Distribution Of Meteorological Data Research Articles

Study on the Susceptibility of Drifting Snow in Ya’an–Qamdo Section of the Railway in Southwest China

To investigate the susceptibility of drifting snow along the Ya’an–Qamdo section of the railway, which is located in a high-altitude and cold plateau in Southwest China with scarce meteorological information, the Weather Research and Forecasting Model (WRF) is used in this paper to simulate the spatio-temporal distribution of meteorological data. According to the varying terrain, the railway section from Ya’an to Qamdo is divided into two regions along 100.8° E for double-layer nested simulation. The original land use data of the WRF model are used in region 1. Due to the increased number of mountains in region 2, the original data are replaced by the MCD12Q1v006 land use data, and the vertical direction layers are densified near the ground to increase simulation accuracy. The simulated results are compared with the observation data. It is found that after densification, the results have been significantly improved. The results obtained by the WRF model can accurately simulate the change trends of temperature, rainfall, and wind speed, and the correlation coefficients are relatively high, which verifies the accuracy of WRF for simulating complex terrain regions. The simulation results further indicate that approximately 300 km of the Ya’an–Qamdo railway may experience drifting snow. Among them, no drifting snow events occur in Ya’an County, and the areas with higher probability are located at the border between Luding County and Tianquan County, followed by Kangding area. The remaining areas have a probability of less than 10%. The WRF model demonstrates its capability in the drifting snow protection of railways with limited meteorological data.

Epidemiological characteristics, seasonality, trends of dog bite injuries, and relationship with meteorological data.

Animal bites are among the most critical in public health problems. Dogs are the leading cause of bite injuries. The study aimed to investigate the epidemiology and clinical features of dog bite cases admitted to an emergency department, as well as their temporal trends, seasonality, and tr relationship with meteorological data. Study data comprised eight years (2012-2019) emergency room records of a tertiary center. Demographic characteristics of the cases, bite anatomical area, treatment applied, hospitalization, and death rates were determined. The incidence rates and distribution of meteorological data by years were examined using ANOVA and Kruskal Wallis tests. Seasonality and temporal trends were investigated for incidence rates using the additive decomposition technique. The temporal relationship of incidence rates with meteorological data was evaluated using the Autoregressive Distributed Delayed Boundary Test. Causality verification was perfoirmed using the Granger test. Dog bite cases consisted of 1,335 records of partients with a mean age of 26.6±0.2 years. Bite cases were most common in the 20-44 age group (44.7%), males (76.4%), and lower extremities (48.2%). The frequency of hospitalization was 4.1%. Annual incidence rates ranged from 52.7-49.9/100,000, with a non-significant increasing trend. The incidence of bites had two peaks, in June and August. A co-integration relationship was observed between incidence rates and air temperature and humidity levels (p<0.001). Effective implementation of prevention programmes is needed for high-risk demographic groups. In addition, a national monitoring and reporting system could evaluate the effectiveness of any prevention programme and reduce the incedence of dog bites.

Visual analysis of meteorological satellite data via model-agnostic meta-learning

Satellites detect the distribution of meteorological data worldwide. However, due to the orbital constraints, the satellite can only reach the same area again after one orbiting cycle. The interval between two detections in the same area is long, and the variation of meteorological data between the two detections is unknown. Moreover, meteorological satellite data are only located near the orbit in one cycle, while the global distribution of meteorological data is unknown. Our method allows to train a regression model with only few meteorological satellite data by taking advantage of the recent advances in deep learning. In detail, we train a model-agnostic meta-learning (MAML) model with data from ground stations instead of meteorological satellites and get the initial network parameters. Based on the initial network parameters trained by MAML, we train the regression models again for different areas. We sample the regression curves of all areas by time and get a time series of global meteorological data distribution. Through case studies conducted together with domain experts, we validate the effectiveness of our method.

