Key Meteorological Variables Research Articles

Water‐vapour variability within a convective boundary‐layer assessed by large‐eddy simulations and IHOP_2002 observations

AbstractThis study presents a comprehensive analysis of the variability of water vapour in a growing convective boundary‐layer (CBL) over land, highlighting the complex links between advection, convective activity and moisture heterogeneity in the boundary layer. A Large‐eddy Simulation (LES) is designed, based on observations, and validated, using an independent data‐set collected during the International H2O Project (IHOP_2002) field‐experiment. Ample information about the moisture distribution in space and time, as well as other important CBL parameters are acquired by mesonet stations, balloon soundings, instruments on‐board two aircraft and the DLR airborne water‐vapour differential‐absorption lidar. Because it can deliver two‐dimensional cross‐sections at high spatial resolution (140 m horizontal, 200 m vertical), the airborne lidar offers valuable insights of small‐scale moisture‐variability throughout the CBL. The LES is able to reproduce the development of the CBL in the morning and early afternoon, as assessed by comparisons of simulated mean profiles of key meteorological variables with sounding data. Simulated profiles of the variance of water‐vapour mixing‐ratio were found to be in good agreement with the lidar‐derived counterparts. Finally, probability‐density functions of potential temperature, vertical velocity and water‐vapour mixing‐ratio calculated from the LES show great consistency with those derived from aircraft in situ measurements in the middle of the CBL. Downdraughts entrained from above the CBL are governing the scale of moisture variability. Characteristic length‐scales are found to be larger for water‐vapour mixing‐ratio than for temperatureThe observed water‐vapour variability exhibits contributions from different scales. The influence of the mesoscale (larger than LES domain size, i.e. 10 km) on the smaller‐scale variability is assessed using LES and observations. The small‐scale variability of water vapour is found to be important and to be driven by the dynamics of the CBL. Both lidar observations and LES evidence that dry downdraughts entrained from above the CBL are governing the scale of moisture variability. Characteristic length‐scales are found to be larger for water‐vapour mixing‐ratio than for temperature and vertical velocity. In particular, intrusions of drier free‐troposphere air from above the growing CBL impose a marked negative skewness on the water‐vapour distribution within it, both as observed and in the simulation. Copyright © 2005 Royal Meteorological Society.

Monte Carlo-based dispersion modeling of off-gassing releases from the fumigant metam-sodium for determining distances to exposure endpoints

Fumigants, such as metam-sodium, can help control pests in the soil and improve the quality and yields for a wide range of crops. Enhanced control of field loss, or off-gassing, to the atmosphere is an important means of promoting fumigant efficacy and sound environmental management of fumigants. Metam-sodium has been used for over 40 years and is one of the most widely used fumigants in the United States. This paper describes a modeling method, the fumigant emissions modeling system (FEMS), which was developed to promote more realistic assessment of downwind concentrations in the vicinity of an applied field. Two models of the US Environmental Protection Agency, i.e., the industrial source complex short-term (ISCST3) model and the toxic modeling system short-term (TOXST) model are used, in conjunction with a pre-processing program, PCRAMMET, and the FEMS programming code to provide a Monte Carlo treatment for all key model inputs. Start times for applications are triggered on a Monte Carlo basis consistent with the annual application frequency. Typically, 1000–10,000 years of applications are simulated, outputting data to display average exceedances per year of selected concentration endpoints as a function of distance from the edge of the treatment zone. A sensitivity analysis is displayed for key meteorological variables, showing uncertainty in emission rates to be a significant input parameter. Some comparisons also are made between ambient concentrations and modeled personal and indoor exposures, showing perspective on the benefits of the indoor environment to buffer peak concentrations.

