15 CFR 950.3 - National Climatic Center (NCC).

Code of Federal Regulations, 2011 CFR

2011-01-01

...; develops analytical and descriptive products to meet user requirements; and provides facilities for the... Meteorological Experiment meteorological data, Global Atmospheric Research Program basic data set, solar...

The cross wavelet analysis of dengue fever variability influenced by meteorological conditions

NASA Astrophysics Data System (ADS)

Lin, Yuan-Chien; Yu, Hwa-Lung; Lee, Chieh-Han

2015-04-01

The multiyear variation of meteorological conditions induced by climate change causes the changing diffusion pattern of infectious disease and serious epidemic situation. Among them, dengue fever is one of the most serious vector-borne diseases distributed in tropical and sub-tropical regions. Dengue virus is transmitted by several species of mosquito and causing lots amount of human deaths every year around the world. The objective of this study is to investigate the impact of meteorological variables to the temporal variation of dengue fever epidemic in southern Taiwan. Several extreme and average indices of meteorological variables, i.e. temperature and humidity, were used for this analysis, including averaged, maximum and minimum temperature, and average rainfall, maximum 1-hr rainfall, and maximum 24-hr rainfall. This study plans to identify and quantify the nonlinear relationship of meteorological variables and dengue fever epidemic, finding the non-stationary time-frequency relationship and phase lag effects of those time series from 1998-2011 by using cross wavelet method. Results show that meteorological variables all have a significant time-frequency correlation region to dengue fever epidemic in frequency about one year (52 weeks). The associated phases can range from 0 to 90 degrees (0-13 weeks lag from meteorological factors to dengue incidences). Keywords: dengue fever, cross wavelet analysis, meteorological factor

Effective and efficient analysis of spatio-temporal data

NASA Astrophysics Data System (ADS)

Zhang, Zhongnan

Spatio-temporal data mining, i.e., mining knowledge from large amount of spatio-temporal data, is a highly demanding field because huge amounts of spatio-temporal data have been collected in various applications, ranging from remote sensing, to geographical information systems (GIS), computer cartography, environmental assessment and planning, etc. The collection data far exceeded human's ability to analyze which make it crucial to develop analysis tools. Recent studies on data mining have extended to the scope of data mining from relational and transactional datasets to spatial and temporal datasets. Among the various forms of spatio-temporal data, remote sensing images play an important role, due to the growing wide-spreading of outer space satellites. In this dissertation, we proposed two approaches to analyze the remote sensing data. The first one is about applying association rules mining onto images processing. Each image was divided into a number of image blocks. We built a spatial relationship for these blocks during the dividing process. This made a large number of images into a spatio-temporal dataset since each image was shot in time-series. The second one implemented co-occurrence patterns discovery from these images. The generated patterns represent subsets of spatial features that are located together in space and time. A weather analysis is composed of individual analysis of several meteorological variables. These variables include temperature, pressure, dew point, wind, clouds, visibility and so on. Local-scale models provide detailed analysis and forecasts of meteorological phenomena ranging from a few kilometers to about 100 kilometers in size. When some of above meteorological variables have some special change tendency, some kind of severe weather will happen in most cases. Using the discovery of association rules, we found that some special meteorological variables' changing has tight relation with some severe weather situation that will happen very soon. This dissertation is composed of three parts: an introduction, some basic knowledges and relative works, and my own three contributions to the development of approaches for spatio-temporal data mining: DYSTAL algorithm, STARSI algorithm, and COSTCOP+ algorithm.

The value of using seasonality and meteorological variables to model intra-urban PM2.5 variation

NASA Astrophysics Data System (ADS)

Olvera Alvarez, Hector A.; Myers, Orrin B.; Weigel, Margaret; Armijos, Rodrigo X.

2018-06-01

A yearlong air monitoring campaign was conducted to assess the impact of local temperature, relative humidity, and wind speed on the temporal and spatial variability of PM2.5 in El Paso, Texas. Monitoring was conducted at four sites purposely selected to capture the local traffic variability. Effects of meteorological events on seasonal PM2.5 variability were identified. For instance, in winter low-wind and low-temperature conditions were associated with high PM2.5 events that contributed to elevated seasonal PM2.5 levels. Similarly, in spring, high PM2.5 events were associated with high-wind and low-relative humidity conditions. Correlation coefficients between meteorological variables and PM2.5 fluctuated drastically across seasons. Specifically, it was observed that for most sites correlations between PM2.5 and meteorological variables either changed from positive to negative or dissolved depending on the season. Overall, the results suggest that mixed effects analysis with season and site as fixed factors and meteorological variables as covariates could increase the explanatory value of LUR models for PM2.5.

Long-term weather predictability: Ural case study

NASA Astrophysics Data System (ADS)

Kubyshen, Alexander; Shopin, Sergey

2016-04-01

The accuracy of the state-of-the-art long-term meteorological forecast (at the seasonal level) is still low. Here it is presented approach (RAMES method) realizing different forecasting methodology. It provides prediction horizon of up to 19-22 years under equal probabilities of determination of parameters in every analyzed period [1]. Basic statements of the method are the following. 1. Long-term forecast on the basis of numerical modeling of the global meteorological process is principally impossible. Extension of long-term prediction horizon could be obtained only by the revealing and using a periodicity of meteorological situations at one point of observation. 2. Conventional calendar is unsuitable for generalization of meteorological data and revealing of cyclicity of meteorological processes. RAMES method uses natural time intervals: one day, synodic month and one year. It was developed a set of special calendars using these natural periods and the Metonic cycle. 3. Long-term time series of meteorological data is not a uniform universal set, it is a sequence of 28 universal sets appropriately superseding each other in time. The specifics of the method are: 1. Usage of the original research toolkit consisting of - a set of calendars based on the Metonic cycle; - a set of charts (coordinate systems) for the construction of sequence diagrams (of daily variability of a meteorological parameter during the analyzed year; of daily variability of a meteorological parameter using long-term dynamical time series of periods-analogues; of monthly and yearly variability of accumulated value of meteorological parameter). 2. Identification and usage of new virtual meteorological objects having several degrees of generalization appropriately located in the used coordinate systems. 3. All calculations are integrated into the single technological scheme providing comparison and mutual verification of calculation results. During the prolonged testing in the Ural region, it was proved the efficiency of the method for forecasting the following meteorological parameters: ­- air temperature (minimum, maximum, daily mean, diurnal variation, last spring and first autumn freeze); - periods of winds with speeds of >5m/s and the maximal expected wind speed; - precipitation periods and amount of precipitations; -­ relative humidity; - atmospheric pressure. Atmospheric events (thunderstorms, fog) and hydrometeors also occupy the appropriate positions at the sequence diagrams that provides a possibility of long-term forecasting also for these events. Accuracy of forecasts was tested in 2006-2009 years. The difference between the forecasted monthly mean temperature and actual values was <0.5°C in 40.9% of cases, between 0.5°C and 1°C in 18.2% of cases, between 1°C and 1.5°C in 18.2% of cases, <2°C in 86% of cases. The RAMES method provides the toolkit to successfully forecast the weather conditions in advance of several years. 1. A.F. Kubyshen, "RAMES method: revealing the periodicity of meteorological processes and it usage for long-term forecast [Metodika «RAMES»: vyjavlenie periodichnosti meteorologicheskih processov i ee ispol'zovanie dlja dolgosrochnogo prognozirovanija]", in A.E. Fedorov (ed.), Sistema «Planeta Zemlja»: 200 let so dnja rozhdenija Izmaila Ivanovicha Sreznevskogo. 100 let so dnja izdanija ego slovarja drevnerusskogo jazyka. LENAND. Moscow. pp. 305-311. (In Russian)

Analysis of source regions and meteorological factors for the variability of spring PM10 concentrations in Seoul, Korea

NASA Astrophysics Data System (ADS)

Lee, Jangho; Kim, Kwang-Yul

2018-02-01

CSEOF analysis is applied for the springtime (March, April, May) daily PM10 concentrations measured at 23 Ministry of Environment stations in Seoul, Korea for the period of 2003-2012. Six meteorological variables at 12 pressure levels are also acquired from the ERA Interim reanalysis datasets. CSEOF analysis is conducted for each meteorological variable over East Asia. Regression analysis is conducted in CSEOF space between the PM10 concentrations and individual meteorological variables to identify associated atmospheric conditions for each CSEOF mode. By adding the regressed loading vectors with the mean meteorological fields, the daily atmospheric conditions are obtained for the first five CSEOF modes. Then, HYSPLIT model is run with the atmospheric conditions for each CSEOF mode in order to back trace the air parcels and dust reaching Seoul. The K-means clustering algorithm is applied to identify major source regions for each CSEOF mode of the PM10 concentrations in Seoul. Three main source regions identified based on the mean fields are: (1) northern Taklamakan Desert (NTD), (2) Gobi Desert and (GD), and (3) East China industrial area (ECI). The main source regions for the mean meteorological fields are consistent with those of previous study; 41% of the source locations are located in GD followed by ECI (37%) and NTD (21%). Back trajectory calculations based on CSEOF analysis of meteorological variables identify distinct source characteristics associated with each CSEOF mode and greatly facilitate the interpretation of the PM10 variability in Seoul in terms of transportation route and meteorological conditions including the source area.

Atmospheric and Space Sciences: Ionospheres and Plasma Environments

NASA Astrophysics Data System (ADS)

Yiǧit, Erdal

2018-01-01

The SpringerBriefs on Atmospheric and Space Sciences in two volumes presents a concise and interdisciplinary introduction to the basic theory, observation & modeling of atmospheric and ionospheric coupling processes on Earth. The goal is to contribute toward bridging the gap between meteorology, aeronomy, and planetary science. In addition recent progress in several related research topics, such atmospheric wave coupling and variability, is discussed. Volume 1 will focus on the atmosphere, while Volume 2 will present the ionospheres and the plasma environments. Volume 2 is aimed primarily at (research) students and young researchers that would like to gain quick insight into the basics of space sciences and current research. In combination with the first volume, it also is a useful tool for professors who would like to develop a course in atmospheric and space physics.

A Reduced Form Model for Ozone Based on Two Decades of ...

EPA Pesticide Factsheets

A Reduced Form Model (RFM) is a mathematical relationship between the inputs and outputs of an air quality model, permitting estimation of additional modeling without costly new regional-scale simulations. A 21-year Community Multiscale Air Quality (CMAQ) simulation for the continental United States provided the basis for the RFM developed in this study. Predictors included the principal component scores (PCS) of emissions and meteorological variables, while the predictand was the monthly mean of daily maximum 8-hour CMAQ ozone for the ozone season at each model grid. The PCS form an orthogonal basis for RFM inputs. A few PCS incorporate most of the variability of emissions and meteorology, thereby reducing the dimensionality of the source-receptor problem. Stochastic kriging was used to estimate the model. The RFM was used to separate the effects of emissions and meteorology on ozone concentrations. by running the RFM with emissions constant (ozone dependent on meteorology), or constant meteorology (ozone dependent on emissions). Years with ozone-conducive meteorology were identified, and meteorological variables best explaining meteorology-dependent ozone were identified. Meteorology accounted for 19% to 55% of ozone variability in the eastern US, and 39% to 92% in the western US. Temporal trends estimated for original CMAQ ozone data and emission-dependent ozone were mostly negative, but the confidence intervals for emission-dependent ozone are much

Quality Control of Meteorological Observations

NASA Technical Reports Server (NTRS)

Collins, William; Dee, Dick; Rukhovets, Leonid

1999-01-01

For the first time, a problem of the meteorological observation quality control (QC) was formulated by L.S. Gandin at the Main Geophysical Observatory in the 70's. Later in 1988 L.S. Gandin began adapting his ideas in complex quality control (CQC) to the operational environment at the National Centers for Environmental Prediction. The CQC was first applied by L.S.Gandin and his colleagues to detection and correction of errors in rawinsonde heights and temperatures using a complex of hydrostatic residuals.Later, a full complex of residuals, vertical and horizontal optimal interpolations and baseline checks were added for the checking and correction of a wide range of meteorological variables. some other of Gandin's ideas were applied and substantially developed at other meteorological centers. A new statistical QC was recently implemented in the Goddard Data Assimilation System. The central component of any quality control is a buddy check which is a test of individual suspect observations against available nearby non-suspect observations. A novel feature of this test is that the error variances which are used for QC decision are re-estimated on-line. As a result, the allowed tolerances for suspect observations can depend on local atmospheric conditions. The system is then better able to accept extreme values observed in deep cyclones, jet streams and so on. The basic statements of this adaptive buddy check are described. Some results of the on-line QC including moisture QC are presented.

Spatial clustering and meteorological drivers of summer ozone in Europe

NASA Astrophysics Data System (ADS)

Carro-Calvo, Leopoldo; Ordóñez, Carlos; García-Herrera, Ricardo; Schnell, Jordan L.

2017-04-01

We present a regionalization of summer near-surface ozone (O3) in Europe. For this purpose we apply a K-means algorithm on a gridded MDA8 O3 (maximum daily average 8-h ozone) dataset covering a European domain [15° W - 30° E, 35°-70° N] at 1° x 1° horizontal resolution for the 1998-2012 period. This dataset was compiled by merging observations from the European Monitoring and Evaluation Programme (EMEP) and the European Environment Agency's air quality database (AirBase). The K-means method allows identifying sets of different regions where the O3 concentrations present coherent spatiotemporal patterns and are thus expected to be driven by similar meteorological factors. After some testing, 9 regions were selected: the British Isles, North-Central Europe, Northern Scandinavia, the Baltic countries, the Iberian Peninsula, Western Europe, South-Central Europe, Eastern Europe and the Balkans. For each region we examine the synoptic situations associated with elevated ozone extremes (days exceeding the 95th percentile of the summer MDA8 O3 distribution). Our analyses reveal that there are basically two different kinds of regions in Europe: (a) those in the centre and south of the continent where ozone extremes are associated with elevated temperature within the same region and (b) those in northern Europe where ozone extremes are driven by southerly advection of air masses from warmer, more polluted areas. Even when the observed patterns were initially identified only for days registering high O3 extremes, all summer days can be projected on such patterns to identify the main modes of meteorological variability of O3. We have found that such modes are partly responsible for the day-to-day variability in the O3 concentrations and can explain a relatively large fraction (from 44 to 88 %, depending on the region) of the interannual variability of summer mean MDA8 O3 during the period of analysis. On the other hand, some major teleconnection patterns have been tested but do not seem to exert a large impact on the variability of surface O3 over most regions. The identification of these independent regions where surface ozone presents a coherent behaviour and responds similarly to specific meteorological modes of variability has multiple applications. For instance, the performance of chemical transport models (CTMs) and chemistry-climate models (CCMs) can be separately assessed over such regions to identify areas where they present large biases that need to be corrected. Our results can also be used to test the models' sensitivity to the day-to-day changing meteorology and to climate change over specific regions.

Analysis and forecast experiments incorporating satellite soundings and cloud and water vapor drift wind information

NASA Technical Reports Server (NTRS)

Goodman, Brian M.; Diak, George R.; Mills, Graham A.

1986-01-01

A system for assimilating conventional meteorological data and satellite-derived data in order to produce four-dimensional gridded data sets of the primary atmospheric variables used for updating limited area forecast models is described. The basic principles of a data assimilation scheme as proposed by Lorenc (1984) are discussed. The design of the system and its incremental assimilation cycles are schematically presented. The assimilation system was tested using radiosonde, buoy, VAS temperature, dew point, gradient wind data, cloud drift, and water vapor motion data. The rms vector errors for the data are analyzed.

The influence of meteorological variables on CO2 and CH4 trends recorded at a semi-natural station.

PubMed

Pérez, Isidro A; Sánchez, M Luisa; García, M Ángeles; Pardo, Nuria; Fernández-Duque, Beatriz

2018-03-01

CO 2 and CH 4 evolution is usually linked with sources, sinks and their changes. However, this study highlights the role of meteorological variables. It aims to quantify their contribution to the trend of these greenhouse gases and to determine which contribute most. Six years of measurements at a semi-natural site in northern Spain were considered. Three sections are established: the first focuses on monthly deciles, the second explores the relationship between pairs of meteorological variables, and the third investigates the relationship between meteorological variables and changes in CO 2 and CH 4 . In the first section, monthly outliers were more marked for CO 2 than for CH 4 . The evolution of monthly deciles was fitted to three simple expressions, linear, quadratic and exponential. The linear and exponential are similar, whereas the quadratic evolution is the most flexible since it provided a variable rate of concentration change and a better fit. With this last evolution, a decrease in the change rate was observed for low CO 2 deciles, whereas an increasing change rate prevailed for the rest and was more accentuated for CH 4 . In the second section, meteorological variables were provided by a trajectory model. Backward trajectories from 1-day prior to reaching the measurement site were used to calculate distance and direction averages as well as the recirculation factor. Terciles of these variables were determined in order to establish three intervals with low, medium and high values. These intervals were used to classify the variables following their interval widths and skewnesses. The best correlation between pairs of meteorological variables was observed for the average distance, in particular with horizontal wind speed. Sinusoidal relationships with the average direction were obtained for average distance and for vertical wind speed. Finally, in the third section, the quadratic evolution was considered in each interval of all the meteorological variables. As regards the main result, the greatest increases were obtained for high potential temperature for both gases followed by low and medium boundary layer height for CO 2 and CH 4 , respectively. Combining both meteorological variables provided increases of 22 ± 9 and 0.070 ± 0.019 ppm for CO 2 and CH 4 , respectively, although the number of observations affected is small, around 7%. Copyright © 2017 Elsevier Ltd. All rights reserved.

Global Atmospheric Monitoring

ERIC Educational Resources Information Center

Wallen, Carl C.

1975-01-01

The global atmospheric monitoring plans of the World Meteorological Organization are detailed. Single and multipurpose basic monitoring systems and the monitoring of chemical properties are discussed. The relationship of the World Meteorological Organization with the United Nations environment program is discussed. A map of the World…

Influence of seasonal and inter-annual hydro-meteorological variability on surface water fecal coliform concentration under varying land-use composition.

PubMed

St Laurent, Jacques; Mazumder, Asit

2014-01-01

Quantifying the influence of hydro-meteorological variability on surface source water fecal contamination is critical to the maintenance of safe drinking water. Historically, this has not been possible due to the scarcity of data on fecal indicator bacteria (FIB). We examined the relationship between hydro-meteorological variability and the most commonly measured FIB, fecal coliform (FC), concentration for 43 surface water sites within the hydro-climatologically complex region of British Columbia. The strength of relationship was highly variable among sites, but tended to be stronger in catchments with nival (snowmelt-dominated) hydro-meteorological regimes and greater land-use impacts. We observed positive relationships between inter-annual FC concentration and hydro-meteorological variability for around 50% of the 19 sites examined. These sites are likely to experience increased fecal contamination due to the projected intensification of the hydrological cycle. Seasonal FC concentration variability appeared to be driven by snowmelt and rainfall-induced runoff for around 30% of the 43 sites examined. Earlier snowmelt in nival catchments may advance the timing of peak contamination, and the projected decrease in annual snow-to-precipitation ratio is likely to increase fecal contamination levels during summer, fall, and winter among these sites. Safeguarding drinking water quality in the face of such impacts will require increased monitoring of FIB and waterborne pathogens, especially during periods of high hydro-meteorological variability. This data can then be used to develop predictive models, inform source water protection measures, and improve drinking water treatment. Copyright © 2013 Elsevier Ltd. All rights reserved.

Meteorologically Driven Simulations of Dengue Epidemics in San Juan, PR

PubMed Central

Morin, Cory W.; Monaghan, Andrew J.; Hayden, Mary H.; Barrera, Roberto; Ernst, Kacey

2015-01-01

Meteorological factors influence dengue virus ecology by modulating vector mosquito population dynamics, viral replication, and transmission. Dynamic modeling techniques can be used to examine how interactions among meteorological variables, vectors and the dengue virus influence transmission. We developed a dengue fever simulation model by coupling a dynamic simulation model for Aedes aegypti, the primary mosquito vector for dengue, with a basic epidemiological Susceptible-Exposed-Infectious-Recovered (SEIR) model. Employing a Monte Carlo approach, we simulated dengue transmission during the period of 2010–2013 in San Juan, PR, where dengue fever is endemic. The results of 9600 simulations using varied model parameters were evaluated by statistical comparison (r2) with surveillance data of dengue cases reported to the Centers for Disease Control and Prevention. To identify the most influential parameters associated with dengue virus transmission for each period the top 1% of best-fit model simulations were retained and compared. Using the top simulations, dengue cases were simulated well for 2010 (r2 = 0.90, p = 0.03), 2011 (r2 = 0.83, p = 0.05), and 2012 (r2 = 0.94, p = 0.01); however, simulations were weaker for 2013 (r2 = 0.25, p = 0.25) and the entire four-year period (r2 = 0.44, p = 0.002). Analysis of parameter values from retained simulations revealed that rain dependent container habitats were more prevalent in best-fitting simulations during the wetter 2010 and 2011 years, while human managed (i.e. manually filled) container habitats were more prevalent in best-fitting simulations during the drier 2012 and 2013 years. The simulations further indicate that rainfall strongly modulates the timing of dengue (e.g., epidemics occurred earlier during rainy years) while temperature modulates the annual number of dengue fever cases. Our results suggest that meteorological factors have a time-variable influence on dengue transmission relative to other important environmental and human factors. PMID:26275146

An Extended Objective Evaluation of the 29-km Eta Model for Weather Support to the United States Space Program

NASA Technical Reports Server (NTRS)

Nutter, Paul; Manobianco, John

1998-01-01

This report describes the Applied Meteorology Unit's objective verification of the National Centers for Environmental Prediction 29-km eta model during separate warm and cool season periods from May 1996 through January 1998. The verification of surface and upper-air point forecasts was performed at three selected stations important for 45th Weather Squadron, Spaceflight Meteorology Group, and National Weather Service, Melbourne operational weather concerns. The statistical evaluation identified model biases that may result from inadequate parameterization of physical processes. Since model biases are relatively small compared to the random error component, most of the total model error results from day-to-day variability in the forecasts and/or observations. To some extent, these nonsystematic errors reflect the variability in point observations that sample spatial and temporal scales of atmospheric phenomena that cannot be resolved by the model. On average, Meso-Eta point forecasts provide useful guidance for predicting the evolution of the larger scale environment. A more substantial challenge facing model users in real time is the discrimination of nonsystematic errors that tend to inflate the total forecast error. It is important that model users maintain awareness of ongoing model changes. Such changes are likely to modify the basic error characteristics, particularly near the surface.

Annual Atmospheric Corrosion of Carbon Steel Worldwide. An Integration of ISOCORRAG, ICP/UNECE and MICAT Databases

PubMed Central

Chico, Belén; de la Fuente, Daniel; Díaz, Iván; Simancas, Joaquín; Morcillo, Manuel

2017-01-01

In the 1980s, three ambitious international programmes on atmospheric corrosion (ISOCORRAG, ICP/UNECE and MICAT), involving the participation of a total of 38 countries on four continents, Europe, America, Asia and Oceania, were launched. Though each programme has its own particular characteristics, the similarity of the basic methodologies used makes it possible to integrate the databases obtained in each case. This paper addresses such an integration with the aim of establishing simple universal damage functions (DF) between first year carbon steel corrosion in the different atmospheres and available environmental variables, both meteorological (temperature (T), relative humidity (RH), precipitation (P), and time of wetness (TOW)) and pollution (SO2 and NaCl). In the statistical processing of the data, it has been chosen to differentiate between marine atmospheres and those in which the chloride deposition rate is insignificant (<3 mg/m2.d). In the DF established for non-marine atmospheres a great influence of the SO2 content in the atmosphere was seen, as well as lesser effects by the meteorological parameters of RH and T. Both NaCl and SO2 pollutants, in that order, are seen to be the most influential variables in marine atmospheres, along with a smaller impact of TOW. PMID:28772966

Cross-scale impact of climate temporal variability on ecosystem water and carbon fluxes

DOE PAGES

Paschalis, Athanasios; Fatichi, Simone; Katul, Gabriel G.; ...

2015-08-07

While the importance of ecosystem functioning is undisputed in the context of climate change and Earth system modeling, the role of short-scale temporal variability of hydrometeorological forcing (~1 h) on the related ecosystem processes remains to be fully understood. Additionally, various impacts of meteorological forcing variability on water and carbon fluxes across a range of scales are explored here using numerical simulations. Synthetic meteorological drivers that highlight dynamic features of the short temporal scale in series of precipitation, temperature, and radiation are constructed. These drivers force a mechanistic ecohydrological model that propagates information content into the dynamics of water andmore » carbon fluxes for an ensemble of representative ecosystems. The focus of the analysis is on a cross-scale effect of the short-scale forcing variability on the modeled evapotranspiration and ecosystem carbon assimilation. Interannual variability of water and carbon fluxes is emphasized in the analysis. The main study inferences are summarized as follows: (a) short-scale variability of meteorological input does affect water and carbon fluxes across a wide range of time scales, spanning from the hourly to the annual and longer scales; (b) different ecosystems respond to the various characteristics of the short-scale variability of the climate forcing in various ways, depending on dominant factors limiting system productivity; (c) whenever short-scale variability of meteorological forcing influences primarily fast processes such as photosynthesis, its impact on the slow-scale variability of water and carbon fluxes is small; and (d) whenever short-scale variability of the meteorological forcing impacts slow processes such as movement and storage of water in the soil, the effects of the variability can propagate to annual and longer time scales.« less

Cross-scale impact of climate temporal variability on ecosystem water and carbon fluxes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Paschalis, Athanasios; Fatichi, Simone; Katul, Gabriel G.

While the importance of ecosystem functioning is undisputed in the context of climate change and Earth system modeling, the role of short-scale temporal variability of hydrometeorological forcing (~1 h) on the related ecosystem processes remains to be fully understood. Additionally, various impacts of meteorological forcing variability on water and carbon fluxes across a range of scales are explored here using numerical simulations. Synthetic meteorological drivers that highlight dynamic features of the short temporal scale in series of precipitation, temperature, and radiation are constructed. These drivers force a mechanistic ecohydrological model that propagates information content into the dynamics of water andmore » carbon fluxes for an ensemble of representative ecosystems. The focus of the analysis is on a cross-scale effect of the short-scale forcing variability on the modeled evapotranspiration and ecosystem carbon assimilation. Interannual variability of water and carbon fluxes is emphasized in the analysis. The main study inferences are summarized as follows: (a) short-scale variability of meteorological input does affect water and carbon fluxes across a wide range of time scales, spanning from the hourly to the annual and longer scales; (b) different ecosystems respond to the various characteristics of the short-scale variability of the climate forcing in various ways, depending on dominant factors limiting system productivity; (c) whenever short-scale variability of meteorological forcing influences primarily fast processes such as photosynthesis, its impact on the slow-scale variability of water and carbon fluxes is small; and (d) whenever short-scale variability of the meteorological forcing impacts slow processes such as movement and storage of water in the soil, the effects of the variability can propagate to annual and longer time scales.« less

Pressure Contact Sounding Data for NASA's Atmospheric Variability Experiment (AVE 3)

NASA Technical Reports Server (NTRS)

Fuelberg, H. E.; Hill, C. K.; Turner, R. E.; Long, K. E.

1975-01-01

The basic rawinsonde data are described at each pressure contact from the surface to sounding termination for the 41 stations participating in the AVE III measurement program that began at 0000 GMT on February 6 and ended at 1200 GMT on February 7, 1975. Soundings were taken at 3-hour intervals during a large period of the experiment from most stations within the United States east of about 105 degrees west longitude. Methods of data processing, change in reduction scheme since the AVE II pilot experiment, and data accuracy are briefly discussed. An example of contact data is presented, and microfiche cards of all the contact data are included in the appendix. The AVE III project was conducted to better understand and establish the extent of applications for meteorological satellite sensor data through correlative ground truth experiments and to provide basic experimental data for use in studies of atmospheric scales of-motion interrelationships.

Evaluating the performance of ENVI-met model in diurnal cycles for different meteorological conditions

NASA Astrophysics Data System (ADS)

Acero, Juan A.; Arrizabalaga, Jon

2018-01-01

Urban areas are known to modify meteorological variables producing important differences in small spatial scales (i.e. microscale). These affect human thermal comfort conditions and the dispersion of pollutants, especially those emitted inside the urban area, which finally influence quality of life and the use of public open spaces. In this study, the diurnal evolution of meteorological variables measured in four urban spaces is compared with the results provided by ENVI-met (v 4.0). Measurements were carried out during 3 days with different meteorological conditions in Bilbao in the north of the Iberian Peninsula. The evaluation of the model accuracy (i.e. the degree to which modelled values approach measured values) was carried out with several quantitative difference metrics. The results for air temperature and humidity show a good agreement of measured and modelled values independently of the regional meteorological conditions. However, in the case of mean radiant temperature and wind speed, relevant differences are encountered highlighting the limitation of the model to estimate these meteorological variables precisely during diurnal cycles, in the considered evaluation conditions (sites and weather).

Synthetic Vision Systems in GA Cockpit-Evaluation of Basic Maneuvers Performed by Low Time GA Pilots During Transition from VMC to IMC

NASA Technical Reports Server (NTRS)

Takallu, M. A.; Wong, D. T.; Uenking, M. D.

2002-01-01

An experimental investigation was conducted to study the effectiveness of modern flight displays in general aviation cockpits for mitigating Low Visibility Loss of Control and the Controlled Flight Into Terrain accidents. A total of 18 General Aviation (GA) pilots with private pilot, single engine land rating, with no additional instrument training beyond private pilot license requirements, were recruited to evaluate three different display concepts in a fixed-based flight simulator at the NASA Langley Research Center's General Aviation Work Station. Evaluation pilots were asked to continue flight from Visual Meteorological Conditions (VMC) into Instrument Meteorological Conditions (IMC) while performing a series of 4 basic precision maneuvers. During the experiment, relevant pilot/vehicle performance variables, pilot control inputs and physiological data were recorded. Human factors questionnaires and interviews were administered after each scenario. Qualitative and quantitative data have been analyzed and the results are presented here. Pilot performance deviations from the established target values (errors) were computed and compared with the FAA Practical Test Standards. Results of the quantitative data indicate that evaluation pilots committed substantially fewer errors when using the Synthetic Vision Systems (SVS) displays than when they were using conventional instruments. Results of the qualitative data indicate that evaluation pilots perceived themselves to have a much higher level of situation awareness while using the SVS display concept.

Meteorological variables to aid forecasting deep slab avalanches on persistent weak layers

USGS Publications Warehouse

Marienthal, Alex; Hendrikx, Jordy; Birkeland, Karl; Irvine, Kathryn M.

2015-01-01

Deep slab avalanches are particularly challenging to forecast. These avalanches are difficult to trigger, yet when they release they tend to propagate far and can result in large and destructive avalanches. We utilized a 44-year record of avalanche control and meteorological data from Bridger Bowl ski area in southwest Montana to test the usefulness of meteorological variables for predicting seasons and days with deep slab avalanches. We defined deep slab avalanches as those that failed on persistent weak layers deeper than 0.9 m, and that occurred after February 1st. Previous studies often used meteorological variables from days prior to avalanches, but we also considered meteorological variables over the early months of the season. We used classification trees and random forests for our analyses. Our results showed seasons with either dry or wet deep slabs on persistent weak layers typically had less precipitation from November through January than seasons without deep slabs on persistent weak layers. Days with deep slab avalanches on persistent weak layers often had warmer minimum 24-hour air temperatures, and more precipitation over the prior seven days, than days without deep slabs on persistent weak layers. Days with deep wet slab avalanches on persistent weak layers were typically preceded by three days of above freezing air temperatures. Seasonal and daily meteorological variables were found useful to aid forecasting dry and wet deep slab avalanches on persistent weak layers, and should be used in combination with continuous observation of the snowpack and avalanche activity.

METEOROLOGICAL AND TRANSPORT MODELING

EPA Science Inventory

Advanced air quality simulation models, such as CMAQ, as well as other transport and dispersion models, require accurate and detailed meteorology fields. These meteorology fields include primary 3-dimensional dynamical and thermodynamical variables (e.g., winds, temperature, mo...

Surface Meteorological Station - Astoria, OR (AST) - Raw Data

DOE Data Explorer

Gottas, Daniel

2017-10-23

A variety of instruments are used to measure various quantities related to meteorology, precipitation, and radiation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.

Surface Meteorological Station - ESRL Short Tower, Bonneville - Raw Data

DOE Data Explorer

McCaffrey, Katherine

2017-10-23

A diversity of instruments are used to measure various quantities related to meteorology and precipitation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.

Surface Meteorological Station - ESRL Short Tower, Condon - Reviewed Data

DOE Data Explorer

McCaffrey, Katherine

2017-10-23

A diversity of instruments are used to measure various quantities related to meteorology, precipitation, and radiation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.

Surface Meteorological Station - ESRL Short Tower, Troutdale - Reviewed Data

DOE Data Explorer

Gottas, Daniel

2017-12-11

A diversity of instruments are used to measure various quantities related to meteorology, precipitation, and radiation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.

Surface Meteorological Station - ESRL Short Tower, Prineville - Raw Data

DOE Data Explorer

McCaffrey, Katherine

2017-10-23

A diversity of instruments are used to measure various quantities related to meteorology, precipitation, and radiation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.

Surface Meteorological Station - ESRL Short Tower, Troutdale - Raw Data

DOE Data Explorer

Gottas, Daniel

2017-12-11

A diversity of instruments are used to measure various quantities related to meteorology, precipitation, and radiation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.

Surface Meteorological Station - ESRL Short Tower, Prineville - Reviewed Data

DOE Data Explorer

McCaffrey, Katherine

2017-10-23

A diversity of instruments are used to measure various quantities related to meteorology, precipitation, and radiation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.

Surface Meteorological Station - ESRL Short Tower, Bonneville - Reviewed Data

DOE Data Explorer

McCaffrey, Katherine

2017-10-23

A diversity of instruments are used to measure various quantities related to meteorology and precipitation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.

Surface Meteorological Station - North Bend, OR (OTH) - Raw Data

DOE Data Explorer

Gottas, Daniel

2017-10-23

A variety of instruments are used to measure various quantities related to meteorology, precipitation, and radiation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.

Surface Meteorological Station - ESRL Short Tower, Condon - Raw Data

DOE Data Explorer

McCaffrey, Katherine

2017-10-23

A diversity of instruments are used to measure various quantities related to meteorology, precipitation, and radiation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.

Surface Meteorological Station - Forks, WA (FKS) - Raw Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gottas, Daniel

A variety of instruments are used to measure various quantities related to meteorology, precipitation, and radiation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.

Surface Meteorological Station - Forks, WA (FKS) - Reviewed Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gottas, Daniel

A variety of instruments are used to measure various quantities related to meteorology, precipitation, and radiation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.

Estimating urban ground-level PM10 using MODIS 3km AOD product and meteorological parameters from WRF model

NASA Astrophysics Data System (ADS)

Ghotbi, Saba; Sotoudeheian, Saeed; Arhami, Mohammad

2016-09-01

Satellite remote sensing products of AOD from MODIS along with appropriate meteorological parameters were used to develop statistical models and estimate ground-level PM10. Most of previous studies obtained meteorological data from synoptic weather stations, with rather sparse spatial distribution, and used it along with 10 km AOD product to develop statistical models, applicable for PM variations in regional scale (resolution of ≥10 km). In the current study, meteorological parameters were simulated with 3 km resolution using WRF model and used along with the rather new 3 km AOD product (launched in 2014). The resulting PM statistical models were assessed for a polluted and largely variable urban area, Tehran, Iran. Despite the critical particulate pollution problem, very few PM studies were conducted in this area. The issue of rather poor direct PM-AOD associations existed, due to different factors such as variations in particles optical properties, in addition to bright background issue for satellite data, as the studied area located in the semi-arid areas of Middle East. Statistical approach of linear mixed effect (LME) was used, and three types of statistical models including single variable LME model (using AOD as independent variable) and multiple variables LME model by using meteorological data from two sources, WRF model and synoptic stations, were examined. Meteorological simulations were performed using a multiscale approach and creating an appropriate physic for the studied region, and the results showed rather good agreements with recordings of the synoptic stations. The single variable LME model was able to explain about 61%-73% of daily PM10 variations, reflecting a rather acceptable performance. Statistical models performance improved through using multivariable LME and incorporating meteorological data as auxiliary variables, particularly by using fine resolution outputs from WRF (R2 = 0.73-0.81). In addition, rather fine resolution for PM estimates was mapped for the studied city, and resulting concentration maps were consistent with PM recordings at the existing stations.

Bayesian dynamic modeling of time series of dengue disease case counts.

PubMed

Martínez-Bello, Daniel Adyro; López-Quílez, Antonio; Torres-Prieto, Alexander

2017-07-01

The aim of this study is to model the association between weekly time series of dengue case counts and meteorological variables, in a high-incidence city of Colombia, applying Bayesian hierarchical dynamic generalized linear models over the period January 2008 to August 2015. Additionally, we evaluate the model's short-term performance for predicting dengue cases. The methodology shows dynamic Poisson log link models including constant or time-varying coefficients for the meteorological variables. Calendar effects were modeled using constant or first- or second-order random walk time-varying coefficients. The meteorological variables were modeled using constant coefficients and first-order random walk time-varying coefficients. We applied Markov Chain Monte Carlo simulations for parameter estimation, and deviance information criterion statistic (DIC) for model selection. We assessed the short-term predictive performance of the selected final model, at several time points within the study period using the mean absolute percentage error. The results showed the best model including first-order random walk time-varying coefficients for calendar trend and first-order random walk time-varying coefficients for the meteorological variables. Besides the computational challenges, interpreting the results implies a complete analysis of the time series of dengue with respect to the parameter estimates of the meteorological effects. We found small values of the mean absolute percentage errors at one or two weeks out-of-sample predictions for most prediction points, associated with low volatility periods in the dengue counts. We discuss the advantages and limitations of the dynamic Poisson models for studying the association between time series of dengue disease and meteorological variables. The key conclusion of the study is that dynamic Poisson models account for the dynamic nature of the variables involved in the modeling of time series of dengue disease, producing useful models for decision-making in public health.

Surface Meteorological Station - ESRL Short Tower, Wasco Airport - Raw Data

DOE Data Explorer

Gottas, Daniel

2017-12-11

A diversity of instruments are used to measure various quantities related to meteorology, precipitation, and radiation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.

Surface Meteorological Station - ESRL Short Tower, Wasco Airport - Reviewed Data

DOE Data Explorer

Gottas, Daniel

2017-12-11

A diversity of instruments are used to measure various quantities related to meteorology, precipitation, and radiation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.

Meteorological tower design for severe weather and remote locations

Treesearch

Kelly Elder; Ilkoo Angutikjuak; Jessica Baker; Matt Belford; Tom Bennett; Karl Birkeland; Daniel Bowker; Doug Chabot; April Cheuvront; Mark Dixon; Dylan Elder; Lee Elder; Shari Gearheard; Greg Giedt; Kim Grant; Sam Green; Ethan Greene; Nick Houfek; Caleb Huntington; Henry Huntington; Thomas Huntington; Daniel Janigian; Crane Johnson; Glen Liston; Rob Maris; Andrea Marsh; Hans-Peter Marshall; Aidan Meiners; Alex Meiners; Theo Meiners; Limakee Palluq; Josh Pope; Esa Qillaq; Joelli Sanguya; Sam Sehnert; Ron Simenhois; Banning Starr; Roger Tyler

2012-01-01

We have developed a robust meteorological tower for deployment in locations with extreme conditions and for applications that require relatively maintenance-free structures. The basic design consists of a triangular base with two horizontal rails on each side, and uprights at the triangle vertices for various instrument configurations. The fabrication materials include...

Use of a scenario-neutral approach to identify the key hydro-meteorological attributes that impact runoff from a natural catchment

NASA Astrophysics Data System (ADS)

Guo, Danlu; Westra, Seth; Maier, Holger R.

2017-11-01

Scenario-neutral approaches are being used increasingly for assessing the potential impact of climate change on water resource systems, as these approaches allow the performance of these systems to be evaluated independently of climate change projections. However, practical implementations of these approaches are still scarce, with a key limitation being the difficulty of generating a range of plausible future time series of hydro-meteorological data. In this study we apply a recently developed inverse stochastic generation approach to support the scenario-neutral analysis, and thus identify the key hydro-meteorological variables to which the system is most sensitive. The stochastic generator simulates synthetic hydro-meteorological time series that represent plausible future changes in (1) the average, extremes and seasonal patterns of rainfall; and (2) the average values of temperature (Ta), relative humidity (RH) and wind speed (uz) as variables that drive PET. These hydro-meteorological time series are then fed through a conceptual rainfall-runoff model to simulate the potential changes in runoff as a function of changes in the hydro-meteorological variables, and runoff sensitivity is assessed with both correlation and Sobol' sensitivity analyses. The method was applied to a case study catchment in South Australia, and the results showed that the most important hydro-meteorological attributes for runoff were winter rainfall followed by the annual average rainfall, while the PET-related meteorological variables had comparatively little impact. The high importance of winter rainfall can be related to the winter-dominated nature of both the rainfall and runoff regimes in this catchment. The approach illustrated in this study can greatly enhance our understanding of the key hydro-meteorological attributes and processes that are likely to drive catchment runoff under a changing climate, thus enabling the design of tailored climate impact assessments to specific water resource systems.

Estimating historical groundwater levels based on relations with hydrologic and meteorological variables in the U.S. glacial aquifer system

NASA Astrophysics Data System (ADS)

Dudley, R. W.; Hodgkins, G. A.; Nielsen, M. G.; Qi, S. L.

2018-07-01

A number of previous studies have examined relations between groundwater levels and hydrologic and meteorological variables over parts of the glacial aquifer system, but systematic analyses across the entire U.S. glacial aquifer system are lacking. We tested correlations between monthly groundwater levels measured at 1043 wells in the U.S. glacial aquifer system considered to be minimally influenced by human disturbance and selected hydrologic and meteorological variables with the goal of extending historical groundwater records where there were strong correlations. Groundwater levels in the East region correlated most strongly with short-term (1 and 3 month) averages of hydrologic and meteorological variables, while those in the Central and West Central regions yielded stronger correlations with hydrologic and meteorological variables averaged over longer time intervals (6-12 months). Variables strongly correlated with high and low annual groundwater levels were identified as candidate records for use in statistical linear models as a means to fill in and extend historical high and low groundwater levels respectively. Overall, 37.4% of study wells meeting data criteria had successful models for high and (or) low groundwater levels; these wells shared characteristics of relatively higher local precipitation, higher local land-surface slope, lower amounts of clay within the surficial sediments, and higher base-flow index. Streamflow and base flow served as explanatory variables in about two thirds of both high- and low-groundwater-level models in all three regions, and generally yielded more and better models compared to precipitation and Palmer Drought Severity Index. The use of variables such as streamflow with substantially longer and more complete records than those of groundwater wells provide a means for placing contemporary groundwater levels in a longer historical context and can support site-specific analyses such as groundwater modeling.

Modelling the meteorological forest fire niche in heterogeneous pyrologic conditions.