Quantification of macropore flow in Danish soils using near-saturated hydraulic properties

Larger pores allowing macropore flow can rapidly transport contaminants as well as nutrients from the soil surface to the subsoil and groundwater. Modeling of the involved macropore flow is complex due to large number of required parameters. One way to deal with this difficulty is the precise evaluation of the soil hydraulic conductivity function using robust pedotransfer functions for the near-saturated part. Aiming to develop pedotransfer functions to estimate saturated and unsaturated hydraulic conductivities [Ks and K(h)] as well as water contents at specific pressure heads, we used Gaussian Process Regression (GPR), a non-parametric machine learning model, to obtain van Genuchten (1980) retention and conductivity parameters for Danish soils based on soil texture and organic matter. We defined K10 as K(h) at − 10 cm pressure head. The difference between the logarithm of Ks and K10 [log(Ks) − log(K10)] was denominated log(Kjump) and was assumed to convey the potential degree of macropore flow considering only soil hydraulic properties. Macropore flow in Denmark was evaluated through the developed PTFs for log(Ks) and log(K10) with an average RMSE of 0.635 and 0.594 (for K in cm d−1) under a confidence interval obtained by bootstrapping. Dynamics of pressure head in an initially saturated soil column with a zero-flux top boundary condition and a 2 m deep groundwater level were simulated using the Hydrus-1D model. The result of this modeling showed that soils in the eastern part of Denmark had a higher likelihood to experience macropore flow. Finally, using the spatial distribution of meteorological data, a macropore flow map of Denmark was produced. Precipitation was a dominating factor on macropore flow in sandy clay loam and sandy loam soils when compared to sandy and loamy sand soils. Finally, maps of relative risk classes of macropore flow qualitatively differentiated land areas in Denmark for vulnerability of nutrient loss and groundwater contamination.

The added value of spatially distributed meteorological data for simulating hydrological processes in a small Mediterranean catchment

The purpose of this paper was to demonstrate the added value of the spatial distribution of rainfall and potential evapotranspiration (PE) in the prediction of the discharge for a small Mediterranean catchment located in the Medjerda basin in Tunisia, i.e. the Raghay. We compare therefore the performance of a conceptual hydrological model available in the ATHYS platform, using global and spatial distributed input data. The model was implemented in two different ways. The first implementation was in a spatially distributed mode, and the second one was in a non-distributed lumped mode by using spatially averaged data weighed with a Thiessen-interpolated factor. The performance of the model was analysed for the distributed mode and for the lumped mode with a cross-validation test and through several modelling evaluation criteria. Simultaneously, the impact of the spatial distribution of meteorological data was assessed for the two cases when estimating the model parameters, the flow and water amounts, and the flow duration curves. The cross-validation of the split-sample test shows a preference for the spatially distributed model based on accuracy criteria and graphical comparison. The distributed mode required, however, more simulation time. Finally, the results reported for the Raghay indicated that the added value of the spatial distribution of rainfall and PE is not constant for the whole series of data, depending on the spatial and temporal variability of climate data over the catchment that should be assessed prior to the modelling implementations.

Input data processing tools for the integrated hydrologic model GSFLOW

Integrated hydrologic modeling (IHM) encompasses a vast number of processes and specifications, variable in time and space, and development of models can be arduous. Model input construction techniques have not been formalized or made easily reproducible. Creating the input files for integrated hydrologic models requires complex GIS processing of raster and vector datasets from various sources. Developing stream network topology that is consistent with the model grid-scale digital elevation model (DEM) is important for robust simulation of surface water and groundwater exchanges. Distribution of meteorological data over the model domain is difficult in complex terrain at the model-grid scale, but is necessary for realistic simulations. As model development requires extensive GIS and computer programming expertise, the use of IHMs has mostly been limited to research groups with available financial, human, and technical resources. Here we present a series of open-source Python scripts that are combined with ESRI ArcGIS to provide a formalized technique for the parameterization and development of inputs for the readily available IHM called GSFLOW. This Python toolkit automates many of the necessary and laborious processes of parameterization, including stream network development, land coverages, and meteorological distribution over the model domain. The final products of the toolkit are PRMS ready Parameter Files, along with several input parameters for a MODFLOW model, including input for the Streamflow Routing Package. A demonstration of the toolkit is provided to illustrate its capabilities.

Thermal comfort along the marathon course of the 2020 Tokyo Olympics.