Species richness, diversity and biomass production of desert annuals in an ungrazed Rhanterium epapposum community over three growth seasons in Kuwait

Two blocks, each consisting of twelve 0.25 m2permanent plots, were established in a community dominated by desert annuals inan area of Kuwait protected from grazing, and community attributes wereinvestigated over three growth seasons. A linear relationship was found betweenestimated desert annual cover and biomass production. Species frequency wasassessed by the number of 100 cm2 subplots occupied byaspecies in a block (“block incidence”). Over the three seasons,vegetation features varied considerably. The first season (spring 1997) wascharacterised by very sparse vegetation cover, low vegetation block incidence(sum of individual block incidences of each species present in a block) and lowspecies richness. The second season (spring 1998) had high vegetation cover,relatively low vegetation block incidence, but high species richness. In thethird season (spring 2000), vegetation cover was very sparse (as in 1997), butboth blocks had the highest vegetation block incidence of the three years andspecies richness was moderately high, although individual plants were verysmall. The growth period was much shorter in the very productive andspecies-rich 1998 season than in 2000. Fluctuations in biomass and speciesincidence coincided with pronounced variation in key meteorological variables,in particular the onset of the first rains, the amount of rainfall during thegrowing season and its temporal distribution. Species composition, however,appears mainly to be determined by the amount and possibly the timing of thefirst rainfalls of the season to trigger germination. There was little evidenceto suggest that particularly favourable or unfavourable microsites existed inplots for more than one vegetation period, either for individual species or fordiversity in general. Despite substantial between-season fluctuations inincidence, the relative abundances of the more frequent species to each otherremained broadly similar over the three seasons, with Plantagoboissieri being the predominant species and accounting for thelargest proportion of biomass.

Estimating Long‐term Trends in Tropospheric Ozone Levels

SummaryThis paper develops Bayesian methodology for estimating long‐term trends in the daily maxima of tropospheric ozone. The methods are then applied to study long‐term trends in ozone at six monitoring sites in the state of Texas. The methodology controls for the effects of meteorological variables because it is known that variables such as temperature, wind speed and humidity substantially affect the formation of tropospheric ozone. A semiparametric regression model is estimated in which a nonparametric trivariate surface is used to model the relationship between ozone and these meteorological variables because, while it is known that the relatinship is a complex nonlinear one, its functional form is unknown. The model also allows for the effects of wind direction and seasonality. The errors are modeled as an autoregression, which is methodologically challenging because the observations are unequally spaced over time. Each function in the model is represented as a linear combination of basis functions located at all of the design points. We also estimate an appropriate data transformation simulataneously with the functions. The functions are estimated nonparametrically by a Bayesian hierarchical model that uses indicator variables to allow a non‐zero probability that the coefficient of each basis term is zero. The entire model, including the nonparametric surfaces, data transformation and autoregression for the unequally spaced errors, is estimated using a Markov chain Monte Carlo sampling scheme with a computationally efficient transition kernel for generating the indicator variables. The empirical results indicate that key meteorological variables explain most of the variation in daily ozone maxima through a nonlinear interaction and that their effects are consistent across the six sites. However, the estimated trends vary considerably from site to site, even within the same city.

Estimations of climate change impacts on crop production in the Republic of Moldova

Statistical methods for assessing crop sensitivity and vulnerability to climate change in Moldova were demonstrated and the following procedures were discussed: (1) projections of likely agroclimatic change; (2) assessments of crop sensitivity to climate change; and (3) assessments of the impact of climate change on crops. In order to predict the future agroclimate, key meteorological variables were transformed statistically to correspond to changes in plants' heat and water supply characteristics. Sensitivity of crop production was examined for corn and winter wheat. By combining the agroclimatic changes with crop response, possible impacts have been estimated and form a basis for possible adaptation strategies. It was shown that regional climate change can result in elevated aridity of Moldova's territory, especially during periods of crop growth. Cultivation of cereal crops in new agroclimatic conditions without adaptation measures will negatively affect yields, especially of winter wheat, whose yield decrease may be 18–39% by 2020s and 22–50% by 2050s. Corn yields may increase by 0–3% and 1–6%, respectively. As an example of adaptation, it is shown that the use of an increased number of late hybrids results in a 25–35% increase in corn yields.