PubMed

De Angelis, Antonella; Ricotta, Carlo; Conedera, Marco; Pezzatti, Gianni Boris

2015-01-01

Fire regimes are strongly related to weather conditions that directly and indirectly influence fire ignition and propagation. Identifying the most important meteorological fire drivers is thus fundamental for daily fire risk forecasting. In this context, several fire weather indices have been developed focussing mainly on fire-related local weather conditions and fuel characteristics. The specificity of the conditions for which fire danger indices are developed makes its direct transfer and applicability problematic in different areas or with other fuel types. In this paper we used the low-to-intermediate fire-prone region of Canton Ticino as a case study to develop a new daily fire danger index by implementing a niche modelling approach (Maxent). In order to identify the most suitable weather conditions for fires, different combinations of input variables were tested (meteorological variables, existing fire danger indices or a combination of both). Our findings demonstrate that such combinations of input variables increase the predictive power of the resulting index and surprisingly even using meteorological variables only allows similar or better performances than using the complex Canadian Fire Weather Index (FWI). Furthermore, the niche modelling approach based on Maxent resulted in slightly improved model performance and in a reduced number of selected variables with respect to the classical logistic approach. Factors influencing final model robustness were the number of fire events considered and the specificity of the meteorological conditions leading to fire ignition.

Modelling the Meteorological Forest Fire Niche in Heterogeneous Pyrologic Conditions

PubMed Central

De Angelis, Antonella; Ricotta, Carlo; Conedera, Marco; Pezzatti, Gianni Boris

2015-01-01

Fire regimes are strongly related to weather conditions that directly and indirectly influence fire ignition and propagation. Identifying the most important meteorological fire drivers is thus fundamental for daily fire risk forecasting. In this context, several fire weather indices have been developed focussing mainly on fire-related local weather conditions and fuel characteristics. The specificity of the conditions for which fire danger indices are developed makes its direct transfer and applicability problematic in different areas or with other fuel types. In this paper we used the low-to-intermediate fire-prone region of Canton Ticino as a case study to develop a new daily fire danger index by implementing a niche modelling approach (Maxent). In order to identify the most suitable weather conditions for fires, different combinations of input variables were tested (meteorological variables, existing fire danger indices or a combination of both). Our findings demonstrate that such combinations of input variables increase the predictive power of the resulting index and surprisingly even using meteorological variables only allows similar or better performances than using the complex Canadian Fire Weather Index (FWI). Furthermore, the niche modelling approach based on Maxent resulted in slightly improved model performance and in a reduced number of selected variables with respect to the classical logistic approach. Factors influencing final model robustness were the number of fire events considered and the specificity of the meteorological conditions leading to fire ignition. PMID:25679957

Association of monthly frequencies of diverse diseases in the calls to the public emergency service of the city of Buenos Aires during 1999-2004 with meteorological variables and seasons.

PubMed

Alexander, P

2013-01-01

This work aims to study associations between monthly averages of meteorological variables and monthly frequencies of diverse diseases in the calls to the public ambulance emergency service of the city of Buenos Aires during the years 1999-2004. Throughout this time period no changes were made in the classification codes of the illnesses. Heart disease, arrhythmia, heart failure, cardiopulmonary arrest, angina pectoris, psychiatric diseases, stroke, transient ischemic attack, syncope and the total number of calls were analyzed against 11 weather variables and the four seasons. All illnesses exhibited some seasonal behavior, except cardiorespiratory arrest and angina pectoris. The largest frequencies of illnesses that exhibited some association with the meteorological variables used to occur in winter, except the psychiatric cases. Heart failure, stroke, psychiatric diseases and the total number of calls showed significant correlations with the 11 meteorological variables considered, and the largest indices (absolute values above 0.6) were found for the former two pathologies. On the other side, cardiorespiratory arrest and angina pectoris revealed no significant correlations and nearly null indices. Variables associated with temperature were the meteorological proxies with the largest correlations against diseases. Pressure and humidity mostly exhibited positive correlations, which is the opposite of variables related to temperature. Contrary to all other diseases, psychiatric pathologies showed a clear predominance of positive correlations. Finally, the association degree of the medical dataset with recurrent patterns was further evaluated through Fourier analysis, to assess the presence of statistically significant behavior. In the Northern Hemisphere high morbidity and mortality rates in December are usually assigned to diverse factors in relation to the holidays, but such an effect is not observed in the present analysis. There seems to be no clearly preferred meteorological proxy among the different types of temperatures used. It is shown that the amount of occurrences depends mainly on season rather on its strength quantified by temperature.

Association of monthly frequencies of diverse diseases in the calls to the public emergency service of the city of Buenos Aires during 1999-2004 with meteorological variables and seasons

NASA Astrophysics Data System (ADS)

Alexander, P.

2013-01-01

This work aims to study associations between monthly averages of meteorological variables and monthly frequencies of diverse diseases in the calls to the public ambulance emergency service of the city of Buenos Aires during the years 1999-2004. Throughout this time period no changes were made in the classification codes of the illnesses. Heart disease, arrhythmia, heart failure, cardiopulmonary arrest, angina pectoris, psychiatric diseases, stroke, transient ischemic attack, syncope and the total number of calls were analyzed against 11 weather variables and the four seasons. All illnesses exhibited some seasonal behavior, except cardiorespiratory arrest and angina pectoris. The largest frequencies of illnesses that exhibited some association with the meteorological variables used to occur in winter, except the psychiatric cases. Heart failure, stroke, psychiatric diseases and the total number of calls showed significant correlations with the 11 meteorological variables considered, and the largest indices (absolute values above 0.6) were found for the former two pathologies. On the other side, cardiorespiratory arrest and angina pectoris revealed no significant correlations and nearly null indices. Variables associated with temperature were the meteorological proxies with the largest correlations against diseases. Pressure and humidity mostly exhibited positive correlations, which is the opposite of variables related to temperature. Contrary to all other diseases, psychiatric pathologies showed a clear predominance of positive correlations. Finally, the association degree of the medical dataset with recurrent patterns was further evaluated through Fourier analysis, to assess the presence of statistically significant behavior. In the Northern Hemisphere high morbidity and mortality rates in December are usually assigned to diverse factors in relation to the holidays, but such an effect is not observed in the present analysis. There seems to be no clearly preferred meteorological proxy among the different types of temperatures used. It is shown that the amount of occurrences depends mainly on season rather on its strength quantified by temperature.

Machine learning methods reveal the temporal pattern of dengue incidence using meteorological factors in metropolitan Manila, Philippines.

PubMed

Carvajal, Thaddeus M; Viacrusis, Katherine M; Hernandez, Lara Fides T; Ho, Howell T; Amalin, Divina M; Watanabe, Kozo

2018-04-17

Several studies have applied ecological factors such as meteorological variables to develop models and accurately predict the temporal pattern of dengue incidence or occurrence. With the vast amount of studies that investigated this premise, the modeling approaches differ from each study and only use a single statistical technique. It raises the question of whether which technique would be robust and reliable. Hence, our study aims to compare the predictive accuracy of the temporal pattern of Dengue incidence in Metropolitan Manila as influenced by meteorological factors from four modeling techniques, (a) General Additive Modeling, (b) Seasonal Autoregressive Integrated Moving Average with exogenous variables (c) Random Forest and (d) Gradient Boosting. Dengue incidence and meteorological data (flood, precipitation, temperature, southern oscillation index, relative humidity, wind speed and direction) of Metropolitan Manila from January 1, 2009 - December 31, 2013 were obtained from respective government agencies. Two types of datasets were used in the analysis; observed meteorological factors (MF) and its corresponding delayed or lagged effect (LG). After which, these datasets were subjected to the four modeling techniques. The predictive accuracy and variable importance of each modeling technique were calculated and evaluated. Among the statistical modeling techniques, Random Forest showed the best predictive accuracy. Moreover, the delayed or lag effects of the meteorological variables was shown to be the best dataset to use for such purpose. Thus, the model of Random Forest with delayed meteorological effects (RF-LG) was deemed the best among all assessed models. Relative humidity was shown to be the top-most important meteorological factor in the best model. The study exhibited that there are indeed different predictive outcomes generated from each statistical modeling technique and it further revealed that the Random forest model with delayed meteorological effects to be the best in predicting the temporal pattern of Dengue incidence in Metropolitan Manila. It is also noteworthy that the study also identified relative humidity as an important meteorological factor along with rainfall and temperature that can influence this temporal pattern.

Exploring the spatio-temporal relationship between two key aeroallergens and meteorological variables in the United Kingdom

NASA Astrophysics Data System (ADS)

Khwarahm, Nabaz; Dash, Jadunandan; Atkinson, Peter M.; Newnham, R. M.; Skjøth, C. A.; Adams-Groom, B.; Caulton, Eric; Head, K.

2014-05-01

Constructing accurate predictive models for grass and birch pollen in the air, the two most important aeroallergens, for areas with variable climate conditions such as the United Kingdom, require better understanding of the relationships between pollen count in the air and meteorological variables. Variations in daily birch and grass pollen counts and their relationship with daily meteorological variables were investigated for nine pollen monitoring sites for the period 2000-2010 in the United Kingdom. An active pollen count sampling method was employed at each of the monitoring stations to sample pollen from the atmosphere. The mechanism of this method is based on the volumetric spore traps of Hirst design (Hirst in Ann Appl Biol 39(2):257-265, 1952). The pollen season (start date, finish date) for grass and birch were determined using a first derivative method. Meteorological variables such as daily rainfall; maximum, minimum and average temperatures; cumulative sum of Sunshine duration; wind speed; and relative humidity were related to the grass and birch pollen counts for the pre-peak, post peak and the entire pollen season. The meteorological variables were correlated with the pollen count data for the following temporal supports: same-day, 1-day prior, 1-day mean prior, 3-day mean prior, 7-day mean prior. The direction of influence (positive/negative) of meteorological variables on pollen count varied for birch and grass, and also varied when the pollen season was treated as a whole season, or was segmented into the pre-peak and post-peak seasons. Maximum temperature, sunshine duration and rainfall were the most important variables influencing the count of grass pollen in the atmosphere. Both maximum temperature (pre-peak) and sunshine produced a strong positive correlation, and rain produced a strong negative correlation with grass pollen count in the air. Similarly, average temperature, wind speed and rainfall were the most important variables influencing the count of birch pollen in the air. Both wind speed and rain produced a negative correlation with birch pollen count in the air and average temperature produced a positive correlation.

An Intercomparison of the Dynamical Cores of Global Atmospheric Circulation Models for Mars

NASA Technical Reports Server (NTRS)

Hollingsworth, Jeffery L.; Bridger, Alison F. C.; Haberle, Robert M.

1998-01-01

This is a Final Report for a Joint Research Interchange (JRI) between NASA Ames Research Center and San Jose State University, Department of Meteorology. The focus of this JRI has been to evaluate the dynamical 'cores' of two global atmospheric circulation models for Mars that are in operation at the NASA Ames Research Center. The two global circulation models in use are fundamentally different: one uses spherical harmonics in its horizontal representation of field variables; the other uses finite differences on a uniform longitude-latitude grid. Several simulations have been conducted to assess how the dynamical processors of each of these circulation models perform using identical 'simple physics' parameterizations. A variety of climate statistics (e.g., time-mean flows and eddy fields) have been compared for realistic solstitial mean basic states. Results of this research have demonstrated that the two Mars circulation models with completely different spatial representations and discretizations produce rather similar circulation statistics for first-order meteorological fields, suggestive of a tendency for convergence of numerical solutions. Second and higher-order fields can, however, vary significantly between the two models.

An Intercomparison of the Dynamical Cores of Global Atmospheric Circulation Models for Mars

NASA Technical Reports Server (NTRS)

Hollingsworth, Jeffery L.; Bridger, Alison F. C.; Haberle, Robert M.

1998-01-01

This is a Final Report for a Joint Research Interchange (JRI) between NASA Ames Research Cen- ter and San Jose State University, Department of Meteorology. The focus of this JRI has been to evaluate the dynamical "cores" of two global atmospheric circulation models for Mars that are in operation at the NASA Ames Research Center. ne two global circulation models in use are fundamentally different: one uses spherical harmonics in its horizontal representation of field variables; the other uses finite differences on a uniform longitude-latitude grid. Several simulations have been conducted to assess how the dynamical processors of each of these circulation models perform using identical "simple physics" parameterizations. A variety of climate statistics (e.g., time-mean flows and eddy fields) have been compared for realistic solstitial mean basic states. Results of this research have demonstrated that the two Mars circulation models with completely different spatial representations and discretizations produce rather similar circulation statistics for first-order meteorological fields, suggestive of a tendency for convergence of numerical solutions. Second and higher-order fields can, however, vary significantly between the two models.

Spatio-temporal response of maize yield to edaphic and meteorological conditions in a saline farmland

USDA-ARS?s Scientific Manuscript database

Spatio-temporal variability of crop production strongly depends on soil heterogeneity, meteorological conditions, and their interaction. Canopy reflectance can be used to describe crop status and yield spatial variability. The objectives of this work were to understand the spatio-temporal variabilit...

Heat Stress Equation Development and Usage for Dryden Flight Research Center (DFRC)

NASA Technical Reports Server (NTRS)

Houtas, Franzeska; Teets, Edward H., Jr.

2012-01-01

Heat Stress Indices are equations that integrate some or all variables (e.g. temperature, relative humidity, wind speed), directly or indirectly, to produce a number for thermal stress on humans for a particular environment. There are a large number of equations that have been developed which range from simple equations that may ignore basic factors (e.g. wind effects on thermal loading, fixed contribution from solar heating) to complex equations that attempt to incorporate all variables. Each equation is evaluated for a particular use, as well as considering the ease of use and reliability of the results. The meteorology group at the Dryden Flight Research Center has utilized and enhanced the American College of Sports Medicine equation to represent the specific environment of the Mojave Desert. The Dryden WBGT Heat Stress equation has been vetted and implemented as an automated notification to the entire facility for the safety of all personnel and visitors.

The SPARC Intercomparison of Middle Atmosphere Climatologies

NASA Technical Reports Server (NTRS)

Randel, William; Fleming, Eric; Geller, Marvin; Gelman, Mel; Hamilton, Kevin; Karoly, David; Ortland, Dave; Pawson, Steve; Swinbank, Richard; Udelhofen, Petra

2003-01-01

Our current confidence in 'observed' climatological winds and temperatures in the middle atmosphere (over altitudes approx. 10-80 km) is assessed by detailed intercomparisons of contemporary and historic data sets. These data sets include global meteorological analyses and assimilations, climatologies derived from research satellite measurements, and historical reference atmosphere circulation statistics. We also include comparisons with historical rocketsonde wind and temperature data, and with more recent lidar temperature measurements. The comparisons focus on a few basic circulation statistics, such as temperature, zonal wind, and eddy flux statistics. Special attention is focused on tropical winds and temperatures, where large differences exist among separate analyses. Assimilated data sets provide the most realistic tropical variability, but substantial differences exist among current schemes.

A climate index indicative of cloudiness derived from satellite infrared sounder data

NASA Technical Reports Server (NTRS)

Abel, M. D.; Cox, S. K.

1981-01-01

In many current studies conducted to enhance the usefulness of meteorological satellite radiance data, one common objective is to infer conventional weather variables. The present investigation, on the other hand, is mainly concerned with the efficient retrieval (minimization of errors) of a nonstandard atmospheric descriptor. The atmosphere's Vertical Infrared Radiative Emitting Structure (VIRES) is retrieved. VIRES is described by the broadband infrared weighting function curve. The shapes of these weighting curves are primarily a function of the three-dimensional cloud structure. The weighting curves are retrieved by a method which uses satellite spectral radiance data. The basic theory involved in the VIRES retrieval procedure parallels the technique used to retrieve temperature soundings.

Daily weather variables and affective disorder admissions to psychiatric hospitals

NASA Astrophysics Data System (ADS)

McWilliams, Stephen; Kinsella, Anthony; O'Callaghan, Eadbhard

2014-12-01

Numerous studies have reported that admission rates in patients with affective disorders are subject to seasonal variation. Notwithstanding, there has been limited evaluation of the degree to which changeable daily meteorological patterns influence affective disorder admission rates. A handful of small studies have alluded to a potential link between psychiatric admission rates and meteorological variables such as environmental temperature (heat waves in particular), wind direction and sunshine. We used the Kruskal-Wallis test, ARIMA and time-series regression analyses to examine whether daily meteorological variables—namely wind speed and direction, barometric pressure, rainfall, hours of sunshine, sunlight radiation and temperature—influence admission rates for mania and depression across 12 regions in Ireland over a 31-year period. Although we found some very weak but interesting trends for barometric pressure in relation to mania admissions, daily meteorological patterns did not appear to affect hospital admissions overall for mania or depression. Our results do not support the small number of papers to date that suggest a link between daily meteorological variables and affective disorder admissions. Further study is needed.

Assessing the impacts of seasonal and vertical atmospheric conditions on air quality over the Pearl River Delta region

NASA Astrophysics Data System (ADS)

Tong, Cheuk Hei Marcus; Yim, Steve Hung Lam; Rothenberg, Daniel; Wang, Chien; Lin, Chuan-Yao; Chen, Yongqin David; Lau, Ngar Cheung

2018-05-01

Air pollution is an increasingly concerning problem in many metropolitan areas due to its adverse public health and environmental impacts. Vertical atmospheric conditions have strong effects on vertical mixing of air pollutants, which directly affects surface air quality. The characteristics and magnitude of how vertical atmospheric conditions affect surface air quality, which are critical to future air quality projections, have not yet been fully understood. This study aims to enhance understanding of the annual and seasonal sensitivities of air pollution to both surface and vertical atmospheric conditions. Based on both surface and vertical meteorological characteristics provided by 1994-2003 monthly dynamic downscaling data from the Weather and Research Forecast Model, we develop generalized linear models (GLMs) to study the relationships between surface air pollutants (ozone, respirable suspended particulates, and sulfur dioxide) and atmospheric conditions in the Pearl River Delta (PRD) region. Applying Principal Component Regression (PCR) to address multi-collinearity, we study the contributions of various meteorological variables to pollutants' concentration levels based on the loading and model coefficient of major principal components. Our results show that relatively high pollutant concentration occurs under relatively low mid-level troposphere temperature gradients, low relative humidity, weak southerly wind (or strong northerly wind) and weak westerly wind (or strong easterly wind). Moreover, the correlations vary among pollutant species, seasons, and meteorological variables at various altitudes. In general, pollutant sensitivity to meteorological variables is found to be greater in winter than in other seasons, and the sensitivity of ozone to meteorology differs from that of the other two pollutants. Applying our GLMs to anomalous air pollution episodes, we find that meteorological variables up to mid troposphere (∼700 mb) play an important role in influencing surface air quality, pinpointing the significant and unique associations between meteorological variables at higher altitudes and surface air quality.

OpenDrift v1.0: a generic framework for trajectory modelling

NASA Astrophysics Data System (ADS)

Dagestad, Knut-Frode; Röhrs, Johannes; Breivik, Øyvind; Ådlandsvik, Bjørn

2018-04-01

OpenDrift is an open-source Python-based framework for Lagrangian particle modelling under development at the Norwegian Meteorological Institute with contributions from the wider scientific community. The framework is highly generic and modular, and is designed to be used for any type of drift calculations in the ocean or atmosphere. A specific module within the OpenDrift framework corresponds to a Lagrangian particle model in the traditional sense. A number of modules have already been developed, including an oil drift module, a stochastic search-and-rescue module, a pelagic egg module, and a basic module for atmospheric drift. The framework allows for the ingestion of an unspecified number of forcing fields (scalar and vectorial) from various sources, including Eulerian ocean, atmosphere and wave models, but also measurements or a priori values for the same variables. A basic backtracking mechanism is inherent, using sign reversal of the total displacement vector and negative time stepping. OpenDrift is fast and simple to set up and use on Linux, Mac and Windows environments, and can be used with minimal or no Python experience. It is designed for flexibility, and researchers may easily adapt or write modules for their specific purpose. OpenDrift is also designed for performance, and simulations with millions of particles may be performed on a laptop. Further, OpenDrift is designed for robustness and is in daily operational use for emergency preparedness modelling (oil drift, search and rescue, and drifting ships) at the Norwegian Meteorological Institute.

Spatial clustering and meteorological drivers of summer ozone in Europe

NASA Astrophysics Data System (ADS)

Carro-Calvo, Leopoldo; Ordóñez, Carlos; García-Herrera, Ricardo; Schnell, Jordan L.

2017-10-01

We have applied the k-means clustering technique on a maximum daily 8-h running average near-surface ozone (MDA8 O3) gridded dataset over Europe at 1° × 1° resolution for summer 1998-2012. This has resulted in a spatial division of nine regions where ozone presents coherent spatiotemporal patterns. The role of meteorology in the variability of ozone at different time scales has been investigated by using daily meteorological fields from the NCEP-NCAR meteorological reanalysis. In the five regions of central-southern Europe ozone extremes (exceedances of the summer 95th percentile) occur mostly under anticyclonic circulation or weak sea level pressure gradients which trigger elevated temperatures and the recirculation of air masses. In the four northern regions extremes are associated with high-latitude anticyclones that divert the typical westerly flow at those latitudes and cause the advection of aged air masses from the south. The impact of meteorology on the day-to-day variability of ozone has been assessed by means of two different types of multiple linear models. These include as predictors meteorological fields averaged within the regions (;region-based; approach) or synoptic indices indicating the degree of resemblance between the daily meteorological fields over a large domain (25°-70° N, 35° W - 35° E) and their corresponding composites for extreme ozone days (;index-based; approach). With the first approach, a reduced set of variables, always including daily maximum temperature within the region, explains 47-66% of the variability (adjusted R2) in central-southern Europe, while more complex models are needed to explain 27-49% of the variability in the northern regions. The index-based approach yields better results for the regions of northern Europe, with adjusted R2 = 40-57%. Finally, both methodologies have also been applied to reproduce the interannual variability of ozone, with the best models explaining 66-88% of the variance in central-southern Europe and 45-66% in the north. Thus, the regionalisation carried out in this work has allowed establishing clear distinctions between the meteorological drivers of ozone in northern Europe and in the rest of the continent. These drivers are consistent across the different time scales examined (extremes, day-to-day and interannual), which gives confidence in the robustness of the results.

Observed and predicted sensitivities of extreme surface ozone to meteorological drivers in three US cities

NASA Astrophysics Data System (ADS)

Fix, Miranda J.; Cooley, Daniel; Hodzic, Alma; Gilleland, Eric; Russell, Brook T.; Porter, William C.; Pfister, Gabriele G.

2018-03-01

We conduct a case study of observed and simulated maximum daily 8-h average (MDA8) ozone (O3) in three US cities for summers during 1996-2005. The purpose of this study is to evaluate the ability of a high resolution atmospheric chemistry model to reproduce observed relationships between meteorology and high or extreme O3. We employ regional coupled chemistry-transport model simulations to make three types of comparisons between simulated and observational data, comparing (1) tails of the O3 response variable, (2) distributions of meteorological predictor variables, and (3) sensitivities of high and extreme O3 to meteorological predictors. This last comparison is made using two methods: quantile regression, for the 0.95 quantile of O3, and tail dependence optimization, which is used to investigate even higher O3 extremes. Across all three locations, we find substantial differences between simulations and observational data in both meteorology and meteorological sensitivities of high and extreme O3.

Development of specifications for surface and subsurface oceanic environmental data

NASA Technical Reports Server (NTRS)

Wolff, P. M.

1976-01-01

The existing need for synoptic subsurface observations was demonstrated giving special attention to the requirements of meteorology. The current state of synoptic oceanographic observations was assessed; a preliminary design for the Basic Observational Network needed to fulfill the minimum needs of synoptic meteorology and oceanography was presented. There is an existing critical need for such a network in the support of atmospheric modeling and operational meteorological prediction, and through utilization of the regional water mass concept an adequate observational system can be designed which is realistic in terms of cost and effort.

Barrier island forest ecosystem: role of meteorologic nutrient inputs.

PubMed

Art, H W; Bormann, F H; Voigt, G K; Woodwell, G M

1974-04-05

The Sunken Forest, located on Fire Island, a barrier island in the Atlantic Ocean off Long Island, New York, is an ecosystem in which most of the basic cation input is in the form of salt spray. This meteorologic input is sufficient to compensate for the lack of certain nutrients in the highly weathered sandy soils. In other ecosystems these nutrients are generally supplied by weathering of soil particles. The compensatory effect of meteorologic input allows for primary production rates in the Sunken Forest similar to those of inland temperate forests.

Development of the Finse Alpine Research Station towards a platform for multi-disciplinary research on Land-Atmosphere Interaction in Cold Environments (LATICE)

NASA Astrophysics Data System (ADS)

Burkhart, John F.; Decker, Sven; Filhol, Simon; Hulth, John; Nesje, Atle; Schuler, Thomas V.; Sobolowski, Stefan; Tallaksen, Lena M.

2017-04-01

The Finse Alpine Research Station provides convenient access to the Hardangervidda mountain plateau in Southern Norway (60 deg N, 1222 m asl). The station is located above the tree-line in vicinity to the west-eastern mountain water divide and is easily accessible by train from Bergen and Oslo. The station itself offers housing and basic laboratory facilities and has been used for ecological monitoring. Over the past years, studies on small-scale snow distribution and ground temperature have been performed and accompanied by a suite of meteorological measurements. Supported by strategic investments by the University of Oslo and ongoing research projects, these activities are currently expanded and the site is developed towards a mountain field laboratory for studies on Land-Atmosphere Interaction in Cold Environments, facilitated by the LATICE project (www.mn.uio.no/latice). Additional synergy comes from close collaborations with a range of institutions that perform operational monitoring close to Finse, including long-term time series of meteorological data and global radiation. Through our activities, this infrastructure has been complemented by a permanent tower for continuous Eddy-Covariance measurements along with associated gas fluxes. A second, mobile covariance system is in preparation and will become operational in 2017. In addition, a wireless sensor network is set up to grasp the spatial distributions of basic meteorological variables, snow depth and glacier mass balance on the nearby Hardangerjøkulen ice cap. While the research focus so far was on small scale processes (snow redistribution), this is now being expanded to cover hydrological processes on the catchment and regional scale. To this end, two discharge stations have been installed to gauge discharge from two contrasting catchments (glacier dominated and non-glacierized). In this presentation, we provide an overview over existing and planned infrastructure, field campaigns and research activities, accompanied by available data, the result of some preliminary analysis and discuss opportunities for future collaboration.

Meteorological Variables and Behavior of Learners with Autism: An Examination of Possible Relationships

ERIC Educational Resources Information Center

VanBuskirk, Sabrina E.; Simpson, Richard L.

2013-01-01

For this study, we collected classroom behavioral data for three children with autism relative to daily meteorological conditions. Meteorological data, including barometric pressure, humidity, outdoor temperature, and moon illumination, were obtained from the National Weather Service. Relationships between children's individual target behaviors…

Bayesian dynamic modeling of time series of dengue disease case counts

PubMed Central

López-Quílez, Antonio; Torres-Prieto, Alexander

2017-01-01

The aim of this study is to model the association between weekly time series of dengue case counts and meteorological variables, in a high-incidence city of Colombia, applying Bayesian hierarchical dynamic generalized linear models over the period January 2008 to August 2015. Additionally, we evaluate the model’s short-term performance for predicting dengue cases. The methodology shows dynamic Poisson log link models including constant or time-varying coefficients for the meteorological variables. Calendar effects were modeled using constant or first- or second-order random walk time-varying coefficients. The meteorological variables were modeled using constant coefficients and first-order random walk time-varying coefficients. We applied Markov Chain Monte Carlo simulations for parameter estimation, and deviance information criterion statistic (DIC) for model selection. We assessed the short-term predictive performance of the selected final model, at several time points within the study period using the mean absolute percentage error. The results showed the best model including first-order random walk time-varying coefficients for calendar trend and first-order random walk time-varying coefficients for the meteorological variables. Besides the computational challenges, interpreting the results implies a complete analysis of the time series of dengue with respect to the parameter estimates of the meteorological effects. We found small values of the mean absolute percentage errors at one or two weeks out-of-sample predictions for most prediction points, associated with low volatility periods in the dengue counts. We discuss the advantages and limitations of the dynamic Poisson models for studying the association between time series of dengue disease and meteorological variables. The key conclusion of the study is that dynamic Poisson models account for the dynamic nature of the variables involved in the modeling of time series of dengue disease, producing useful models for decision-making in public health. PMID:28671941

Meteorological adjustment of yearly mean values for air pollutant concentration comparison

NASA Technical Reports Server (NTRS)

Sidik, S. M.; Neustadter, H. E.

1976-01-01

Using multiple linear regression analysis, models which estimate mean concentrations of Total Suspended Particulate (TSP), sulfur dioxide, and nitrogen dioxide as a function of several meteorologic variables, two rough economic indicators, and a simple trend in time are studied. Meteorologic data were obtained and do not include inversion heights. The goodness of fit of the estimated models is partially reflected by the squared coefficient of multiple correlation which indicates that, at the various sampling stations, the models accounted for about 23 to 47 percent of the total variance of the observed TSP concentrations. If the resulting model equations are used in place of simple overall means of the observed concentrations, there is about a 20 percent improvement in either: (1) predicting mean concentrations for specified meteorological conditions; or (2) adjusting successive yearly averages to allow for comparisons devoid of meteorological effects. An application to source identification is presented using regression coefficients of wind velocity predictor variables.

Model variations in predicting incidence of Plasmodium falciparum malaria using 1998-2007 morbidity and meteorological data from south Ethiopia.

PubMed

Loha, Eskindir; Lindtjørn, Bernt

2010-06-16

Malaria transmission is complex and is believed to be associated with local climate changes. However, simple attempts to extrapolate malaria incidence rates from averaged regional meteorological conditions have proven unsuccessful. Therefore, the objective of this study was to determine if variations in specific meteorological factors are able to consistently predict P. falciparum malaria incidence at different locations in south Ethiopia. Retrospective data from 42 locations were collected including P. falciparum malaria incidence for the period of 1998-2007 and meteorological variables such as monthly rainfall (all locations), temperature (17 locations), and relative humidity (three locations). Thirty-five data sets qualified for the analysis. Ljung-Box Q statistics was used for model diagnosis, and R squared or stationary R squared was taken as goodness of fit measure. Time series modelling was carried out using Transfer Function (TF) models and univariate auto-regressive integrated moving average (ARIMA) when there was no significant predictor meteorological variable. Of 35 models, five were discarded because of the significant value of Ljung-Box Q statistics. Past P. falciparum malaria incidence alone (17 locations) or when coupled with meteorological variables (four locations) was able to predict P. falciparum malaria incidence within statistical significance. All seasonal AIRMA orders were from locations at altitudes above 1742 m. Monthly rainfall, minimum and maximum temperature was able to predict incidence at four, five and two locations, respectively. In contrast, relative humidity was not able to predict P. falciparum malaria incidence. The R squared values for the models ranged from 16% to 97%, with the exception of one model which had a negative value. Models with seasonal ARIMA orders were found to perform better. However, the models for predicting P. falciparum malaria incidence varied from location to location, and among lagged effects, data transformation forms, ARIMA and TF orders. This study describes P. falciparum malaria incidence models linked with meteorological data. Variability in the models was principally attributed to regional differences, and a single model was not found that fits all locations. Past P. falciparum malaria incidence appeared to be a superior predictor than meteorology. Future efforts in malaria modelling may benefit from inclusion of non-meteorological factors.

Effects of meteorological factors on incidence of scarlet fever during different periods in different districts of China.

PubMed

Duan, Yu; Yang, Li-Juan; Zhang, Yan-Jie; Huang, Xiao-Lei; Pan, Gui-Xia; Wang, Jing

2017-03-01

To reveal the difference of meteorological effect on scarlet fever in Beijing and Hong Kong, China, during different periods among 2004-2014. The data of monthly incidence of scarlet fever and meteorological variables from 2004 to 2014 in Beijing and Hong Kong were collected from Chinese science data center of public health, meteorological data website and Hong Kong observatory website. The whole study period was separated into two periods by the outbreak year 2011 (Jan 2004-Dec 2010 and Jan 2011-Dec 2014). A generalized additive Poisson model was conducted to estimate the effect of meteorological variables on monthly incidence of scarlet fever during two periods in Beijing and Hong Kong, China. Incidence of scarlet fever in two districts were compared and found the average incidence during period of 2004-2010 were significantly different (Z=203.973, P<0.001) while average incidence became generally equal during 2011-2014 (Z=2.125, P>0.05). There was also significant difference in meteorological variables between Beijing and Hong Kong during whole study period, except air pressure (Z=0.165, P=0.869). After fitting GAM model, it could be found monthly mean temperature showed a negative effect (RR=0.962, 95%CI: 0.933, 0.992) on scarlet fever in Hong Kong during the period of 2004-2010. By comparison, for data in Beijing during the period of 2011-2014, the RRs of monthly mean temperature range growing 1°C and monthly sunshine duration growing 1h was equal to 1.196(1.022, 1.399) and 1.006(1.001, 1.012), respectively. The changes of meteorological effect on scarlet fever over time were not significant both in Beijing and Hong Kong. This study suggests that meteorological variables were important factors for incidence of scarlet fever during different period in Beijing and Hong Kong. It also support that some meteorological effects were opposite in different period although these differences might not completely statistically significant. Copyright © 2017 Elsevier B.V. All rights reserved.

75 FR 52692 - Approval and Promulgation of Implementation Plans; New Mexico; Interstate Transport of Pollution...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-08-27

... an indication of potential variability in future projections due to differences in actual meteorology... maintaining attainment of the NAAQS at these locations if there are adverse variations in meteorology or... the Central Regional Air Planning Association (CENRAP) modeling of 2002 emissions and meteorology.\\22...

Meteorological fluctuations define long-term crop yield patterns in conventional and organic production systems

USDA-ARS?s Scientific Manuscript database

Periodic variability in meteorological patterns presents significant challenges to crop production consistency and yield stability. Meteorological influences on corn and soybean grain yields were analyzed over an 18-year period at a long-term experiment in Beltsville, Maryland, U.S.A., comparing c...

Meteorology, Emissions, and Grid Resolution: Effects on Discrete and Probabilistic Model Performance

EPA Science Inventory

In this study, we analyze the impacts of perturbations in meteorology and emissions and variations in grid resolution on air quality forecast simulations. The meteorological perturbations con-sidered in this study introduce a typical variability of ~1°C, 250 - 500 m, 1 m/s, and 1...

The effect of soil moisture anomalies on maize yield in Germany

NASA Astrophysics Data System (ADS)

Peichl, Michael; Thober, Stephan; Meyer, Volker; Samaniego, Luis

2018-03-01

Crop models routinely use meteorological variations to estimate crop yield. Soil moisture, however, is the primary source of water for plant growth. The aim of this study is to investigate the intraseasonal predictability of soil moisture to estimate silage maize yield in Germany. We also evaluate how approaches considering soil moisture perform compare to those using only meteorological variables. Silage maize is one of the most widely cultivated crops in Germany because it is used as a main biomass supplier for energy production in the course of the German Energiewende (energy transition). Reduced form fixed effect panel models are employed to investigate the relationships in this study. These models are estimated for each month of the growing season to gain insights into the time-varying effects of soil moisture and meteorological variables. Temperature, precipitation, and potential evapotranspiration are used as meteorological variables. Soil moisture is transformed into anomalies which provide a measure for the interannual variation within each month. The main result of this study is that soil moisture anomalies have predictive skills which vary in magnitude and direction depending on the month. For instance, dry soil moisture anomalies in August and September reduce silage maize yield more than 10 %, other factors being equal. In contrast, dry anomalies in May increase crop yield up to 7 % because absolute soil water content is higher in May compared to August due to its seasonality. With respect to the meteorological terms, models using both temperature and precipitation have higher predictability than models using only one meteorological variable. Also, models employing only temperature exhibit elevated effects.

Meteorological factors and timing of the initiating event of human parturition

NASA Astrophysics Data System (ADS)

Hirsch, Emmet; Lim, Courtney; Dobrez, Deborah; Adams, Marci G.; Noble, William

2011-03-01

The aim of this study was to determine whether meteorological factors are associated with the timing of either onset of labor with intact membranes or rupture of membranes prior to labor—together referred to as `the initiating event' of parturition. All patients delivering at Evanston Hospital after spontaneous labor or rupture of membranes at ≥20 weeks of gestation over a 6-month period were studied. Logistic regression models of the initiating event of parturition using clinical variables (maternal age, gestational age, parity, multiple gestation and intrauterine infection) with and without the addition of meteorological variables (barometric pressure, temperature and humidity) were compared. A total of 1,088 patients met the inclusion criteria. Gestational age, multiple gestation and chorioamnionitis were associated with timing of initiation of parturition ( P < 0.01). The addition of meteorological to clinical variables generated a statistically significant improvement in prediction of the initiating event; however, the magnitude of this improvement was small (less than 2% difference in receiver-operating characteristic score). These observations held regardless of parity, fetal number and gestational age. Meteorological factors are associated with the timing of parturition, but the magnitude of this association is small.

Usage of multivariate geostatistics in interpolation processes for meteorological precipitation maps

NASA Astrophysics Data System (ADS)

Gundogdu, Ismail Bulent

2017-01-01

Long-term meteorological data are very important both for the evaluation of meteorological events and for the analysis of their effects on the environment. Prediction maps which are constructed by different interpolation techniques often provide explanatory information. Conventional techniques, such as surface spline fitting, global and local polynomial models, and inverse distance weighting may not be adequate. Multivariate geostatistical methods can be more significant, especially when studying secondary variables, because secondary variables might directly affect the precision of prediction. In this study, the mean annual and mean monthly precipitations from 1984 to 2014 for 268 meteorological stations in Turkey have been used to construct country-wide maps. Besides linear regression, the inverse square distance and ordinary co-Kriging (OCK) have been used and compared to each other. Also elevation, slope, and aspect data for each station have been taken into account as secondary variables, whose use has reduced errors by up to a factor of three. OCK gave the smallest errors (1.002 cm) when aspect was included.

Drought effects on US maize and soybean production: spatiotemporal patterns and historical changes

NASA Astrophysics Data System (ADS)

Zipper, Samuel C.; Qiu, Jiangxiao; Kucharik, Christopher J.

2016-09-01

Maximizing agricultural production on existing cropland is one pillar of meeting future global food security needs. To close crop yield gaps, it is critical to understand how climate extremes such as drought impact yield. Here, we use gridded, daily meteorological data and county-level annual yield data to quantify meteorological drought sensitivity of US maize and soybean production from 1958 to 2007. Meteorological drought negatively affects crop yield over most US crop-producing areas, and yield is most sensitive to short-term (1-3 month) droughts during critical development periods from July to August. While meteorological drought is associated with 13% of overall yield variability, substantial spatial variability in drought effects and sensitivity exists, with central and southeastern US becoming increasingly sensitive to drought over time. Our study illustrates fine-scale spatiotemporal patterns of drought effects, highlighting where variability in crop production is most strongly associated with drought, and suggests that management strategies that buffer against short-term water stress may be most effective at sustaining long-term crop productivity.

Effect of meteorological variables on Plasmodium vivax and Plasmodium falciparum malaria in outbreak prone districts of Rajasthan, India.

PubMed

Lingala, Mercy A L

Malaria is a public health problem caused by Plasmodium parasite and transmitted by anopheline mosquitoes. Arid and semi-arid regions of western India are prone to malaria outbreaks. Malaria outbreak prone districts viz. Bikaner, Barmer and Jodhpur were selected to study the effect of meteorological variables on Plasmodium vivax and Plasmodium falciparum malaria outbreaks for the period of 2009-2012. The data of monthly malaria cases and meteorological variables was analysed using SPSS 20v. Spearman correlation analysis was conducted to examine the strength of the relationship between meteorological variables, P. vivax and P. falciparum malaria cases. Pearson's correlation analysis was carried out among the meteorological variables to observe the independent effect of each independent variable on the outcome. Results indicate that malaria outbreaks have occurred in Bikaner and Barmer due to continuous rains for more than two months. Rainfall has shown to be an important predictor of malaria outbreaks in Rajasthan. P. vivax is more significantly correlated with rainfall, minimum temperature (P<0.01) and less significantly with relative humidity (P<0.05); whereas P. falciparum is significantly correlated with rainfall, relative humidity (P<0.01) and less significantly with temperature (P<0.05). The determination of the lag period for P. vivax is relative humidity and for P. falciparum is temperature. The lag period between malaria cases and rainfall is shorter for P. vivax than P. falciparum. In conclusion, the knowledge generated is not only useful to take prompt malaria control interventions but also helpful to develop better forecasting model in outbreak prone regions. Copyright © 2017 The Author. Published by Elsevier Ltd.. All rights reserved.

Artificial neural network model for ozone concentration estimation and Monte Carlo analysis

NASA Astrophysics Data System (ADS)

Gao, Meng; Yin, Liting; Ning, Jicai

2018-07-01

Air pollution in urban atmosphere directly affects public-health; therefore, it is very essential to predict air pollutant concentrations. Air quality is a complex function of emissions, meteorology and topography, and artificial neural networks (ANNs) provide a sound framework for relating these variables. In this study, we investigated the feasibility of using ANN model with meteorological parameters as input variables to predict ozone concentration in the urban area of Jinan, a metropolis in Northern China. We firstly found that the architecture of network of neurons had little effect on the predicting capability of ANN model. A parsimonious ANN model with 6 routinely monitored meteorological parameters and one temporal covariate (the category of day, i.e. working day, legal holiday and regular weekend) as input variables was identified, where the 7 input variables were selected following the forward selection procedure. Compared with the benchmarking ANN model with 9 meteorological and photochemical parameters as input variables, the predicting capability of the parsimonious ANN model was acceptable. Its predicting capability was also verified in term of warming success ratio during the pollution episodes. Finally, uncertainty and sensitivity analysis were also performed based on Monte Carlo simulations (MCS). It was concluded that the ANN could properly predict the ambient ozone level. Maximum temperature, atmospheric pressure, sunshine duration and maximum wind speed were identified as the predominate input variables significantly influencing the prediction of ambient ozone concentrations.

Meteorological Influence on the 2009 Influenza A (H1N1) Pandemic in Mainland China.

NASA Astrophysics Data System (ADS)

Zhao, X.; Cai, J.; Feng, D.; Bai, Y.; Xu, B.

2015-12-01

Since May 2009, a novel influenza A (H1N1) pandemic has spread rapidly in mainland China from Mexico. Although there has been substantial analysis of this influenza, reliable work estimating its spatial dynamics and determinants remain scarce. The survival and transmission of this pandemic virus not only depends on its biological properties, but also a correlation with external environmental factors. In this study, we collected daily influenza A (H1N1) cases and corresponding annual meteorological factors in mainland China from May 2009 to April 2010. By analyzing these data at county-level, a similarity index, which considered the spatio-temporal characteristics of the disease, was proposed to evaluate the role and lag time of meteorological factors in the influenza transmission. The results indicated that the influenza spanned a large geographical area, following an overall trend from east to west across the country. The spatio-temporal transmission of the disease was affected by a series of meteorological variables, especially absolute humidity with a 3-week lag. These findings confirmed that the absolute humidity and other meteorological variables contributed to the local occurrence and dispersal of influenza A (H1N1). The impact of meteorological variables and their lag effects could be involved in the improvement of effective strategies to control and prevent disease outbreaks.

Estimation of Monthly Near Surface Air Temperature Using Geographically Weighted Regression in China

NASA Astrophysics Data System (ADS)

Wang, M. M.; He, G. J.; Zhang, Z. M.; Zhang, Z. J.; Liu, X. G.