The Olympic Games will be held in Tokyo in 2020 and the period will be the hottest period of the year in Japan. Marathon is a sport with a large heat load, and it is said that the risk of heat stroke rises more than other sports activities. The thermal environment of the 2020 Tokyo Olympic marathon course is analyzed by using wet-bulb globe temperature (WBGT) and Universal Thermal Climate Index (UTCI) map of the center area of Tokyo. The change due to the place, the effect of the shadow of the building, and the position on the course was analyzed from the distribution of WBGT and UTCI in the short-term analysis of sunny day from August 2 to August 6, 2014. To make the distribution map, we calculated distributions of sky view factor and mean radiant temperature of the 10km × 7.5km analyzed area in the center of Tokyo. Distributions of air temperature and humidity are calculated from Metropolitan Environmental Temperature and Rainfall Observation System data, which is a high-resolution measurement network. It was possible to incorporate the local variation of temperature and humidity of the analyzed area. In the result, the WBGT is about 1°C lower and the UTCI is about 4-8°C lower in the shadow of buildings from 9:00 to 10:00 than in the sunny side. As a cooling method, making a shadow is a relatively effective method. The variation along the course considering the distribution of meteorological data within the area is about 0.5°C WBGT and 1°C UTCI range. If we allow the error of this range, one-point meteorological data can be applied for the estimation along the course. Passing the right side (left side in the case of return) of the course could keep the accumulated value slightly lower along the course in the morning because the marathon course roughly runs from west to east and buildings' shadow is on the relatively right side (south side). But practically, the effect of changing the position on the course was small. The long-term analysis on the degree of risk for each hour was also carried out by using one-point data of the first 10days of August from 2007 to 2016. The risk increased rapidly after 8:00. It will be safer if the marathon race is finished before 9:00 or if the race is held after 19:00.

Screening procedure for airborne pollutants emitted from a high-tech industrial complex in Taiwan

Despite the modernization of computational techniques, atmospheric dispersion modeling remains a complicated task as it involves the use of large amounts of interrelated data with wide variability. The continuously growing list of regulated air pollutants also increases the difficulty of this task. To address these challenges, this study aimed to develop a screening procedure for a long-term exposure scenario by generating a site-specific lookup table of hourly averaged dispersion factors (χ/Q), which could be evaluated by downwind distance, direction, and effective plume height only. To allow for such simplification, the average plume rise was weighted with the frequency distribution of meteorological data so that the prediction of χ/Q could be decoupled from the meteorological data. To illustrate this procedure, 20 receptors around a high-tech complex in Taiwan were selected. Five consecutive years of hourly meteorological data were acquired to generate a lookup table of χ/Q, as well as two regression formulas of plume rise as functions of downwind distance, buoyancy flux, and stack height. To calculate the concentrations for the selected receptors, a six-step Excel algorithm was programmed with four years of emission records and 10 most critical toxics were screened out. A validation check using Industrial Source Complex (ISC3) model with the same meteorological and emission data showed an acceptable overestimate of 6.7% in the average concentration of 10 nearby receptors. The procedure proposed in this study allows practical and focused emission management for a large industrial complex and can therefore be integrated into an air quality decision-making system.

Testing the normality of the residuals of surface temperature data at VLBI/GPS co-located sites by goodness of fit tests