METEOROLOGICAL ANALYSIS OF DAILY MAXIMUM GROUND-LEVEL OZONE FOR THE NIAGARA REGION

Ground-level ozone has become a problem of major concern in urban airsheds in Canada, owing to its adverse effects on humans and crops. As a secondary pollutant, its formation is dependent on the presence of certain precursor gases in conjunction with appropriate meteorological conditions. Several studies have examined the relationship between maximum concentrations and key meteorological variables at the regional scale during episodic conditions. This study sought to understand this relationship at the local scale using surface and upper-air meteorological data for the Niagara Region. In the methodological approach, factor analysis and linear regression methods were used to determine the best combination of variables that would explain the highest percentage of variance in daily maximum ground-level ozone associated with different event categories. Each event category had a combination of unique meteorological characteristics. Factor analysis yielded seven factors that together constituted 20 of the orig...

A bivariate autoregressive model for estimation of prevalence of fasciolosis in cattle

AbstractFasciolosis is a serious economic disease of cattle and sheep with a worldwide distribution. In the annual control of the disease, it is vital that the issue of specific veterinary advice on the strategic use of control measures should be based on precise estimates of the levels of risk of the occurrence of the disease. This paper describes the construction of a mathematical model to forecast the level of risk of the disease in cattle in any year. The model has been developed in Northern Ireland using a computerized information database retrieval system for abattoir condemnation data. The information recorded by the system details the specific cause and location of all condemnations in cattle, sheep and pigs for all abattoirs in Northern Ireland. Analysis of a subset of these data (1976 to 1991) for cattle resulted in a univariate time series model for the recorded prevalence of condemnations due to fasciolosis in cattle livers. The overall mathematical forecasting model was constructed by regressing the residual terms of the univariate time series model with the corresponding time series of all the key meteorological variables. The best model (R2 =0·8) was obtained using the mean air temperature for the period fune to August inclusive. The model could be adopted for use in any region of the world where relevant abattoir condemnation and meteorological data are available.

The sensitivity of modeled sulfate wet deposition to the meteorological values used as input

Abstract Variations in the wet deposition of sulfate, resulting from uncertainties in key meteorological variables, have been calculated using a scavenging model with a steady-state meteorology and a time-dependent, non-linear chemical mechanism. Input data to initialize this model are representative of data measured during an actual storm event. The results are presented in terms of the deposition uncertainty that would result if the maximum prescribed uncertainty occurred simultaneously in all input meteorological variables. For stratiform clouds, sulfate deposition uncertainties of up to 33% of the computed value were calculated. For convective clouds, the sulfate deposition uncertainties ranged from near 30% at low grid-averaged precipitation rates to near 60% at higher precipitation rates. The largest contributor to deposition uncertainty was the area of convective cloud coverage. For a mixed convective-stratiform cloud system, uncertainties in deposition remained below 40% as long as the convective contribution to total precipitation was less than 75% of the total rainfall amount. In the mixed convective-stratiform system, the uncertainties in deposition were associated principally with uncertain areas of cloud coverage and uncertain precipitation contributions by each cloud type. Uncertainties in precipitation rate, cloud top height, and cloud base height were of secondary importance for the mixed cloud system and contributed roughly equally to the remaining deposition uncertainty. In all simulations, uncertainties calculated as the standard deviation from the known value are about half of the maximum value.

Time series analysis of the prevalence of Ascaris suum infections in pigs using abattoir condemnation data

AbstractIn most regions of the world, condemnations of porcine livers are almost always due to parasitic Ascaris suum infections. Thus, accurate data for all abattoirs in a region will reflect the prevalence of this disease in the pig population of that region. In Northern Ireland, a computerized information database retrieval system for abattoir data has been available since 1986. The information recorded by the system details the specific cause and location of all condemnations in cattle, sheep and pigs for all abattoirs in Northern Ireland since 1969. In this paper, results of analysis of a subset of these data (1969 to 1989) for pigs is presented and a univariate time series model identified for the recorded prevalence of condemnations due to cirrhotic lesions in pig livers. The results of the analysis demonstrate a very highly significant upward trend in the occurrence of Ascaris suum infections. The residual terms for the model were correlated with the corresponding time series of all the key meteorological variables. The association between the level of lesions and the mean level of air temperature over the early summer period is highlighted. The overall proportion of variation accounted for in the time series of the annual level of cirrhotic condemnations was 96%.