2018-04-01

Near surface air temperature (NSAT) is a primary descriptor of terrestrial environment conditions. The availability of NSAT with high spatial resolution is deemed necessary for several applications such as hydrology, meteorology and ecology. In this study, a regression-based NSAT mapping method is proposed. This method is combined remote sensing variables with geographical variables, and uses geographically weighted regression to estimate NSAT. The altitude was selected as geographical variable; and the remote sensing variables include land surface temperature (LST) and Normalized Difference vegetation index (NDVI). The performance of the proposed method was assessed by predict monthly minimum, mean, and maximum NSAT from point station measurements in China, a domain with a large area, complex topography, and highly variable station density, and the NSAT maps were validated against the meteorology observations. Validation results with meteorological data show the proposed method achieved an accuracy of 1.58 °C. It is concluded that the proposed method for mapping NSAT is very operational and has good precision.

Measurement of inter- and intra-annual variability of landscape fire activity at a continental scale: The Australian case

Treesearch

Grant J. Williamson; Lynda D. Prior; Matt Jolly; Mark A. Cochrane; Brett P. Murphy; David M. J. S. Bowman

2016-01-01

Climate dynamics at diurnal, seasonal and inter-annual scales shape global fire activity, although difficulties of assembling reliable fire and meteorological data with sufficient spatio-temporal resolution have frustrated quantification of this variability. Using Australia as a case study, we combine data from 4760 meteorological stations with 12 years of satellite-...

Modeling the Effects of Meteorological Conditions on the Neutron Flux

DTIC Science & Technology

2017-05-22

a statistical model that predicts environmental neutron background as a function of five meteorological variables: inverse barometric pressure...variable of the model was inverse barometric pressure with a contribution an order of magnitude larger than any other variable’s contribution. The...is based on the sensitivity of each sensor. . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.2 Neutron counts from the LNS and inverse pressure

Correlation analysis for the attack of bacillary dysentery and meteorological factors based on the Chinese medicine theory of Yunqi and the medical-meteorological forecast model.

PubMed

Ma, Shi-Lei; Tang, Qiao-Ling; Liu, Hong-Wei; He, Juan; Gao, Si-Hua

2013-03-01

To explore the impact of meteorological factors on the outbreak of bacillary dysentery, so as to provide suggestions for disease prevention. Based on the Chinese medicine theory of Yunqi, the descriptive statistics, single-factor correlation analysis and back-propagation artificial neural net-work were conducted using data on five basic meteorological factors and data on incidence of bacillary dysentery in Beijing, China, for the period 1970-2004. The incidence of bacillary dysentery showed significant positive correlation relationship with the precipitation, relative humidity, vapor pressure, and temperature, respectively. The incidence of bacillary dysentery showed a negatively correlated relationship with the wind speed and the change trend of average wind speed. The results of medical-meteorological forecast model showed a relatively high accuracy rate. There is a close relationship between the meteorological factors and the incidence of bacillary dysentery, but the contributions of which to the onset of bacillary dysentery are different to each other.

Assessing the impact of local meteorological variables on surface ozone in Hong Kong during 2000-2015 using quantile and multiple line regression models

NASA Astrophysics Data System (ADS)

Zhao, Wei; Fan, Shaojia; Guo, Hai; Gao, Bo; Sun, Jiaren; Chen, Laiguo

2016-11-01

The quantile regression (QR) method has been increasingly introduced to atmospheric environmental studies to explore the non-linear relationship between local meteorological conditions and ozone mixing ratios. In this study, we applied QR for the first time, together with multiple linear regression (MLR), to analyze the dominant meteorological parameters influencing the mean, 10th percentile, 90th percentile and 99th percentile of maximum daily 8-h average (MDA8) ozone concentrations in 2000-2015 in Hong Kong. The dominance analysis (DA) was used to assess the relative importance of meteorological variables in the regression models. Results showed that the MLR models worked better at suburban and rural sites than at urban sites, and worked better in winter than in summer. QR models performed better in summer for 99th and 90th percentiles and performed better in autumn and winter for 10th percentile. And QR models also performed better in suburban and rural areas for 10th percentile. The top 3 dominant variables associated with MDA8 ozone concentrations, changing with seasons and regions, were frequently associated with the six meteorological parameters: boundary layer height, humidity, wind direction, surface solar radiation, total cloud cover and sea level pressure. Temperature rarely became a significant variable in any season, which could partly explain the peak of monthly average ozone concentrations in October in Hong Kong. And we found the effect of solar radiation would be enhanced during extremely ozone pollution episodes (i.e., the 99th percentile). Finally, meteorological effects on MDA8 ozone had no significant changes before and after the 2010 Asian Games.

Ensemble data assimilation of total column ozone using a coupled meteorology-chemistry model and its impact on the structure of Typhoon Nabi (2005)

NASA Astrophysics Data System (ADS)

Lim, S.; Park, S. K.; Zupanski, M.

2015-09-01

Ozone (O3) plays an important role in chemical reactions and is usually incorporated in chemical data assimilation (DA). In tropical cyclones (TCs), O3 usually shows a lower concentration inside the eyewall and an elevated concentration around the eye, impacting meteorological as well as chemical variables. To identify the impact of O3 observations on TC structure, including meteorological and chemical information, we developed a coupled meteorology-chemistry DA system by employing the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and an ensemble-based DA algorithm - the maximum likelihood ensemble filter (MLEF). For a TC case that occurred over East Asia, Typhoon Nabi (2005), our results indicate that the ensemble forecast is reasonable, accompanied with larger background state uncertainty over the TC, and also over eastern China. Similarly, the assimilation of O3 observations impacts meteorological and chemical variables near the TC and over eastern China. The strongest impact on air quality in the lower troposphere was over China, likely due to the pollution advection. In the vicinity of the TC, however, the strongest impact on chemical variables adjustment was at higher levels. The impact on meteorological variables was similar in both over China and near the TC. The analysis results are verified using several measures that include the cost function, root mean square (RMS) error with respect to observations, and degrees of freedom for signal (DFS). All measures indicate a positive impact of DA on the analysis - the cost function and RMS error have decreased by 16.9 and 8.87 %, respectively. In particular, the DFS indicates a strong positive impact of observations in the TC area, with a weaker maximum over northeastern China.

Determination of the optimal training principle and input variables in artificial neural network model for the biweekly chlorophyll-a prediction: a case study of the Yuqiao Reservoir, China.

PubMed

Liu, Yu; Xi, Du-Gang; Li, Zhao-Liang

2015-01-01

Predicting the levels of chlorophyll-a (Chl-a) is a vital component of water quality management, which ensures that urban drinking water is safe from harmful algal blooms. This study developed a model to predict Chl-a levels in the Yuqiao Reservoir (Tianjin, China) biweekly using water quality and meteorological data from 1999-2012. First, six artificial neural networks (ANNs) and two non-ANN methods (principal component analysis and the support vector regression model) were compared to determine the appropriate training principle. Subsequently, three predictors with different input variables were developed to examine the feasibility of incorporating meteorological factors into Chl-a prediction, which usually only uses water quality data. Finally, a sensitivity analysis was performed to examine how the Chl-a predictor reacts to changes in input variables. The results were as follows: first, ANN is a powerful predictive alternative to the traditional modeling techniques used for Chl-a prediction. The back program (BP) model yields slightly better results than all other ANNs, with the normalized mean square error (NMSE), the correlation coefficient (Corr), and the Nash-Sutcliffe coefficient of efficiency (NSE) at 0.003 mg/l, 0.880 and 0.754, respectively, in the testing period. Second, the incorporation of meteorological data greatly improved Chl-a prediction compared to models solely using water quality factors or meteorological data; the correlation coefficient increased from 0.574-0.686 to 0.880 when meteorological data were included. Finally, the Chl-a predictor is more sensitive to air pressure and pH compared to other water quality and meteorological variables.

CentNet—A deployable 100-station network for surface exchange research

NASA Astrophysics Data System (ADS)

Oncley, S.; Horst, T. W.; Semmer, S.; Militzer, J.; Maclean, G.; Knudson, K.

2014-12-01

Climate, air quality, atmospheric composition, surface hydrology, and ecological processes are directly affected by the Earth's surface. Complexity of this surface exists at multiple spatial scales, which complicates the understanding of these processes. NCAR/EOL currently provides a facility to the research community to make direct eddy-covariance flux observations to quantify surface-atmosphere interactions. However, just as model resolution has continued to increase, there is a need to increase the spatial density of flux measurements to capture the wide variety of scales that contribute to exchange processes close to the surface. NCAR/EOL now has developed the CentNet facility, that is envisioned to have on the order of 100 surface flux stations deployable for periods of months to years. Each station would measure standard meteorological variables, all components of the surface energy balance (including turbulence fluxes and radiation), atmospheric composition, and other quantities to characterize the surface. Thus, CentNet can support observational research in the biogeosciences, hydrology, urban meteorology, basic meteorology, and turbulence. CentNet has been designed to be adaptable to a wide variety of research problems while keeping operations manageable. Tower infrastructure has been designed to be lightweight, easily deployed, and with a minimal set-up footprint. CentNet uses sensor networks to increase spatial sampling at each station. The data system saves every sample on site to retain flexibility in data analysis. We welcome guidance on development and funding priorities as we build CentNet.

Spatiotemporal estimation of historical PM2.5 concentrations using PM10, meteorological variables, and spatial effect

NASA Astrophysics Data System (ADS)

Li, Lianfa; Wu, Anna H.; Cheng, Iona; Chen, Jiu-Chiuan; Wu, Jun

2017-10-01

Monitoring of fine particulate matter with diameter <2.5 μm (PM2.5) started from 1999 in the US and even later in many other countries. The lack of historical PM2.5 data limits epidemiological studies of long-term exposure of PM2.5 and health outcomes such as cancer. In this study, we aimed to design a flexible approach to reliably estimate historical PM2.5 concentrations by incorporating spatial effect and the measurements of existing co-pollutants such as particulate matter with diameter <10 μm (PM10) and meteorological variables. Monitoring data of PM10, PM2.5, and meteorological variables covering the entire state of California were obtained from 1999 through 2013. We developed a spatiotemporal model that quantified non-linear associations between PM2.5 concentrations and the following predictor variables: spatiotemporal factors (PM10 and meteorological variables), spatial factors (land-use patterns, traffic, elevation, distance to shorelines, and spatial autocorrelation), and season. Our model accounted for regional-(county) scale spatial autocorrelation, using spatial weight matrix, and local-scale spatiotemporal variability, using local covariates in additive non-linear model. The spatiotemporal model was evaluated, using leaving-one-site-month-out cross validation. Our final daily model had an R2 of 0.81, with PM10, meteorological variables, and spatial autocorrelation, explaining 55%, 10%, and 10% of the variance in PM2.5 concentrations, respectively. The model had a cross-validation R2 of 0.83 for monthly PM2.5 concentrations (N = 8170) and 0.79 for daily PM2.5 concentrations (N = 51,421) with few extreme values in prediction. Further, the incorporation of spatial effects reduced bias in predictions. Our approach achieved a cross validation R2 of 0.61 for the daily model when PM10 was replaced by total suspended particulate. Our model can robustly estimate historical PM2.5 concentrations in California when PM2.5 measurements were not available.

Validation of China-wide interpolated daily climate variables from 1960 to 2011

NASA Astrophysics Data System (ADS)

Yuan, Wenping; Xu, Bing; Chen, Zhuoqi; Xia, Jiangzhou; Xu, Wenfang; Chen, Yang; Wu, Xiaoxu; Fu, Yang

2015-02-01

Temporally and spatially continuous meteorological variables are increasingly in demand to support many different types of applications related to climate studies. Using measurements from 600 climate stations, a thin-plate spline method was applied to generate daily gridded climate datasets for mean air temperature, maximum temperature, minimum temperature, relative humidity, sunshine duration, wind speed, atmospheric pressure, and precipitation over China for the period 1961-2011. A comprehensive evaluation of interpolated climate was conducted at 150 independent validation sites. The results showed superior performance for most of the estimated variables. Except for wind speed, determination coefficients ( R 2) varied from 0.65 to 0.90, and interpolations showed high consistency with observations. Most of the estimated climate variables showed relatively consistent accuracy among all seasons according to the root mean square error, R 2, and relative predictive error. The interpolated data correctly predicted the occurrence of daily precipitation at validation sites with an accuracy of 83 %. Moreover, the interpolation data successfully explained the interannual variability trend for the eight meteorological variables at most validation sites. Consistent interannual variability trends were observed at 66-95 % of the sites for the eight meteorological variables. Accuracy in distinguishing extreme weather events differed substantially among the meteorological variables. The interpolated data identified extreme events for the three temperature variables, relative humidity, and sunshine duration with an accuracy ranging from 63 to 77 %. However, for wind speed, air pressure, and precipitation, the interpolation model correctly identified only 41, 48, and 58 % of extreme events, respectively. The validation indicates that the interpolations can be applied with high confidence for the three temperatures variables, as well as relative humidity and sunshine duration based on the performance of these variables in estimating daily variations, interannual variability, and extreme events. Although longitude, latitude, and elevation data are included in the model, additional information, such as topography and cloud cover, should be integrated into the interpolation algorithm to improve performance in estimating wind speed, atmospheric pressure, and precipitation.

Urban airborne matter in central and southern Chile: Effects of meteorological conditions on fine and coarse particulate matter

NASA Astrophysics Data System (ADS)

Yáñez, Marco A.; Baettig, Ricardo; Cornejo, Jorge; Zamudio, Francisco; Guajardo, Jorge; Fica, Rodrigo

2017-07-01

Air pollution is one of the major global environmental problems affecting human health and life quality. Many cities of Chile are heavily polluted with PM2.5 and PM10, mainly in the cold season, and there is little understanding of how the variation in particle matter differs between cities and how this is affected by the meteorological conditions. The objective of this study was to assess the effect of meteorological variables on respirable particulate matter (PM) of the main cities in the central-south valley of Chile during the cold season (May to August) between 2014 and 2016. We used hourly PM2.5 and PMcoarse (PM10- PM2.5) information along with wind speed, temperature and relative humidity, and other variables derived from meteorological parameters. Generalized additive models (GAMs) were fitted for each of the eight cities selected, covering a latitudinal range of 929 km, from Santiago to Osorno. Great variation in PM was found between cities during the cold months, and that variation exhibited a marked latitudinal pattern. Overall, the more northerly cities tended to be less polluted in PM2.5 and more polluted in PMcoarse than the more southerly cities, and vice versa. The results show that other derived variables from meteorology were better related with PM than the use of traditional daily means. The main variables selected with regard to PM2.5 content were mean wind speed and minimum temperature (negative relationship). Otherwise, the main variables selected with regard to PMcoarse content were mean wind speed (negative), and the daily range in temperature (positive). Variables derived from relative humidity contributed differently to the models, having a higher effect on PMcoarse than PM2.5, and exhibiting both negative and positive effects. For the different cities the deviance explained by the GAMs ranged from 37.6 to 79.1% for PM2.5 and from 18.5 to 63.7% for PMcoarse. The percentage of deviance explained by the models for PM2.5 exhibited a latitudinal pattern, which was not observed in PMcoarse. This highlights the greater predictability of PM2.5 according to meteorological parameters in the cities to the south. Southern cities located spatially close to one another had similar patterns in both the selected variables for the models and the trends. The meteorological factor influencing the cities had a major impact on PM concentrations. The findings of this study may aid understanding of PM variation across the country, in the way of improving forecasting models.

Model variations in predicting incidence of Plasmodium falciparum malaria using 1998-2007 morbidity and meteorological data from south Ethiopia

PubMed Central

2010-01-01

Background Malaria transmission is complex and is believed to be associated with local climate changes. However, simple attempts to extrapolate malaria incidence rates from averaged regional meteorological conditions have proven unsuccessful. Therefore, the objective of this study was to determine if variations in specific meteorological factors are able to consistently predict P. falciparum malaria incidence at different locations in south Ethiopia. Methods Retrospective data from 42 locations were collected including P. falciparum malaria incidence for the period of 1998-2007 and meteorological variables such as monthly rainfall (all locations), temperature (17 locations), and relative humidity (three locations). Thirty-five data sets qualified for the analysis. Ljung-Box Q statistics was used for model diagnosis, and R squared or stationary R squared was taken as goodness of fit measure. Time series modelling was carried out using Transfer Function (TF) models and univariate auto-regressive integrated moving average (ARIMA) when there was no significant predictor meteorological variable. Results Of 35 models, five were discarded because of the significant value of Ljung-Box Q statistics. Past P. falciparum malaria incidence alone (17 locations) or when coupled with meteorological variables (four locations) was able to predict P. falciparum malaria incidence within statistical significance. All seasonal AIRMA orders were from locations at altitudes above 1742 m. Monthly rainfall, minimum and maximum temperature was able to predict incidence at four, five and two locations, respectively. In contrast, relative humidity was not able to predict P. falciparum malaria incidence. The R squared values for the models ranged from 16% to 97%, with the exception of one model which had a negative value. Models with seasonal ARIMA orders were found to perform better. However, the models for predicting P. falciparum malaria incidence varied from location to location, and among lagged effects, data transformation forms, ARIMA and TF orders. Conclusions This study describes P. falciparum malaria incidence models linked with meteorological data. Variability in the models was principally attributed to regional differences, and a single model was not found that fits all locations. Past P. falciparum malaria incidence appeared to be a superior predictor than meteorology. Future efforts in malaria modelling may benefit from inclusion of non-meteorological factors. PMID:20553590

Choice of Control Variables in Variational Data Assimilation and Its Analysis and Forecast Impact

NASA Astrophysics Data System (ADS)

Xie, Yuanfu; Sun, Jenny; Fang, Wei-ting

2014-05-01

Choice of control variables directly impacts the analysis qualify of a variational data assimilation and its forecasts. A theory on selecting control variables for wind and moisture field is introduced for 3DVAR or 4DVAR. For a good control variable selection, Parseval's theory is applied to 3-4DVAR and the behavior of different control variables is illustrated in physical and Fourier space in terms of minimization condition, meteorological dynamic scales and practical implementation. The computational and meteorological benefits will be discussed. Numerical experiments have been performed using WRF-DA for wind control variables and CRTM for moisture control variables. It is evident of the WRF forecast improvement and faster convergence of CRTM satellite data assimilation.

Dynamical Mapping of Anopheles darlingi Densities in a Residual Malaria Transmission Area of French Guiana by Using Remote Sensing and Meteorological Data.

PubMed

Adde, Antoine; Roux, Emmanuel; Mangeas, Morgan; Dessay, Nadine; Nacher, Mathieu; Dusfour, Isabelle; Girod, Romain; Briolant, Sébastien

2016-01-01

Local variation in the density of Anopheles mosquitoes and the risk of exposure to bites are essential to explain the spatial and temporal heterogeneities in the transmission of malaria. Vector distribution is driven by environmental factors. Based on variables derived from satellite imagery and meteorological observations, this study aimed to dynamically model and map the densities of Anopheles darlingi in the municipality of Saint-Georges de l'Oyapock (French Guiana). Longitudinal sampling sessions of An. darlingi densities were conducted between September 2012 and October 2014. Landscape and meteorological data were collected and processed to extract a panel of variables that were potentially related to An. darlingi ecology. Based on these data, a robust methodology was formed to estimate a statistical predictive model of the spatial-temporal variations in the densities of An. darlingi in Saint-Georges de l'Oyapock. The final cross-validated model integrated two landscape variables-dense forest surface and built surface-together with four meteorological variables related to rainfall, evapotranspiration, and the minimal and maximal temperatures. Extrapolation of the model allowed the generation of predictive weekly maps of An. darlingi densities at a resolution of 10-m. Our results supported the use of satellite imagery and meteorological data to predict malaria vector densities. Such fine-scale modeling approach might be a useful tool for health authorities to plan control strategies and social communication in a cost-effective, targeted, and timely manner.

Integrated firn elevation change model for glaciers and ice caps

NASA Astrophysics Data System (ADS)

Saß, Björn; Sauter, Tobias; Braun, Matthias

2016-04-01

We present the development of a firn compaction model in order to improve the volume to mass conversion of geodetic glacier mass balance measurements. The model is applied on the Arctic ice cap Vestfonna. Vestfonna is located on the island Nordaustlandet in the north east of Svalbard. Vestfonna covers about 2400 km² and has a dome like shape with well-defined outlet glaciers. Elevation and volume changes measured by e.g. satellite techniques are becoming more and more popular. They are carried out over observation periods of variable length and often covering different meteorological and snow hydrological regimes. The elevation change measurements compose of various components including dynamic adjustments, firn compaction and mass loss by downwasting. Currently, geodetic glacier mass balances are frequently converted from elevation change measurements using a constant conversion factor of 850 kg m-³ or the density of ice (917 kg m-³) for entire glacier basins. However, the natural conditions are rarely that static. Other studies used constant densities for the ablation (900 kg m-³) and accumulation (600 kg m-³) areas, whereby density variations with varying meteorological and climate conditions are not considered. Hence, each approach bears additional uncertainties from the volume to mass conversion that are strongly affected by the type and timing of the repeat measurements. We link and adapt existing models of surface energy balance, accumulation and snow and firn processes in order to improve the volume to mass conversion by considering the firn compaction component. Energy exchange at the surface is computed by a surface energy balance approach and driven by meteorological variables like incoming short-wave radiation, air temperature, relative humidity, air pressure, wind speed, all-phase precipitation, and cloud cover fraction. Snow and firn processes are addressed by a coupled subsurface model, implemented with a non-equidistant layer discretisation. On our poster we present a general view on the model structure, the input data (model forcing) and finally, an exemplary test case with basic approaches of validation.

Understanding and seasonal forecasting of hydrological drought in the Anthropocene

NASA Astrophysics Data System (ADS)

Yuan, Xing; Zhang, Miao; Wang, Linying; Zhou, Tian

2017-11-01

Hydrological drought is not only caused by natural hydroclimate variability but can also be directly altered by human interventions including reservoir operation, irrigation, groundwater exploitation, etc. Understanding and forecasting of hydrological drought in the Anthropocene are grand challenges due to complicated interactions among climate, hydrology and humans. In this paper, five decades (1961-2010) of naturalized and observed streamflow datasets are used to investigate hydrological drought characteristics in a heavily managed river basin, the Yellow River basin in north China. Human interventions decrease the correlation between hydrological and meteorological droughts, and make the hydrological drought respond to longer timescales of meteorological drought. Due to large water consumptions in the middle and lower reaches, there are 118-262 % increases in the hydrological drought frequency, up to 8-fold increases in the drought severity, 21-99 % increases in the drought duration and the drought onset is earlier. The non-stationarity due to anthropogenic climate change and human water use basically decreases the correlation between meteorological and hydrological droughts and reduces the effect of human interventions on hydrological drought frequency while increasing the effect on drought duration and severity. A set of 29-year (1982-2010) hindcasts from an established seasonal hydrological forecasting system are used to assess the forecast skill of hydrological drought. In the naturalized condition, the climate-model-based approach outperforms the climatology method in predicting the 2001 severe hydrological drought event. Based on the 29-year hindcasts, the former method has a Brier skill score of 11-26 % against the latter for the probabilistic hydrological drought forecasting. In the Anthropocene, the skill for both approaches increases due to the dominant influence of human interventions that have been implicitly incorporated by the hydrological post-processing, while the difference between the two predictions decreases. This suggests that human interventions can outweigh the climate variability for the hydrological drought forecasting in the Anthropocene, and the predictability for human interventions needs more attention.

Modeling and roles of meteorological factors in outbreaks of highly pathogenic avian influenza H5N1.

PubMed

Biswas, Paritosh K; Islam, Md Zohorul; Debnath, Nitish C; Yamage, Mat

2014-01-01

The highly pathogenic avian influenza A virus subtype H5N1 (HPAI H5N1) is a deadly zoonotic pathogen. Its persistence in poultry in several countries is a potential threat: a mutant or genetically reassorted progenitor might cause a human pandemic. Its world-wide eradication from poultry is important to protect public health. The global trend of outbreaks of influenza attributable to HPAI H5N1 shows a clear seasonality. Meteorological factors might be associated with such trend but have not been studied. For the first time, we analyze the role of meteorological factors in the occurrences of HPAI outbreaks in Bangladesh. We employed autoregressive integrated moving average (ARIMA) and multiplicative seasonal autoregressive integrated moving average (SARIMA) to assess the roles of different meteorological factors in outbreaks of HPAI. Outbreaks were modeled best when multiplicative seasonality was incorporated. Incorporation of any meteorological variable(s) as inputs did not improve the performance of any multivariable models, but relative humidity (RH) was a significant covariate in several ARIMA and SARIMA models with different autoregressive and moving average orders. The variable cloud cover was also a significant covariate in two SARIMA models, but air temperature along with RH might be a predictor when moving average (MA) order at lag 1 month is considered.

Influence of Climate on PM2.5 Concentrations over Europe : a Meteorological Analysis using a 9-year Model Simulation

NASA Astrophysics Data System (ADS)

Lecoeur, À.; Seigneur, C.; Terray, L.; Pagé, C.

2012-04-01

In the early 1970s, it has been demonstrated that a large number of deaths and health problems are associated with particulate pollution. As a consequence, several governments have set health-based air quality standards to protect public health. Particulate matter with an aerodynamical diameter of 2.5 μg.m-3 or less (PM2.5) is particularly concerned by these measures. As PM2.5 concentrations are strongly dependent on meteorological conditions, it is important to investigate the relationships between PM2.5 and meteorological parameters. This will help to understand the processes at play and anticipate the effects of climate change on PM2.5 air quality. Most of the previous work agree that temperature, wind speed, humidity, rain rate and mixing height are the meteorological variables that impact PM2.5 concentrations the most. A large number of those studies used Global Circulation Models (GCM) and Chemical Transport Models (CTM) and focus on the USA. They typically predict a diminution of PM2.5 concentrations in the future, with some geographical and/or temporal discrepancies, when only the climate evolution is considered. When considering changes in emissions along with climate, no consensus has yet been found. Furthermore, the correlations between PM2.5 concentrations and meteorological variables are often low, which prevents a straightforward analysis of their relationships. In this work, we consider that PM2.5 concentrations depend on both large-scale atmospheric circulation and local meteorological variables. We thus investigate the influence of present climate on PM2.5 concentrations over Europe by representing it using a weather regimes/types approach. We start by exploring the relationships between classical weather regimes, meteorological variables and PM2.5 concentrations over five stations in Europe, using the EMEP air quality database. The pressure at sea level is used in the classification as it effectively describes the atmospheric circulation. We experimentally verify some intuitive results: weather regimes associated with weak (resp. high) precipitation, wind and low (resp. high) temperatures correspond to higher (resp. lower) PM2.5 concentrations. We also observe that rain rate is the variable that impacts PM2.5 concentrations the most. Next, we search for better relationships by adding this second variable to the classification: we therefore build new weather regimes, called weather types. Because of the low number of the EMEP observations, we compute PM2.5 concentrations with the Polyphemus/Polair3D CTM for years between 2000 and 2008 in order to obtain a spatially and temporally complete dataset of PM2.5 concentrations and chemical components, which can be used to relate PM2.5 concentrations to meteorological regimes and specific variables. By classifying both a large-scale variable and a local variable that influence the PM2.5 concentrations and using gridded data of the modeled concentrations of PM2.5, we obtain a more robust analysis. The results of this work will provide the basis to predict the effects of climate change (via the evolution of weather regimes/types frequencies) on PM2.5 chemical composition and concentrations.

heterogeneous mixture distributions for multi-source extreme rainfall

NASA Astrophysics Data System (ADS)

Ouarda, T.; Shin, J.; Lee, T. S.

2013-12-01

Mixture distributions have been used to model hydro-meteorological variables showing mixture distributional characteristics, e.g. bimodality. Homogeneous mixture (HOM) distributions (e.g. Normal-Normal and Gumbel-Gumbel) have been traditionally applied to hydro-meteorological variables. However, there is no reason to restrict the mixture distribution as the combination of one identical type. It might be beneficial to characterize the statistical behavior of hydro-meteorological variables from the application of heterogeneous mixture (HTM) distributions such as Normal-Gamma. In the present work, we focus on assessing the suitability of HTM distributions for the frequency analysis of hydro-meteorological variables. In the present work, in order to estimate the parameters of HTM distributions, the meta-heuristic algorithm (Genetic Algorithm) is employed to maximize the likelihood function. In the present study, a number of distributions are compared, including the Gamma-Extreme value type-one (EV1) HTM distribution, the EV1-EV1 HOM distribution, and EV1 distribution. The proposed distribution models are applied to the annual maximum precipitation data in South Korea. The Akaike Information Criterion (AIC), the root mean squared errors (RMSE) and the log-likelihood are used as measures of goodness-of-fit of the tested distributions. Results indicate that the HTM distribution (Gamma-EV1) presents the best fitness. The HTM distribution shows significant improvement in the estimation of quantiles corresponding to the 20-year return period. It is shown that extreme rainfall in the coastal region of South Korea presents strong heterogeneous mixture distributional characteristics. Results indicate that HTM distributions are a good alternative for the frequency analysis of hydro-meteorological variables when disparate statistical characteristics are presented.

Control of Methane Production and Exchange in Northern Peatlands

NASA Technical Reports Server (NTRS)

Crill, Patrick

1997-01-01

This proposal has successfully supported studies that have developed unique long ten-n datasets of methane (CH4) emissions and carbon dioxide (CO2) exchange in order to quantify the controls on CH4 production and exchange especially the linkages to the carbon cycle in northern peatlands. The primary research site has been a small fen in southeastern New Hampshire where a unique multi-year data baseline of CH4 flux measurements was begun (with NASA funding) in 1989. The fen has also been instrumented for continuous hydrological and meteorological observations and year-round porewater sampling. Multiyear datasets of methane flux are very valuable and very rare. Datasets using the same sampling techniques at the same sites are the only way to assess the effect of the integrated ecosystem response to climatological variability. The research has had two basic objectives: 1. To quantify the effect of seasonal and interannual variability on CH4flux. 2. To examine process level controls on methane dynamics.

Implications of climate variability for monitoring the effectiveness of global mercury policy

NASA Astrophysics Data System (ADS)

Giang, A.; Monier, E.; Couzo, E. A.; Pike-thackray, C.; Selin, N. E.

2016-12-01

We investigate how climate variability affects ability to detect policy-related anthropogenic changes in mercury emissions in wet deposition monitoring data using earth system and atmospheric chemistry modeling. The Minamata Convention, a multilateral environmental agreement that aims to protect human health and the environment from anthropogenic emissions and releases of mercury, includes provisions for monitoring treaty effectiveness. Because meteorology can affect mercury chemistry and transport, internal variability is an important contributor to uncertainty in how effective policy may be in reducing the amount of mercury entering ecosystems through wet deposition. We simulate mercury chemistry using the GEOS-Chem global transport model to assess the influence of meteorology in the context of other uncertainties in mercury cycling and policy. In these simulations, we find that interannual variability in meteorology may be a dominant contributor to the spatial pattern and magnitude of historical regional wet deposition trends. To further assess the influence of climate variability in the GEOS-Chem mercury simulation, we use a 5-member ensemble of meteorological fields from the MIT Integrated Global System Model under present and future climate. Each member involves randomly initialized 20 year simulations centered around 2000 and 2050 (under a no-policy and a climate stabilization scenario). Building on previous efforts to understand climate-air quality interactions for ground-level O3 and particulate matter, we estimate from the ensemble the range of trends in mercury wet deposition given natural variability, and, to extend our previous results on regions that are sensitive to near-source vs. remote anthropogenic signals, we identify geographic regions where mercury wet deposition is most sensitive to this variability. We discuss how an improved understanding of natural variability can inform the Conference of Parties on monitoring strategy and policy ambition.

Weather Variability Associated with Aedes (Stegomyia) aegypti (Dengue Vector) Oviposition Dynamics in Northwestern Argentina

PubMed Central

Estallo, Elizabet L.; Ludueña-Almeida, Francisco F.; Introini, María V.; Zaidenberg, Mario; Almirón, Walter R.

2015-01-01

This study aims to develop a forecasting model by assessing the weather variability associated with seasonal fluctuation of Aedes aegypti oviposition dynamic at a city level in Orán, in northwestern Argentina. Oviposition dynamics were assessed by weekly monitoring of 90 ovitraps in the urban area during 2005-2007. Correlations were performed between the number of eggs collected weekly and weather variables (rainfall, photoperiod, vapor pressure of water, temperature, and relative humidity) with and without time lags (1 to 6 weeks). A stepwise multiple linear regression analysis was performed with the set of meteorological variables from the first year of study with the variables in the time lags that best correlated with the oviposition. Model validation was conducted using the data from the second year of study (October 2006- 2007). Minimum temperature and rainfall were the most important variables. No eggs were found at temperatures below 10°C. The most significant time lags were 3 weeks for minimum temperature and rains, 3 weeks for water vapor pressure, and 6 weeks for maximum temperature. Aedes aegypti could be expected in Orán three weeks after rains with adequate min temperatures. The best-fit forecasting model for the combined meteorological variables explained 70 % of the variance (adj. R2). The correlation between Ae. aegypti oviposition observed and estimated by the forecasting model resulted in rs = 0.80 (P < 0.05). The forecasting model developed would allow prediction of increases and decreases in the Ae. aegypti oviposition activity based on meteorological data for Orán city and, according to the meteorological variables, vector activity can be predicted three or four weeks in advance. PMID:25993415

Dune mobility in the St. Anthony Dune Field, Idaho, USA: Effects of meteorological variables and lag time

NASA Astrophysics Data System (ADS)

Hoover, R. H.; Gaylord, D. R.; Cooper, C. M.

2018-05-01

The St. Anthony Dune Field (SADF) is a 300 km2 expanse of active to stabilized transverse, barchan, barchanoid, and parabolic sand dunes located in a semi-arid climate in southeastern Idaho. The northeastern portion of the SADF, 16 km2, was investigated to examine meteorological influences on dune mobility. Understanding meteorological predictors of sand-dune migration for the SADF informs landscape evolution and impacts assessment of eolian activity on sensitive agricultural lands in the western United States, with implications for semi-arid environments globally. Archival aerial photos from 1954 to 2011 were used to calculate dune migration rates which were subsequently compared to regional meteorological data, including temperature, precipitation and wind speed. Observational analyses based on aerial photo imagery and meteorological data indicate that dune migration is influenced by weather for up to 5-10 years and therefore decadal weather patterns should be taken into account when using dune migration rates as proxies from climate fluctuation. Statistical examination of meteorological variables in this study indicates that 24% of the variation of sand dune migration rates is attributed to temperature, precipitation and wind speed, which is increased to 45% when incorporating lag time.

Distributions of eight meteorological variables at Cape Kennedy, Florida and Vandenberg Air Force Base, California

NASA Technical Reports Server (NTRS)

Graves, M. E.; King, R. L.; Brown, S. C.

1973-01-01

Extreme values, median values, and nine percentile values are tabulated for eight meteorological variables at Cape Kennedy, Florida and at Vandenberg Air Force Base, California. The variables are temperature, relative humidity, station pressure, water vapor pressure, water vapor mixing ratio, density, and enthalpy. For each month eight hours are tabulated, namely, 0100, 0400, 0700, 1000, 1300, 1600, 1900, and 2200 local time. These statistics are intended for general use for the space shuttle design trade-off analysis and are not to be used for specific design values.

Meteorological Modes of Variability for Fine Particulate Matter (PM2.5) Air Quality in the United States: Implications for PM2.5 Sensitivity to Climate Change

EPA Science Inventory

We applied a multiple linear regression model to understand the relationships of PM_2.5 with meteorological variables in the contiguous US and from there to infer the sensitivity of PM_2.5 to climate change. We used 2004-2008 PM_2.5 observations fro...

Active Learning in an Introductory Meteorology Class

NASA Astrophysics Data System (ADS)

Marchese, P. J.; Bluestone, C.

2007-12-01

Active learning modules were introduced to the primarily minority population in the introductory meteorology class at Queensborough Community College (QCC). These activities were developed at QCC and other 4 year colleges and designed to reinforce basic meteorological concepts. The modules consisted of either Interactive Lecture Demonstrations (ILD) or discovery-based activities. During the ILD the instructor would describe an experiment that would be demonstrated in class. Students would predict what the outcome would be and compare their expected results to the actual outcome of the experiment. In the discovery-based activities students would learn about physical concepts by performing basic experiments. These activities differed from the traditional lab in that it avoided "cookbook" procedures and emphasized having the students learn about the concept using the scientific method. As a result of these activities student scores measuring conceptual understanding, as well as factual knowledge, increased as compared to student scores in a more affluent community college. Students also had higher self- efficacy scores. Lower scoring students demonstrated the greatest benefit, while the better students had little (or no) changes.

75 FR 71029 - Approval and Promulgation of State Implementation Plans; State of Colorado; Interstate Transport...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-11-22

... variability in emissions and/ or meteorology), may have difficulty maintaining the standard. As explained in... year with particularly severe meteorology (weather that is conducive to ozone and/or particulate...

Assessment of ozone variations and meteorological effects in an urban area in the Mediterranean Coast.

PubMed

Dueñas, C; Fernández, M C; Cañete, S; Carretero, J; Liger, E

2002-11-01

Ozone concentrations are valuable indicators of possible health and environmental impacts. However, they are also used to monitor changes and trends in the sources of both ozone and its precursors. For this purpose, the influence of meteorological variables is a confusing factor. This study presents an analysis of a year of ozone concentrations measured in a coastal Spanish city. Firstly, the aim of this study was to perceive the daily, monthly and seasonal variation patterns of ozone concentrations. Diurnal cycles are presented by season and the fit of the data to a normal distribution is tested. In order to assess ozone behaviour under temperate weather conditions, local meteorological variables (wind direction and speed, temperature, relative humidity, pressure and rainfall) were monitored together with ozone concentrations. The main relationships we could observe in these analyses were then used to obtain a regression equation linking diurnal ozone concentrations in summer with meteorological parameters.

Evaluation of Hydrologic and Meteorological Impacts on Dengue Fever Incidences in Southern Taiwan using Time- Frequency Method

NASA Astrophysics Data System (ADS)

Tsai, Christina; Yeh, Ting-Gu

2017-04-01

Extreme weather events are occurring more frequently as a result of climate change. Recently dengue fever has become a serious issue in southern Taiwan. It may have characteristic temporal scales that can be identified. Some researchers have hypothesized that dengue fever incidences are related to climate change. This study applies time-frequency analysis to time series data concerning dengue fever and hydrologic and meteorological variables. Results of three time-frequency analytical methods - the Hilbert Huang transform (HHT), the Wavelet Transform (WT) and the Short Time Fourier Transform (STFT) are compared and discussed. A more effective time-frequency analysis method will be identified to analyze relevant time series data. The most influential time scales of hydrologic and meteorological variables that are associated with dengue fever are determined. Finally, the linkage between hydrologic/meteorological factors and dengue fever incidences can be established.

The effects of daily weather variables on psychosis admissions to psychiatric hospitals

NASA Astrophysics Data System (ADS)

McWilliams, Stephen; Kinsella, Anthony; O'Callaghan, Eadbhard

2013-07-01

Several studies have noted seasonal variations in admission rates of patients with psychotic illnesses. However, the changeable daily meteorological patterns within seasons have never been examined in any great depth in the context of admission rates. A handful of small studies have posed interesting questions regarding a potential link between psychiatric admission rates and meteorological variables such as environmental temperature (especially heat waves) and sunshine. In this study, we used simple non-parametric testing and more complex ARIMA and time-series regression analysis to examine whether daily meteorological patterns (wind speed and direction, barometric pressure, rainfall, sunshine, sunlight and temperature) exert an influence on admission rates for psychotic disorders across 12 regions in Ireland. Although there were some weak but interesting trends for temperature, barometric pressure and sunshine, the meteorological patterns ultimately did not exert a clinically significant influence over admissions for psychosis. Further analysis is needed.

The asteroid rendezvous spacecraft. An adaptation study of TIROS/DMSP technology

NASA Technical Reports Server (NTRS)

1982-01-01

The feasibility of using the TIROS/DMSP Earth orbiting meteorological satellite in application to a near Earth asteroid rendezvous mission. System and subsystems analysis was carried out to develop a configuration of the spacecraft suitable for this mission. Mission analysis studies were also done and maneuver/rendezvous scenarios developed for baseline missions to both Anteros and Eros. The fact that the Asteroid mission is the most complex of the Pioneer class missions currently under consideration notwithstanding, the basic conclusion very strongly supports the suitability of the basic TIROS bus for this mission in all systems and subsystems areas, including science accommodation. Further, the modifications which are required due to the unique mission are very low risk and can be accomplished readily. The key issue is that in virtually every key subsystem, the demands of the Asteroid mission are a subset of the basic meteorological satellite mission. This allows a relatively simple reconfiguration to be accomplished without a major system redesign.

Meteorological factors affecting dengue incidence in Davao, Philippines.

PubMed

Iguchi, Jesavel A; Seposo, Xerxes T; Honda, Yasushi

2018-05-15

Dengue fever is a major public health concern in the Philippines, and has been a significant cause of hospitalizations and deaths among young children. Previous literature links climate change to dengue, and with increasingly unpredictable changing climate patterns, there is a need to understand how these meteorological variables affect dengue incidence in a highly endemic area. Weekly dengue incidences (2011-2015) in Davao Region, Philippines were obtained from the Department of Health. Same period of weekly local meteorological variables were obtained from the National Climatic Data Center (NCDC) and the National Oceanic and Atmospheric Administration (NOAA). Wavelet coherence analysis was used to determine the presence of non-stationary relationships, while a quasi-Poisson regression combined with distributed lag nonlinear model (DLNM) was used to analyze the association between meteorological variables and dengue incidences. Significant periodicity was detected in the 7 to 14-week band between the year 2011-2012 and a 26-week periodicity from the year 2013-2014. Overall cumulative risks were particularly high for rainfall at 32 mm (RR: 1.67, 95% CI: 1.07-2.62), while risks were observed to increase with increasing dew point. On the other hand, lower average temperature of 26 °C has resulted to an increased RR of dengue (RR: 1.96, 95% CI: 0.47-8.15) while higher temperature from 27 °C to 31 °C has lower RR. The observed possible threshold levels of these meteorological variables can be integrated into an early warning system to enhance dengue prediction for better vector control and management in the future.

Sub-daily Statistical Downscaling of Meteorological Variables Using Neural Networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kumar, Jitendra; Brooks, Bjørn-Gustaf J.; Thornton, Peter E

2012-01-01

A new open source neural network temporal downscaling model is described and tested using CRU-NCEP reanal ysis and CCSM3 climate model output. We downscaled multiple meteorological variables in tandem from monthly to sub-daily time steps while also retaining consistent correlations between variables. We found that our feed forward, error backpropagation approach produced synthetic 6 hourly meteorology with biases no greater than 0.6% across all variables and variance that was accurate within 1% for all variables except atmospheric pressure, wind speed, and precipitation. Correlations between downscaled output and the expected (original) monthly means exceeded 0.99 for all variables, which indicates thatmore » this approach would work well for generating atmospheric forcing data consistent with mass and energy conserved GCM output. Our neural network approach performed well for variables that had correlations to other variables of about 0.3 and better and its skill was increased by downscaling multiple correlated variables together. Poor replication of precipitation intensity however required further post-processing in order to obtain the expected probability distribution. The concurrence of precipitation events with expected changes in sub ordinate variables (e.g., less incident shortwave radiation during precipitation events) were nearly as consistent in the downscaled data as in the training data with probabilities that differed by no more than 6%. Our downscaling approach requires training data at the target time step and relies on a weak assumption that climate variability in the extrapolated data is similar to variability in the training data.« less

Meteorological radar services: a brief discussion and a solution in practice

NASA Astrophysics Data System (ADS)

Nicolaides, K. A.