Evaluating the distribution patterns of surface temperature data at Very Long Baseline Interferometry (VLBI)/Global Positioning System (GPS) co-located sites w.r.t. normality is one of the most important issues in modeling surface temperature data over long periods. Such evaluation can generate algorithms for filling in missing data at measurement sites. Some algorithms in the literature, such as those in the study of Cho et al. J Coast Res 65. doi: 10. 2112/SI65-321. 1, (2013), require trend, harmonic, and residual components to fill in the missing data. Trend and harmonic components estimate an optimal model that can be used to assist such algorithms when filling in missing data. The present study is based on the investigation of the normal distribution of the residuals of a surface temperature time series at VLBI/GPS co-located sites, after removing the trend and seasonal effects through harmonic components (inter-daily variations). This study uses surface temperature data collected from the VLBI/GPS co-located sites of two different regions in Europe: Matera (Italy) and Wettzell (Germany). The data collected from these sites form a time series, and time series analyses and conventional k-sigma outlier detection are implemented on these data sets before subjecting them to goodness of fit tests for normality. The residual components of the time series are acquired through a decomposing trend and signal effect from the original time series, assuming that the residuals of the time series are normally distributed. In testing the hypothesis that an observed frequency distribution fits the normal distribution, the following tests are used: Pearson χ2, Kolmogorov-Smirnov, Anderson-Darling, Shapiro-Wilk or Shapiro-Francia, D’Agostino, Jarque-Bera, skewness, and kurtosis tests. Some graphical methods are also applied to support the results of the goodness of fit tests for normality. Some proposals on the application of the goodness of fit tests are put forward, such as the evaluation of the estimation model for trend and harmonic components by considering the properties of the implemented goodness of fit tests. The results of this study can be used to determine the optimal model for estimating trend and harmonic components. The output of the present study is expected to have an important role in modeling surface temperature distributions at co-located VLBI/GPS sites for filling in missing data. Above all, meteorological data, such as temperature, pressure, and humidity, are of specific interest for modeling tropospheric delay, the main error factor in positioning in space geodesy, which in turn makes investigations on the distribution of meteorological data more attractive in geoscience.

A Meteorological Data Distribution System Using Remote Method Invocation Technology

A meteorological data distribution system (MDDS) is presented. The system was implemented in the Spanish Juan Carlos I Antarctic station during the 2002-2003 campaign. Meteorological data are distributed to any point of the base using the LAN, which allows distributing data up to a 3.3-km radius from the acquisition point. The acquisition control procedures are also integrated in the same system. A wide range of difficulties have been overcome during the development of the MDDS, including the integration of heterogeneous components, the integrity of the message system, the future extension and scalability of the system, fault tolerance, process concurrency, and the temporary synchronization of all the applications involved. These have been achieved through three steps, namely 1) design of a complete object-oriented model of the system; 2) implementation of this model using a scalable and portable development environment, such as Java; and 3) use of an intercommunication application technology that fits all the system requirements (high level of abstraction and easy to program and to deploy) as the remote method invocation technology from Java. This development is part of a more ambitious project, called LabVir, that is devoted to implement distributed measurements in Spanish oceanographic vessels and in the Antarctic base using Web technology interfaces. In a more general framework, the LabVir project will make the experimentation tools accessible from any existing navigator, creating a distributed research environment, that is accessible and user friendly from any place with hypertext transfer protocol connectivity

Enhancing African meteorological services with MDD

AbstractFour Meteorological Data Distribution (MDD) systems were installed in African meteorological services in late 1992. The PC‐based systems are inexpensive and are intended to he sustainable technology. The MDDs were well received and in each country the MDD immediately enhanced the local information base, improving the quantity of station data available as well as providing processed products. The MDDs have enabled the recipient meteorological departments to improve the services they offer to government, to aviation and to the public. The Natural Resources Institute (NRI) is now working with local users to develop further applications. The future of MDD in Africa is not yet clear. MDD may be integrated into multi‐tasking workstations but such technology may not yet be appropriate for small meteorological services.

Simulation of Discharge using Different Methods of Meteorological Data Distribution, Basin Discretization and Snow Modelling

This contribution investigates the performance of discharge modelling when proceeding from rather simple methods of snowmelt modelling to more sophisticated, distributed approaches. In all cases, the conceptual HBV runoff model is used for the calculation of discharge from the Romanche River basin (French Alps, area = 224 km2,12.5 % glacierized), feeding the Lac de Chambon reservoir used for hydroelectric power production. The results show that when using temperature-index approaches, no gain in performance is achieved by the more detailed basin discretization. However, a clear gain is observed when the precipitation and air temperature distribution of the elaborate meteorological data analysis system “SAFRAN” is used as input to the conceptual snow model. When using the original version of the physically-based “CROCUS” snow model, a strong overestimation of discharge resulted during the main snow melt season. A revised SAFRAN model version employing a new radiation routine, and the use of a realistic distribution of snow cover in each sub-unit considered by CROCUS resulted in a discharge simulation performance which is as good as the one from the calibrated conceptual models. Sensitivity analyses of the various snow models investigating the hydrological consequences of climatic change show surprisingly similar results.