2014-08-01

The Department of Meteorology is the organization designated by the Civil Aviation Department and by the National Supervisory Authority of the Republic of Cyprus, as an air navigation service provider, based on the regulations of the Single European Sky. Department of Meteorology holds and maintains also an ISO: 9001/2008, Quality System, for the provision of meteorological and climatological services to aeronautic and maritime community, but also to the general public. In order to fulfill its obligations the Department of Meteorology customs the rather dense meteorological stations network, with long historical data series, installed and maintained by the Department, in parallel with modelling and Numerical Weather Prediction (NWP), along with training and gaining of expertise. Among the available instruments in the community of meteorologists is the meteorological radar, a basic tool for the needs of very short/short range forecasting (nowcasting). The Department of Meteorology installed in the mid 90's a C-band radar over «Throni» location and expanded its horizons in nowcasting, aviation safety and warnings issuance. The radar has undergone several upgrades but today technology has over passed its rather old technology. At the present the Department of Meteorology is in the process of buying Meteorological Radar Services as a result of a public procurement procedure. Two networked X-band meteorological radar will be installed (the project now is in the phase of infrastructure establishment while the hardware is in the process of assemble), and maintained from Space Hellas (the contractor) for a 13 years' time period. The present article must be faced as a review article of the efforts of the Department of Meteorology to support its weather forecasters with data from meteorological radar.

Extended T-index models for glacier surface melting: a case study from Chorabari Glacier, Central Himalaya, India

NASA Astrophysics Data System (ADS)

Karakoti, Indira; Kesarwani, Kapil; Mehta, Manish; Dobhal, D. P.

2016-10-01

Two enhanced temperature-index (T-index) models are proposed by incorporating meteorological parameters viz. relative humidity, wind speed and net radiation. The models are an attempt to explore different climatic variables other than temperature affecting glacier surface melting. Weather data were recorded at Chorabari Glacier using an automatic weather station during the summers of 2010 (July 10 to September 10) and 2012 (June 10 to October 25). The modelled surface melt is validated against the measured point surface melting at the snout. Performance of the developed models is evaluated by comparing with basic temperature-index model and is quantified through different efficiency criteria. The results suggest that proposed models yield considerable improvement in surface melt simulation . Consequently, the study reveals that glacier surface melt depends not only on temperature but also on weather parameters viz. relative humidity, wind speed and net radiation play a significant role in glacier surface melting. This approach provides a major improvement on basic temperature-index method and offers an alternative to energy balance model.

Current research on aviation weather (bibliography)

NASA Technical Reports Server (NTRS)

Durham, D. E.; Frost, W.

1978-01-01

This bibliography of 326 readily usable references of basic and applied research programs related to the various areas of aviation meteorology was assembled. A literature search was conducted which surveyed the major abstract publications such as the International Aerospace Abstracts, the Meteorological and Geoastrophysical Abstracts, and the Scientific and Technical Aerospace Reports. In addition, NASA and DOT computer literature searches were run; and NASA, NOAA, and FAA research project managers were requested to provide writeups on their ongoing research.

Filling the gaps in meteorological continuous data measured at FLUXNET sites with ERA-Interim reanalysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vuichard, N.; Papale, D.

In this study, exchanges of carbon, water and energy between the land surface and the atmosphere are monitored by eddy covariance technique at the ecosystem level. Currently, the FLUXNET database contains more than 500 registered sites, and up to 250 of them share data (free fair-use data set). Many modelling groups use the FLUXNET data set for evaluating ecosystem models' performance, but this requires uninterrupted time series for the meteorological variables used as input. Because original in situ data often contain gaps, from very short (few hours) up to relatively long (some months) ones, we develop a new and robustmore » method for filling the gaps in meteorological data measured at site level. Our approach has the benefit of making use of continuous data available globally (ERA-Interim) and a high temporal resolution spanning from 1989 to today. These data are, however, not measured at site level, and for this reason a method to downscale and correct the ERA-Interim data is needed. We apply this method to the level 4 data (L4) from the La Thuile collection, freely available after registration under a fair-use policy. The performance of the developed method varies across sites and is also function of the meteorological variable. On average over all sites, applying the bias correction method to the ERA-Interim data reduced the mismatch with the in situ data by 10 to 36 %, depending on the meteorological variable considered. In comparison to the internal variability of the in situ data, the root mean square error (RMSE) between the in situ data and the unbiased ERA-I (ERA-Interim) data remains relatively large (on average over all sites, from 27 to 76 % of the standard deviation of in situ data, depending on the meteorological variable considered). The performance of the method remains poor for the wind speed field, in particular regarding its capacity to conserve a standard deviation similar to the one measured at FLUXNET stations.« less

Filling the gaps in meteorological continuous data measured at FLUXNET sites with ERA-Interim reanalysis

DOE PAGES

Vuichard, N.; Papale, D.

2015-07-13

In this study, exchanges of carbon, water and energy between the land surface and the atmosphere are monitored by eddy covariance technique at the ecosystem level. Currently, the FLUXNET database contains more than 500 registered sites, and up to 250 of them share data (free fair-use data set). Many modelling groups use the FLUXNET data set for evaluating ecosystem models' performance, but this requires uninterrupted time series for the meteorological variables used as input. Because original in situ data often contain gaps, from very short (few hours) up to relatively long (some months) ones, we develop a new and robustmore » method for filling the gaps in meteorological data measured at site level. Our approach has the benefit of making use of continuous data available globally (ERA-Interim) and a high temporal resolution spanning from 1989 to today. These data are, however, not measured at site level, and for this reason a method to downscale and correct the ERA-Interim data is needed. We apply this method to the level 4 data (L4) from the La Thuile collection, freely available after registration under a fair-use policy. The performance of the developed method varies across sites and is also function of the meteorological variable. On average over all sites, applying the bias correction method to the ERA-Interim data reduced the mismatch with the in situ data by 10 to 36 %, depending on the meteorological variable considered. In comparison to the internal variability of the in situ data, the root mean square error (RMSE) between the in situ data and the unbiased ERA-I (ERA-Interim) data remains relatively large (on average over all sites, from 27 to 76 % of the standard deviation of in situ data, depending on the meteorological variable considered). The performance of the method remains poor for the wind speed field, in particular regarding its capacity to conserve a standard deviation similar to the one measured at FLUXNET stations.« less

Impacts of 2000-2050 Climate Change on Fine Particulate Matter (PM2.5) Air Quality in China Based on Statistical Projections Using an Ensemble of Global Climate Models

NASA Astrophysics Data System (ADS)

Leung, D. M.; Tai, A. P. K.; Shen, L.; Moch, J. M.; van Donkelaar, A.; Mickley, L. J.

2017-12-01

Fine particulate matter (PM2.5) air quality is strongly dependent on not only on emissions but also meteorological conditions. Here we examine the dominant synoptic circulation patterns that control day-to-day PM2.5 variability over China. We perform principal component (PC) analysis on 1998-2016 NCEP/NCAR Reanalysis I daily meteorological fields to diagnose distinct synoptic meteorological modes, and perform PC regression on spatially interpolated 2014-2016 daily mean PM2.5 concentrations in China to identify modes dominantly explaining PM2.5 variability. We find that synoptic systems, e.g., cold-frontal passages, maritime inflow and frontal precipitation, can explain up to 40% of the day-to-day PM2.5 variability in major metropolitan regions in China. We further investigate how annually changing frequencies of synoptic systems, as well as changing local meteorology, drive interannual PM2.5 variability. We apply a spectral analysis on the PC time series to obtain the 1998-2016 annual median synoptic frequency, and use a forward-selection multiple linear regression (MLR) model of satellite-derived 1998-2015 annual mean PM2.5 concentrations on local meteorology and synoptic frequency, selecting predictors that explain the highest fraction of interannual PM2.5 variability while guarding against multicollinearity. To estimate the effect of climate change on future PM2.5 air quality, we project a multimodel ensemble of 15 CMIP5 models under the RCP8.5 scenario on the PM2.5-to-meteorology sensitivities derived for the present-day from the MLR model. Our results show that climate change could be responsible for increases in PM2.5 of more than 25 μg m-3 in northwestern China and 10 mg m-3 in northeastern China by the 2050s. Increases in synoptic frequency of cold-frontal passages cause only a modest 1 μg m-3 decrease in PM2.5 in North China Plain. Our analyses show that climate change imposes a significant penalty on air quality over China and poses serious threat on human health under the RCP8.5 future.

Appraisal of Weather Research and Forecasting Model Downscaling of Hydro-meteorological Variables and their Applicability for Discharge Prediction: Prognostic Approach for Ungauged Basin

NASA Astrophysics Data System (ADS)

Srivastava, P. K.; Han, D.; Rico-Ramirez, M. A.; Bray, M.; Islam, T.; Petropoulos, G.; Gupta, M.

2015-12-01

Hydro-meteorological variables such as Precipitation and Reference Evapotranspiration (ETo) are the most important variables for discharge prediction. However, it is not always possible to get access to them from ground based measurements, particularly in ungauged catchments. The mesoscale model WRF (Weather Research & Forecasting model) can be used for prediction of hydro-meteorological variables. However, hydro-meteorologists would like to know how well the downscaled global data products are as compared to ground based measurements and whether it is possible to use the downscaled data for ungauged catchments. Even with gauged catchments, most of the stations have only rain and flow gauges installed. Measurements of other weather hydro-meteorological variables such as solar radiation, wind speed, air temperature, and dew point are usually missing and thus complicate the problems. In this study, for downscaling the global datasets, the WRF model is setup over the Brue catchment with three nested domains (D1, D2 and D3) of horizontal grid spacing of 81 km, 27 km and 9 km are used. The hydro-meteorological variables are downscaled using the WRF model from the National Centers for Enviromental Prediction (NCEP) reanalysis datasets and subsequently used for the ETo estimation using the Penman Monteith equation. The analysis of weather variables and precipitation are compared against the ground based datasets, which indicate that the datasets are in agreement with the observed datasets for complete monitoring period as well as during the seasons except precipitation whose performance is poorer in comparison to the measured rainfall. After a comparison, the WRF estimated precipitation and ETo are then used as a input parameter in the Probability Distributed Model (PDM) for discharge prediction. The input data and model parameter sensitivity analysis and uncertainty estimation are also taken into account for the PDM calibration and prediction following the Generalised Likelihood Uncertainty Estimation (GLUE) approach. The overall analysis suggests that the uncertainty estimates in predicted discharge using WRF downscaled ETo have comparable performance to ground based observed datasets and hence is promising for discharge prediction in the absence of ground based measurements.

Long-term fluctuations of hailstorms in South Moravia, Czech Republic: synthesis of different data sources

NASA Astrophysics Data System (ADS)

Chromá, Kateřina; Brázdil, Rudolf; Dolák, Lukáš; Řezníčková, Ladislava; Valášek, Hubert; Zahradníček, Pavel

2016-04-01

Hailstorms belong to natural phenomena causing great material damage in present time, similarly as it was in the past. In Moravia (eastern part of the Czech Republic), systematic meteorological observations started generally in the latter half of the 19th century. Therefore, in order to create long-term series of hailstorms, it is necessary to search for other sources of information. Different types of documentary evidence are used in historical climatology, such as annals, chronicles, diaries, private letters, newspapers etc. Besides them, institutional documentary evidence of economic and administrative character (e.g. taxation records) has particular importance. This study aims to create a long-term series of hailstorms in South Moravia using various types of documentary evidence (such as taxation records, family archives, chronicles and newspapers which are the most important) and systematic meteorological observations in the station network. Although available hailstorm data cover the 1541-2014 period, incomplete documentary evidence allows reasonable analysis of fluctuations in hailstorm frequency only since the 1770s. The series compiled from documentary data and systematic meteorological observations is used to identify periods of lower and higher hailstorm frequency. Existing data may be used also for the study of spatial hailstorm variability. Basic uncertainties of compiled hailstorm series are discussed. Despite some bias in hailstorm data, South-Moravian hailstorm series significantly extends our knowledge about this phenomenon in the south-eastern part of the Czech Republic. The study is a part of the research project "Hydrometeorological extremes in Southern Moravia derived from documentary evidence" supported by the Grant Agency of the Czech Republic, reg. no. 13-19831S.

Identifying controlling factors of ground-level ozone levels over southwestern Taiwan using a decision tree

NASA Astrophysics Data System (ADS)

Chu, Hone-Jay; Lin, Chuan-Yao; Liau, Churn-Jung; Kuo, Yi-Ming

2012-12-01

Kaohsiung City and the suburban region of southwestern Taiwan have suffered from severe air pollution since becoming the largest center of heavy industry in Taiwan. The complex process of ozone (O3) formation and its precursor compounds (the volatile organic compounds (VOCs) and nitrogen oxide (NOx) emissions), accompanied by meteorological conditions, make controlling ozone difficult. Using a decision tree is especially appropriate for analyzing time series data that contain ozone levels and meteorological and explanatory variables for ozone formation. Results show that dominant variables such as temperature, wind speed, VOCs, and NOx can play vital roles in describing ozone variations among observations. That temperature and wind speed are highly correlated with ozone levels indicates that these meteorological conditions largely affect ozone variability. The results also demonstrate that spatial heterogeneity of ozone patterns are in coastal and inland areas caused by sea-land breeze and pollutant sources during high ozone episodes over southwestern Taiwan. This study used a decision tree to obtain quantitative insight into spatial distributions of precursor compound emissions and effects of meteorological conditions on ozone levels that are useful for refining monitoring plans and developing management strategies.

Decreasing reference evapotranspiration in a warming climate - A case of Changjiang (Yangtze) River catchment during 1970-2000

NASA Astrophysics Data System (ADS)

Xu, C. Y.; Gong, L. B.; Tong, J.; Chen, D. L.

2006-07-01

This study deals with temporal trends in the Penman-Monteith reference evapotranspiration estimated from standard meteorological observations, observed pan evaporation, and four related meteorological variables during 1970-2000 in the Yangtze River catchment. Relative contributions of the four meteorological variables to changes in the reference evapotranspiration are quantified. The results show that both the reference evapotranspiration and the pan evaporation have significant. decreasing trends in the upper, the middle as well as in the whole Changjiang (Yangtze) River catchment at the 5% significance level, while the air temperature shows a significant increasing trend. The decreasing trend detected in the reference evapotranspiration can be attributed to the significant decreasing trends in the net radiation and the wind speed.

Meteorological factors for PM10 concentration levels in Northern Spain

NASA Astrophysics Data System (ADS)

Santurtún, Ana; Mínguez, Roberto; Villar-Fernández, Alejandro; González Hidalgo, Juan Carlos; Zarrabeitia, María Teresa

2013-04-01

Atmospheric particulate matter (PM) is made up of a mixture of solid and aqueous species which enter the atmosphere by anthropogenic and natural pathways. The levels and composition of ambient air PM depend on the climatology and on the geography (topography, soil cover, proximity to arid zones or to the coast) of a given region. Spain has particular difficulties in achieving compliance with the limit values established by the European Union (based on recommendations from the World Health Organization) for particulate matter on the order of 10 micrometers of diameter or less (PM10), but not only antropogenical emissions are responsible for this: some studies show that PM10 concentrations originating from these kinds of sources are similar to what is found in other European countries, while some of the geographical features of the Iberian Peninsula (such as African mineral dust intrusion, soil aridity or rainfall) are proven to be a factor for higher PM concentrations. This work aims to describe PM10 concentration levels in Cantabria (Northern Spain) and their relationship with the following meteorological variables: rainfall, solar radiation, temperature, barometric pressure and wind speed. Data consists of daily series obtained from hourly data records for the 2000-2010 period, of PM10 concentrations from 4 different urban-background stations, and daily series of the meteorological variables provided by Spanish National Meteorology Agency. The method used for establishing the relationships between these variables consists of several steps: i) fitting a non-stationary probability density function for each variable accounting for long-term trends, seasonality during the year and possible seasonality during the week to distinguish between work and weekend days, ii) using the marginal distribution function obtained, transform the time series of historical values of each variable into a normalized Gaussian time series. This step allows using consistently time series models, iii) fitting of a times series model (Autoregressive moving average, ARMA) to the transformed historical values in order to eliminate the temporal autocorrelation structure of each stochastic process, obtaining a white noise for each variable, and finally, iv) the calculation of cross correlations between white noises at different time lags. These cross correlations allow characterization of the true correlation between signals, avoiding the problems induced by data scaling or autocorrelations inherent to each signal. Results provide the relationship and possible contribution to PM10 concentration levels associated with each meteorological variable. This information can be used to improve PM10 concentration levels forecasting using existing meteorological forecasts.

Meteorological variables affect fertility rate after intrauterine artificial insemination in sheep in a seasonal-dependent manner: a 7-year study.

PubMed

Palacios, C; Abecia, J A

2015-05-01

A total number of 48,088 artificial inseminations (AIs) have been controlled during seven consecutive years in 79 dairy sheep Spanish farms (41° N). Mean, maximum and minimum ambient temperatures (Ts), temperature amplitude (TA), mean relative humidity (RH), mean solar radiation (SR) and total rainfall of each insemination day and 15 days later were recorded. Temperature-humidity index (THI) and effective temperature (ET) have been calculated. A binary logistic regression model to estimate the risk of not getting pregnant compared to getting pregnant, through the odds ratio (OR), was performed. Successful winter inseminations were carried out under higher SR (P < 0.01) and summer inseminations under lower SR values (P < 0.05). Successful inseminations during the summer were performed under significantly lower maximum T (P < 0.01), while winter inseminations resulted in pregnancy when they were carried out under higher maximum (P < 0.05) and minimum Ts (P < 0.01). Up to five meteorological variables presented OR >1 (maximum T, ET and rainfall on AI day, and ET and rainfall on day 15), and two variables presented OR <1 (SR on AI day and maximum T on day 15). However, the effect of meteorological factors affected fertility in opposite ways, so T becomes a protective or risk factor on fertility depending on season. In conclusion, the percentage of pregnancy after AI in sheep is significantly affected by meteorological variables in a seasonal-dependent manner, so the parameters such as temperature reverse their effects in the hot or cold seasons. A forecast of the meteorological conditions could be a useful tool when AI dates are being scheduled.

Meteorological variables affect fertility rate after intrauterine artificial insemination in sheep in a seasonal-dependent manner: a 7-year study

NASA Astrophysics Data System (ADS)

Palacios, C.; Abecia, J. A.

2015-05-01

A total number of 48,088 artificial inseminations (AIs) have been controlled during seven consecutive years in 79 dairy sheep Spanish farms (41° N). Mean, maximum and minimum ambient temperatures ( Ts), temperature amplitude (TA), mean relative humidity (RH), mean solar radiation (SR) and total rainfall of each insemination day and 15 days later were recorded. Temperature-humidity index (THI) and effective temperature (ET) have been calculated. A binary logistic regression model to estimate the risk of not getting pregnant compared to getting pregnant, through the odds ratio (OR), was performed. Successful winter inseminations were carried out under higher SR ( P < 0.01) and summer inseminations under lower SR values ( P < 0.05). Successful inseminations during the summer were performed under significantly lower maximum T ( P < 0.01), while winter inseminations resulted in pregnancy when they were carried out under higher maximum ( P < 0.05) and minimum Ts ( P < 0.01). Up to five meteorological variables presented OR >1 (maximum T, ET and rainfall on AI day, and ET and rainfall on day 15), and two variables presented OR <1 (SR on AI day and maximum T on day 15). However, the effect of meteorological factors affected fertility in opposite ways, so T becomes a protective or risk factor on fertility depending on season. In conclusion, the percentage of pregnancy after AI in sheep is significantly affected by meteorological variables in a seasonal-dependent manner, so the parameters such as temperature reverse their effects in the hot or cold seasons. A forecast of the meteorological conditions could be a useful tool when AI dates are being scheduled.

The influence of intra- and inter-annual meteorological variability on dengue transmission: a multi-level modeling analysis

NASA Astrophysics Data System (ADS)

Wen, Tzai-Hung; Chen, Tzu-Hsin

2017-04-01

Dengue fever is one of potentially life-threatening mosquito-borne diseases and IPCC Fifth Assessment Report (AR5) has confirmed that dengue incidence is sensitive to the critical weather conditions, such as effects of temperature. However, previous literature focused on the effects of monthly or weekly average temperature or accumulative precipitation on dengue incidence. The influence of intra- and inter-annual meteorological variability on dengue outbreak is under investigated. The purpose of the study focuses on measuring the effect of the intra- and inter-annual variations of temperature and precipitation on dengue outbreaks. We developed the indices of intra-annual temperature variability are maximum continuity, intermittent, and accumulation of most suitable temperature (MST) for dengue vectors; and also the indices of intra-annual precipitation variability, including the measure of continuity of wetness or dryness during a pre-epidemic period; and rainfall intensity during an epidemic period. We used multi-level modeling to investigate the intra- and inter-annual meteorological variations on dengue outbreaks in southern Taiwan from 1998-2015. Our results indicate that accumulation and maximum continuity of MST are more significant than average temperature on dengue outbreaks. The effect of continuity of wetness during the pre-epidemic period is significantly more positive on promoting dengue outbreaks than the rainfall effect during the epidemic period. Meanwhile, extremely high or low rainfall density during an epidemic period do not promote the spread of dengue epidemics. Our study differentiates the effects of intra- and inter-annual meteorological variations on dengue outbreaks and also provides policy implications for further dengue control under the threats of climate change. Keywords: dengue fever, meteorological variations, multi-level model

EXAMINATION OF MODEL PREDICTIONS AT DIFFERENT HORIZONTAL GRID RESOLUTIONS

EPA Science Inventory

While fluctuations in meteorological and air quality variables occur on a continuum of spatial scales, the horizontal grid spacing of coupled meteorological and photochemical models sets a lower limit on the spatial scales that they can resolve. However, both computational costs ...

Algorithm Estimates Microwave Water-Vapor Delay

NASA Technical Reports Server (NTRS)

Robinson, Steven E.

1989-01-01

Accuracy equals or exceeds conventional linear algorithms. "Profile" algorithm improved algorithm using water-vapor-radiometer data to produce estimates of microwave delays caused by water vapor in troposphere. Does not require site-specific and weather-dependent empirical parameters other than standard meteorological data, latitude, and altitude for use in conjunction with published standard atmospheric data. Basic premise of profile algorithm, wet-path delay approximated closely by solution to simplified version of nonlinear delay problem and generated numerically from each radiometer observation and simultaneous meteorological data.

Characterizing Temperature Variability and Associated Large Scale Meteorological Patterns Across South America

NASA Astrophysics Data System (ADS)

Detzer, J.; Loikith, P. C.; Mechoso, C. R.; Barkhordarian, A.; Lee, H.

2017-12-01

South America's climate varies considerably owing to its large geographic range and diverse topographical features. Spanning the tropics to the mid-latitudes and from high peaks to tropical rainforest, the continent experiences an array of climate and weather patterns. Due to this considerable spatial extent, assessing temperature variability at the continent scale is particularly challenging. It is well documented in the literature that temperatures have been increasing across portions of South America in recent decades, and while there have been many studies that have focused on precipitation variability and change, temperature has received less scientific attention. Therefore, a more thorough understanding of the drivers of temperature variability is critical for interpreting future change. First, k-means cluster analysis is used to identify four primary modes of temperature variability across the continent, stratified by season. Next, composites of large scale meteorological patterns (LSMPs) are calculated for months assigned to each cluster. Initial results suggest that LSMPs, defined using meteorological variables such as sea level pressure (SLP), geopotential height, and wind, are able to identify synoptic scale mechanisms important for driving temperature variability at the monthly scale. Some LSMPs indicate a relationship with known recurrent modes of climate variability. For example, composites of geopotential height suggest that the Southern Annular Mode is an important, but not necessarily dominant, component of temperature variability over southern South America. This work will be extended to assess the drivers of temperature extremes across South America.

Sensitivity of the interannual variability of mineral aerosol simulations to meteorological forcing dataset

DOE PAGES

Smith, Molly B.; Mahowald, Natalie M.; Albani, Samuel; ...

2017-03-07

Interannual variability in desert dust is widely observed and simulated, yet the sensitivity of these desert dust simulations to a particular meteorological dataset, as well as a particular model construction, is not well known. Here we use version 4 of the Community Atmospheric Model (CAM4) with the Community Earth System Model (CESM) to simulate dust forced by three different reanalysis meteorological datasets for the period 1990–2005. We then contrast the results of these simulations with dust simulated using online winds dynamically generated from sea surface temperatures, as well as with simulations conducted using other modeling frameworks but the same meteorological forcings, in order tomore » determine the sensitivity of climate model output to the specific reanalysis dataset used. For the seven cases considered in our study, the different model configurations are able to simulate the annual mean of the global dust cycle, seasonality and interannual variability approximately equally well (or poorly) at the limited observational sites available. Altogether, aerosol dust-source strength has remained fairly constant during the time period from 1990 to 2005, although there is strong seasonal and some interannual variability simulated in the models and seen in the observations over this time period. Model interannual variability comparisons to observations, as well as comparisons between models, suggest that interannual variability in dust is still difficult to simulate accurately, with averaged correlation coefficients of 0.1 to 0.6. Because of the large variability, at least 1 year of observations at most sites are needed to correctly observe the mean, but in some regions, particularly the remote oceans of the Southern Hemisphere, where interannual variability may be larger than in the Northern Hemisphere, 2–3 years of data are likely to be needed.« less

Dynamical Mapping of Anopheles darlingi Densities in a Residual Malaria Transmission Area of French Guiana by Using Remote Sensing and Meteorological Data

PubMed Central

Adde, Antoine; Roux, Emmanuel; Mangeas, Morgan; Dessay, Nadine; Nacher, Mathieu; Dusfour, Isabelle; Girod, Romain; Briolant, Sébastien

2016-01-01

Local variation in the density of Anopheles mosquitoes and the risk of exposure to bites are essential to explain the spatial and temporal heterogeneities in the transmission of malaria. Vector distribution is driven by environmental factors. Based on variables derived from satellite imagery and meteorological observations, this study aimed to dynamically model and map the densities of Anopheles darlingi in the municipality of Saint-Georges de l’Oyapock (French Guiana). Longitudinal sampling sessions of An. darlingi densities were conducted between September 2012 and October 2014. Landscape and meteorological data were collected and processed to extract a panel of variables that were potentially related to An. darlingi ecology. Based on these data, a robust methodology was formed to estimate a statistical predictive model of the spatial-temporal variations in the densities of An. darlingi in Saint-Georges de l’Oyapock. The final cross-validated model integrated two landscape variables—dense forest surface and built surface—together with four meteorological variables related to rainfall, evapotranspiration, and the minimal and maximal temperatures. Extrapolation of the model allowed the generation of predictive weekly maps of An. darlingi densities at a resolution of 10-m. Our results supported the use of satellite imagery and meteorological data to predict malaria vector densities. Such fine-scale modeling approach might be a useful tool for health authorities to plan control strategies and social communication in a cost-effective, targeted, and timely manner. PMID:27749938

Meteorological analysis of symptom data for people with seasonal affective disorder.

PubMed

Sarran, Christophe; Albers, Casper; Sachon, Patrick; Meesters, Ybe

2017-11-01

It is thought that variation in natural light levels affect people with Seasonal Affective Disorder (SAD). Several meteorological factors related to luminance can be forecast but little is known about which factors are most indicative of worsening SAD symptoms. The aim of this meteorological analysis is to determine which factors are linked to SAD symptoms. The symptoms of 291 individuals with SAD in and near Groningen have been evaluated over the period 2003-2009. Meteorological factors linked to periods of low natural light (sunshine, global radiation, horizontal visibility, cloud cover and mist) and others (temperature, humidity and pressure) were obtained from weather observation stations. A Bayesian zero adjusted auto-correlated multilevel Poisson model was carried out to assess which variables influence the SAD symptom score BDI-II. The outcome of the study suggests that the variable sunshine duration, for both the current and previous week, and global radiation for the previous week, are significantly linked to SAD symptoms. Crown Copyright © 2017. Published by Elsevier B.V. All rights reserved.

Assessing relationships between cattle egret migration and meteorology in the southwest Pacific: a review

NASA Astrophysics Data System (ADS)

Bridgman, H. A.; Maddock, M.; Geering, D. J.

The evolution of research into meteorological factors affecting the migration of the Cattle Egret (Ardeola ibis coromandus) in the southwestern Pacific region (Australia, New Zealand and the Tasman Sea) - from ground-based studies dependent on volunteer observers to a pilot satellite-tracking project - is reviewed and the results are related to the literature on bird migration. The predominant pattern is a seasonal migration from breeding colonies in southeast Queensland and northern New South Wales which takes place in stages along the east coastal plain under favourable meteorological conditions. Migration outward (southward) occurs in February through April and return to the breeding colonies occurs in October and November. Wintering destinations include Tasmania, southern Victoria and parts of New Zealand. Favourable meteorological conditions for migration southward include:moderate north to northwest airflow behind a high; light and variable winds in a high or col; and light and variable winds over New South Wales with moderate westerlies over Victoria and Tasmania. A satellite-tracking project helped to validate findings from the ground-based studies, provided additional information not otherwise obtainable, and demonstrated the potential of the technique to further clarify the relation between timing and staging of migration, and meteorology.

The use of normalized climatological anomalies to rank synoptic-scale events and their relation to Weather Types

NASA Astrophysics Data System (ADS)

Ramos, A. M.; Lorenzo, M. N.; Gimeno, L.; Nieto, R.; Añel, J. A.

2009-09-01

Several methods have been developed to rank meteorological events in terms of severity, social impact or economic impacts. These classifications are not always objective since they depend of several factors, for instance, the observation network is biased towards the densely populated urban areas against rural or oceanic areas. It is also very important to note that not all rare synoptic-scale meteorological events attract significant media attention. In this work we use a comprehensive method of classifying synoptic-scale events adapted from Hart and Grumm, 2001, to the European region (30N-60N, 30W-15E). The main motivation behind this method is that the more unusual the event (a cold outbreak, a heat wave, or a flood), for a given region, the higher ranked it must be. To do so, we use four basic meteorological variables (Height, Temperature, Wind and Specific Humidity) from NCEP reanalysis dataset over the range of 1000hPa to 200hPa at a daily basis from 1948 to 2004. The climatology used embraces the 1961-1990 period. For each variable, the analysis of raking climatological anomalies was computed taking into account the daily normalized departure from climatology at different levels. For each day (from 1948 to 2004) we have four anomaly measures, one for each variable, and another, a combined where the anomaly (total anomaly) is the average of the anomaly of the four variables. Results will be analyzed on a monthly, seasonal and annual basis. Seasonal trends and variability will also be shown. In addition, and given the extent of the database, the expected return periods associated with the anomalies are revealed. Moreover, we also use an automated version of the Lamb weather type (WT) classification scheme (Jones et al, 1993) adapted for the Galicia area (Northwestern corner of the Iberian Peninsula) by Lorenzo et al (2008) in order to compute the daily local circulation regimes in this area. By combining the corresponding daily WT with the five anomaly measures we can evaluate if there is any preferable WT responsible for high or low values of anomalies. Hart, R.E and R.H. Grumm (2001) Using normalized climatological anomalies to rank synoptic-scale events objectivily. Monthly Weather Review, 129, 2426-2442. Jones, P. D., M. Hulme, K. R. Briffa (1993) A comparison of Lamb circulation types with anobjective classification scheme. International Journal of Climatology, 13: 655- 663. Lorenzo M.N., J.J. Taboada and L.Gimeno (2008). Links between circulation weather types and teleconnection patterns and their influence on precipitation patterns in Galicia (NW Spain). International Journal of Climatology 28(11): 1493:1505 DOI: 10.1002/joc.1646.

A study of air-to-ground sound propagation using an instrumented meteorological tower

NASA Technical Reports Server (NTRS)

Kasper, P. K.; Pappa, R. S.; Keefe, L. R.; Sutherland, L. C.

1975-01-01

The results of an exploratory NASA study, leading to a better understanding of the effects of meteorological conditions on the propagation of aircraft noise, are reported. The experimental program utilized a known sound source fixed atop an instrumented meteorological tower. The basic experimental scheme consisted of measuring the amplitude of sound radiated toward the ground along a line of microphones fixed to a tower guy wire. Experimental results show the feasibility of this approach in the acquisition of data indicating the variations encountered in the time-averaged and instantaneous amplitudes of propagated sound. The investigation included a consideration of ground reflections, a comparison of measured attenuations with predicted atmospheric absorption losses, and an evaluation of the amplitude fluctuations of recorded sound pressures.

Determination of the Solar Energy Microclimate of the United States Using Satellite Data

NASA Technical Reports Server (NTRS)

Vonderharr, T. H.; Ellis, J. S.

1978-01-01

The determination of total solar energy reaching the ground over the United States using measurements from meteorological satellites as the basic data set is examined. The methods of satellite data processing are described. Uncertainty analysis and comparison of results with well calibrated surface pyranometers are used to estimate the probable error in the satellite-based determination of ground insolation. It is 10 to 15 percent for daily information, and about 5 percent for monthly values. However, the natural space and time variability of insolation is much greater than the uncertainty in the method. The most important aspect of the satellite-based technique is the ability to determine the solar energy reaching the ground over small areas where no other measurements are available. Thus, it complements the widely spaced solar radiation measurement network of ground stations.

The Cooperative VAS Program with the Marshall Space Flight Center

NASA Technical Reports Server (NTRS)

Diak, George R.; Menzel, W. Paul

1988-01-01

Work was divided between the analysis/forecast model development and evaluation of the impact of satellite data in mesoscale numerical weather prediction (NWP), development of the Multispectral Atmospheric Mapping Sensor (MAMS), and other related research. The Cooperative Institute for Meteorological Satellite Studies (CIMSS) Synoptic Scale Model (SSM) has progressed from a relatively basic analysis/forecast system to a package which includes such features as nonlinear vertical mode initialization, comprehensive Planetary Boundary Layer (PBL) physics, and the core of a fully four-dimensional data assimilation package. The MAMS effort has produced a calibrated visible and infrared sensor that produces imager at high spatial resolution. The MAMS was developed in order to study small scale atmospheric moisture variability, to monitor and classify clouds, and to investigate the role of surface characteristics in the production of clouds, precipitation, and severe storms.

USING METEOROLOGICAL MODEL OUTPUT AS A SURROGATE FOR ON-SITE OBSERVATIONS TO PREDICT DEPOSITION VELOCITY

EPA Science Inventory

The National Oceanic and Atmospheric Administration's Multi-Layer Model (NOAA-MLM) is used by several operational dry deposition networks for estimating the deposition velocity of O , SO , HNO , and particles. The NOAA-MLM requires hourly values of meteorological variables and...

The influence of weather on migraine – are migraine attacks predictable?

PubMed Central

Hoffmann, Jan; Schirra, Tonio; Lo, Hendra; Neeb, Lars; Reuter, Uwe; Martus, Peter

2015-01-01

Objective The study aimed at elucidating a potential correlation between specific meteorological variables and the prevalence and intensity of migraine attacks as well as exploring a potential individual predictability of a migraine attack based on meteorological variables and their changes. Methods Attack prevalence and intensity of 100 migraineurs were correlated with atmospheric pressure, relative air humidity, and ambient temperature in 4-h intervals over 12 consecutive months. For each correlation, meteorological parameters at the time of the migraine attack as well as their variation within the preceding 24 h were analyzed. For migraineurs showing a positive correlation, logistic regression analysis was used to assess the predictability of a migraine attack based on meteorological information. Results In a subgroup of migraineurs, a significant weather sensitivity could be observed. In contrast, pooled analysis of all patients did not reveal a significant association. An individual prediction of a migraine attack based on meteorological data was not possible, mainly as a result of the small prevalence of attacks. Interpretation The results suggest that only a subgroup of migraineurs is sensitive to specific weather conditions. Our findings may provide an explanation as to why previous studies, which commonly rely on a pooled analysis, show inconclusive results. The lack of individual attack predictability indicates that the use of preventive measures based on meteorological conditions is not feasible. PMID:25642431

Impact analysis of traffic-related air pollution based on real-time traffic and basic meteorological information.

PubMed

Pan, Long; Yao, Enjian; Yang, Yang

2016-12-01

With the rapid development of urbanization and motorization in China, traffic-related air pollution has become a major component of air pollution which constantly jeopardizes public health. This study proposes an integrated framework for estimating the concentration of traffic-related air pollution with real-time traffic and basic meteorological information and also for further evaluating the impact of traffic-related air pollution. First, based on the vehicle emission factor models sensitive to traffic status, traffic emissions are calculated according to the real-time link-based average traffic speed, traffic volume, and vehicular fleet composition. Then, based on differences in meteorological conditions, traffic pollution sources are divided into line sources and point sources, and the corresponding methods to determine the dynamic affecting areas are also proposed. Subsequently, with basic meteorological data, Gaussian dispersion model and puff integration model are applied respectively to estimate the concentration of traffic-related air pollution. Finally, the proposed estimating framework is applied to calculate the distribution of CO concentration in the main area of Beijing, and the population exposure is also calculated to evaluate the impact of traffic-related air pollution on public health. Results show that there is a certain correlation between traffic indicators (i.e., traffic speed and traffic intensity) of the affecting area and traffic-related CO concentration of the target grid, which indicates the methods to determine the affecting areas are reliable. Furthermore, the reliability of the proposed estimating framework is verified by comparing the predicted and the observed ambient CO concentration. In addition, results also show that the traffic-related CO concentration is higher in morning and evening peak hours, and has a heavier impact on public health within the Fourth Ring Road of Beijing due to higher population density and higher CO concentration under calm wind condition in this area. Copyright © 2016 Elsevier Ltd. All rights reserved.

A globally calibrated scheme for generating daily meteorology from monthly statistics: Global-WGEN (GWGEN) v1.0

NASA Astrophysics Data System (ADS)

Sommer, Philipp S.; Kaplan, Jed O.

2017-10-01

While a wide range of Earth system processes occur at daily and even subdaily timescales, many global vegetation and other terrestrial dynamics models historically used monthly meteorological forcing both to reduce computational demand and because global datasets were lacking. Recently, dynamic land surface modeling has moved towards resolving daily and subdaily processes, and global datasets containing daily and subdaily meteorology have become available. These meteorological datasets, however, cover only the instrumental era of the last approximately 120 years at best, are subject to considerable uncertainty, and represent extremely large data files with associated computational costs of data input/output and file transfer. For periods before the recent past or in the future, global meteorological forcing can be provided by climate model output, but the quality of these data at high temporal resolution is low, particularly for daily precipitation frequency and amount. Here, we present GWGEN, a globally applicable statistical weather generator for the temporal downscaling of monthly climatology to daily meteorology. Our weather generator is parameterized using a global meteorological database and simulates daily values of five common variables: minimum and maximum temperature, precipitation, cloud cover, and wind speed. GWGEN is lightweight, modular, and requires a minimal set of monthly mean variables as input. The weather generator may be used in a range of applications, for example, in global vegetation, crop, soil erosion, or hydrological models. While GWGEN does not currently perform spatially autocorrelated multi-point downscaling of daily weather, this additional functionality could be implemented in future versions.

Air Quality and Meteorological Boundary Conditions during the MCMA-2003 Field Campaign

NASA Astrophysics Data System (ADS)

Sosa, G.; Arriaga, J.; Vega, E.; Magaña, V.; Caetano, E.; de Foy, B.; Molina, L. T.; Molina, M. J.; Ramos, R.; Retama, A.; Zaragoza, J.; Martínez, A. P.; Márquez, C.; Cárdenas, B.; Lamb, B.; Velasco, E.; Allwine, E.; Pressley, S.; Westberg, H.; Reyes, R.

2004-12-01

A comprehensive field campaign to characterize photochemical smog in the Mexico City Metropolitan Area (MCMA) was conducted during April 2003. An important number of equipment was deployed all around the urban core and its surroundings to measure gas and particles composition from the various sources and receptor sites. In addition to air quality measurements, meteorology variables were also taken by regular weather meteorological stations, tethered balloons, radiosondes, sodars and lidars. One important issue with regard to the field campaign was the characterization of the boundary conditions in order to feed meteorological and air quality models. Four boundary sites were selected to measure continuously criteria pollutants, VOC and meteorological variables at surface level. Vertical meteorological profiles were measured at three other sites : radiosondes in Tacubaya site were launched every six hours daily; tethered balloons were launched at CENICA and FES-Cuautitlan sites according to the weather conditions, and one sodar was deployed at UNAM site in the south of the city. Additionally to these measurements, two fixed meteorological monitoring networks deployed along the city were available to complement these measurements. In general, we observed that transport of pollutants from the city to the boundary sites changes every day, according to the coupling between synoptic and local winds. This effect were less important at elevated sites such as Cerro de la Catedral and ININ, where synoptic wind were more dominant during the field campaign. Also, local sources nearby boundary sites hide the influence of pollution coming from the city some days, particularly at the La Reforma site.

Recent changes and drivers of the atmospheric evaporative demand in the Canary Islands

NASA Astrophysics Data System (ADS)

Vicente-Serrano, Sergio M.; Azorin-Molina, Cesar; Sanchez-Lorenzo, Arturo; El Kenawy, Ahmed; Martín-Hernández, Natalia; Peña-Gallardo, Marina; Beguería, Santiago; Tomas-Burguera, Miquel

2016-08-01

We analysed recent evolution and meteorological drivers of the atmospheric evaporative demand (AED) in the Canary Islands for the period 1961-2013. We employed long and high-quality time series of meteorological variables to analyse current AED changes in this region and found that AED has increased during the investigated period. Overall, the annual ETo, which was estimated by means of the FAO-56 Penman-Monteith equation, increased significantly by 18.2 mm decade-1 on average, with a stronger trend in summer (6.7 mm decade-1). In this study we analysed the contribution of (i) the aerodynamic (related to the water vapour that a parcel of air can store) and (ii) radiative (related to the available energy to evaporate a quantity of water) components to the decadal variability and trends of ETo. More than 90 % of the observed ETo variability at the seasonal and annual scales can be associated with the variability in the aerodynamic component. The variable that recorded more significant changes in the Canary Islands was relative humidity, and among the different meteorological factors used to calculate ETo, relative humidity was the main driver of the observed ETo trends. The observed trend could have negative consequences in a number of water-depending sectors if it continues in the future.

Synoptic meteorological modes of variability for fine particulate matter (PM2.5) air quality in major metropolitan regions of China

NASA Astrophysics Data System (ADS)

Leung, Danny M.; Tai, Amos P. K.; Mickley, Loretta J.; Moch, Jonathan M.; van Donkelaar, Aaron; Shen, Lu; Martin, Randall V.

2018-05-01

In his study, we use a combination of multivariate statistical methods to understand the relationships of PM2.5 with local meteorology and synoptic weather patterns in different regions of China across various timescales. Using June 2014 to May 2017 daily total PM2.5 observations from ˜ 1500 monitors, all deseasonalized and detrended to focus on synoptic-scale variations, we find strong correlations of daily PM2.5 with all selected meteorological variables (e.g., positive correlation with temperature but negative correlation with sea-level pressure throughout China; positive and negative correlation with relative humidity in northern and southern China, respectively). The spatial patterns suggest that the apparent correlations with individual meteorological variables may arise from common association with synoptic systems. Based on a principal component analysis of 1998-2017 meteorological data to diagnose distinct meteorological modes that dominate synoptic weather in four major regions of China, we find strong correlations of PM2.5 with several synoptic modes that explain 10 to 40 % of daily PM2.5 variability. These modes include monsoonal flows and cold frontal passages in northern and central China associated with the Siberian High, onshore flows in eastern China, and frontal rainstorms in southern China. Using the Beijing-Tianjin-Hebei (BTH) region as a case study, we further find strong interannual correlations of regionally averaged satellite-derived annual mean PM2.5 with annual mean relative humidity (RH; positive) and springtime fluctuation frequency of the Siberian High (negative). We apply the resulting PM2.5-to-climate sensitivities to the Intergovernmental Panel on Climate Change (IPCC) Coupled Model Intercomparison Project Phase 5 (CMIP5) climate projections to predict future PM2.5 by the 2050s due to climate change, and find a modest decrease of ˜ 0.5 µg m-3 in annual mean PM2.5 in the BTH region due to more frequent cold frontal ventilation under the RCP8.5 future, representing a small climate benefit, but the RH-induced PM2.5 change is inconclusive due to the large inter-model differences in RH projections.

A dynamic aerodynamic resistance approach to calculate high resolution sensible heat fluxes in urban areas

NASA Astrophysics Data System (ADS)

Crawford, Ben; Grimmond, Sue; Kent, Christoph; Gabey, Andrew; Ward, Helen; Sun, Ting; Morrison, William

2017-04-01

Remotely sensed data from satellites have potential to enable high-resolution, automated calculation of urban surface energy balance terms and inform decisions about urban adaptations to environmental change. However, aerodynamic resistance methods to estimate sensible heat flux (QH) in cities using satellite-derived observations of surface temperature are difficult in part due to spatial and temporal variability of the thermal aerodynamic resistance term (rah). In this work, we extend an empirical function to estimate rah using observational data from several cities with a broad range of surface vegetation land cover properties. We then use this function to calculate spatially and temporally variable rah in London based on high-resolution (100 m) land cover datasets and in situ meteorological observations. In order to calculate high-resolution QH based on satellite-observed land surface temperatures, we also develop and employ novel methods to i) apply source area-weighted averaging of surface and meteorological variables across the study spatial domain, ii) calculate spatially variable, high-resolution meteorological variables (wind speed, friction velocity, and Obukhov length), iii) incorporate spatially interpolated urban air temperatures from a distributed sensor network, and iv) apply a modified Monte Carlo approach to assess uncertainties with our results, methods, and input variables. Modeled QH using the aerodynamic resistance method is then compared to in situ observations in central London from a unique network of scintillometers and eddy-covariance measurements.

Study of spacecraft direct readout meteorological systems

NASA Technical Reports Server (NTRS)

Bartlett, R.; Elam, W.; Hoedemaker, R.

1973-01-01

Characteristics are defined of the next generation direct readout meteorological satellite system with particular application to Tiros N. Both space and ground systems are included. The recommended space system is composed of four geosynchronous satellites and two low altitude satellites in sun-synchronous orbit. The goesynchronous satellites transmit to direct readout ground stations via a shared S-band link, relayed FOFAX satellite cloud cover pictures (visible and infrared) and weather charts (WEFAX). Basic sensor data is transmitted to regional Data Utilization Stations via the same S-band link. Basic sensor data consists of 0.5 n.m. sub-point resolution data in the 0.55 - 0.7 micron spectral region, and 4.0 n.m. resolution data in the 10.5 - 12.6 micron spectral region. The two low altitude satellites in sun-synchronous orbit provide data to direct readout ground stations via a 137 MHz link, a 400 Mhz link, and an S-band link.

Random Forest Application for NEXRAD Radar Data Quality Control

NASA Astrophysics Data System (ADS)

Keem, M.; Seo, B. C.; Krajewski, W. F.

2017-12-01

Identification and elimination of non-meteorological radar echoes (e.g., returns from ground, wind turbines, and biological targets) are the basic data quality control steps before radar data use in quantitative applications (e.g., precipitation estimation). Although WSR-88Ds' recent upgrade to dual-polarization has enhanced this quality control and echo classification, there are still challenges to detect some non-meteorological echoes that show precipitation-like characteristics (e.g., wind turbine or anomalous propagation clutter embedded in rain). With this in mind, a new quality control method using Random Forest is proposed in this study. This classification algorithm is known to produce reliable results with less uncertainty. The method introduces randomness into sampling and feature selections and integrates consequent multiple decision trees. The multidimensional structure of the trees can characterize the statistical interactions of involved multiple features in complex situations. The authors explore the performance of Random Forest method for NEXRAD radar data quality control. Training datasets are selected using several clear cases of precipitation and non-precipitation (but with some non-meteorological echoes). The model is structured using available candidate features (from the NEXRAD data) such as horizontal reflectivity, differential reflectivity, differential phase shift, copolar correlation coefficient, and their horizontal textures (e.g., local standard deviation). The influence of each feature on classification results are quantified by variable importance measures that are automatically estimated by the Random Forest algorithm. Therefore, the number and types of features in the final forest can be examined based on the classification accuracy. The authors demonstrate the capability of the proposed approach using several cases ranging from distinct to complex rain/no-rain events and compare the performance with the existing algorithms (e.g., MRMS). They also discuss operational feasibility based on the observed strength and weakness of the method.

When the Fog Clears: Long-Term Monitoring of Fog and Fog-Dependent Biota in the Namib Desert

NASA Astrophysics Data System (ADS)

Logan, J. R. V.

2014-12-01

The Gobabeb Research and Training Centre in western Namibia is currently undertaking several efforts to enhance long-term atmospheric and fog monitoring in the central Namib Desert and to measure how fog-dependent biota are responding to global change. In an environment that receives regular sea fog and a mean annual rainfall of only 25 mm, Gobabeb is ideally situated to study the drivers and ecological role of fog in arid environments. Currently more than ten meteorological projects perform measurements at or close to Gobabeb. These projects include continuous trace gas measurements, fog isotope sampling, in situ surface radiation measurements, land surface temperature and other satellite validation studies, and multiple aerosol/dust monitoring projects; most of these projects are also components in other global monitoring networks. To these projects, Gobabeb has recently added a network of nine autonomous weather stations spanning the central Namib that will continuously collect basic meteorological data over an area of approximately 70x70 km. Using this data in conjunction with modeling efforts will expand our understanding of fog formation and the linkages between fog and the Benguela Current off Namibia's coast. Historical weather data from previous meteorological stations and satellite observations will also enable development of a fog time series for the last 50 years to determine climate variability driven by possible changes in the Benguela Current system. To complement these efforts, Gobabeb is also expanding its decades-old ecological research programs to explore the impacts of the fog on the region's biota at various time and spatial scales. Gobabeb's long-term, multidisciplinary projects can serve as a prototype for monitoring in other fog-affected systems, together increasing our understanding of coastal fog dynamics, land-atmosphere-ocean connections, and the impacts of fog-related global change.

[Individual tree diameter increment model for natural Betula platyphylla forests based on meteorological factors].

PubMed

Zhang, Hai Ping; Li, Feng Ri; Dong, Li Hu; Liu, Qiang

2017-06-18

Based on the 212 re-measured permanent plots for natural Betula platyphylla fore-sts in Daxing'an Mountains and Xiaoxing'an Mountains and 30 meteorological stations data, an individual tree growth model based on meteorological factors was constructed. The differences of stand and meteorological factors between Daxing'an Mountains and Xiaoxing'an Mountains were analyzed and the diameter increment model including the regional effects was developed by dummy variable approach. The results showed that the minimum temperature (T g min ) and mean precipitation (P g m ) in growing season were the main meteorological factors which affected the diameter increment in the two study areas. T g min and P g m were positively correlated with the diameter increment, but the influence strength of T g min was obviously different between the two research areas. The adjusted coefficient of determination (R a 2 ) of the diameter increment model with meteorological factors was 0.56 and had an 11% increase compared to the one without meteorological factors. It was concluded that meteorological factors could well explain the diameter increment of B. platyphylla. R a 2 of the model with regional effects was 0.59, and increased by 18% compared to the one without regional effects, and effectively solved the incompatible problem of parameters between the two research areas. The validation results showed that the individual tree diameter growth model with regional effect had the best prediction accuracy in estimating the diameter increment of B. platyphylla. The mean error, mean absolute error, mean error percent and mean prediction error percent were 0.0086, 0.4476, 5.8% and 20.0%, respectively. Overall, dummy variable model of individual tree diameter increment based on meteorological factors could well describe the diameter increment process of natural B. platyphylla in Daxing'an Mountains and Xiaoxing'an Mountains.

Research relative to weather radar measurement techniques

NASA Technical Reports Server (NTRS)

Smith, Paul L.

1992-01-01

Research relative to weather radar measurement techniques, which involves some investigations related to measurement techniques applicable to meteorological radar systems in Thailand, is reported. A major part of the activity was devoted to instruction and discussion with Thai radar engineers, technicians, and meteorologists concerning the basic principles of radar meteorology and applications to specific problems, including measurement of rainfall and detection of wind shear/microburst hazards. Weather radar calibration techniques were also considered during this project. Most of the activity took place during two visits to Thailand, in December 1990 and February 1992.

Instantaneous-to-daily GPP upscaling schemes based on a coupled photosynthesis-stomatal conductance model: correcting the overestimation of GPP by directly using daily average meteorological inputs.

PubMed

Wang, Fumin; Gonsamo, Alemu; Chen, Jing M; Black, T Andrew; Zhou, Bin

2014-11-01

Daily canopy photosynthesis is usually temporally upscaled from instantaneous (i.e., seconds) photosynthesis rate. The nonlinear response of photosynthesis to meteorological variables makes the temporal scaling a significant challenge. In this study, two temporal upscaling schemes of daily photosynthesis, the integrated daily model (IDM) and the segmented daily model (SDM), are presented by considering the diurnal variations of meteorological variables based on a coupled photosynthesis-stomatal conductance model. The two models, as well as a simple average daily model (SADM) with daily average meteorological inputs, were validated using the tower-derived gross primary production (GPP) to assess their abilities in simulating daily photosynthesis. The results showed IDM closely followed the seasonal trend of the tower-derived GPP with an average RMSE of 1.63 g C m(-2) day(-1), and an average Nash-Sutcliffe model efficiency coefficient (E) of 0.87. SDM performed similarly to IDM in GPP simulation but decreased the computation time by >66%. SADM overestimated daily GPP by about 15% during the growing season compared to IDM. Both IDM and SDM greatly decreased the overestimation by SADM, and improved the simulation of daily GPP by reducing the RMSE by 34 and 30%, respectively. The results indicated that IDM and SDM are useful temporal upscaling approaches, and both are superior to SADM in daily GPP simulation because they take into account the diurnally varying responses of photosynthesis to meteorological variables. SDM is computationally more efficient, and therefore more suitable for long-term and large-scale GPP simulations.

Evaluation of the effects of climate and man intervention on ground waters and their dependent ecosystems using time series analysis

NASA Astrophysics Data System (ADS)

Gemitzi, Alexandra; Stefanopoulos, Kyriakos

2011-06-01

SummaryGroundwaters and their dependent ecosystems are affected both by the meteorological conditions as well as from human interventions, mainly in the form of groundwater abstractions for irrigation needs. This work aims at investigating the quantitative effects of meteorological conditions and man intervention on groundwater resources and their dependent ecosystems. Various seasonal Auto-Regressive Integrated Moving Average (ARIMA) models with external predictor variables were used in order to model the influence of meteorological conditions and man intervention on the groundwater level time series. Initially, a seasonal ARIMA model that simulates the abstraction time series using as external predictor variable temperature ( T) was prepared. Thereafter, seasonal ARIMA models were developed in order to simulate groundwater level time series in 8 monitoring locations, using the appropriate predictor variables determined for each individual case. The spatial component was introduced through the use of Geographical Information Systems (GIS). Application of the proposed methodology took place in the Neon Sidirochorion alluvial aquifer (Northern Greece), for which a 7-year long time series (i.e., 2003-2010) of piezometric and groundwater abstraction data exists. According to the developed ARIMA models, three distinct groups of groundwater level time series exist; the first one proves to be dependent only on the meteorological parameters, the second group demonstrates a mixed dependence both on meteorological conditions and on human intervention, whereas the third group shows a clear influence from man intervention. Moreover, there is evidence that groundwater abstraction has affected an important protected ecosystem.

A joint modelling exercise designed to assess the respective impact of emission changes and meteorological variability on the observed air quality trends in major urban hotspots.

NASA Astrophysics Data System (ADS)

Colette, Augustin; Bessagnet, Bertrand; Dangiola, Ariela; D'Isidoro, Massimo; Gauss, Michael; Granier, Claire; Hodnebrog, Øivind; Jakobs, Hermann; Kanakidou, Maria; Khokhar, Fahim; Law, Kathy; Maurizi, Alberto; Meleux, Frederik; Memmesheimer, Michael; Nyiri, Agnes; Rouil, Laurence; Stordal, Frode; Tampieri, Francesco

2010-05-01

With the growth of urban agglomerations, assessing the drivers of variability of air quality in and around the main anthropogenic emission hotspots has become a major societal concern as well as a scientific challenge. These drivers include emission changes and meteorological variability; both of them can be investigated by means of numerical modelling of trends over the past few years. A collaborative effort has been developed in the framework of the CityZen European project to address this question. Several chemistry and transport models (CTMs) are deployed in this activity: four regional models (BOLCHEM, CHIMERE, EMEP and EURAD) and three global models (CTM2, MOZART, and TM4). The period from 1998 to 2007 has been selected for the historic reconstruction. The focus for the present preliminary presentation is Europe. A consistent set of emissions is used by all partners (EMEP for the European domain and IPCC-AR5 beyond) while a variety of meteorological forcing is used to gain robustness in the ensemble spread amongst models. The results of this experiment will be investigated to address the following questions: - Is the envelope of models able to reproduce the observed trends of the key chemical constituents? - How the variability amongst models changes in time and space and what does it tell us about the processes driving the observed trends? - Did chemical regimes and aerosol formation processes changed in selected hotspots? Answering the above questions will contribute to fulfil the ultimate goal of the present study: distinguishing the respective contribution of meteorological variability and emissions changes on air quality trends in major anthropogenic emissions hotspots.

Weather analysis and interpretation procedures developed for the US/Canada wheat and barley exploratory experiment

NASA Technical Reports Server (NTRS)

Trenchard, M. H. (Principal Investigator)

1980-01-01

Procedures and techniques for providing analyses of meteorological conditions at segments during the growing season were developed for the U.S./Canada Wheat and Barley Exploratory Experiment. The main product and analysis tool is the segment-level climagraph which depicts temporally meteorological variables for the current year compared with climatological normals. The variable values for the segment are estimates derived through objective analysis of values obtained at first-order station in the region. The procedures and products documented represent a baseline for future Foreign Commodity Production Forecasting experiments.

The impact of Doppler lidar wind observations on a single-level meteorological analysis

NASA Technical Reports Server (NTRS)

Riishojgaard, L. P.; Atlas, R.; Emmitt, G. D.

2001-01-01

Through the use of observation operators, modern data assimilation systems have the capability to ingest observations of quantities that are not themselves model variables, but are mathematically related to those variables. An example of this are the so-called LOS (line of sight) winds that a Doppler wind Lidar can provide. The model - or data assimilation system - needs information about both components of the horizontal wind vectors, whereas the observations in this case only provide the projection of the wind vector onto a given direction. The analyzed value is then calculated essentially based on a comparison between the observation itself and the model-simulated value of the observed quantity. However, in order to assess the expected impact of such an observing system, it is important to examine the extent to which a meteorological analysis can be constrained by the LOS winds. The answer to this question depends on the fundamental character of the atmospheric flow fields that are analyzed, but more importantly it also depends on the real and assumed error covariance characteristics of these fields. A single-level wind analysis system designed to explore these issues has been built at the NASA Data Assimilation Office. In this system, simulated wind observations can be evaluated in terms of their impact on the analysis quality under various assumptions about their spatial distribution and error characteristics and about the error covariance of the background fields. The basic design of the system will be presented along with experimental results obtained with it. In particular, the value of simultaneously measuring LOS winds along two different directions for a given location will be discussed.

Evaluation of an atmospheric model with surface and ABL meteorological data for energy applications in structured areas

NASA Astrophysics Data System (ADS)

Triantafyllou, A. G.; Kalogiros, J.; Krestou, A.; Leivaditou, E.; Zoumakis, N.; Bouris, D.; Garas, S.; Konstantinidis, E.; Wang, Q.

2018-03-01

This paper provides the performance evaluation of the meteorological component of The Air Pollution Model (TAPM), a nestable prognostic model, in predicting meteorological variables in urban areas, for both its surface layer and atmospheric boundary layer (ABL) turbulence parameterizations. The model was modified by incorporating four urban land surface types, replacing the existing single urban surface. Control runs were carried out over the wider area of Kozani, an urban area in NW Greece. The model was evaluated for both surface and ABL meteorological variables by using measurements of near-surface and vertical profiles of wind and temperature. The data were collected by using monitoring surface stations in selected sites as well as an acoustic sounder (SOnic Detection And Ranging (SODAR), up to 300 m above ground) and a radiometer profiler (up to 600 m above ground). The results showed the model demonstrated good performance in predicting the near-surface meteorology in the Kozani region for both a winter and a summer month. In the ABL, the comparison showed that the model's forecasts generally performed well with respect to the thermal structure (temperature profiles and ABL height) but overestimated wind speed at the heights of comparison (mostly below 200 m) up to 3-4 ms-1.

A Synoptic Weather Typing Approach to Assess Climate Change Impacts on Meteorological and Hydrological Risks at Local Scale in South-Central Canada

NASA Astrophysics Data System (ADS)

Cheng, Chad Shouquan; Li, Qian; Li, Guilong

2010-05-01

The synoptic weather typing approach has become popular in evaluating the impacts of climate change on a variety of environmental problems. One of the reasons is its ability to categorize a complex set of meteorological variables as a coherent index, which can facilitate analyses of local climate change impacts. The weather typing method has been applied in Environment Canada to analyze climatic change impacts on various meteorological/hydrological risks, such as freezing rain, heavy rainfall, high-/low-flow events, air pollution, and human health. These studies comprise of three major parts: (1) historical simulation modeling to verify the hazardous events, (2) statistical downscaling to provide station-scale future climate information, and (3) estimates of changes in frequency and magnitude of future hazardous meteorological/hydrological events in this century. To achieve these goals, in addition to synoptic weather typing, the modeling conceptualizations in meteorology and hydrology and various linear/nonlinear regression techniques were applied. Furthermore, a formal model result verification process has been built into the entire modeling exercise. The results of the verification, based on historical observations of the outcome variables predicted by the models, showed very good agreement. This paper will briefly summarize these research projects, focusing on the modeling exercise and results.

Interannual variability of ammonia concentrations over the United States: sources and implications

NASA Astrophysics Data System (ADS)

Schiferl, Luke D.; Heald, Colette L.; Van Damme, Martin; Clarisse, Lieven; Clerbaux, Cathy; Coheur, Pierre-François; Nowak, John B.; Neuman, J. Andrew; Herndon, Scott C.; Roscioli, Joseph R.; Eilerman, Scott J.

2016-09-01

The variability of atmospheric ammonia (NH3), emitted largely from agricultural sources, is an important factor when considering how inorganic fine particulate matter (PM2.5) concentrations and nitrogen cycling are changing over the United States. This study combines new observations of ammonia concentration from the surface, aboard aircraft, and retrieved by satellite to both evaluate the simulation of ammonia in a chemical transport model (GEOS-Chem) and identify which processes control the variability of these concentrations over a 5-year period (2008-2012). We find that the model generally underrepresents the ammonia concentration near large source regions (by 26 % at surface sites) and fails to reproduce the extent of interannual variability observed at the surface during the summer (JJA). Variability in the base simulation surface ammonia concentration is dominated by meteorology (64 %) as compared to reductions in SO2 and NOx emissions imposed by regulation (32 %) over this period. Introduction of year-to-year varying ammonia emissions based on animal population, fertilizer application, and meteorologically driven volatilization does not substantially improve the model comparison with observed ammonia concentrations, and these ammonia emissions changes have little effect on the simulated ammonia concentration variability compared to those caused by the variability of meteorology and acid-precursor emissions. There is also little effect on the PM2.5 concentration due to ammonia emissions variability in the summer when gas-phase changes are favored, but variability in wintertime emissions, as well as in early spring and late fall, will have a larger impact on PM2.5 formation. This work highlights the need for continued improvement in both satellite-based and in situ ammonia measurements to better constrain the magnitude and impacts of spatial and temporal variability in ammonia concentrations.

Rainfall pattern variability as climate change impact in The Wallacea Region

NASA Astrophysics Data System (ADS)

Pujiastuti, I.; Nurjani, E.

2018-04-01

The objective of the study is to observe the characteristic variability of rainfall pattern in the city located in every rainfall type, local (Kendari), monsoon (Manado), and equatorial (Palu). The result will be compared to determine which has the most significantly precipitation changing due to climate change impact. Rainfall variability in Indonesia illustrates precipitation variation thus the important variability is the variability of monthly rainfall. Monthly precipitation data for the period of 1961-2010 are collected from Indonesian Agency for Meteorological, Climatological, and Geophysical Agency. This data is calculated with the normal test statistical method to analyze rainfall variability. The result showed the pattern of trend and variability of rainfall in every city with the own characteristic which determines the rainfall type. Moreover, there is comparison of rainfall pattern changing between every rainfall type. This information is useful for climate change mitigation and adaptation strategies especially in water resource management form precipitation as well as the occurrence of meteorological disasters.

Development of a fire weather index using meteorological observations within the Northeast United States

Treesearch

Michael J. Erickson; Joseph J. Charney; Brian A. Colle

2016-01-01

A fire weather index (FWI) is developed using wildfire occurrence data and Automated Surface Observing System weather observations within a subregion of the northeastern United States (NEUS) from 1999 to 2008. Average values of several meteorological variables, including near-surface temperature, relative humidity, dewpoint, wind speed, and cumulative daily...

Meteorological Factors for Dengue Fever Control and Prevention in South China.

PubMed

Gu, Haogao; Leung, Ross Ka-Kit; Jing, Qinlong; Zhang, Wangjian; Yang, Zhicong; Lu, Jiahai; Hao, Yuantao; Zhang, Dingmei

2016-08-31

Dengue fever (DF) is endemic in Guangzhou and has been circulating for decades, causing significant economic loss. DF prevention mainly relies on mosquito control and change in lifestyle. However, alert fatigue may partially limit the success of these countermeasures. This study investigated the delayed effect of meteorological factors, as well as the relationships between five climatic variables and the risk for DF by boosted regression trees (BRT) over the period of 2005-2011, to determine the best timing and strategy for adapting such preventive measures. The most important meteorological factor was daily average temperature. We used BRT to investigate the lagged relationship between dengue clinical burden and climatic variables, with the 58 and 62 day lag models attaining the largest area under the curve. The climatic factors presented similar patterns between these two lag models, which can be used as references for DF prevention in the early stage. Our results facilitate the development of the Mosquito Breeding Risk Index for early warning systems. The availability of meteorological data and modeling methods enables the extension of the application to other vector-borne diseases endemic in tropical and subtropical countries.

Precipitation Interpolation by Multivariate Bayesian Maximum Entropy Based on Meteorological Data in Yun- Gui-Guang region, Mainland China

NASA Astrophysics Data System (ADS)

Wang, Chaolin; Zhong, Shaobo; Zhang, Fushen; Huang, Quanyi

2016-11-01

Precipitation interpolation has been a hot area of research for many years. It had close relation to meteorological factors. In this paper, precipitation from 91 meteorological stations located in and around Yunnan, Guizhou and Guangxi Zhuang provinces (or autonomous region), Mainland China was taken into consideration for spatial interpolation. Multivariate Bayesian maximum entropy (BME) method with auxiliary variables, including mean relative humidity, water vapour pressure, mean temperature, mean wind speed and terrain elevation, was used to get more accurate regional distribution of annual precipitation. The means, standard deviations, skewness and kurtosis of meteorological factors were calculated. Variogram and cross- variogram were fitted between precipitation and auxiliary variables. The results showed that the multivariate BME method was precise with hard and soft data, probability density function. Annual mean precipitation was positively correlated with mean relative humidity, mean water vapour pressure, mean temperature and mean wind speed, negatively correlated with terrain elevation. The results are supposed to provide substantial reference for research of drought and waterlog in the region.

On the predictability of land surface fluxes from meteorological variables

NASA Astrophysics Data System (ADS)

Haughton, Ned; Abramowitz, Gab; Pitman, Andy J.

2018-01-01

Previous research has shown that land surface models (LSMs) are performing poorly when compared with relatively simple empirical models over a wide range of metrics and environments. Atmospheric driving data appear to provide information about land surface fluxes that LSMs are not fully utilising. Here, we further quantify the information available in the meteorological forcing data that are used by LSMs for predicting land surface fluxes, by interrogating FLUXNET data, and extending the benchmarking methodology used in previous experiments. We show that substantial performance improvement is possible for empirical models using meteorological data alone, with no explicit vegetation or soil properties, thus setting lower bounds on a priori expectations on LSM performance. The process also identifies key meteorological variables that provide predictive power. We provide an ensemble of empirical benchmarks that are simple to reproduce and provide a range of behaviours and predictive performance, acting as a baseline benchmark set for future studies. We reanalyse previously published LSM simulations and show that there is more diversity between LSMs than previously indicated, although it remains unclear why LSMs are broadly performing so much worse than simple empirical models.

Benchmarking of Typical Meteorological Year datasets dedicated to Concentrated-PV systems

NASA Astrophysics Data System (ADS)

Realpe, Ana Maria; Vernay, Christophe; Pitaval, Sébastien; Blanc, Philippe; Wald, Lucien; Lenoir, Camille

2016-04-01

Accurate analysis of meteorological and pyranometric data for long-term analysis is the basis of decision-making for banks and investors, regarding solar energy conversion systems. This has led to the development of methodologies for the generation of Typical Meteorological Years (TMY) datasets. The most used method for solar energy conversion systems was proposed in 1978 by the Sandia Laboratory (Hall et al., 1978) considering a specific weighted combination of different meteorological variables with notably global, diffuse horizontal and direct normal irradiances, air temperature, wind speed, relative humidity. In 2012, a new approach was proposed in the framework of the European project FP7 ENDORSE. It introduced the concept of "driver" that is defined by the user as an explicit function of the pyranometric and meteorological relevant variables to improve the representativeness of the TMY datasets with respect the specific solar energy conversion system of interest. The present study aims at comparing and benchmarking different TMY datasets considering a specific Concentrated-PV (CPV) system as the solar energy conversion system of interest. Using long-term (15+ years) time-series of high quality meteorological and pyranometric ground measurements, three types of TMY datasets generated by the following methods: the Sandia method, a simplified driver with DNI as the only representative variable and a more sophisticated driver. The latter takes into account the sensitivities of the CPV system with respect to the spectral distribution of the solar irradiance and wind speed. Different TMY datasets from the three methods have been generated considering different numbers of years in the historical dataset, ranging from 5 to 15 years. The comparisons and benchmarking of these TMY datasets are conducted considering the long-term time series of simulated CPV electric production as a reference. The results of this benchmarking clearly show that the Sandia method is not suitable for CPV systems. For these systems, the TMY datasets obtained using dedicated drivers (DNI only or more precise one) are more representative to derive TMY datasets from limited long-term meteorological dataset.

A longitudinal study of mortality and air pollution for São Paulo, Brazil.

PubMed

Botter, Denise A; Jørgensen, Bent; Peres, Antonieta A Q

2002-09-01

We study the effects of various air-pollution variables on the daily death counts for people over 65 years in São Paulo, Brazil, from 1991 to 1993, controlling for meteorological variables. We use a state space model where the air-pollution variables enter via the latent process, and the meteorological variables via the observation equation. The latent process represents the potential mortality due to air pollution, and is estimated by Kalman filter techniques. The effect of air pollution on mortality is found to be a function of the variation in the sulphur dioxide level for the previous 3 days, whereas the other air-pollution variables (total suspended particulates, nitrogen dioxide, carbon monoxide, ozone) are not significant when sulphur dioxide is in the equation. There are significant effects of humidity and up to lag 3 of temperature, and a significant seasonal variation.

Decadal evaluation of regional climate, air quality, and their interactions using WRF/Chem Version 3.6.1

NASA Astrophysics Data System (ADS)

Yahya, K.; Wang, K.; Campbell, P.; Glotfelty, T.; He, J.; Zhang, Y.

2015-08-01

The Weather Research and Forecasting model with Chemistry (WRF/Chem) v3.6.1 with the Carbon Bond 2005 (CB05) gas-phase mechanism is evaluated for its first decadal application during 2001-2010 using the Representative Concentration Pathway (RCP 8.5) emissions to assess its capability and appropriateness for long-term climatological simulations. The initial and boundary conditions are downscaled from the modified Community Earth System Model/Community Atmosphere Model (CESM/CAM5) v1.2.2. The meteorological initial and boundary conditions are bias-corrected using the National Center for Environmental Protection's Final (FNL) Operational Global Analysis data. Climatological evaluations are carried out for meteorological, chemical, and aerosol-cloud-radiation variables against data from surface networks and satellite retrievals. The model performs very well for the 2 m temperature (T2) for the 10 year period with only a small cold bias of -0.3 °C. Biases in other meteorological variables including relative humidity at 2 m, wind speed at 10 m, and precipitation tend to be site- and season-specific; however, with the exception of T2, consistent annual biases exist for most of the years from 2001 to 2010. Ozone mixing ratios are slightly overpredicted at both urban and rural locations but underpredicted at rural locations. PM2.5 concentrations are slightly overpredicted at rural sites, but slightly underpredicted at urban/suburban sites. In general, the model performs relatively well for chemical and meteorological variables, and not as well for aerosol-cloud-radiation variables. Cloud-aerosol variables including aerosol optical depth, cloud water path, cloud optical thickness, and cloud droplet number concentration are generally underpredicted on average across the continental US. Overpredictions of several cloud variables over eastern US result in underpredictions of radiation variables and overpredictions of shortwave and longwave cloud forcing which are important climate variables. While the current performance is deemed to be acceptable, improvements to the bias-correction method for CESM downscaling and the model parameterizations of cloud dynamics and thermodynamics, as well as aerosol-cloud interactions can potentially improve model performance for long-term climate simulations.

On the role of "internal variability" on soil erosion assessment

NASA Astrophysics Data System (ADS)

Kim, Jongho; Ivanov, Valeriy; Fatichi, Simone

2017-04-01

Empirical data demonstrate that soil loss is highly non-unique with respect to meteorological or even runoff forcing and its frequency distributions exhibit heavy tails. However, all current erosion assessments do not describe the large associated uncertainties of temporal erosion variability and make unjustified assumptions by relying on central tendencies. Thus, the predictive skill of prognostic models and reliability of national-scale assessments have been repeatedly questioned. In this study, we attempt to reveal that the high variability in soil losses can be attributed to two sources: (1) 'external variability' referring to the uncertainties originating at macro-scale, such as climate, topography, and land use, which has been extensively studied; (2) 'geomorphic internal variability' referring to the micro-scale variations of pedologic properties (e.g., surface erodibility in soils with multi-sized particles), hydrologic properties (e.g., soil structure and degree of saturation), and hydraulic properties (e.g., surface roughness and surface topography). Using data and a physical hydraulic, hydrologic, and erosion and sediment transport model, we show that the geomorphic internal variability summarized by spatio-temporal variability in surface erodibility properties is a considerable source of uncertainty in erosion estimates and represents an overlooked but vital element of geomorphic response. The conclusion is that predictive frameworks of soil erosion should embed stochastic components together with deterministic assessments, if they do not want to largely underestimate uncertainty. Acknowledgement: This study was supported by the Basic Science Research Program of the National Research Foundation of Korea funded by the Ministry of Education (2016R1D1A1B03931886).

Effect of meteorological variables on the incidence of hand, foot, and mouth disease in children: a time-series analysis in Guangzhou, China.

PubMed

Huang, Yong; Deng, Te; Yu, Shicheng; Gu, Jing; Huang, Cunrui; Xiao, Gexin; Hao, Yuantao

2013-03-13

Over the last decade, major outbreaks of hand, foot, and mouth disease (HFMD) have been reported in Asian countries, resulting in thousands of deaths among children. However, less is known regarding the effect of meteorological variables on the incidence of HFMD in children. This study aims at quantifying the relationship between meteorological variables and the incidence of HFMD among children in Guangzhou, China. The association between weekly HFMD cases in children aged <15 years and meteorological variables in Guangzhou from 2008 to 2011 were analyzed using the generalized additive model (GAM) and time-series method, after controlling for long-term trend and seasonality, holiday effects, influenza period and delayed effects. Temperature and relative humidity with one week lag were significantly associated with HFMD infection among children. We found that a 1°C increase in temperature led to an increase of 1.86% (95% CI: 0.92, 2.81%) in the weekly number of cases in the 0-14 years age group. A one percent increase in relative humidity may lead to an increase of 1.42% (95% CI: 0.97, 1.87%) in the weekly number of cases in the 0-14 years age group. This study provides quantitative evidence that the incidence of HFMD in children was associated with high average temperature and high relative humidity. The one-week delay in the effects of temperature and relative humidity on HFMD is consistent with the enterovirus incubation period and the potential time lag between onset of children's sickness and parental awareness and response.

Extreme value modeling for the analysis and prediction of time series of extreme tropospheric ozone levels: a case study.

PubMed

Escarela, Gabriel

2012-06-01

The occurrence of high concentrations of tropospheric ozone is considered as one of the most important issues of air management programs. The prediction of dangerous ozone levels for the public health and the environment, along with the assessment of air quality control programs aimed at reducing their severity, is of considerable interest to the scientific community and to policy makers. The chemical mechanisms of tropospheric ozone formation are complex, and highly variable meteorological conditions contribute additionally to difficulties in accurate study and prediction of high levels of ozone. Statistical methods offer an effective approach to understand the problem and eventually improve the ability to predict maximum levels of ozone. In this paper an extreme value model is developed to study data sets that consist of periodically collected maxima of tropospheric ozone concentrations and meteorological variables. The methods are applied to daily tropospheric ozone maxima in Guadalajara City, Mexico, for the period January 1997 to December 2006. The model adjusts the daily rate of change in ozone for concurrent impacts of seasonality and present and past meteorological conditions, which include surface temperature, wind speed, wind direction, relative humidity, and ozone. The results indicate that trend, annual effects, and key meteorological variables along with some interactions explain the variation in daily ozone maxima. Prediction performance assessments yield reasonably good results.

Selection of meteorological conditions to apply in an Ecotron facility

NASA Astrophysics Data System (ADS)

Leemans, Vincent; De Cruz, Lesley; Dumont, Benjamin; Hamdi, Rafiq; Delaplace, Pierre; Heinesh, Bernard; Garré, Sarah; Verheggen, François; Theodorakopoulos, Nicolas; Longdoz, Bernard

2017-04-01

This presentation aims to propose a generic method to produce meteorological input data that is useful for climate research infrastructures such as an Ecotron, where researchers will face the need to generate representative actual or future climatic conditions. Depending on the experimental objectives and the research purposes, typical conditions or more extreme values such as dry or wet climatic scenarios might be requested. Four variables were considered here, the near-surface air temperature, the near-surface relative humidity, the cloud cover and precipitation. The meteorological datasets, among which a specific meteorological year can be picked up, are produced by the ALARO-0 model from the RMIB (Royal Meteorological Institute of Belgium). Two future climate scenarios (RCP 4.5 and 8.5) and two time periods (2041-2070 and 2071-2100) were used as well as a historical run of the model (1981-2010) which is used as a reference. When the data from a historical run were compared to the observed historical data, biases were noticed. A linear correction was proposed for all the variables except for precipitation, for which a non-linear correction (using a power function) was chosen to maintain a zero-precipitation occurrences. These transformations were able to remove most of the differences between the observed and historical run of the model for the means and for the standard deviations. For the relative humidity, because of non-linearities, only one half of the average bias was corrected and a different path might have to be chosen. For the selection of a meteorological year, a position and a dispersion parameter have been proposed to characterise each meteorological year for each variable. For precipitation, a third parameter quantifying the importance of dry and wet periods has been defined. In order to select a specific climate, for each of these nine parameters the experimenter should provide a percentile and a weight to prioritize the importance of each variable in the process of a global climate selection. The proposed algorithm computed the weighted distance for each year between the parameters and the point representing the position of the percentile in the nine-dimensional space. The five closest values were then selected and represented in different graphs. The proposed method is able to provide a decision aid in the selection of the meteorological conditions to be generated within an Ecotron. However, with a limited number of years available in each case (thirty years for each RCP and each time period), there is no perfect match and the ultimate trade-off will be the responsibility of the researcher. For typical years, close to the median, the relative frequency is higher and the trade-off is more easy than for more extreme years where the relative frequency is low.

How well do meteorological indicators represent agricultural and forest drought across Europe?

NASA Astrophysics Data System (ADS)

Bachmair, S.; Tanguy, M.; Hannaford, J.; Stahl, K.

2018-03-01

Drought monitoring and early warning (M&EW) systems are an important component of agriculture/silviculture drought risk assessment. Many operational information systems rely mostly on meteorological indicators, and a few incorporate vegetation state information. However, the relationships between meteorological drought indicators and agricultural/silvicultural drought impacts vary across Europe. The details of this variability have not been elucidated sufficiently on a continental scale in Europe to inform drought risk management at administrative scales. The objective of this study is to fill this gap and evaluate how useful the variety of meteorological indicators are to assess agricultural/silvicultural drought across Europe. The first part of the analysis systematically linked meteorological drought indicators to remote sensing based vegetation indices (VIs) for Europe at NUTs3 administrative regions scale using correlation analysis for crops and forests. In a second step, a stepwise multiple linear regression model was deployed to identify variables explaining the spatial differences observed. Finally, corn crop yield in Germany was chosen as a case study to verify VIs’ representativeness of agricultural drought impacts. Results show that short accumulation periods of SPI and SPEI are best linked to crop vegetation stress in most cases, which further validates the use of SPI3 in existing operational drought monitors. However, large regional differences in correlations are also revealed. Climate (temperature and precipitation) explained the largest proportion of variance, suggesting that meteorological indices are less informative of agricultural/silvicultural drought in colder/wetter parts of Europe. These findings provide important context for interpreting meteorological indices on widely used national to continental M&EW systems, leading to a better understanding of where/when such M&EW tools can be indicative of likely agricultural stress and impacts.

Influence of ozone and meteorological parameters on levels of polycyclic aromatic hydrocarbons in the air

NASA Astrophysics Data System (ADS)

Pehnec, Gordana; Jakovljević, Ivana; Šišović, Anica; Bešlić, Ivan; Vađić, Vladimira

2016-04-01

Concentrations of ten polycyclic aromatic hydrocarbons (PAHs) in the PM10 particle fraction were measured together with ozone and meteorological parameters at an urban site (Zagreb, Croatia) over a one-year period. Data were subjected to regression analysis in order to determine the relationship between the measured pollutants and selected meteorological variables. All of the PAHs showed seasonal variations with high concentrations in winter and autumn and very low concentrations during summer and spring. All of the ten PAHs concentrations also correlated well with each other. A statistically significant negative correlation was found between the concentrations of PAHs and ozone concentrations and concentrations of PAHs and temperature, as well as a positive correlation between concentrations of PAHs and PM10 mass concentration and relative humidity. Multiple regression analysis showed that concentrations of PM10 and ozone, temperature, relative humidity and pressure accounted for 43-70% of PAHs variability. Concentrations of PM10 and temperature were significant variables for all of the measured PAH's concentrations in all seasons. Ozone concentrations were significant for only some of the PAHs, particularly 6-ring PAHs.

Changes in the type of precipitation and associated cloud types in Eastern Romania (1961-2008)

NASA Astrophysics Data System (ADS)

Manea, Ancuta; Birsan, Marius-Victor; Tudorache, George; Cărbunaru, Felicia

2016-03-01

Recent climate change is characterized (among other things) by changes in the frequency of some meteorological phenomena. This paper deals with the long-term changes in various precipitation types, and the connection between their variability and cloud type frequencies, at 11 meteorological stations from Eastern Romania over 1961-2008. These stations were selected with respect to data record completeness for all considered variables (weather phenomena and cloud type). The meteorological variables involved in the present study are: monthly number of days with rain, snowfall, snow showers, rain and snow (sleet), sleet showers and monthly frequency of the Cumulonimbus, Nimbostratus and Stratus clouds. Our results show that all stations present statistically significant decreasing trends in the number of days with rain in the warm period of the year. Changes in the frequency of days for each precipitation type show statistically significant decreasing trends for non-convective (stratiform) precipitation - rain, drizzle, sleet and snowfall -, while the frequencies of rain shower and snow shower (convective precipitation) are increasing. Cloud types show decreasing trends for Nimbostratus and Stratus, and increasing trends for Cumulonimbus.

Variation of surface ozone in Campo Grande, Brazil: meteorological effect analysis and prediction.

PubMed

Pires, J C M; Souza, A; Pavão, H G; Martins, F G

2014-09-01

The effect of meteorological variables on surface ozone (O3) concentrations was analysed based on temporal variation of linear correlation and artificial neural network (ANN) models defined by genetic algorithms (GAs). ANN models were also used to predict the daily average concentration of this air pollutant in Campo Grande, Brazil. Three methodologies were applied using GAs, two of them considering threshold models. In these models, the variables selected to define different regimes were daily average O3 concentration, relative humidity and solar radiation. The threshold model that considers two O3 regimes was the one that correctly describes the effect of important meteorological variables in O3 behaviour, presenting also a good predictive performance. Solar radiation, relative humidity and rainfall were considered significant for both O3 regimes; however, wind speed (dispersion effect) was only significant for high concentrations. According to this model, high O3 concentrations corresponded to high solar radiation, low relative humidity and wind speed. This model showed to be a powerful tool to interpret the O3 behaviour, being useful to define policy strategies for human health protection regarding air pollution.

Aviation--An Individualized Approach

ERIC Educational Resources Information Center

Seeds, Fred F.

1974-01-01

Describes an individualized aviation course for high school seniors. The course, broken down into Learner Education Guides with students progressing at their own learning rates, consists of the history of aviation, career opportunities, the space program, basic aeronautics, navigation, meteorology, Federal Aviation Administration regulations and…

Design of extensible meteorological data acquisition system based on FPGA

NASA Astrophysics Data System (ADS)

Zhang, Wen; Liu, Yin-hua; Zhang, Hui-jun; Li, Xiao-hui

2015-02-01

In order to compensate the tropospheric refraction error generated in the process of satellite navigation and positioning. Temperature, humidity and air pressure had to be used in concerned models to calculate the value of this error. While FPGA XC6SLX16 was used as the core processor, the integrated silicon pressure sensor MPX4115A and digital temperature-humidity sensor SHT75 are used as the basic meteorological parameter detection devices. The core processer was used to control the real-time sampling of ADC AD7608 and to acquire the serial output data of SHT75. The data was stored in the BRAM of XC6SLX16 and used to generate standard meteorological parameters in NEMA format. The whole design was based on Altium hardware platform and ISE software platform. The system was described in the VHDL language and schematic diagram to realize the correct detection of temperature, humidity, air pressure. The 8-channel synchronous sampling characteristics of AD7608 and programmable external resources of FPGA laid the foundation for the increasing of analog or digital meteorological element signal. The designed meteorological data acquisition system featured low cost, high performance, multiple expansions.

Computer simulations of space-borne meteorological systems on the CYBER 205

NASA Technical Reports Server (NTRS)

Halem, M.

1984-01-01

Because of the extreme expense involved in developing and flight testing meteorological instruments, an extensive series of numerical modeling experiments to simulate the performance of meteorological observing systems were performed on CYBER 205. The studies compare the relative importance of different global measurements of individual and composite systems of the meteorological variables needed to determine the state of the atmosphere. The assessments are made in terms of the systems ability to improve 12 hour global forecasts. Each experiment involves the daily assimilation of simulated data that is obtained from a data set called nature. This data is obtained from two sources: first, a long two-month general circulation integration with the GLAS 4th Order Forecast Model and second, global analysis prepared by the National Meteorological Center, NOAA, from the current observing systems twice daily.

Detection of mesoscale zones of atmospheric instabilities using remote sensing and weather forecasting model data

NASA Astrophysics Data System (ADS)

Winnicki, I.; Jasinski, J.; Kroszczynski, K.; Pietrek, S.

2009-04-01

The paper presents elements of research conducted in the Faculty of Civil Engineering and Geodesy of the Military University of Technology, Warsaw, Poland, concerning application of mesoscale models and remote sensing data to determining meteorological conditions of aircraft flight directly related with atmospheric instabilities. The quality of meteorological support of aviation depends on prompt and effective forecasting of weather conditions changes. The paper presents a computer module for detecting and monitoring zones of cloud cover, precipitation and turbulence along the aircraft flight route. It consists of programs and scripts for managing, processing and visualizing meteorological and remote sensing databases. The application was developed in Matlab® for Windows®. The module uses products of COAMPS (Coupled Ocean/Atmosphere Mesoscale Prediction System) mesoscale non-hydrostatic model of the atmosphere developed by the US Naval Research Laboratory, satellite images acquisition system from the MSG-2 (Meteosat Second Generation) of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) and meteorological radars data acquired from the Institute of Meteorology and Water Management (IMGW), Warsaw, Poland. The satellite images acquisition system and the COAMPS model are run operationally in the Faculty of Civil Engineering and Geodesy. The mesoscale model is run on an IA64 Feniks multiprocessor 64-bit computer cluster. The basic task of the module is to enable a complex analysis of data sets of miscellaneous information structure and to verify COAMPS results using satellite and radar data. The research is conducted using uniform cartographic projection of all elements of the database. Satellite and radar images are transformed into the Lambert Conformal projection of COAMPS. This facilitates simultaneous interpretation and supports decision making process for safe execution of flights. Forecasts are based on horizontal distributions and vertical profiles of meteorological parameters produced by the module. Verification of forecasts includes research of spatial and temporal correlations of structures generated by the model, e.g.: cloudiness, meteorological phenomena (fogs, precipitation, turbulence) and structures identified on current satellite images. The developed module determines meteorological parameters fields for vertical profiles of the atmosphere. Interpolation procedures run at user selected standard (pressure) or height levels of the model enable to determine weather conditions along any route of aircraft. Basic parameters of the procedures determining e.g. flight safety include: cloud base, visibility, cloud cover, turbulence coefficient, icing and precipitation intensity. Determining icing and turbulence characteristics is based on standard and new methods (from other mesoscale models). The research includes also investigating new generation mesoscale models, especially remote sensing data assimilation. This is required by necessity to develop and introduce objective methods of forecasting weather conditions. Current research in the Faculty of Civil Engineering and Geodesy concerns validation of the mesoscale module performance.

As-Built documentation of programs to implement the Robertson and Doraiswamy/Thompson models

NASA Technical Reports Server (NTRS)

Valenziano, D. J. (Principal Investigator)

1981-01-01

The software which implements two spring wheat phenology models is described. The main program routines for the Doraiswamy/Thompson crop phenology model and the basic Robertson crop phenology model are DTMAIN and BRMAIN. These routines read meteorological data files and coefficient files, accept the planting date information and other information from the user, and initiate processing. Daily processing for the basic Robertson program consists only of calculation of the basic Robertson increment of crop development. Additional processing in the Doraiswamy/Thompson program includes the calculation of a moisture stress index and correction of the basic increment of development. Output for both consists of listings of the daily results.

Effects of Meteorological Variables on the Incidence of Rupture of Intracranial Aneurysms in Central New Jersey.

PubMed

Kellogg, Marissa; Petrov, Dimitriy; Agarwal, Nitin; Patel, Nitesh V; Hansberry, David Richard; Agarwal, Prateek; Brimacombe, Michael; Gandhi, Chirag D; Prestigiacomo, Charles

2017-05-01

Introduction  Previous studies have suggested relationships between the rupture of intracranial aneurysms and meteorological variables such as season, barometric pressure, and temperature. Our objective was to examine the relationship between the incidence of hospital admissions secondary to aneurysmal subarachnoid hemorrhage (aSAH) and meteorological variables in central New Jersey. Methods  The study population consisted of 312 patients who presented to University Hospital in Newark, New Jersey, between January 1, 2003, and December 31, 2008, with aSAH. Days in the 6-year period were classified as nonbleed days (no aSAH), bleed days (one or more aSAHs within 1 calendar day), cluster days (two or more aSAHs within 2 calendar days), and multiple-bleed days (two or more aSAHs within 1 calendar day). Results  The only significant meteorological risk factor for the occurrence of multiple-bleed days was high barometric pressure (1018.5 versus 1016.5 millibars [mbars]; p <  0.04), but an increase in barometric pressure (+ 2.8 mbars) over the 2 days prior to the multiple-bleed day, although not statistically significant, may be a risk factor ( p <  0.09). Barometric pressure was also noted to be increased on bleed days (1017.2 versus 1016.5 mbars) and cluster days (1017.7 versus 1016.5 mbars), but this relationship was not significant ( p <  0.1 and p <  0.1, respectively). Although aSAH days demonstrated consistently lower temperatures than non-aSAH days and dropping temperatures were consistently found in the days preceding the aSAH, these relationships were not significant. Conclusion  Among meteorological factors, high barometric pressure and low temperature may be risk factors for the onset of aSAH. Georg Thieme Verlag KG Stuttgart · New York.

Uncertainties in Episodic Ozone Modeling Stemming from Uncertainties in the Meteorological Fields.

NASA Astrophysics Data System (ADS)

Biswas, Jhumoor; Trivikrama Rao, S.

2001-02-01

This paper examines the uncertainty associated with photochemical modeling using the Variable-Grid Urban Airshed Model (UAM-V) with two different prognostic meteorological models. The meteorological fields for ozone episodes that occurred during 17-20 June, 12-15 July, and 30 July-2 August in the summer of 1995 were derived from two meteorological models, the Regional Atmospheric Modeling System (RAMS) and the Fifth-Generation Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model (MM5). The simulated ozone concentrations from the two photochemical modeling systems, namely, RAMS/UAM-V and MM5/UAM-V, are compared with each other and with ozone observations from several monitoring sites in the eastern United States. The overall results indicate that neither modeling system performs significantly better than the other in reproducing the observed ozone concentrations. The results reveal that there is a significant variability, about 20% at the 95% level of confidence, in the modeled 1-h ozone concentration maxima from one modeling system to the other for a given episode. The model-to-model variability in the simulated ozone levels is for most part attributable to the unsystematic type of errors. The directionality for emission controls (i.e., NOx versus VOC sensitivity) is also evaluated with UAM-V using hypothetical emission reductions. The results reveal that not only the improvement in ozone but also the VOC-sensitive and NOx-sensitive regimes are influenced by the differences in the meteorological fields. Both modeling systems indicate that a large portion of the eastern United States is NOx limited, but there are model-to-model and episode-to-episode differences at individual grid cells regarding the efficacy of emission reductions.

A meteorological distribution system for high-resolution terrestrial modeling (MicroMet)

Treesearch

Glen E. Liston; Kelly Elder

2006-01-01

An intermediate-complexity, quasi-physically based, meteorological model (MicroMet) has been developed to produce high-resolution (e.g., 30-m to 1-km horizontal grid increment) atmospheric forcings required to run spatially distributed terrestrial models over a wide variety of landscapes. The following eight variables, required to run most terrestrial models, are...

Mesoscale numerical modeling of meteorological events in a strong topographic gradient in the northeastern part of Mexico

NASA Astrophysics Data System (ADS)

Pineda-Martinez, Luis F.; Carbajal, Noel

2009-08-01

A series of numerical experiments were carried out to study the effect of meteorological events such as warm and cold air masses on climatic features and variability of a understudied region with strong topographic gradients in the northeastern part of Mexico. We applied the mesoscale model MM5. We investigated the influence of soil moisture availability in the performance of the model under two representative events for winter and summer. The results showed that a better resolution in land use cover improved the agreement among observed and calculated data. The topography induces atmospheric circulation patterns that determine the spatial distribution of climate and seasonal behavior. The numerical experiments reveal regions favorable to forced convection on the eastern side of the mountain chains Eastern Sierra Madre and Sierra de Alvarez. These processes affect the vertical and horizontal structure of the meteorological variables along the topographic gradient.

Meteorological Influences on the Seasonality of Lyme Disease in the United States

PubMed Central

Moore, Sean M.; Eisen, Rebecca J.; Monaghan, Andrew; Mead, Paul

2014-01-01

Lyme disease (Borrelia burgdorferi infection) is the most common vector-transmitted disease in the United States. The majority of human Lyme disease (LD) cases occur in the summer months, but the timing of the peak occurrence varies geographically and from year to year. We calculated the beginning, peak, end, and duration of the main LD season in 12 highly endemic states from 1992 to 2007 and then examined the association between the timing of these seasonal variables and several meteorological variables. An earlier beginning to the LD season was positively associated with higher cumulative growing degree days through Week 20, lower cumulative precipitation, a lower saturation deficit, and proximity to the Atlantic coast. The timing of the peak and duration of the LD season were also associated with cumulative growing degree days, saturation deficit, and cumulative precipitation, but no meteorological predictors adequately explained the timing of the end of the LD season. PMID:24470565

Validation and statistical analysis of temperature, humidity profiles and Integrated Water Vapor (IWV) from microwave measurements over Granada (Spain)

NASA Astrophysics Data System (ADS)

Bedoya, Andres; Navas-Guzmán, Francisco; Guerrero-Rascado, Juan Luis; Alados-Arboledas, Lucas

2017-04-01

Profiles of meteorological variables such as temperature, relative humidity and integrated water vapor derived from a ground-based microwave radiometer (MWR, RPG-HATPRO) are continuously monitored since 2012 at Granada station (Southeastern Spain). During this period up to 210 collocated meteorological balloons, equipped with a radiosonde DFM-09 (GRAWMET), were launched. This study is carried out with a twofold goal. On one hand, a validation of the MWR products such as temperature and water vapor mixing ratio profiles and the IWV from MWR is carried out comparing with radiosonde measurements. The behavior of MWR retrievals under clear and cloudy conditions and for special situations such as inversions has been analyzed. On the other hand, the whole period with continuous measurements is used for a statistical evaluation of the meteorological variables derived from MWR in order to thermodynamically characterize the atmosphere over Granada.

Selected meteorological data for an arid climate over bare soil near Beatty, Nye County, Nevada, November 1977 through May 1980

USGS Publications Warehouse

Brown, Robin G.; Nichols, William D.

1990-01-01

Meteorological data were collected over bare soil at a site for low-level radioactive-waste burial near Beatty, Nevada, from November 1977 to May 1980. The data include precipitation, windspeed, wind direction, incident solar radiation, reflected solar radiation, net radiation, dry- and wet-bulb air temperatures at three heights, soil temperature at five depths, and soil-heat flux at three depths. Mean relative humidity was computed for each day of the collection period for which data are available.A discussion is presented of the study site and the instrumentation and procedures used for collecting and processing the data. Selected data from November 1977 to May 1980 are presented in tabular form. Diurnal fluctuations of selected meteorological variables for representative summer and winter periods are graphically presented. The effects on selected variables of a partial solar eclipse are also discussed

Numerical experiments on short-term meteorological effects on solar variability

NASA Technical Reports Server (NTRS)

Somerville, R. C. J.; Hansen, J. E.; Stone, P. H.; Quirk, W. J.; Lacis, A. A.

1975-01-01

A set of numerical experiments was conducted to test the short-range sensitivity of a large atmospheric general circulation model to changes in solar constant and ozone amount. On the basis of the results of 12-day sets of integrations with very large variations in these parameters, it is concluded that realistic variations would produce insignificant meteorological effects. Any causal relationships between solar variability and weather, for time scales of two weeks or less, rely upon changes in parameters other than solar constant or ozone amounts, or upon mechanisms not yet incorporated in the model.

Comparing interpolation techniques for annual temperature mapping across Xinjiang region

NASA Astrophysics Data System (ADS)

Ren-ping, Zhang; Jing, Guo; Tian-gang, Liang; Qi-sheng, Feng; Aimaiti, Yusupujiang

2016-11-01

Interpolating climatic variables such as temperature is challenging due to the highly variable nature of meteorological processes and the difficulty in establishing a representative network of stations. In this paper, based on the monthly temperature data which obtained from the 154 official meteorological stations in the Xinjiang region and surrounding areas, we compared five spatial interpolation techniques: Inverse distance weighting (IDW), Ordinary kriging, Cokriging, thin-plate smoothing splines (ANUSPLIN) and Empirical Bayesian kriging(EBK). Error metrics were used to validate interpolations against independent data. Results indicated that, the ANUSPLIN performed best than the other four interpolation methods.

A Mulitivariate Statistical Model Describing the Compound Nature of Soil Moisture Drought

NASA Astrophysics Data System (ADS)

Manning, Colin; Widmann, Martin; Bevacqua, Emanuele; Maraun, Douglas; Van Loon, Anne; Vrac, Mathieu

2017-04-01

Soil moisture in Europe acts to partition incoming energy into sensible and latent heat fluxes, thereby exerting a large influence on temperature variability. Soil moisture is predominantly controlled by precipitation and evapotranspiration. When these meteorological variables are accumulated over different timescales, their joint multivariate distribution and dependence structure can be used to provide information of soil moisture. We therefore consider soil moisture drought as a compound event of meteorological drought (deficits of precipitation) and heat waves, or more specifically, periods of high Potential Evapotraspiration (PET). We present here a statistical model of soil moisture based on Pair Copula Constructions (PCC) that can describe the dependence amongst soil moisture and its contributing meteorological variables. The model is designed in such a way that it can account for concurrences of meteorological drought and heat waves and describe the dependence between these conditions at a local level. The model is composed of four variables; daily soil moisture (h); a short term and a long term accumulated precipitation variable (Y1 and Y_2) that account for the propagation of meteorological drought to soil moisture drought; and accumulated PET (Y_3), calculated using the Penman Monteith equation, which can represent the effect of a heat wave on soil conditions. Copula are multivariate distribution functions that allow one to model the dependence structure of given variables separately from their marginal behaviour. PCCs then allow in theory for the formulation of a multivariate distribution of any dimension where the multivariate distribution is decomposed into a product of marginal probability density functions and two-dimensional copula, of which some are conditional. We apply PCC here in such a way that allows us to provide estimates of h and their uncertainty through conditioning on the Y in the form h=h|y_1,y_2,y_3 (1) Applying the model to various Fluxnet sites across Europe, we find the model has good skill and can particularly capture periods of low soil moisture well. We illustrate the relevance of the dependence structure of these Y variables to soil moisture and show how it may be generalised to offer information of soil moisture on a widespread scale where few observations of soil moisture exist. We then present results from a validation study of a selection of EURO CORDEX climate models where we demonstrate the skill of these models in representing these dependencies and so offer insight into the skill seen in the representation of soil moisture in these models.

Comparing Global Atmospheric CO2 Flux and Transport Models with Remote Sensing (and Other) Observations

NASA Technical Reports Server (NTRS)

Kawa, S. R.; Collatz, G. J.; Pawson, S.; Wennberg, P. O.; Wofsy, S. C.; Andrews, A. E.

2010-01-01

We report recent progress derived from comparison of global CO2 flux and transport models with new remote sensing and other sources of CO2 data including those from satellite. The overall objective of this activity is to improve the process models that represent our understanding of the workings of the atmospheric carbon cycle. Model estimates of CO2 surface flux and atmospheric transport processes are required for initial constraints on inverse analyses, to connect atmospheric observations to the location of surface sources and sinks, to provide the basic framework for carbon data assimilation, and ultimately for future projections of carbon-climate interactions. Models can also be used to test consistency within and between CO2 data sets under varying geophysical states. Here we focus on simulated CO2 fluxes from terrestrial vegetation and atmospheric transport mutually constrained by analyzed meteorological fields from the Goddard Modeling and Assimilation Office for the period 2000 through 2009. Use of assimilated meteorological data enables direct model comparison to observations across a wide range of scales of variability. The biospheric fluxes are produced by the CASA model at 1x1 degrees on a monthly mean basis, modulated hourly with analyzed temperature and sunlight. Both physiological and biomass burning fluxes are derived using satellite observations of vegetation, burned area (as in GFED-3), and analyzed meteorology. For the purposes of comparison to CO2 data, fossil fuel and ocean fluxes are also included in the transport simulations. In this presentation we evaluate the model's ability to simulate CO2 flux and mixing ratio variability in comparison to remote sensing observations from TCCON, GOSAT, and AIRS as well as relevant in situ observations. Examples of the influence of key process representations are shown from both forward and inverse model comparisons. We find that the model can resolve much of the synoptic, seasonal, and interannual variability in the observations, although reasons for persistent discrepancies in northern hemisphere vegetation uptake are examined. At this time, we do not find any serious shortcomings in the model transport representation, but this is still the subject of close scrutiny. In general, the fidelity of these simulations leads us to anticipate incorporation of real-time, highly resolved remote sensing and other observations into quantitative analyses that will reduce uncertainty in CO2 fluxes and revolutionize our understanding of the key processes controlling atmospheric CO2 and its evolution with time.

The Ogallala Agro-Climate Tool (Technical Description)

USDA-ARS?s Scientific Manuscript database

A Visual Basic agro-climate application capable of estimating irrigation demand and crop water use over the Ogallala Aquifer region is described here. The application’s meteorological database consists of daily precipitation and temperature data from 141 U.S. Historical Climatology Network stations ...

Variability of floods, droughts and windstorms over the past 500 years in Central Europe based on documentary and instrumental data

NASA Astrophysics Data System (ADS)

Brazdil, Rudolf

2016-04-01

Hydrological and meteorological extremes (HMEs) in Central Europe during the past 500 years can be reconstructed based on instrumental and documentary data. Documentary data about weather and related phenomena represent the basic source of information for historical climatology and hydrology, dealing with reconstruction of past climate and HMEs, their perception and impacts on human society. The paper presents the basic distribution of documentary data on (i) direct descriptions of HMEs and their proxies on the one hand and on (ii) individual and institutional data sources on the other. Several groups of documentary evidence such as narrative written records (annals, chronicles, memoirs), visual daily weather records, official and personal correspondence, special prints, financial and economic records (with particular attention to taxation data), newspapers, pictorial documentation, chronograms, epigraphic data, early instrumental observations, early scientific papers and communications are demonstrated with respect to extraction of information about HMEs, which concerns usually of their occurrence, severity, seasonality, meteorological causes, perception and human impacts. The paper further presents the analysis of 500-year variability of floods, droughts and windstorms on the base of series, created by combination of documentary and instrumental data. Results, advantages and drawbacks of such approach are documented on the examples from the Czech Lands. The analysis of floods concentrates on the River Vltava (Prague) and the River Elbe (Děčín) which show the highest frequency of floods occurring in the 19th century (mainly of winter synoptic type) and in the second half of the 16th century (summer synoptic type). Reported are also the most disastrous floods (August 1501, March and August 1598, February 1655, June 1675, February 1784, March 1845, February 1862, September 1890, August 2002) and the European context of floods in the severe winter 1783/84. Drought fluctuations in the Czech Lands are represented by the chronology of drought frequency on the one hand and by the reconstructed series of drought indices (SPI, SPEI, Z-Index and PDSI) on the other. Wind extremes are documented on the example of Czech windstorm chronology derived from documentary data (including tornadoes) with an example of "windstorm of the 18th century" (20-21 December 1740). Finally, scientific potential and perspectives of historical-climatological (historical-hydrological) research of HMEs are presented.

Dynamical Downscaling of Meteorology from a Global Model by WRF towards Resolving US PM2.5 Distributions for the Mid 21st Century

NASA Astrophysics Data System (ADS)

Kunwar, S.; Bowden, J.; Milly, G.; Previdi, M. J.; Fiore, A. M.; West, J. J.

2017-12-01

In the coming decades, anthropogenically induced climate change will likely impact PM2.5 through both changing meteorology and feedback in natural emissions. A major goal of our project is to assess changes in PM2.5 levels over the continental US due to climate variability and change for the period 2005-2065. We will achieve this by using regional models to dynamically downscale coarse resolution (20 × 20) meteorology and air chemistry from a global model to finer spatial resolution (12 km), improving air quality projections for regions and subregions of the US (NE, SE, SW, NW, Midwest, Intermountain West). We downscale from GFDL CM3 simulations of the RCP8.5 scenario for the years 2006-2100 with aerosol and ozone precursor emissions fixed at 2005 levels. We carefully select model years from the global simulations that sample the range of PM2.5 distributions for different US regions at mid 21st century (2050-2065). Here we will show results for the meteorological downscaling (using WRF version 3.8.1) for this project, including a performance evaluation for meteorological variables with respect to the global model. In the future, the downscaled meteorology presented here will be used to drive air quality downscaling in CMAQ (version 5.2). Analysis of the resulting PM2.5 statistics for US regions, as well as the drivers for PM2.5 changes, will be important in supporting informed policies for air quality (also health and visibility) planning for different US regions for the next five decades.

Characterizing UT/LS O3 from Global Ozonesonde Profiles Using a Clustering Technique and Meteorological Reanalyses

NASA Astrophysics Data System (ADS)

Stauffer, R. M.; Thompson, A. M.

2017-12-01

Previous studies employing the self-organizing map (SOM) clustering technique to US ozonesonde data proved valuable for quantifying UT/LS O3 variability, and linking meteorological and chemical drivers to the shape of the ozone (O3) profile from the troposphere to the lower stratosphere. Focus has thus far been limited to specific geographical regions, but SOM has demonstrated the advantages of clustering over monthly climatological O3 averages, which mask day-to-day variability in the O3 profile and the correspondence between O3 and meteorology. We expand SOM to a global set of ozonesonde profiles, mostly from WOUDC, spanning 1980-present from 30 sites to evaluate global O3 climatologies and quantify links to geophysical processes for various meteorological regimes. Four clusters of O3 mixing ratio profiles are generated for each site, which show dominant profile shapes that correspond to site latitude. Offsets among O3 profile clusters and monthly O3 climatologies are 100s of ppbv in the UT/LS at higher latitude sites with active dynamics. Examination of meteorological reanalyses reveals a clear relationship among SOM clusters and covarying meteorological fields (geopotential height, potential vorticity, and tropopause height) for most sites. Tropical SOM clusters show marked dependence on velocity potential anomalies calculated from reanalysis winds, with low UT/LS O3 amounts corresponding to enhanced upper-level divergence, and vice versa. In addition to creating SOM cluster-based O3 climatologies, these results are meant to inform future approaches to validation of chemical transport models and satellite retrievals, which often struggle in the UT/LS region.

METEO in the TALNET project after 5 years - meteorology for talented high schools students

NASA Astrophysics Data System (ADS)

Pisoft, P.; Miksovsky, J.

2010-09-01

TALNET is a project aiming to systematically identify and work with gifted youth (13-19 years). Specifically, it applies online educational activities combined with face to face activities. It has been organised by the Faculty of Maths and Physics (MFF) of Charles University in Prague (UK) and National Institute for Youth (NIDM) since 2003, later in cooperation with other faculties, e.g. Natural Sciences (PrF UK), universities and science and research institutes in the Czech Republic and abroad, e.g. DLR, Germany. Topics of the educational activities come from natural sciences (such as physics, astronomy, biology, chemistry, meteorology etc.) and mathematics. The presented project's part METEO embraces lessons primarily focused on basics of meteorology and atmospheric physics in general and it has been part of the Talnet project for 5 years. The meteorological lectures consist of description of, e.g., climate system, meteorological quantities, weather forecasting, ozone and the stratosphere, climate change or atmospheric optics. On top of the lectures, the students are encouraged to work on enclosed homework such as meteorological time series analysis, clouds observation and classification, halo simulation and so on. The METEO course lasts one semester and the students make their seminar thesis at the end. The presented materials will consist of examples of the contemporary lectures and their organization, homeworks or seminar theses.

Availability of selected meteorological data in computer-based files of the U.S. Geological Survey, Montana, North Dakota, South Dakota, and Wyoming

USGS Publications Warehouse

Link, Brenda L.; Cary, L.E.

1986-01-01

Meteorological data were located, acquired, and stored from selected stations in Montana and North Dakota coal regions and adjacent areas including South Dakota and Wyoming. Data that were acquired have potential use in small watershed modeling studies. Emphasis was placed on acquiring data that was collected during the period 1970 to the present (1984). A map shows the location and type of stations selected. A narration summarizing conventions used in acquiring and storing the meteorological data is provided along with the various retrieval options available. Individual station descriptions are followed by tables listing the meteorological variables collected, period of obtained record, percentage of data recovery, and the instruments used and their description. (USGS)

Decadal application of WRF/Chem for regional air quality and climate modeling over the U.S. under the representative concentration pathways scenarios. Part 1: Model evaluation and impact of downscaling

NASA Astrophysics Data System (ADS)

Yahya, Khairunnisa; Wang, Kai; Campbell, Patrick; Chen, Ying; Glotfelty, Timothy; He, Jian; Pirhalla, Michael; Zhang, Yang

2017-03-01

An advanced online-coupled meteorology-chemistry model, i.e., the Weather Research and Forecasting Model with Chemistry (WRF/Chem), is applied for current (2001-2010) and future (2046-2055) decades under the representative concentration pathways (RCP) 4.5 and 8.5 scenarios to examine changes in future climate, air quality, and their interactions. In this Part I paper, a comprehensive model evaluation is carried out for current decade to assess the performance of WRF/Chem and WRF under both scenarios and the benefits of downscaling the North Carolina State University's (NCSU) version of the Community Earth System Model (CESM_NCSU) using WRF/Chem. The evaluation of WRF/Chem shows an overall good performance for most meteorological and chemical variables on a decadal scale. Temperature at 2-m is overpredicted by WRF (by ∼0.2-0.3 °C) but underpredicted by WRF/Chem (by ∼0.3-0.4 °C), due to higher radiation from WRF. Both WRF and WRF/Chem show large overpredictions for precipitation, indicating limitations in their microphysics or convective parameterizations. WRF/Chem with prognostic chemical concentrations, however, performs much better than WRF with prescribed chemical concentrations for radiation variables, illustrating the benefit of predicting gases and aerosols and representing their feedbacks into meteorology in WRF/Chem. WRF/Chem performs much better than CESM_NCSU for most surface meteorological variables and O3 hourly mixing ratios. In addition, WRF/Chem better captures observed temporal and spatial variations than CESM_NCSU. CESM_NCSU performance for radiation variables is comparable to or better than WRF/Chem performance because of the model tuning in CESM_NCSU that is routinely made in global models.

Enhancing seasonal climate prediction capacity for the Pacific countries

NASA Astrophysics Data System (ADS)

Kuleshov, Y.; Jones, D.; Hendon, H.; Charles, A.; Cottrill, A.; Lim, E.-P.; Langford, S.; de Wit, R.; Shelton, K.

2012-04-01

Seasonal and inter-annual climate variability is a major factor in determining the vulnerability of many Pacific Island Countries to climate change and there is need to improve weekly to seasonal range climate prediction capabilities beyond what is currently available from statistical models. In the seasonal climate prediction project under the Australian Government's Pacific Adaptation Strategy Assistance Program (PASAP), we describe a comprehensive project to strengthen the climate prediction capacities in National Meteorological Services in 14 Pacific Island Countries and East Timor. The intent is particularly to reduce the vulnerability of current services to a changing climate, and improve the overall level of information available assist with managing climate variability. Statistical models cannot account for aspects of climate variability and change that are not represented in the historical record. In contrast, dynamical physics-based models implicitly include the effects of a changing climate whatever its character or cause and can predict outcomes not seen previously. The transition from a statistical to a dynamical prediction system provides more valuable and applicable climate information to a wide range of climate sensitive sectors throughout the countries of the Pacific region. In this project, we have developed seasonal climate outlooks which are based upon the current dynamical model POAMA (Predictive Ocean-Atmosphere Model for Australia) seasonal forecast system. At present, meteorological services of the Pacific Island Countries largely employ statistical models for seasonal outlooks. Outcomes of the PASAP project enhanced capabilities of the Pacific Island Countries in seasonal prediction providing National Meteorological Services with an additional tool to analyse meteorological variables such as sea surface temperatures, air temperature, pressure and rainfall using POAMA outputs and prepare more accurate seasonal climate outlooks.

Comparison of short-term associations with meteorological variables between COPD and pneumonia hospitalization among the elderly in Hong Kong—a time-series study

NASA Astrophysics Data System (ADS)

Lam, Holly Ching-yu; Chan, Emily Ying-yang; Goggins, William Bernard

2018-05-01

Pneumonia and chronic obstructive pulmonary diseases (COPD) are the commonest causes of respiratory hospitalization among older adults. Both diseases have been reported to be associated with ambient temperature, but the associations have not been compared between the diseases. Their associations with other meteorological variables have also not been well studied. This study aimed to evaluate the associations between meteorological variables, pneumonia, and COPD hospitalization among adults over 60 and to compare these associations between the diseases. Daily cause-specific hospitalization counts in Hong Kong during 2004-2011 were regressed on daily meteorological variables using distributed lag nonlinear models. Associations were compared between diseases by ratio of relative risks. Analyses were stratified by season and age group (60-74 vs. ≥ 75). In hot season, high temperature (> 28 °C) and high relative humidity (> 82%) were statistically significantly associated with more pneumonia in lagged 0-2 and lagged 0-10 days, respectively. Pneumonia hospitalizations among the elderly (≥ 75) also increased with high solar radiation and high wind speed. During the cold season, consistent hockey-stick associations with temperature and relative humidity were found for both admissions and both age groups. The minimum morbidity temperature and relative humidity were at about 21-22 °C and 82%. The lagged effects of low temperature were comparable for both diseases (lagged 0-20 days). The low-temperature-admissions associations with COPD were stronger and were strongest among the elderly. This study found elevated pneumonia and COPD admissions risks among adults ≥ 60 during periods of extreme weather conditions, and the associations varied by season and age group. Vulnerable groups should be advised to avoid exposures, such as staying indoor and maintaining satisfactory indoor conditions, to minimize risks.

Regional meteorological drivers and long term trends of winter-spring nitrate dynamics across watersheds in northeastern North America

Treesearch

Jill Crossman; M. Catherine Eimers; Nora J. Casson; Douglas A. Burns; John L. Campbell; Gene E. Likens; Myron J. Mitchell; Sarah J. Nelson; James B. Shanley; Shaun A. Watmough; Kara L. Webster

2016-01-01

This study evaluated the contribution of winter rain-on-snow (ROS) events to annual and seasonal nitrate (N-NO3) export and identified the regional meteorological drivers of inter-annual variability in ROS N-NO3 export (ROS-N) at 9 headwater streams located across Ontario, Canada and the northeastern United States. Although...

Multi-scale modelling to evaluate building energy consumption at the neighbourhood scale.

PubMed

Mauree, Dasaraden; Coccolo, Silvia; Kaempf, Jérôme; Scartezzini, Jean-Louis

2017-01-01

A new methodology is proposed to couple a meteorological model with a building energy use model. The aim of such a coupling is to improve the boundary conditions of both models with no significant increase in computational time. In the present case, the Canopy Interface Model (CIM) is coupled with CitySim. CitySim provides the geometrical characteristics to CIM, which then calculates a high resolution profile of the meteorological variables. These are in turn used by CitySim to calculate the energy flows in an urban district. We have conducted a series of experiments on the EPFL campus in Lausanne, Switzerland, to show the effectiveness of the coupling strategy. First, measured data from the campus for the year 2015 are used to force CIM and to evaluate its aptitude to reproduce high resolution vertical profiles. Second, we compare the use of local climatic data and data from a meteorological station located outside the urban area, in an evaluation of energy use. In both experiments, we demonstrate the importance of using in building energy software, meteorological variables that account for the urban microclimate. Furthermore, we also show that some building and urban forms are more sensitive to the local environment.

Multi-scale modelling to evaluate building energy consumption at the neighbourhood scale

PubMed Central

Coccolo, Silvia; Kaempf, Jérôme; Scartezzini, Jean-Louis

2017-01-01

A new methodology is proposed to couple a meteorological model with a building energy use model. The aim of such a coupling is to improve the boundary conditions of both models with no significant increase in computational time. In the present case, the Canopy Interface Model (CIM) is coupled with CitySim. CitySim provides the geometrical characteristics to CIM, which then calculates a high resolution profile of the meteorological variables. These are in turn used by CitySim to calculate the energy flows in an urban district. We have conducted a series of experiments on the EPFL campus in Lausanne, Switzerland, to show the effectiveness of the coupling strategy. First, measured data from the campus for the year 2015 are used to force CIM and to evaluate its aptitude to reproduce high resolution vertical profiles. Second, we compare the use of local climatic data and data from a meteorological station located outside the urban area, in an evaluation of energy use. In both experiments, we demonstrate the importance of using in building energy software, meteorological variables that account for the urban microclimate. Furthermore, we also show that some building and urban forms are more sensitive to the local environment. PMID:28880883

Application of WRF/Chem-MADRID and WRF/Polyphemus in Europe - Part 1: Model description, evaluation of meteorological predictions, and aerosol-meteorology interactions

NASA Astrophysics Data System (ADS)

Zhang, Y.; Sartelet, K.; Wu, S.-Y.; Seigneur, C.

2013-07-01

Comprehensive model evaluation and comparison of two 3-D air quality modeling systems (i.e., the Weather Research and Forecast model (WRF)/Polyphemus and WRF with chemistry and the Model of Aerosol Dynamics, Reaction, Ionization, and Dissolution (MADRID) (WRF/Chem-MADRID)) are conducted over Western Europe. Part 1 describes the background information for the model comparison and simulation design, the application of WRF for January and July 2001 over triple-nested domains in Western Europe at three horizontal grid resolutions: 0.5°, 0.125°, and 0.025°, and the effect of aerosol/meteorology interactions on meteorological predictions. Nine simulated meteorological variables (i.e., downward shortwave and longwave radiation fluxes (SWDOWN and LWDOWN), outgoing longwave radiation flux (OLR), temperature at 2 m (T2), specific humidity at 2 m (Q2), relative humidity at 2 m (RH2), wind speed at 10 m (WS10), wind direction at 10 m (WD10), and precipitation (Precip)) are evaluated using available observations in terms of spatial distribution, domainwide daily and site-specific hourly variations, and domainwide performance statistics. The vertical profiles of temperature, dew points, and wind speed/direction are also evaluated using sounding data. WRF demonstrates its capability in capturing diurnal/seasonal variations and spatial gradients and vertical profiles of major meteorological variables. While the domainwide performance of LWDOWN, OLR, T2, Q2, and RH2 at all three grid resolutions is satisfactory overall, large positive or negative biases occur in SWDOWN, WS10, and Precip even at 0.125° or 0.025° in both months and in WD10 in January. In addition, discrepancies between simulations and observations exist in T2, Q2, WS10, and Precip at mountain/high altitude sites and large urban center sites in both months, in particular, during snow events or thunderstorms. These results indicate the model's difficulty in capturing meteorological variables in complex terrain and subgrid-scale meteorological phenomena, due to inaccuracies in model initialization parameterization (e.g., lack of soil temperature and moisture nudging), limitations in the physical parameterizations (e.g., shortwave radiation, cloud microphysics, cumulus parameterizations, and ice nucleation treatments) as well as limitations in surface heat and moisture budget parameterizations (e.g., snow-related processes, subgrid-scale surface roughness elements, and urban canopy/heat island treatments and CO2 domes). While the use of finer grid resolutions of 0.125° and 0.025° shows some improvements for WS10, WD10, Precip, and some mesoscale events (e.g., strong forced convection and heavy precipitation), it does not significantly improve the overall statistical performance for all meteorological variables except for Precip. The WRF/Chem simulations with and without aerosols show that aerosols lead to reduced net shortwave radiation fluxes, 2 m temperature, 10 m wind speed, planetary boundary layer (PBL) height, and precipitation and increase aerosol optical depth, cloud condensation nuclei, cloud optical depth, and cloud droplet number concentrations over most of the domain. These results indicate a need to further improve the model representations of the above parameterizations as well as aerosol-meteorology interactions at all scales.

Effects of Meteorological Variability on the Thermosphere-Ionosphere System during the Moderate Geomagnetic Disturbed January 2013 Period As Simulated By Time-GCM

NASA Astrophysics Data System (ADS)

Maute, A. I.; Hagan, M. E.; Richmond, A. D.; Liu, H.; Yudin, V. A.

2014-12-01

The ionosphere-thermosphere system is affected by solar and magnetospheric processes and by meteorological variability. Ionospheric observations of total electron content during the current solar cycle have shown that variability associated with meteorological forcing is important during solar minimum, and can have significant ionospheric effects during solar medium to maximum conditions. Numerical models can be used to study the comparative importance of geomagnetic and meterological forcing.This study focuses on the January 2013 Stratospheric Sudden Warming (SSW) period, which is associated with a very disturbed middle atmosphere as well as with moderately disturbed solar geomagntic conditions. We employ the NCAR Thermosphere-Ionosphere-Mesosphere-Electrodynamics General Circulation Model (TIME-GCM) with a nudging scheme using Whole-Atmosphere-Community-Climate-Model-Extended (WACCM-X)/Goddard Earth Observing System Model, Version 5 (GEOS5) results to simulate the effects of the meteorological and solar wind forcing on the upper atmosphere. The model results are evaluated by comparing with observations e.g., TEC, NmF2, ion drifts. We study the effect of the SSW on the wave spectrum, and the associated changes in the low latitude vertical drifts. These changes are compared to the impact of the moderate geomagnetic forcing on the TI-system during the January 2013 time period by conducting numerical experiments. We will present select highlights from our study and elude to the comparative importance of the forcing from above and below as simulated by the TIME-GCM.

A Simple Model of Cirrus Horizontal Inhomogeneity and Cloud Fraction

NASA Technical Reports Server (NTRS)

Smith, Samantha A.; DelGenio, Anthony D.

1998-01-01

A simple model of horizontal inhomogeneity and cloud fraction in cirrus clouds has been formulated on the basis that all internal horizontal inhomogeneity in the ice mixing ratio is due to variations in the cloud depth, which are assumed to be Gaussian. The use of such a model was justified by the observed relationship between the normalized variability of the ice water mixing ratio (and extinction) and the normalized variability of cloud depth. Using radar cloud depth data as input, the model reproduced well the in-cloud ice water mixing ratio histograms obtained from horizontal runs during the FIRE2 cirrus campaign. For totally overcast cases the histograms were almost Gaussian, but changed as cloud fraction decreased to exponential distributions which peaked at the lowest nonzero ice value for cloud fractions below 90%. Cloud fractions predicted by the model were always within 28% of the observed value. The predicted average ice water mixing ratios were within 34% of the observed values. This model could be used in a GCM to produce the ice mixing ratio probability distribution function and to estimate cloud fraction. It only requires basic meteorological parameters, the depth of the saturated layer and the standard deviation of cloud depth as input.

Wildland-Urban Interface Fires and Socioeconomic Conditions: A Case Study of a Northwestern Patagonia City

NASA Astrophysics Data System (ADS)

de Torres Curth, Monica; Biscayart, Carolina; Ghermandi, Luciana; Pfister, Gabriela

2012-04-01

In many regions of the world, fires are primarily of anthropogenic origin. In northwestern Patagonia, the number of fires is not correlated with meteorological variables, but is concentrated in urban areas. This study was conducted in the wildland-urban interface (WUI) area of San Carlos de Bariloche (Patagonia, Argentina), within the Nahuel Huapi National Park. WUI fires are particularly problematic because, besides people and goods, they represent a danger to protected areas. We studied the relationship between fire records and socioeconomic indicators within the WUI of San Carlos de Bariloche. We conducted a Multiple Correspondence Factorial Analysis and an Ascendant Hierarchical Classification of the city neighborhoods. The results show that the neighborhoods in Bariloche can be divided into three classes: High Socioeconomic Fire Risk neighborhoods, including neighborhoods with the highest fire rates, where people have low instruction level, high levels of unsatisfied basic needs and high unemployment levels; Low Socioeconomic Fire Risk neighborhoods, that groups neighborhoods which present the opposite characterization, and Moderate Socioeconomic Fire Risk neighborhoods, which are more heterogeneous. Once neighborhoods were classified, a Socioeconomic Fire Risk map was generated, supplementing the existing WUI Fire Danger map. Our results emphasize the relevance of socioeconomic variables to fire policies.

Long-term meteorologically independent trend analysis of ozone air quality at an urban site in the greater Houston area.

PubMed

Botlaguduru, Venkata S V; Kommalapati, Raghava R; Huque, Ziaul

2018-04-19

The Houston-Galveston-Brazoria (HGB) area of Texas has a history of ozone exceedances and is currently classified under moderate nonattainment status for the 2008 8-hr ozone standard of 75 ppb. The HGB area is characterized by intense solar radiation, high temperature, and humidity, which influence day-to-day variations in ozone concentrations. Long-term air quality trends independent of meteorological influence need to be constructed for ascertaining the effectiveness of air quality management in this area. The Kolmogorov-Zurbenko (KZ) filter technique used to separate different scales of motion in a time series, is applied in the current study for maximum daily 8-hr (MDA8) ozone concentrations at an urban site (EPA AQS Site ID: 48-201-0024, Aldine) in the HGB area. This site located within 10 miles of downtown Houston and the George Bush Intercontinental Airport, was selected for developing long-term meteorologically independent MDA8 ozone trends for the years 1990-2016. Results from this study indicate a consistent decrease in meteorologically independent MDA8 ozone between 2000-2016. This pattern could be partially attributed to a reduction in underlying NO X emissions, particularly that of lowering nitrogen dioxide (NO 2 ) levels, and a decrease in the release of highly reactive volatile organic compounds (HRVOC). Results also suggest solar radiation to be most strongly correlated to ozone, with temperature being the secondary meteorological control variable. Relative humidity and wind speed have tertiary influence at this site. This study observed that meteorological variability accounts for a high of 61% variability in baseline ozone (low-frequency component, sum of long-term and seasonal components), while 64% of the change in long-term MDA8 ozone post-2000 could be attributed to NO X emissions reduction. Long-term MDA8 ozone trend component was estimated to be decreasing at a linear rate of 0.412 ± 0.007 ppb/yr for the years 2000-2016, and 0.155 ± 0.005 ppb/yr for the overall period of 1990-2016. Implications Statement The effectiveness of air emission controls can be evaluated by developing long-term air quality trends independent of meteorological influences. KZ filter technique is a well-established method to separate an air quality time-series into: short-term, seasonal and long-term components. This paper applies the KZ filter technique to MDA8 ozone data between 1990-2016 at an urban site in the Greater Houston area and estimates the variance accounted for, by the primary meteorological control variables. Estimates for linear trends of MDA8 ozone are calculated and underlying causes are investigated to provide a guidance for further investigation into air quality management of the Greater Houston Area.

Ozone time scale decomposition and trend assessment from surface observations

NASA Astrophysics Data System (ADS)

Boleti, Eirini; Hueglin, Christoph; Takahama, Satoshi

2017-04-01

Emissions of ozone precursors have been regulated in Europe since around 1990 with control measures primarily targeting to industries and traffic. In order to understand how these measures have affected air quality, it is now important to investigate concentrations of tropospheric ozone in different types of environments, based on their NOx burden, and in different geographic regions. In this study, we analyze high quality data sets for Switzerland (NABEL network) and whole Europe (AirBase) for the last 25 years to calculate long-term trends of ozone concentrations. A sophisticated time scale decomposition method, called the Ensemble Empirical Mode Decomposition (EEMD) (Huang,1998;Wu,2009), is used for decomposition of the different time scales of the variation of ozone, namely the long-term trend, seasonal and short-term variability. This allows subtraction of the seasonal pattern of ozone from the observations and estimation of long-term changes of ozone concentrations with lower uncertainty ranges compared to typical methodologies used. We observe that, despite the implementation of regulations, for most of the measurement sites ozone daily mean values have been increasing until around mid-2000s. Afterwards, we observe a decline or a leveling off in the concentrations; certainly a late effect of limitations in ozone precursor emissions. On the other hand, the peak ozone concentrations have been decreasing for almost all regions. The evolution in the trend exhibits some differences between the different types of measurement. In addition, ozone is known to be strongly affected by meteorology. In the applied approach, some of the meteorological effects are already captured by the seasonal signal and already removed in the de-seasonalized ozone time series. For adjustment of the influence of meteorology on the higher frequency ozone variation, a statistical approach based on Generalized Additive Models (GAM) (Hastie,1990;Wood,2006), which corrects for meteorological effects, has been developed in order to a) investigate if trends are masked by meteorological variability and b) to understand which part of the observed trends is meteorology driven. By correlating short-term variation of ozone, as obtained from the EEMD, with the corresponding short-term variation of relevant meteorological parameters, we subtract the variation of ozone concentrations that is related to the meteorological effects explained by the GAM. We find that higher frequency meteorological correction reduces further the uncertainty in trend estimation by a small factor. In addition, the seasonal variability of ozone as obtained from the EEMD has been studied in more detail for possible changes in its behavior. A shortening of the seasonal cycle was observed, i.e. reduction of maximum and in-crease of minimum concentration per year, while the occurrence of maximum is shifted to earlier times during a year. In summary, we present a sophisticated and consistent approach for detecting and categorizing trends and meteorological influences on ozone concentrations in long-term measurements across Europe.

Seven years (2008-2014) of meteorological observations plus a synthetic El Nino drought for BCI Panama.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Powell, Thomas; Kueppers, Lara; Paton, Steve

This dataset is a derivative product of raw meteorological data collected at Barro Colorado Island, Panama (see acknowledgements below). This dataset contains the following: 1) a seven-year record (2008-2014) of meteorological observations from BCI that is in a comma delimited text format, 2) an R-script that converts the observed meteorology into an hdf5 format that can be read by the ED2 model, 3) two decades of meteorological drivers in hdf5 format that are based on the 7-year record of observations and include a synthetic 2-yr El Nino drought, 4) a ReadMe.txt file that explains how the data in the hdf5more » meteorological drivers correspond to the observations. The raw meteorological data were further QC'd as part of the NGEE-Tropics project to derive item 1 above. The R-script makes the appropriate unit conversions for all observed meteorological variables to be compatible with the ED2 model. The R-script also converts RH into specific humidity, splits total shortwave radiation into its 4-stream parts, and calculates longwave radiation from air temperature and RH. The synthetic El Nino drought is based on selected months from the observed meteorology where in each, precipitation (only) of the selected months was modified to reflect the precipitation patterns of the 1982/83 El Nino observed at BCI.« less

San Antonio Mountain Experiment (SAMEX).

NASA Astrophysics Data System (ADS)

McCutchan, Morris H.; Fox, Douglas G.; Furman, R. William

1982-10-01

The San Antonio Mountain Experiment (SAMEX) involves a 3325 m. conically shaped, isolated mountain in north-central New Mexico where hourly observations of temperature, relative humidity, wind speed, wind direction, and precipitation are being taken at nine locations over a three- to five-year period that began in 1980. The experiment is designed to isolate the effect of topography on these meteorological variables by using a geometric configuration sufficiently simple to lead to generalized results. One remote automatic weather station (RAWS) is located at the peak (3322 m); four are located at midslope (3033 m) on southwest, southeast, northeast, and northwest aspects; and four are at the base (2743 m) on southwest, southeast, northeast, and northwest aspects. The surface observations are supplemented by rawinsonde, pibal, tethersonde, and constant-level balloon observations at selected times during each year. The unique set of meteorological data collected in the experiment will be used to 1) determine the effect of elevation and aspect on the meteorological variables; 2) compare the temperature, humidity, and wind components on the mountain with observations and/or predictions of these variables in the free air nearby; and 3) validate temperature, humidity, and wind models in complex terrain.

Chemistry Simulations Using MERRA-2 Reanalysis with the GMI CTM and Replay in Support of the Atmospheric Composition Community

NASA Technical Reports Server (NTRS)

Oman, Luke D.; Strahan, Susan E.

2016-01-01

Simulations using reanalyzed meteorological conditions have been long used to understand causes of atmospheric composition change over the recent past. Using the new Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) meteorology, chemistry simulations are being conducted to create products covering 1980-2016 for the atmospheric composition community. These simulations use the Global Modeling Initiative (GMI) chemical mechanism in two different models: the GMI Chemical Transport Model (CTM) and the GEOS-5 model developed Replay mode. Replay mode means an integration of the GEOS-5 general circulation model that is incrementally adjusted each time step toward the MERRA-2 analysis. The GMI CTM is a 1 x 1.25 simulation and the MERRA-2 GMI Replay simulation uses the native MERRA-2 approximately horizontal resolution on the cubed sphere. The Replay simulations is driven by the online use of key MERRA-2 meteorological variables (i.e. U, V, T, and surface pressure) with all other variables calculated in response to those variables. A specialized set of transport diagnostics is included in both runs to better understand trace gas transport and changes over the recent past.

PREDICTING CLINICALLY DIAGNOSED DYSENTERY INCIDENCE OBTAINED FROM MONTHLY CASE REPORTING BASED ON METEOROLOGICAL VARIABLES IN DALIAN, LIAONING PROVINCE, CHINA, 2005-2011 USING A DEVELOPED MODEL.

PubMed

An, Qingyu; Yao, Wei; Wu, Jun

2015-03-01

This study describes our development of a model to predict the incidence of clinically diagnosed dysentery in Dalian, Liaoning Province, China, using time series analysis. The model was developed using the seasonal autoregressive integrated moving average (SARIMA). Spearman correlation analysis was conducted to explore the relationship between meteorological variables and the incidence of clinically diagnosed dysentery. The meteorological variables which significantly correlated with the incidence of clinically diagnosed dysentery were then used as covariables in the model, which incorporated the monthly incidence of clinically diagnosed dysentery from 2005 to 2010 in Dalian. After model development, a simulation was conducted for the year 2011 and the results of this prediction were compared with the real observed values. The model performed best when the temperature data for the preceding month was used to predict clinically diagnosed dysentery during the following month. The developed model was effective and reliable in predicting the incidence of clinically diagnosed dysentery for most but not all months, and may be a useful tool for dysentery disease control and prevention, but further studies are needed to fine tune the model.

A Methodology for the Parametric Reconstruction of Non-Steady and Noisy Meteorological Time Series

NASA Astrophysics Data System (ADS)

Rovira, F.; Palau, J. L.; Millán, M.

2009-09-01

Climatic and meteorological time series often show some persistence (in time) in the variability of certain features. One could regard annual, seasonal and diurnal time variability as trivial persistence in the variability of some meteorological magnitudes (as, e.g., global radiation, air temperature above surface, etc.). In these cases, the traditional Fourier transform into frequency space will show the principal harmonics as the components with the largest amplitude. Nevertheless, meteorological measurements often show other non-steady (in time) variability. Some fluctuations in measurements (at different time scales) are driven by processes that prevail on some days (or months) of the year but disappear on others. By decomposing a time series into time-frequency space through the continuous wavelet transformation, one is able to determine both the dominant modes of variability and how those modes vary in time. This study is based on a numerical methodology to analyse non-steady principal harmonics in noisy meteorological time series. This methodology combines both the continuous wavelet transform and the development of a parametric model that includes the time evolution of the principal and the most statistically significant harmonics of the original time series. The parameterisation scheme proposed in this study consists of reproducing the original time series by means of a statistically significant finite sum of sinusoidal signals (waves), each defined by using the three usual parameters: amplitude, frequency and phase. To ensure the statistical significance of the parametric reconstruction of the original signal, we propose a standard statistical t-student analysis of the confidence level of the amplitude in the parametric spectrum for the different wave components. Once we have assured the level of significance of the different waves composing the parametric model, we can obtain the statistically significant principal harmonics (in time) of the original time series by using the Fourier transform of the modelled signal. Acknowledgements The CEAM Foundation is supported by the Generalitat Valenciana and BANCAIXA (València, Spain). This study has been partially funded by the European Commission (FP VI, Integrated Project CIRCE - No. 036961) and by the Ministerio de Ciencia e Innovación, research projects "TRANSREG” (CGL2007-65359/CLI) and "GRACCIE” (CSD2007-00067, Program CONSOLIDER-INGENIO 2010).

Meteorology and energy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1979-07-01

In the consideration of the meteorological aspects of energy problems, the latter is divided into three main groups: energy production, energy transport and exploration, and new energy resources. Increased energy production will have an impact on the environment. Although at present there is insufficient information for precise forecasts, meteorologists and hydrologists will be able to make reasonable assumptions for the future. Human use of energy is strongly influenced by variations of weather. Such systems as electric power transmission networks, shipping of hydrocarbons by sea, and pipelines for the transportation of large quantities of oil and gas, are all particularly sensitivemore » to weather and climate. The meteorologist provides basic data on weather and climate to facilitate energy exploration. The new energy resources addressed in this article are solar, wind, geothermal, and nuclear. The World Meteorological Organization's Executive Committee established a set of priorities in dealing with energy problems. This paper also briefly examines the burden imposed on global energy resources.« less

Airborne black carbon concentrations over an urban region in western India-temporal variability, effects of meteorology, and source regions.

PubMed

Bapna, Mukund; Sunder Raman, Ramya; Ramachandran, S; Rajesh, T A

2013-03-01

This study characterizes over 5 years of high time resolution (5 min), airborne black carbon (BC) concentrations (July 2003 to December 2008) measured over Ahmedabad, an urban region in western India. The data were used to obtain different time averages of BC concentrations, and these averages were then used to assess the diurnal, seasonal, and annual variability of BC over the study region. Assessment of diurnal variations revealed a strong association between BC concentrations and vehicular traffic. Peaks in BC concentration were co-incident with the morning (0730 to 0830, LST) and late evening (1930 to 2030, LST) rush hour traffic. Additionally, diurnal variability in BC concentrations during major festivals (Diwali and Dushera during the months of October/November) revealed an increase in BC concentrations due to fireworks displays. Maximum half hourly BC concentrations during the festival days were as high as 79.8 μg m(-3). However, the high concentrations rapidly decayed suggesting that local meteorology during the festive season was favorable for aerosol dispersion. A multiple linear regression (MLR) model with BC as the dependent variable and meteorological parameters as independent variables was fitted. The variability in temperature, humidity, wind speed, and wind direction accounted for about 49% of the variability in measured BC concentrations. Conditional probability function (CPF) analysis was used to identify the geographical location of local source regions contributing to the effective BC measured (at 880 nm) at the receptor site. The east north-east (ENE) direction to the receptor was identified as a major source region. National highway (NH8) and two coal-fired thermal power stations (at Gandhinagar and Sabarmati) were located in the identified direction, suggesting that local traffic and power plant emissions were likely contributors to the measured BC.

Harmonic analysis of the precipitation in Greece

NASA Astrophysics Data System (ADS)

Nastos, P. T.; Zerefos, C. S.

2009-04-01

Greece is a country with a big variety of climates due to its geographical position, to the many mountain ranges and also to the multifarious and long coastline. The mountainous volumes are of such orientation that influences the distribution of the precipitation, having as a result, Western Greece to present great differentiations from Central and Eastern Greece. The application of harmonic analysis to the annual variability of precipitation is the goal of this study, so that the components, which compose the annual variability, be elicited. For this purpose, the mean monthly precipitation data from 30 meteorological stations of National Meteorological Service were used for the time period 1950-2000. The initial target is to reduce the number of variables and to detect structure in the relationships between variables. The most commonly used technique for this purpose is the application of Factor Analysis to a table having as columns the meteorological stations-variables and rows the monthly mean precipitation, so that 2 main factors were calculated, which explain the 98% of total variability of precipitation in Greece. Factor 1, representing the so-called uniform field and interpreting the most of the total variance, refers in fact to the Mediterranean depressions, affecting mainly the West of Greece and also the East Aegean and the Asia Minor coasts. In the process, the Fourier Analysis was applied to the factor scores extracted from the Factor Analysis, so that 2 harmonic components are resulted, which explain above the 98% of the total variability of each main factor, and are due to different synoptic and thermodynamic processes associated with Greece's precipitation construction. Finally, the calculation of the time of occurrence of the maximum precipitation, for each harmonic component of each one of the two main factors, gives the spatial distribution of appearance of the maximum precipitation in the Hellenic region.

Inter-annual variability of carbon fluxes in temperate forest ecosystems: effects of biotic and abiotic factors

NASA Astrophysics Data System (ADS)

Chen, M.; Keenan, T. F.; Hufkens, K.; Munger, J. W.; Bohrer, G.; Brzostek, E. R.; Richardson, A. D.

2014-12-01

Carbon dynamics in terrestrial ecosystems are influenced by both abiotic and biotic factors. Abiotic factors, such as variation in meteorological conditions, directly drive biophysical and biogeochemical processes; biotic factors, referring to the inherent properties of the ecosystem components, reflect the internal regulating effects including temporal dynamics and memory. The magnitude of the effect of abiotic and biotic factors on forest ecosystem carbon exchange has been suggested to vary at different time scales. In this study, we design and conduct a model-data fusion experiment to investigate the role and relative importance of the biotic and abiotic factors for inter-annual variability of the net ecosystem CO2 exchange (NEE) of temperate deciduous forest ecosystems in the Northeastern US. A process-based model (FöBAAR) is parameterized at four eddy-covariance sites using all available flux and biometric measurements. We conducted a "transplant" modeling experiment, that is, cross- site and parameter simulations with different combinations of site meteorology and parameters. Using wavelet analysis and variance partitioning techniques, analysis of model predictions identifies both spatial variant and spatially invariant parameters. Variability of NEE was primarily modulated by gross primary productivity (GPP), with relative contributions varying from hourly to yearly time scales. The inter-annual variability of GPP and NEE is more regulated by meteorological forcing, but spatial variability in certain model parameters (biotic response) has more substantial effects on the inter-annual variability of ecosystem respiration (Reco) through the effects on carbon pools. Both the biotic and abiotic factors play significant roles in modulating the spatial and temporal variability in terrestrial carbon cycling in the region. Together, our study quantifies the relative importance of both, and calls for better understanding of them to better predict regional CO2 exchanges.

Look! It Is Going to Rain

ERIC Educational Resources Information Center

Trundle, Kathy Cabe; Sackes, Mesut

2010-01-01

It is important to help young children make connections between events in their lives and science concepts in preschool classrooms, so introducing basic meteorology ideas offer a great opportunity to make weather connections and awaken scientific curiosity (Spiropoulou, Kostopoulos, and Jacovides 1999). Therefore, this article presents a science…

Efficient Ways to Learn Weather Radar Polarimetry

ERIC Educational Resources Information Center

Cao, Qing; Yeary, M. B.; Zhang, Guifu

2012-01-01

The U.S. weather radar network is currently being upgraded with dual-polarization capability. Weather radar polarimetry is an interdisciplinary area of engineering and meteorology. This paper presents efficient ways to learn weather radar polarimetry through several basic and practical topics. These topics include: 1) hydrometeor scattering model…

14 CFR 63.35 - Knowledge requirements.

Code of Federal Regulations, 2013 CFR

2013-01-01

... aerodynamics. (3) Basic meteorology with respect to engine operations. (4) Center of gravity computations. (b... written test, is employed as a flight crewmember or mechanic by a U.S. air carrier or commercial operator... training; and (iii) Meets the recurrent training requirements of the applicable part or, for mechanics...

14 CFR 63.35 - Knowledge requirements.

Code of Federal Regulations, 2011 CFR

2011-01-01

... aerodynamics. (3) Basic meteorology with respect to engine operations. (4) Center of gravity computations. (b... written test, is employed as a flight crewmember or mechanic by a U.S. air carrier or commercial operator... training; and (iii) Meets the recurrent training requirements of the applicable part or, for mechanics...

14 CFR 63.35 - Knowledge requirements.

Code of Federal Regulations, 2012 CFR

2012-01-01

... aerodynamics. (3) Basic meteorology with respect to engine operations. (4) Center of gravity computations. (b... written test, is employed as a flight crewmember or mechanic by a U.S. air carrier or commercial operator... training; and (iii) Meets the recurrent training requirements of the applicable part or, for mechanics...

14 CFR 63.35 - Knowledge requirements.

Code of Federal Regulations, 2014 CFR

2014-01-01

... aerodynamics. (3) Basic meteorology with respect to engine operations. (4) Center of gravity computations. (b... written test, is employed as a flight crewmember or mechanic by a U.S. air carrier or commercial operator... training; and (iii) Meets the recurrent training requirements of the applicable part or, for mechanics...

The Southern Hemisphere lower stratosphere during August and September 1987 - Analyses based on the United Kingdom Meteorological Office Global Model

NASA Technical Reports Server (NTRS)

Mckenna, D. S.; Jones, R. L.; Buckland, A. T.; Austin, J.; Tuck, A. F.; Winkler, R. H.; Chan, K. R.

1989-01-01

This paper presents a series of meteorological analyses used to aid the interpretation of the in situ Airborne Antarctic Ozone Experiment (AAOE) observations obtained aboard the ER-2 and DC-8 aircraft and examines the basis and accuracy of the analytical procedure. Maps and sections of meteorological variables derived from the UK Meteorological Office Global Model are presented for ER-2 and DC-8 flight days. It is found that analyzed temperatures and winds are generally in good agreement with AAOE observations at all levels; minor discrepancies were evident only at DC-8 altitudes. Maps of potential vorticity presented on the 428-K potential temperature surface show that the vortex is essentially circumpolar, although there are periods when major distortions are apparent.

Spatial and temporal trends of reference crop evapotranspiration and its influential variables in Yangtze River Delta, eastern China

NASA Astrophysics Data System (ADS)

Xu, Yu; Xu, Youpeng; Wang, Yuefeng; Wu, Lei; Li, Guang; Song, Song

2017-11-01

Reference crop evapotranspiration (ETo) is one of the most important links in hydrologic circulation and greatly affects regional agricultural production and water resource management. Its variation has drawn more and more attention in the context of global warming. We used the Penman-Monteith method of the Food and Agriculture Organization, based on meteorological factors such as air temperature, sunshine duration, wind speed, and relative humidity to calculate the ETo over 46 meteorological stations located in the Yangtze River Delta, eastern China, from 1957 to 2014. The spatial distributions and temporal trends in ETo were analyzed based on the modified Mann-Kendall trend test and linear regression method, while ArcGIS software was employed to produce the distribution maps. The multiple stepwise regression method was applied in the analysis of the meteorological variable time series to identify the causes of any observed trends in ETo. The results indicated that annual ETo showed an obvious spatial pattern of higher values in the north than in the south. Annual increasing trends were found at 34 meteorological stations (73.91 % of the total), which were mainly located in the southeast. Among them, 12 (26.09 % of the total) stations showed significant trends. We saw a dominance of increasing trends in the monthly ETo except for January, February, and August. The high value zone of monthly ETo appeared in the northwest from February to June, mid-south area from July to August, and southeast coastal area from September to January. The research period was divided into two stages—stage I (1957-1989) and stage II (1990-2014)—to investigate the long-term temporal ETo variation. In stage I, almost 85 % of the total stations experienced decreasing trends, while more than half of the meteorological stations showed significant increasing trends in annual ETo during stage II except in February and September. Relative humidity, wind speed, and sunshine duration were identified as the most dominant meteorological variables influencing annual ETo changes. The results are expected to assist water resource managers and policy makers in making better planning decisions in the research region.

Comparative analysis of meteorological and hydrological drought in the Pearl River basin during the period 1960-2012

NASA Astrophysics Data System (ADS)

Xu, K.; Wu, C.; Hu, B.; Niu, J.

2017-12-01

Drought is one of the major natural hazards that can have devastating impacts on the regional environment, agriculture, and water resources. Previous studies have conducted the assessment of historic changes in meteorological drought over various regional scales but rarely considered hydrological drought due to limited hydrological observations. Here, we use a long-term (1960-2012) gridded hydro-meteorological data to present a comparative analysis of meteorological and hydrological drought in the Pearl River basin in southern China using the standardized precipitation index (SPI) and the standardized runoff index (SRI). The variation in SPI and SRI at four different timescales (1-, 3-, 6-, and 12-month) is investigated using the Mann-Kendall (M-K) method and continuous wavelet transform (CWT). The results indicate that the correlation between SPI and SRI is strong over the Pearl River basin and tends to be stronger at the longer timescale. Meanwhile, the periodic oscillation pattern of SPI becomes more consistent with that of SRI with the increased timescale. The SPI can be used as a substitute for SRI to represent the hydrological drought at the long-term scale. Overall there is a noticeably wetting trend mainly in the eastern parts and a significant drying trend mainly in the western regions and the downstream area of the Pearl River basin. The variability of meteorological drought is significant mainly in the eastern and western regions, while the variability of hydrological drought tends to be larger mainly in the western region. CWT analysis indicates a period of 0.75-7 years in both meteorological and hydrological droughts during the period 1960-2012 in the study region.

Variability of winds and temperature in the Bergen area

NASA Astrophysics Data System (ADS)

Schönbein, Daniel; Ólafsson, Haraldur; Asle Olseth, Jan; Furevik, Birgitte

2017-04-01

In recent years, observations have been made by a dense network of automatic weather stations in the Bergen area in W-Norway (Bergen School of Meteorology). Here, cases are presented that feature large spatial variability in winds and temperature and the ability of a numerical model to reproduce this variability is assessed.

Measuring the impact of air pollution on respiratory infection risk in China.

PubMed

Tang, Sanyi; Yan, Qinling; Shi, Wei; Wang, Xia; Sun, Xiaodan; Yu, Pengbo; Wu, Jianhong; Xiao, Yanni

2018-01-01

China is now experiencing major public health challenges caused by air pollution. Few studies have quantified the dynamics of air pollution and its impact on the risk of respiratory infection. We conducted an integrated data analysis to quantify the association among air quality index (AQI), meteorological variables and respiratory infection risk in Shaanxi province of China in the period of November 15th, 2010 to November 14th, 2016. Our analysis illustrated a statistically significantly positive correlation between the number of influenza-like illness (ILI) cases and AQI, and the respiratory infection risk has increased progressively with increased AQI with a time lag of 0-3 days. We also developed mathematical models for the AQI trend and respiratory infection dynamics, incorporating AQI-dependent incidence and AQI-based behaviour change interventions. Our combined data and modelling analysis estimated the basic reproduction number for the respiratory infection during the studying period to be 2.4076, higher than the basic reproduction number of the 2009 pandemic influenza in the same province. Our modelling-based simulations concluded that, in terms of respiratory infection risk reduction, the persistent control of emission in the China's blue-sky programme is much more effective than substantial social-economic interventions implemented only during the smog days. Copyright © 2017 Elsevier Ltd. All rights reserved.

The role of climate and socioeconomic factors on the spatiotemporal variability of cholera in Nigeria

NASA Astrophysics Data System (ADS)

Abdussalam, Auwal; Thornes, John; Leckebusch, Gregor

2015-04-01

Nigeria has a number of climate-sensitive infectious diseases; one of the most important of these diseases that remains a threat to public health is cholera. This study investigates the influences of both meteorological and socioeconomic factors on the spatiotemporal variability of cholera in Nigeria. A stepwise multiple regression models are used to estimate the influence of the year-to-year variations of cholera cases and deaths for individual states in the country and as well for three groups of states that are classified based on annual rainfall amount. Specifically, seasonal mean maximum and minimum temperatures and annual rainfall totals were analysed with annual aggregate count of cholera cases and deaths, taking into account of the socioeconomic factors that are potentially enhancing vulnerability such as: absolute poverty, adult literacy, access to pipe borne water and population density. Result reveals that the most important explanatory meteorological and socioeconomic variables in explaining the spatiotemporal variability of the disease are rainfall totals, seasonal mean maximum temperature, absolute poverty, and accessibility to pipe borne water. The influences of socioeconomic factors appeared to be more pronounced in the northern part of the country, and vice-versa in the case of meteorological factors. Also, cross validated models output suggests a strong possibility of disease prediction, which will help authorities to put effective control measures in place which depend on prevention, and or efficient response.

Developing a methodology to predict PM10 concentrations in urban areas using generalized linear models.

PubMed

Garcia, J M; Teodoro, F; Cerdeira, R; Coelho, L M R; Kumar, Prashant; Carvalho, M G

2016-09-01

A methodology to predict PM10 concentrations in urban outdoor environments is developed based on the generalized linear models (GLMs). The methodology is based on the relationship developed between atmospheric concentrations of air pollutants (i.e. CO, NO2, NOx, VOCs, SO2) and meteorological variables (i.e. ambient temperature, relative humidity (RH) and wind speed) for a city (Barreiro) of Portugal. The model uses air pollution and meteorological data from the Portuguese monitoring air quality station networks. The developed GLM considers PM10 concentrations as a dependent variable, and both the gaseous pollutants and meteorological variables as explanatory independent variables. A logarithmic link function was considered with a Poisson probability distribution. Particular attention was given to cases with air temperatures both below and above 25°C. The best performance for modelled results against the measured data was achieved for the model with values of air temperature above 25°C compared with the model considering all ranges of air temperatures and with the model considering only temperature below 25°C. The model was also tested with similar data from another Portuguese city, Oporto, and results found to behave similarly. It is concluded that this model and the methodology could be adopted for other cities to predict PM10 concentrations when these data are not available by measurements from air quality monitoring stations or other acquisition means.

Impacts of weather versus climate and driver uncertainty on multi-centennial ecosystem model simulations

NASA Astrophysics Data System (ADS)

Rollinson, C.; Simkins, J.; Fer, I.; Desai, A. R.; Dietze, M.

2017-12-01

Simulations of ecosystem dynamics and comparisons with empirical data require accurate, continuous, and often sub-daily meteorology records that are spatially aligned to the scale of the empirical data. A wealth of meteorology data for the past, present, and future is available through site-specific observations, modern reanalysis products, and gridded GCM simulations. However, these products are mismatched in spatial and temporal resolution, often with both different means and seasonal patterns. We have designed and implemented a two-step meteorological downscaling and ensemble generation method that combines multiple meteorology data products through debiasing and temporal downscaling protocols. Our methodology is designed to preserve the covariance among seven meteorological variables for use as drivers in ecosystem model simulations: temperature, precipitation, short- and longwave radiation, surface pressure, humidity, and wind. Furthermore, our method propagates uncertainty through the downscaling process and results in ensembles of meteorology that can be compared to paleoclimate reconstructions and used to analyze the effects of both high- and low-frequency climate anomalies on ecosystem dynamics. Using a multiple linear regression approach, we have combined hourly, 0.125-degree gridded data from the NLDAS (1980-present) with CRUNCEP (1901-2010) and CMIP5 historical (1850-2005), past millennium (850-1849), and future (1950-2100) GCM simulations. This has resulted in an ensemble of continuous, hourly-resolved meteorology from from the paleo era into the future with variability in weather events as well as low-frequency climatic changes. We investigate the influence of extreme sub-daily weather phenomena versus long-term climatic changes in an ensemble of ecosystem models that range in atmospheric and biological complexity. Through data assimilation with paleoclimate reconstructions of past climate, we can improve data-model comparisons using observations of vegetation change from the past 1200 years. Accounting for driver uncertainty in model evaluation can help determine the relative influence of structural versus parameterization errors in ecosystem modelings.

Environmental and Physiographic Controls on Inter-Growing Season Variability of Carbon Dioxide and Water Vapour Fluxes in a Minerotrophic Fen

NASA Astrophysics Data System (ADS)

van der Kamp, G.; Sonnentag, O.; Chen, J. M.; Barr, A.; Hedstrom, N.; Granger, R.

2008-12-01

The interaction of fens with groundwater is spatially and temporally highly variable in response to meteorological conditions, resulting in frequent changes of groundwater fluxes in both vertical and lateral directions (flow reversals) across the mineral soil-peat boundary. However, despite the importance of the topographic and hydrogeological setting of fens, no study has been reported in the literature that explores a fen's atmospheric CO2 and energy flux densities under contrasting meteorological conditions in response to its physiographic setting. In our contribution we report four years of growing season eddy covariance and supporting measurements from the Canada Fluxnet-BERMS fen (formerly BOREAS southern peatland) in Saskatchewan, Canada. We first analyze hydrological data along two piezometer transects across the mineral soil-peat boundary with the objective of assessing changes in water table configuration and thus hydraulic gradients, indicating flow reversals, in response to dry and wet meteorological conditions. Next we quantify and compare growing season totals and diurnal and daily variations in evapotranspiration (ET) and net ecosystem exchange (NEE) and its component fluxes gross ecosystem productivity (GPP) and terrestrial ecosystem respiration (TER) to identify their controls with a major focus on water table depth. While ET growing season totals were similar (~ 310 mm) under dry and wet meteorological conditions, the CO2 sink- source strength of Sandhill fen varied substantially from carbon neutral (NEE = -2 [+-7] g C m-2 per growing season) under dry meteorological condition (2003) to a moderate CO2- sink with NEE ranging between 157 [+- 10] and 190 [+- 11] g C m-2 per growing season under wet meteorological conditions (2004, 2005, and 2006). Using a process-oriented ecosystem model, BEPS-TerrainLab, we investigate how different canopy components at Sandhill contribute to total ET and GPP, and thus water use efficiency, under dry and wet meteorological conditions.

The Quality Control Algorithms Used in the Creation of NASA Kennedy Space Center Lightning Protection System Towers Meteorological Database

NASA Technical Reports Server (NTRS)

Orcutt, John M.; Brenton, James C.

2016-01-01

An accurate database of meteorological data is essential for designing any aerospace vehicle and for preparing launch commit criteria. Meteorological instrumentation were recently placed on the three Lightning Protection System (LPS) towers at Kennedy Space Center (KSC) launch complex 39B (LC-39B), which provide a unique meteorological dataset existing at the launch complex over an extensive altitude range. Data records of temperature, dew point, relative humidity, wind speed, and wind direction are produced at 40, 78, 116, and 139 m at each tower. The Marshall Space Flight Center Natural Environments Branch (EV44) received an archive that consists of one-minute averaged measurements for the period of record of January 2011 - April 2015. However, before the received database could be used EV44 needed to remove any erroneous data from within the database through a comprehensive quality control (QC) process. The QC process applied to the LPS towers' meteorological data is similar to other QC processes developed by EV44, which were used in the creation of meteorological databases for other towers at KSC. The QC process utilized in this study has been modified specifically for use with the LPS tower database. The QC process first includes a check of each individual sensor. This check includes removing any unrealistic data and checking the temporal consistency of each variable. Next, data from all three sensors at each height are checked against each other, checked against climatology, and checked for sensors that erroneously report a constant value. Then, a vertical consistency check of each variable at each tower is completed. Last, the upwind sensor at each level is selected to minimize the influence of the towers and other structures at LC-39B on the measurements. The selection process for the upwind sensor implemented a study of tower-induced turbulence. This paper describes in detail the QC process, QC results, and the attributes of the LPS towers meteorological database.

Representative Values of Icing-Related Variables Aloft in Freezing Rain and Freezing Drizzle

DOT National Transportation Integrated Search

1996-03-01

Radiosonde and surface observations in freezing rain (ZR) and freezing drizzle (ZL), and a limited number of aircraft measurements in ZR, have been examined for information on the magnitude and altitude dependence of meteorological variables associat...

Meteorological variables and bacillary dysentery cases in Changsha City, China.

PubMed

Gao, Lu; Zhang, Ying; Ding, Guoyong; Liu, Qiyong; Zhou, Maigeng; Li, Xiujun; Jiang, Baofa

2014-04-01

This study aimed to investigate the association between meteorological-related risk factors and bacillary dysentery in a subtropical inland Chinese area: Changsha City. The cross-correlation analysis and the Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) model were used to quantify the relationship between meteorological factors and the incidence of bacillary dysentery. Monthly mean temperature, mean relative humidity, mean air pressure, mean maximum temperature, and mean minimum temperature were significantly correlated with the number of bacillary dysentery cases with a 1-month lagged effect. The ARIMAX models suggested that a 1°C rise in mean temperature, mean maximum temperature, and mean minimum temperature might lead to 14.8%, 12.9%, and 15.5% increases in the incidence of bacillary dysentery disease, respectively. Temperature could be used as a forecast factor for the increase of bacillary dysentery in Changsha. More public health actions should be taken to prevent the increase of bacillary dysentery disease with consideration of local climate conditions, especially temperature.

Meteorological Variables and Bacillary Dysentery Cases in Changsha City, China

PubMed Central

Gao, Lu; Zhang, Ying; Ding, Guoyong; Liu, Qiyong; Zhou, Maigeng; Li, Xiujun; Jiang, Baofa

2014-01-01

This study aimed to investigate the association between meteorological-related risk factors and bacillary dysentery in a subtropical inland Chinese area: Changsha City. The cross-correlation analysis and the Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) model were used to quantify the relationship between meteorological factors and the incidence of bacillary dysentery. Monthly mean temperature, mean relative humidity, mean air pressure, mean maximum temperature, and mean minimum temperature were significantly correlated with the number of bacillary dysentery cases with a 1-month lagged effect. The ARIMAX models suggested that a 1°C rise in mean temperature, mean maximum temperature, and mean minimum temperature might lead to 14.8%, 12.9%, and 15.5% increases in the incidence of bacillary dysentery disease, respectively. Temperature could be used as a forecast factor for the increase of bacillary dysentery in Changsha. More public health actions should be taken to prevent the increase of bacillary dysentery disease with consideration of local climate conditions, especially temperature. PMID:24591435

Influence of weather and climate on subjective symptom intensity in atopic eczema

NASA Astrophysics Data System (ADS)

Vocks, E.; Busch, R.; Fröhlich, C.; Borelli, S.; Mayer, H.; Ring, J.

The frequent clinical observation that the course of atopic eczema, a skin disease involving a disturbed cutaneous barrier function, is influenced by climate and weather motivated us to analyse these relationships biometrically. In the Swiss high-mountain area of Davos the intensity of itching experienced by patients with atopic eczema was evaluated and compared to 15 single meteorological variables recorded daily during an entire 7-year observation period. By means of univariate analyses and multiple regressions, itch intensity was found to be correlated with some meteorological variables. A clear-cut inverse correlation exists with air temperature (coefficient of correlation: -0.235, P<0.001), but the effects of water vapour pressure, air pressure and hours of sunshine are less pronounced. The results show that itching in atopic eczema is significantly dependent on meteorological conditions. The data suggest that, in patients with atopic eczema, a certain range of thermo-hygric atmospheric conditions with a balance of heat and water loss on the skin surface is essential for the skin to feel comfortable.

Contribution of Modis Satellite Image to Estimate the Daily Air Temperature in the Casablanca City, Morocco

NASA Astrophysics Data System (ADS)

Bahi, Hicham; Rhinane, Hassan; Bensalmia, Ahmed

2016-10-01

Air temperature is considered to be an essential variable for the study and analysis of meteorological regimes and chronics. However, the implementation of a daily monitoring of this variable is very difficult to achieve. It requires sufficient of measurements stations density, meteorological parks and favourable logistics. The present work aims to establish relationship between day and night land surface temperatures from MODIS data and the daily measurements of air temperature acquired between [2011-20112] and provided by the Department of National Meteorology [DMN] of Casablanca, Morocco. The results of the statistical analysis show significant interdependence during night observations with correlation coefficient of R2=0.921 and Root Mean Square Error RMSE=1.503 for Tmin while the physical magnitude estimated from daytime MODIS observation shows a relatively coarse error with R2=0.775 and RMSE=2.037 for Tmax. A method based on Gaussian process regression was applied to compute the spatial distribution of air temperature from MODIS throughout the city of Casablanca.

14 CFR Appendix D to Part 141 - Commercial Pilot Certification Course

Code of Federal Regulations, 2011 CFR

2011-01-01

... Board; (3) Basic aerodynamics and the principles of flight; (4) Meteorology, to include recognition of critical weather situations, windshear recognition and avoidance, and the use of aeronautical weather... pattern); and (iv) 3 hours in a gyroplane in preparation for the practical test within 60 days preceding...

14 CFR Appendix D to Part 141 - Commercial Pilot Certification Course

Code of Federal Regulations, 2010 CFR

2010-01-01

... Board; (3) Basic aerodynamics and the principles of flight; (4) Meteorology, to include recognition of critical weather situations, windshear recognition and avoidance, and the use of aeronautical weather... pattern); and (iv) 3 hours in a gyroplane in preparation for the practical test within 60 days preceding...

Assessing factors that influence deviations between measured and calculated reference evapotranspiration

NASA Astrophysics Data System (ADS)

Rodny, Marek; Nolz, Reinhard

2017-04-01

Evapotranspiration (ET) is a fundamental component of the hydrological cycle, but challenging to be quantified. Lysimeter facilities, for example, can be installed and operated to determine ET, but they are costly and represent only point measurements. Therefore, lysimeter data are traditionally used to develop, calibrate, and validate models that allow calculating reference evapotranspiration (ET0) based on meteorological data, which can be measured more easily. The standardized form of the well-known FAO Penman-Monteith equation (ASCE-EWRI) is recommended as a standard procedure for estimating ET0 and subsequently plant water requirements. Applied and validated under different climatic conditions, the Penman-Monteith equation is generally known to deliver proper results. On the other hand, several studies documented deviations between measured and calculated ET0 depending on environmental conditions. Potential reasons are, for example, differing or varying surface characteristics of the lysimeter and the location where the weather instruments are placed. Advection of sensible heat (transport of dry and hot air from surrounding areas) might be another reason for deviating ET-values. However, elaborating causal processes is complex and requires comprehensive data of high quality and specific analysis techniques. In order to assess influencing factors, we correlated differences between measured and calculated ET0 with pre-selected meteorological parameters and related system parameters. Basic data were hourly ET0-values from a weighing lysimeter (ET0_lys) with a surface area of 2.85 m2 (reference crop: frequently irrigated grass), weather data (air and soil temperature, relative humidity, air pressure, wind velocity, and solar radiation), and soil water content in different depths. ET0_ref was calculated in hourly time steps according to the standardized procedure after ASCE-EWRI (2005). Deviations between both datasets were calculated as ET0_lys-ET0_ref and separated into positive and negative values. For further interpretation, we calculated daily sums of these values. The respective daily difference (positive or negative) served as independent variable (x) in linear correlation with a selected parameter as dependent variable (y). Quality of correlation was evaluated by means of coefficients of determination (R2). When ET0_lys > ET0_ref, the differences were only weakly correlated with the selected parameters. Hence, the evaluation of the causal processes leading to underestimation of measured hourly ET0 seems to require a more rigorous approach. On the other hand, when ET0_lys < ET0_ref, the differences correlated considerably with the meteorological parameters and related system parameters. Interpreting the particular correlations in detail indicated different (or varying) surface characteristics between the irrigated lysimeter and the nearby (non-irrigated) meteorological station.

Measurement of volatile organic chemicals at selected sites in California

NASA Technical Reports Server (NTRS)

Singh, Hanwant B.; Salas, L.; Viezee, W.; Sitton, B.; Ferek, R.

1992-01-01

Urban air concentrations of 24 selected volatile organic chemicals that may be potentially hazardous to human health and environment were measured during field experiments conducted at two California locations, at Houston, and at Denver. Chemicals measured included chlorofluorocarbons, halomethanes, haloethanes, halopropanes, chloroethylenes, and aromatic hydrocarbons. With emphasis on California sites, data from these studies are analyzed and interpreted with respect to variabilities in ambient air concentrations, diurnal changes, relation to prevailing meteorology, sources and trends. Except in a few instances, mean concentrations are typically between 0 and 5 ppb. Significant variabilities in atmospheric concentrations associated with intense sources and adverse meteorological conditions are shown to exist. In addition to short-term variability, there is evidence of systematic diurnal and seasonal trends. In some instances it is possible to detect declining trends resulting from the effectiveness of control strategies.

Inherent uncertainties in meteorological parameters for wind turbine design

NASA Technical Reports Server (NTRS)

Doran, J. C.

1982-01-01

Major difficulties associated with meteorological measurments such as the inability to duplicate the experimental conditions from one day to the next are discussed. This lack of consistency is compounded by the stochastic nature of many of the meteorological variables of interest. Moreover, simple relationships derived in one location may be significantly altered by topographical or synoptic differences encountered at another. The effect of such factors is a degree of inherent uncertainty if an attempt is made to describe the atmosphere in terms of universal laws. Some of these uncertainties and their causes are examined, examples are presented and some implications for wind turbine design are suggested.

Meteorological and snow distribution data in the Izas Experimental Catchment (Spanish Pyrenees) from 2011 to 2017

NASA Astrophysics Data System (ADS)

Revuelto, Jesús; Azorin-Molina, Cesar; Alonso-González, Esteban; Sanmiguel-Vallelado, Alba; Navarro-Serrano, Francisco; Rico, Ibai; López-Moreno, Juan Ignacio

2017-12-01

This work describes the snow and meteorological data set available for the Izas Experimental Catchment in the Central Spanish Pyrenees, from the 2011 to 2017 snow seasons. The experimental site is located on the southern side of the Pyrenees between 2000 and 2300 m above sea level, covering an area of 55 ha. The site is a good example of a subalpine environment in which the evolution of snow accumulation and melt are of major importance in many mountain processes. The climatic data set consists of (i) continuous meteorological variables acquired from an automatic weather station (AWS), (ii) detailed information on snow depth distribution collected with a terrestrial laser scanner (TLS, lidar technology) for certain dates across the snow season (between three and six TLS surveys per snow season) and (iii) time-lapse images showing the evolution of the snow-covered area (SCA). The meteorological variables acquired at the AWS are precipitation, air temperature, incoming and reflected solar radiation, infrared surface temperature, relative humidity, wind speed and direction, atmospheric air pressure, surface temperature (snow or soil surface), and soil temperature; all were taken at 10 min intervals. Snow depth distribution was measured during 23 field campaigns using a TLS, and daily information on the SCA was also retrieved from time-lapse photography. The data set (https://doi.org/10.5281/zenodo.848277) is valuable since it provides high-spatial-resolution information on the snow depth and snow cover, which is particularly useful when combined with meteorological variables to simulate snow energy and mass balance. This information has already been analyzed in various scientific studies on snow pack dynamics and its interaction with the local climatology or topographical characteristics. However, the database generated has great potential for understanding other environmental processes from a hydrometeorological or ecological perspective in which snow dynamics play a determinant role.

Apparent relationship between solar-sector boundaries and 300-mb vorticity: Possible explanation in terms of upward propagation of planetary-scale waves

NASA Technical Reports Server (NTRS)

Deland, R. J.

1974-01-01

The selection process for sector structure boundary crossings used in vorticity correlation studies is examined and the possible influence of ascending planetary scale waves is assessed. It is proposed that some of the observed correlations between geomagnetic and meteorological variations may be due to meteorological effects on the geometric variables, rather than due to common solar origin.

Correlation of spring spore concentrations and meteorological conditions in Tulsa, Oklahoma

NASA Astrophysics Data System (ADS)

Troutt, C.; Levetin, E.

Different spore types are abundant in the atmosphere depending on the weather conditions. Ascospores generally follow precipitation, while spore types such as Alternaria and Cladosporium are abundant in dry conditions. This project attempted to correlate fungal spore concentrations with meteorological data from Tulsa, Oklahoma during May 1998 and May 1999. Air samples were collected and analyzed by the 12-traverse method. The spore types included were Cladosporium, Alternaria, Epicoccum, Curvularia, Pithomyces, Drechslera, smut spores, ascospores, basidiospores, and other spores. Weather variables included precipitation levels, temperature, dew point, air pressure, wind speed, wind direction and wind gusts. There were over 242.57 mm of rainfall in May 1999 and only 64.01 mm in May 1998. The most abundant spore types during May 1998 and May 1999 were Cladosporium, ascospores, and basidiospores. Results showed that there were significant differences in the dry-air spora between May 1998 and May 1999. There were twice as many Cladosporium in May 1998 as in May 1999; both ascospores and basidiospores showed little change. Multiple regression analysis was used to determine which meteorological variables influenced spore concentrations. Results showed that there was no single model for all spore types. Different combinations of factors were predictors of concentration for the various fungi examined; however, temperature and dew point seemed to be the most important meteorological factors.

Testing the use of standardised indices and GRACE satellite data to estimate the European 2015 groundwater drought in near-real time

NASA Astrophysics Data System (ADS)

Van Loon, Anne F.; Kumar, Rohini; Mishra, Vimal

2017-04-01

In 2015, central and eastern Europe were affected by a severe drought. This event has recently been studied from meteorological and streamflow perspective, but no analysis of the groundwater situation has been performed. One of the reasons is that real-time groundwater level observations often are not available. In this study, we evaluate two alternative approaches to quantify the 2015 groundwater drought over two regions in southern Germany and eastern Netherlands. The first approach is based on spatially explicit relationships between meteorological conditions and historic groundwater level observations. The second approach uses the Gravity Recovery Climate Experiment (GRACE) terrestrial water storage (TWS) and groundwater anomalies derived from GRACE-TWS and (near-)surface storage simulations by the Global Land Data Assimilation System (GLDAS) models. We combined the monthly groundwater observations from 2040 wells to establish the spatially varying optimal accumulation period between the Standardised Groundwater Index (SGI) and the Standardized Precipitation Evapotranspiration Index (SPEI) at a 0.25° gridded scale. The resulting optimal accumulation periods range between 1 and more than 24 months, indicating strong spatial differences in groundwater response time to meteorological input over the region. Based on the estimated optimal accumulation periods and available meteorological time series, we reconstructed the groundwater anomalies up to 2015 and found that in Germany a uniform severe groundwater drought persisted for several months during this year, whereas the Netherlands appeared to have relatively high groundwater levels. The differences between this event and the 2003 European benchmark drought are striking. The 2003 groundwater drought was less uniformly pronounced, both in the Netherlands and Germany. This is because slowly responding wells (the ones with optimal accumulation periods of more than 12 months) still were above average from the wet year of 2002, which experienced severe flooding in central Europe. GRACE-TWS and GRACE-based groundwater anomalies did not capture the spatial variability of the 2003 and 2015 drought events satisfactorily. GRACE-TWS did show that both 2003 and 2015 were relatively dry, but the differences between Germany and the Netherlands in 2015 and the spatially variable groundwater drought pattern in 2003 were not captured. This could be associated with the coarse spatial scale of GRACE. The simulated groundwater anomalies based on GRACE-TWS deviated considerably from the GRACE-TWS signal and from observed groundwater anomalies. The uncertainty in the GRACE-based groundwater anomalies mainly results from uncertainties in the simulation of soil moisture by the different GLDAS models. The GRACE-based groundwater anomalies are therefore not suitable for use in real-time groundwater drought monitoring in our case study regions. The alternative approach based on the spatially variable relationship between meteorological conditions and groundwater levels is more suitable to quantify groundwater drought in near real-time. Compared to the meteorological drought and streamflow drought (described in previous studies), the groundwater drought of 2015 had a more pronounced spatial variability in its response to meteorological conditions, with some areas primarily influenced by short-term meteorological deficits and others influenced by meteorological deficits accumulated over the preceding 2 years or more. In drought management, this information is very useful and our approach to quantify groundwater drought can be used until real-time groundwater observations become readily available.

Application of WRF/Chem-MADRID and WRF/Polyphemus in Europe - Part 1: Model description and evaluation of meteorological predictions

NASA Astrophysics Data System (ADS)

Zhang, Y.; Sartelet, K.; Wu, S.-Y.; Seigneur, C.

2013-02-01

Comprehensive model evaluation and comparison of two 3-D air quality modeling systems (i.e. the Weather Research and Forecast model (WRF)/Polyphemus and WRF with chemistry and the Model of Aerosol Dynamics, Reaction, Ionization, and Dissolution (MADRID) (WRF/Chem-MADRID) are conducted over western Europe. Part 1 describes the background information for the model comparison and simulation design, as well as the application of WRF for January and July 2001 over triple-nested domains in western Europe at three horizontal grid resolutions: 0.5°, 0.125°, and 0.025°. Six simulated meteorological variables (i.e. temperature at 2 m (T2), specific humidity at 2 m (Q2), relative humidity at 2 m (RH2), wind speed at 10 m (WS10), wind direction at 10 m (WD10), and precipitation (Precip)) are evaluated using available observations in terms of spatial distribution, domainwide daily and site-specific hourly variations, and domainwide performance statistics. WRF demonstrates its capability in capturing diurnal/seasonal variations and spatial gradients of major meteorological variables. While the domainwide performance of T2, Q2, RH2, and WD10 at all three grid resolutions is satisfactory overall, large positive or negative biases occur in WS10 and Precip even at 0.025°. In addition, discrepancies between simulations and observations exist in T2, Q2, WS10, and Precip at mountain/high altitude sites and large urban center sites in both months, in particular, during snow events or thunderstorms. These results indicate the model's difficulty in capturing meteorological variables in complex terrain and subgrid-scale meteorological phenomena, due to inaccuracies in model initialization parameterization (e.g. lack of soil temperature and moisture nudging), limitations in the physical parameterizations of the planetary boundary layer (e.g. cloud microphysics, cumulus parameterizations, and ice nucleation treatments) as well as limitations in surface heat and moisture budget parameterizations (e.g. snow-related processes, subgrid-scale surface roughness elements, and urban canopy/heat island treatments and CO2 domes). While the use of finer grid resolutions of 0.125° and 0.025° shows some improvement for WS10, Precip, and some mesoscale events (e.g. strong forced convection and heavy precipitation), it does not significantly improve the overall statistical performance for all meteorological variables except for Precip. These results indicate a need to further improve the model representations of the above parameterizations at all scales.

Variability of Springtime Transpacific Pollution Transport During 2000-2006: The INTEX-5 Mission in the Context of Previous Years

NASA Technical Reports Server (NTRS)

Pfister, G. G.; Emmons, L. K.; Edwards, D. P.; Arellano, A.; Sachse, G.; Campos, T.

2010-01-01

We analyze the transport of pollution across the Pacific during the NASA INTEX-B (Intercontinental Chemical Transport Experiment Part 8) campaign in spring 2006 and examine how this year compares to the time period for 2000 through 2006. In addition to aircraft measurements of carbon monoxide (CO) collected during INTEX-B, we include in this study multi-year satellite retrievals of CO from the Measurements of Pollution in the Troposphere (MOPITT) instrument and simulations from the chemistry transport model MOZART-4. Model tracers are used to examine the contributions of different source regions and source types to pollution levels over the Pacific. Additional modeling studies are performed to separate the impacts of inter-annual variability in meteorology and .dynamics from changes in source strength. interannual variability in the tropospheric CO burden over the Pacific and the US as estimated from the MOPITT data range up to 7% and a somewhat smaller estimate (5%) is derived from the model. When keeping the emissions in the model constant between years, the year-to-year changes are reduced (2%), but show that in addition to changes in emissions, variable meteorological conditions also impact transpacific pollution transport. We estimate that about 113 of the variability in the tropospheric CO loading over the contiguous US is explained by changes in emissions and about 213 by changes in meteorology and transport. Biomass burning sources are found to be a larger driver for inter-annual variability in the CO loading compared to fossil and biofuel sources or photochemical CO production even though their absolute contributions are smaller. Source contribution analysis shows that the aircraft sampling during INTEX-B was fairly representative of the larger scale region, but with a slight bias towards higher influence from Asian contributions.

Preliminary study on the time-related changes of the infrared thermal images of the human body

NASA Astrophysics Data System (ADS)

Li, Ziru; Zhang, Xusheng; Lin, Gang; Chen, Zhigang

2009-08-01

It is of great importance to study the manifestations and the influencing factors of the time-related changes of infrared thermal images (ITI) of human body since the variable body surface temperature distribution seriously affected the application of ITI in medicine. In this paper, manifestations of time-related changes of the ITI of human body from three double-blind randomized trials and their correlation with meteorological factors (e.g. temperature, pressure, humidity, cold front passage and tropical cyclone landing) were studied. The trials were placebo or drug controlled studying the influences of Chinese medicine health food (including Shengsheng capsule with immunity adjustment function, Shengan capsule with sleep improvement function and Shengyi capsule with the function of helping to decrease serum lipid) on the ITI of human body. In the first thirty-six days of the trials images were scanned every six days and image data in the seven observation time spots (including the 0, 6, 12, 18, 24, 30, 36 day of the trial) were used for the time-related study. For every subject the scanned time was fixed in the day within two hours. The ITI features which could reflect the functions of the health foods were studied. The indexes of the features were relative magnitude (temperature difference between the viewing area and the reference area). Results showed that the variation tendencies of the trial group and control group were basically the same in placebo controlled trials and some of the long-term effects of Chinese medicine health food could be reflected significantly in certain time spots in the first thirty-six days. Time-related changes of the ITI of human body were closely related with meteorological factors but there were other influencing factors still need to be studied. As the ITI of human body could reflect the influences of Chinese medicine health foods and are closely related with meteorology, there are bright prospects for the application of ITI in health monitor.

Comparison of meteorological forcing (WFDEI, AGRI4CAST) to in-situ observations in a semi arid catchment. The case of Merguellil in Tunisia.

NASA Astrophysics Data System (ADS)

Le Page, Michel; Gosset, Cindy; Oueslati, Ines; Calvez, Roger; Zribi, Mehrez; Lilli Chabaane, Zohra

2015-04-01

Meteorological forcing is essential to hydrological and hydro-geological modeling. In the case of the semi-arid catchment of Merguellil in Tunisia, long term time series are only available in the plain for a SYNOP station. Other meteorological stations have been installed since 2010. Therefore, this study aims at qualifying the reliability of the meteorological forcing necessary for an integrated model conception. We compare the meteorological data from 7 stations (sources: WMO and our own station), inside and around the Merguellil catchment, with daily gridded data at 25*25 km from AGRI4CAST and 50*50km from WFDEI. AGRI4CAST (Biaveti et al, 2008) is an interpolated dataset based on actual weather stations produced by the Joint Research Centre (JRC) for the Monitoring Agricultural Resources Unit (MARS). The WFDEI second version dataset (Weedon et al, 2014) has been generated using the same methodology as the widely used WATCH Forcing Data (WFD) by making use of the ERA-Interim reanalysis data. The studied meteorological variables are Rs, Tmoy, U2, P, RH and ET0, with the scores RMSE, bias and R pearson. Regarding the AGRI4CAST dataset, the scores are established over different periods according to variables based on stepping between the observed and interpolated data. The scores show good correlations between the observed temperatures, but with a spatial variability bound to the stations elevations. The moderate and interpolated radiations also show a good concordance indicating a good reliability. The R pearson score obtained for the values of relative humidity show a good correlation between the observations and the interpolations, however, the short periods of comparisons do not allow obtaining significant information and the RMSE and bias are important. Wind speed has an important negative bias for a majority of stations (positively for only one). Only one station shows concordances between the data. The study of the data indicates that we shall have to adjust the wind speeds and the relative humidity of the air for the implementation of a model. Finally the reference evapotranspiration seems relatively coherent, in spite of the dispersal observed during the meteorological measures, but with biases rather high and RMSE also rather high (> 1.3 mm). After revised the parameter U2 and RH, AGRI4CAST can possibly be corrected by ancillary ground stations. The analysis of the WFDEI dataset is currently under evaluation. (1) Biavetti, I., Karetsos, S., Ceglar, A., Toreti, A., Panagos P. (2014), European meteorological data: contribution to research, development and policy support, Proc. of SPIE Vol. 9229 922907-1 (2) Weedon, G. P., G. Balsamo, N. Bellouin, S. Gomes, M. J. Best, and P. Viterbo (2014), The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA-Interim reanalysis data, Water Resour. Res., 50, 7505-7514, doi:10.1002/ 2014WR015638.

Determining effective forecast horizons for multi-purpose reservoirs with short- and long-term operating objectives

NASA Astrophysics Data System (ADS)

Luchner, Jakob; Anghileri, Daniela; Castelletti, Andrea

2017-04-01

Real-time control of multi-purpose reservoirs can benefit significantly from hydro-meteorological forecast products. Because of their reliability, the most used forecasts range on time scales from hours to few days and are suitable for short-term operation targets such as flood control. In recent years, hydro-meteorological forecasts have become more accurate and reliable on longer time scales, which are more relevant to long-term reservoir operation targets such as water supply. While the forecast quality of such products has been studied extensively, the forecast value, i.e. the operational effectiveness of using forecasts to support water management, has been only relatively explored. It is comparatively easy to identify the most effective forecasting information needed to design reservoir operation rules for flood control but it is not straightforward to identify which forecast variable and lead time is needed to define effective hedging rules for operational targets with slow dynamics such as water supply. The task is even more complex when multiple targets, with diverse slow and fast dynamics, are considered at the same time. In these cases, the relative importance of different pieces of information, e.g. magnitude and timing of peak flow rate and accumulated inflow on different time lags, may vary depending on the season or the hydrological conditions. In this work, we analyze the relationship between operational forecast value and streamflow forecast horizon for different multi-purpose reservoir trade-offs. We use the Information Selection and Assessment (ISA) framework to identify the most effective forecast variables and horizons for informing multi-objective reservoir operation over short- and long-term temporal scales. The ISA framework is an automatic iterative procedure to discriminate the information with the highest potential to improve multi-objective reservoir operating performance. Forecast variables and horizons are selected using a feature selection technique. The technique determines the most informative combination in a multi-variate regression model to the optimal reservoir releases based on perfect information at a fixed objective trade-off. The improved reservoir operation is evaluated against optimal reservoir operation conditioned upon perfect information on future disturbances and basic reservoir operation using only the day of the year and the reservoir level. Different objective trade-offs are selected for analyzing resulting differences in improved reservoir operation and selected forecast variables and horizons. For comparison, the effective streamflow forecast horizon determined by the ISA framework is benchmarked against the performances obtained with a deterministic model predictive control (MPC) optimization scheme. Both the ISA framework and the MPC optimization scheme are applied to the real-world case study of Lake Como, Italy, using perfect streamflow forecast information. The principal operation targets for Lake Como are flood control and downstream water supply which makes its operation a suitable case study. Results provide critical feedback to reservoir operators on the use of long-term streamflow forecasts and to the hydro-meteorological forecasting community with respect to the forecast horizon needed from reliable streamflow forecasts.

“ How Reliable is the Couple of WRF & VIC Models”

EPA Science Inventory

The ability of the fully coupling of Weather Research & Forecasting Model (WRF) and Variable Infiltration Capacity (VIC) model to produce hydrological and climate variables was evaluated. First, the VIC model was run by using observed meteorological data and calibrated in the Upp...

How useful are meteorological drought indicators to assess agricultural drought impacts across Europe?

NASA Astrophysics Data System (ADS)

Bachmair, Sophie; Tanguy, Maliko; Hannaford, Jamie; Stahl, Kerstin

2016-04-01

Drought monitoring and early warning (M&EW) is an important component of agricultural and silvicultural risk management. Meteorological indicators such as the Standardized Precipitation Index (SPI) are widely used in operational M&EW systems and for drought hazard assessment. Meteorological drought yet does not necessarily equate to agricultural drought given differences in drought susceptibility, e.g. crop-specific vulnerability, soil water holding capacity, irrigation and other management practices. How useful are meteorological indicators such as SPI to assess agricultural drought? Would the inclusion of vegetation indicators into drought M&EW systems add value for the agricultural sector? To answer these questions, it is necessary to investigate the link between meteorological indicators and agricultural impacts of drought. Crop yield or loss data is one source of information for drought impacts, yet mostly available as aggregated data at the annual scale. Remotely sensed vegetation stress data offer another possibility to directly assess agricultural impacts with high spatial and temporal resolution and are already used by some M&EW systems. At the same time, reduced crop yield and satellite-based vegetation stress potentially suffer from multi-causality. The aim of this study is therefore to investigate the relation between meteorological drought indicators and agricultural drought impacts for Europe, and to intercompare different agricultural impact variables. As drought indicators we used SPI and the Standardized Precipitation Evaporation Index (SPEI) for different accumulation periods. The focus regarding drought impact variables was on remotely sensed vegetation stress derived from MODIS NDVI (Normalized Difference Vegetation Index) and LST (Land Surface Temperature) data, but the analysis was complemented with crop yield data and text-based information from the European Drought Impact report Inventory (EDII) for selected countries. A correlation analysis between meteorological drought indicators and remotely sensed vegetation stress at the EU NUTS3 region level revealed a high correlation between the two types of indicators for many regions; however some spatial variability was observed in (i) strength of correlation, (ii) performance of SPI versus SPEI, and (iii) best linked SPI/SPEI time scale. We additionally explored whether geographic properties like climate, soil texture, land use, and location explain the observed spatial patterns. Our study revealed that climatically dryer areas (water limited) showed high correlations between SPI/SPEI and vegetation stress, whereas the wettest parts of Europe (radiation limited regions) showed negative correlations especially for short accumulation periods, suggesting that for these regions, short droughts could actually be beneficial for vegetation growth. These findings suggest that relying solely on meteorological indicators for agricultural risk assessment in some regions might be inadequate. Overall, such information may help to tailor agricultural drought M&EW systems to specific regions.

Exploring objective climate classification for the Himalayan arc and adjacent regions using gridded data sources

NASA Astrophysics Data System (ADS)

Forsythe, N.; Blenkinsop, S.; Fowler, H. J.

2015-05-01

A three-step climate classification was applied to a spatial domain covering the Himalayan arc and adjacent plains regions using input data from four global meteorological reanalyses. Input variables were selected based on an understanding of the climatic drivers of regional water resource variability and crop yields. Principal component analysis (PCA) of those variables and k-means clustering on the PCA outputs revealed a reanalysis ensemble consensus for eight macro-climate zones. Spatial statistics of input variables for each zone revealed consistent, distinct climatologies. This climate classification approach has potential for enhancing assessment of climatic influences on water resources and food security as well as for characterising the skill and bias of gridded data sets, both meteorological reanalyses and climate models, for reproducing subregional climatologies. Through their spatial descriptors (area, geographic centroid, elevation mean range), climate classifications also provide metrics, beyond simple changes in individual variables, with which to assess the magnitude of projected climate change. Such sophisticated metrics are of particular interest for regions, including mountainous areas, where natural and anthropogenic systems are expected to be sensitive to incremental climate shifts.

Temporal abundance of Aedes aegypti in Manaus, Brazil, measured by two trap types for adult mosquitoes

PubMed Central

Degener, Carolin Marlen; de Ázara, Tatiana Mingote Ferreira; Roque, Rosemary Aparecida; Codeço, Cláudia Torres; Nobre, Aline Araújo; Ohly, Jörg Johannes; Geier, Martin; Eiras, Álvaro Eduardo

2014-01-01

A longitudinal study was conducted in Manaus, Brazil, to monitor changes of adult Aedes aegypti (L.) abundance. The objectives were to compare mosquito collections of two trap types, to characterise temporal changes of the mosquito population, to investigate the influence of meteorological variables on mosquito collections and to analyse the association between mosquito collections and dengue incidence. Mosquito monitoring was performed fortnightly using MosquiTRAPs (MQT) and BG-Sentinel (BGS) traps between December 2008-June 2010. The two traps revealed opposing temporal infestation patterns, with highest mosquito collections of MQTs during the dry season and highest collections of BGS during the rainy seasons. Several meteorological variables were significant predictors of mosquito collections in the BGS. The best predictor was the relative humidity, lagged two weeks (in a positive relationship). For MQT, only the number of rainy days in the previous week was significant (in a negative relationship). The correlation between monthly dengue incidence and mosquito abundance in BGS and MQT was moderately positive and negative, respectively. Catches of BGS traps reflected better the dynamic of dengue incidence. The findings help to understand the effects of meteorological variables on mosquito infestation indices of two different traps for adult dengue vectors in Manaus. PMID:25494470

Simulating and explaining passive air sampling rates for semi-volatile compounds on polyurethane foam passive samplers

PubMed Central

Petrich, Nicholas T.; Spak, Scott N.; Carmichael, Gregory R.; Hu, Dingfei; Martinez, Andres; Hornbuckle, Keri C.

2013-01-01

Passive air samplers (PAS) including polyurethane foam (PUF) are widely deployed as an inexpensive and practical way to sample semi-volatile pollutants. However, concentration estimates from PAS rely on constant empirical mass transfer rates, which add unquantified uncertainties to concentrations. Here we present a method for modeling hourly sampling rates for semi-volatile compounds from hourly meteorology using first-principle chemistry, physics, and fluid dynamics, calibrated from depuration experiments. This approach quantifies and explains observed effects of meteorology on variability in compound-specific sampling rates and analyte concentrations; simulates nonlinear PUF uptake; and recovers synthetic hourly concentrations at a reference temperature. Sampling rates are evaluated for polychlorinated biphenyl congeners at a network of Harner model samplers in Chicago, Illinois during 2008, finding simulated average sampling rates within analytical uncertainty of those determined from loss of depuration compounds, and confirming quasi-linear uptake. Results indicate hourly, daily and interannual variability in sampling rates, sensitivity to temporal resolution in meteorology, and predictable volatility-based relationships between congeners. We quantify importance of each simulated process to sampling rates and mass transfer and assess uncertainty contributed by advection, molecular diffusion, volatilization, and flow regime within the PAS, finding PAS chamber temperature contributes the greatest variability to total process uncertainty (7.3%). PMID:23837599

Integrating uncertainty propagation in GNSS radio occultation retrieval: from excess phase to atmospheric bending angle profiles

NASA Astrophysics Data System (ADS)

Schwarz, Jakob; Kirchengast, Gottfried; Schwaerz, Marc

2018-05-01

Global Navigation Satellite System (GNSS) radio occultation (RO) observations are highly accurate, long-term stable data sets and are globally available as a continuous record from 2001. Essential climate variables for the thermodynamic state of the free atmosphere - such as pressure, temperature, and tropospheric water vapor profiles (involving background information) - can be derived from these records, which therefore have the potential to serve as climate benchmark data. However, to exploit this potential, atmospheric profile retrievals need to be very accurate and the remaining uncertainties quantified and traced throughout the retrieval chain from raw observations to essential climate variables. The new Reference Occultation Processing System (rOPS) at the Wegener Center aims to deliver such an accurate RO retrieval chain with integrated uncertainty propagation. Here we introduce and demonstrate the algorithms implemented in the rOPS for uncertainty propagation from excess phase to atmospheric bending angle profiles, for estimated systematic and random uncertainties, including vertical error correlations and resolution estimates. We estimated systematic uncertainty profiles with the same operators as used for the basic state profiles retrieval. The random uncertainty is traced through covariance propagation and validated using Monte Carlo ensemble methods. The algorithm performance is demonstrated using test day ensembles of simulated data as well as real RO event data from the satellite missions CHAllenging Minisatellite Payload (CHAMP); Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC); and Meteorological Operational Satellite A (MetOp). The results of the Monte Carlo validation show that our covariance propagation delivers correct uncertainty quantification from excess phase to bending angle profiles. The results from the real RO event ensembles demonstrate that the new uncertainty estimation chain performs robustly. Together with the other parts of the rOPS processing chain this part is thus ready to provide integrated uncertainty propagation through the whole RO retrieval chain for the benefit of climate monitoring and other applications.

An Investigation of Turbulent Heat Exchange in the Subtropics

DTIC Science & Technology

2014-09-30

meteorological sensors aboard the research vessel the R/V Revelle during the DYNAMO field program. In situ meteorology and high-rate flux sensors operated...continuously while in the sampling period for DYNAMO Leg 3. This included all sensors operating during Leg 2 with the addition of a closed-path LI...stress; wave data; surface and near surface sea temperatures, salinity and currents; and other key variables specifically requested by DYNAMO /LASP PIs

Spatial and temporal variability of reference evapotranspiration and influenced meteorological factors in the Jialing River Basin, China

NASA Astrophysics Data System (ADS)

Herath, Imali Kaushalya; Ye, Xuchun; Wang, Jianli; Bouraima, Abdel-Kabirou

2018-02-01

Reference evapotranspiration (ETr) is one of the important parameters in the hydrological cycle. The spatio-temporal variation of ETr and other meteorological parameters that influence ETr were investigated in the Jialing River Basin (JRB), China. The ETr was estimated using the CROPWAT 8.0 computer model based on the Penman-Montieth equation for the period 1964-2014. Mean temperature (MT), relative humidity (RH), sunshine duration (SD), and wind speed (WS) were the main input parameters of CROPWAT while 12 meteorological stations were evaluated. Linear regression and Mann-Kendall methods were applied to study the spatio-temporal trends while the inverse distance weighted (IDW) method was used to identify the spatial distribution of ETr. Stepwise regression and partial correlation methods were used to identify the meteorological variables that most significantly influenced the changes in ETr. The highest annual ETr was found in the northern part of the basin, whereas the lowest rate was recorded in the western part. In the autumn, the highest ETr was recorded in the southeast part of JRB. The annual ETr reflected neither significant increasing nor decreasing trends. Except for the summer, ETr is slightly increasing in other seasons. The MT significantly increased whereas SD and RH were significantly decreased during the 50-year period. Partial correlation and stepwise regression methods found that the impact of meteorological parameters on ETr varies on an annual and seasonal basis while SD, MT, and RH contributed to the changes of annual and seasonal ETr in the JRB.

Geographical, meteorological and vectorial factors related to malaria re-emergence in Huang-Huai River of central China.

PubMed

Zhou, Shui S; Huang, Fang; Wang, Jian J; Zhang, Shao S; Su, Yun P; Tang, Lin H

2010-11-24

Malaria still represents a significant public health problem in China, and the cases dramatically increased in the areas along the Huang-Huai River of central China after 2001. Considering spatial aggregation of malaria cases and specific vectors, the geographical, meteorological and vectorial factors were analysed to determine the key factors related to malaria re-emergence in these particular areas. The geographic information of 357 malaria cases and 603 water bodies in 113 villages were collected to analyse the relationship between the residence of malaria cases and water body. Spearman rank correlation, multiple regression, curve fitting and trend analysis were used to explain the relationship between the meteorological factors and malaria incidence. Entomological investigation was conducted in two sites to get the vectorial capacity and the basic reproductive rate to determine whether the effect of vector lead to malaria re-emergence. The distances from household of cases to the nearest water-body was positive-skew distributed, the median was 60.9 m and 74% malaria cases were inhabited in the extent of 60 m near the water body, and the risk rate of people live there attacked by malaria was higher than others(OR = 1.6, 95%CI (1.042, 2.463), P < 0.05). The annual average temperature and rainfall may have close relationship with annual incidence. The average monthly temperature and rainfall were the key factors, and the correlation coefficients are 0.501 and 0.304(P < 0.01), respectively. Moreover, 75.3% changes of monthly malaria incidence contributed to the average monthly temperature (T(mean)), the average temperature of last two months(T(mean₀₁)) and the average rainfall of current month (R(mean)) and the regression equation was Y = -2.085 + 0.839I₁ + 0.998T(mean₀) - 0.86T(mean₀₁) + 0.16R(mean₀). All the collected mosquitoes were Anopheles sinensis. The vectorial capacity and the basic reproductive rate of An. sinensis in two sites were 0.6969, 0.4983 and 2.1604, 1.5447, respectively. The spatial distribution between malaria cases and water-body, the changing of meteorological factors, and increasing vectorial capacity and basic reproductive rate of An. sinensis leaded to malaria re-emergence in these areas.

Geographical, meteorological and vectorial factors related to malaria re-emergence in Huang-Huai River of central China

PubMed Central

2010-01-01

Background Malaria still represents a significant public health problem in China, and the cases dramatically increased in the areas along the Huang-Huai River of central China after 2001. Considering spatial aggregation of malaria cases and specific vectors, the geographical, meteorological and vectorial factors were analysed to determine the key factors related to malaria re-emergence in these particular areas. Methods The geographic information of 357 malaria cases and 603 water bodies in 113 villages were collected to analyse the relationship between the residence of malaria cases and water body. Spearman rank correlation, multiple regression, curve fitting and trend analysis were used to explain the relationship between the meteorological factors and malaria incidence. Entomological investigation was conducted in two sites to get the vectorial capacity and the basic reproductive rate to determine whether the effect of vector lead to malaria re-emergence. Results The distances from household of cases to the nearest water-body was positive-skew distributed, the median was 60.9 m and 74% malaria cases were inhabited in the extent of 60 m near the water body, and the risk rate of people live there attacked by malaria was higher than others(OR = 1.6, 95%CI (1.042, 2.463), P < 0.05). The annual average temperature and rainfall may have close relationship with annual incidence. The average monthly temperature and rainfall were the key factors, and the correlation coefficients are 0.501 and 0.304(P < 0.01), respectively. Moreover, 75.3% changes of monthly malaria incidence contributed to the average monthly temperature (Tmean), the average temperature of last two months(Tmean01) and the average rainfall of current month (Rmean) and the regression equation was Y = -2.085 + 0.839I1 + 0.998Tmean0 - 0.86Tmean01 + 0.16Rmean0. All the collected mosquitoes were Anopheles sinensis. The vectorial capacity and the basic reproductive rate of An. sinensis in two sites were 0.6969, 0.4983 and 2.1604, 1.5447, respectively. Conclusion The spatial distribution between malaria cases and water-body, the changing of meteorological factors, and increasing vectorial capacity and basic reproductive rate of An. sinensis leaded to malaria re-emergence in these areas. PMID:21092326

Comparison of short-term associations with meteorological variables between COPD and pneumonia hospitalization among the elderly in Hong Kong-a time-series study.

PubMed

Lam, Holly Ching-Yu; Chan, Emily Ying-Yang; Goggins, William Bernard

2018-05-05

Pneumonia and chronic obstructive pulmonary diseases (COPD) are the commonest causes of respiratory hospitalization among older adults. Both diseases have been reported to be associated with ambient temperature, but the associations have not been compared between the diseases. Their associations with other meteorological variables have also not been well studied. This study aimed to evaluate the associations between meteorological variables, pneumonia, and COPD hospitalization among adults over 60 and to compare these associations between the diseases. Daily cause-specific hospitalization counts in Hong Kong during 2004-2011 were regressed on daily meteorological variables using distributed lag nonlinear models. Associations were compared between diseases by ratio of relative risks. Analyses were stratified by season and age group (60-74 vs. ≥ 75). In hot season, high temperature (> 28 °C) and high relative humidity (> 82%) were statistically significantly associated with more pneumonia in lagged 0-2 and lagged 0-10 days, respectively. Pneumonia hospitalizations among the elderly (≥ 75) also increased with high solar radiation and high wind speed. During the cold season, consistent hockey-stick associations with temperature and relative humidity were found for both admissions and both age groups. The minimum morbidity temperature and relative humidity were at about 21-22 °C and 82%. The lagged effects of low temperature were comparable for both diseases (lagged 0-20 days). The low-temperature-admissions associations with COPD were stronger and were strongest among the elderly. This study found elevated pneumonia and COPD admissions risks among adults ≥ 60 during periods of extreme weather conditions, and the associations varied by season and age group. Vulnerable groups should be advised to avoid exposures, such as staying indoor and maintaining satisfactory indoor conditions, to minimize risks.

The influence of meteorological conditions on the progress and dynamics of pollen phenophases of selected species.

NASA Astrophysics Data System (ADS)

Jatczak, K.; Linkowska, J.; Rapiejko, P.

2010-09-01

In Poland phenological data is used mainly as a natural indicator of the influence of climate changes on environment. In relation to the growing interest of phenology in scientific research, we substantially extended observation ranges, concentrating mainly on phenophases of selected species that are important for allergology. Phenological data application in complex analysis together with meteorological and aerobiological data, give an opportunity for drawing conclusions on variability of the starting date of pollen season and its dynamics in a meteorological aspect. Species have their regional phenological characteristics, however the characteristics depends on meteorological conditions in a particular year. Therefore, the calculation of pheno-meteorological parameters is important for pollen release prediction. Availability of phenological database can also be useful in the field of preventive health care, through phenological data application in different atmospheric models (NWP models, phenological models, pollen release models) for numerical forecasting of pollen concentration in the air. Genetic conditions, industrial development, increase of air pollution are regarded as the main determinants of allergic diseases. The results of pheno - aero- meteorological analysis enable the estimation of the influence of natural environmental changes on the increasing prevalence of allergic diseases in Poland.

Early meteorological records from Latin-America and the Caribbean during the 18th and 19th centuries

NASA Astrophysics Data System (ADS)

Domínguez-Castro, Fernando; Vaquero, José Manuel; Gallego, María Cruz; Farrona, Ana María Marín; Antuña-Marrero, Juan Carlos; Cevallos, Erika Elizabeth; Herrera, Ricardo García; de La Guía, Cristina; Mejía, Raúl David; Naranjo, José Manuel; Del Rosario Prieto, María; Ramos Guadalupe, Luis Enrique; Seiner, Lizardo; Trigo, Ricardo Machado; Villacís, Marcos

2017-11-01

This paper provides early instrumental data recovered for 20 countries of Latin-America and the Caribbean (Argentina, Bahamas, Belize, Brazil, British Guiana, Chile, Colombia, Costa Rica, Cuba, Ecuador, France (Martinique and Guadalupe), Guatemala, Jamaica, Mexico, Nicaragua, Panama, Peru, Puerto Rico, El Salvador and Suriname) during the 18th and 19th centuries. The main meteorological variables retrieved were air temperature, atmospheric pressure, and precipitation, but other variables, such as humidity, wind direction, and state of the sky were retrieved when possible. In total, more than 300,000 early instrumental data were rescued (96% with daily resolution). Especial effort was made to document all the available metadata in order to allow further post-processing. The compilation is far from being exhaustive, but the dataset will contribute to a better understanding of climate variability in the region, and to enlarging the period of overlap between instrumental data and natural/documentary proxies.

Early meteorological records from Latin-America and the Caribbean during the 18th and 19th centuries.

PubMed

Domínguez-Castro, Fernando; Vaquero, José Manuel; Gallego, María Cruz; Farrona, Ana María Marín; Antuña-Marrero, Juan Carlos; Cevallos, Erika Elizabeth; Herrera, Ricardo García; de la Guía, Cristina; Mejía, Raúl David; Naranjo, José Manuel; Del Rosario Prieto, María; Ramos Guadalupe, Luis Enrique; Seiner, Lizardo; Trigo, Ricardo Machado; Villacís, Marcos

2017-11-14

This paper provides early instrumental data recovered for 20 countries of Latin-America and the Caribbean (Argentina, Bahamas, Belize, Brazil, British Guiana, Chile, Colombia, Costa Rica, Cuba, Ecuador, France (Martinique and Guadalupe), Guatemala, Jamaica, Mexico, Nicaragua, Panama, Peru, Puerto Rico, El Salvador and Suriname) during the 18th and 19th centuries. The main meteorological variables retrieved were air temperature, atmospheric pressure, and precipitation, but other variables, such as humidity, wind direction, and state of the sky were retrieved when possible. In total, more than 300,000 early instrumental data were rescued (96% with daily resolution). Especial effort was made to document all the available metadata in order to allow further post-processing. The compilation is far from being exhaustive, but the dataset will contribute to a better understanding of climate variability in the region, and to enlarging the period of overlap between instrumental data and natural/documentary proxies.

Increasing Juniperus virginiana L. pollen in the Tulsa atmosphere: long-term trends, variability, and influence of meteorological conditions

NASA Astrophysics Data System (ADS)

Flonard, Michaela; Lo, Esther; Levetin, Estelle

2018-02-01

In the Tulsa area, the Cupressaceae is largely represented by eastern red cedar ( Juniperus virginiana L.). The encroachment of this species into the grasslands of Oklahoma has been well documented, and it is believed this trend will continue. The pollen is known to be allergenic and is a major component of the Tulsa atmosphere in February and March. This study examined airborne Cupressaceae pollen data from 1987 to 2016 to determine long-term trends, pollen seasonal variability, and influence of meteorological variables on airborne pollen concentrations. Pollen was collected through means of a Burkard sampler and analyzed with microscopy. Daily pollen concentrations and yearly pollen metrics showed a high degree of variability. In addition, there were significant increases over time in the seasonal pollen index and in peak concentrations. These increases parallel the increasing population of J. virginiana in the region. Pollen data were split into pre- and post-peak categories for statistical analyses, which revealed significant differences in correlations of the two datasets when analyzed with meteorological conditions. While temperature and dew point, among others were significant in both datasets, other factors, like relative humidity, were significant only in one dataset. Analyses using wind direction showed that southerly and southwestern winds contributed to increased pollen concentrations. This study confirms that J. virginiana pollen has become an increasing risk for individuals sensitive to this pollen and emphasizes the need for long-term aerobiological monitoring in other areas.

Prediction of ozone concentration in tropospheric levels using artificial neural networks and support vector machine at Rio de Janeiro, Brazil

NASA Astrophysics Data System (ADS)

Luna, A. S.; Paredes, M. L. L.; de Oliveira, G. C. G.; Corrêa, S. M.

2014-12-01

It is well known that air quality is a complex function of emissions, meteorology and topography, and statistical tools provide a sound framework for relating these variables. The observed data were contents of nitrogen dioxide (NO2), nitrogen monoxide (NO), nitrogen oxides (NOx), carbon monoxide (CO), ozone (O3), scalar wind speed (SWS), global solar radiation (GSR), temperature (TEM), moisture content in the air (HUM), collected by a mobile automatic monitoring station at Rio de Janeiro City in two places of the metropolitan area during 2011 and 2012. The aims of this study were: (1) to analyze the behavior of the variables, using the method of PCA for exploratory data analysis; (2) to propose forecasts of O3 levels from primary pollutants and meteorological factors, using nonlinear regression methods like ANN and SVM, from primary pollutants and meteorological factors. The PCA technique showed that for first dataset, variables NO, NOx and SWS have a greater impact on the concentration of O3 and the other data set had the TEM and GSR as the most influential variables. The obtained results from the nonlinear regression techniques ANN and SVM were remarkably closely and acceptable to one dataset presenting coefficient of determination for validation respectively 0.9122 and 0.9152, and root mean square error of 7.66 and 7.85, respectively. For these datasets, the PCA, SVM and ANN had demonstrated their robustness as useful tools for evaluation, and forecast scenarios for air quality.

Impact of meteorological factors on the incidence of bacillary dysentery in Beijing, China: A time series analysis (1970-2012).

PubMed

Yan, Long; Wang, Hong; Zhang, Xuan; Li, Ming-Yue; He, Juan

2017-01-01

Influence of meteorological variables on the transmission of bacillary dysentery (BD) is under investigated topic and effective forecasting models as public health tool are lacking. This paper aimed to quantify the relationship between meteorological variables and BD cases in Beijing and to establish an effective forecasting model. A time series analysis was conducted in the Beijing area based upon monthly data on weather variables (i.e. temperature, rainfall, relative humidity, vapor pressure, and wind speed) and on the number of BD cases during the period 1970-2012. Autoregressive integrated moving average models with explanatory variables (ARIMAX) were built based on the data from 1970 to 2004. Prediction of monthly BD cases from 2005 to 2012 was made using the established models. The prediction accuracy was evaluated by the mean square error (MSE). Firstly, temperature with 2-month and 7-month lags and rainfall with 12-month lag were found positively correlated with the number of BD cases in Beijing. Secondly, ARIMAX model with covariates of temperature with 7-month lag (β = 0.021, 95% confidence interval(CI): 0.004-0.038) and rainfall with 12-month lag (β = 0.023, 95% CI: 0.009-0.037) displayed the highest prediction accuracy. The ARIMAX model developed in this study showed an accurate goodness of fit and precise prediction accuracy in the short term, which would be beneficial for government departments to take early public health measures to prevent and control possible BD popularity.

Decadal evaluation of regional climate, air quality, and their interactions over the continental US and their interactions using WRF/Chem version 3.6.1

NASA Astrophysics Data System (ADS)

Yahya, Khairunnisa; Wang, Kai; Campbell, Patrick; Glotfelty, Timothy; He, Jian; Zhang, Yang

2016-02-01

The Weather Research and Forecasting model with Chemistry (WRF/Chem) v3.6.1 with the Carbon Bond 2005 (CB05) gas-phase mechanism is evaluated for its first decadal application during 2001-2010 using the Representative Concentration Pathway 8.5 (RCP 8.5) emissions to assess its capability and appropriateness for long-term climatological simulations. The initial and boundary conditions are downscaled from the modified Community Earth System Model/Community Atmosphere Model (CESM/CAM5) v1.2.2. The meteorological initial and boundary conditions are bias-corrected using the National Center for Environmental Protection's Final (FNL) Operational Global Analysis data. Climatological evaluations are carried out for meteorological, chemical, and aerosol-cloud-radiation variables against data from surface networks and satellite retrievals. The model performs very well for the 2 m temperature (T2) for the 10-year period, with only a small cold bias of -0.3 °C. Biases in other meteorological variables including relative humidity at 2 m, wind speed at 10 m, and precipitation tend to be site- and season-specific; however, with the exception of T2, consistent annual biases exist for most of the years from 2001 to 2010. Ozone mixing ratios are slightly overpredicted at both urban and rural locations with a normalized mean bias (NMB) of 9.7 % but underpredicted at rural locations with an NMB of -8.8 %. PM2.5 concentrations are moderately overpredicted with an NMB of 23.3 % at rural sites but slightly underpredicted with an NMB of -10.8 % at urban/suburban sites. In general, the model performs relatively well for chemical and meteorological variables, and not as well for aerosol-cloud-radiation variables. Cloud-aerosol variables including aerosol optical depth, cloud water path, cloud optical thickness, and cloud droplet number concentration are generally underpredicted on average across the continental US. Overpredictions of several cloud variables over the eastern US result in underpredictions of radiation variables (such as net shortwave radiation - GSW - with a mean bias - MB - of -5.7 W m-2) and overpredictions of shortwave and longwave cloud forcing (MBs of ˜ 7 to 8 W m-2), which are important climate variables. While the current performance is deemed to be acceptable, improvements to the bias-correction method for CESM downscaling and the model parameterizations of cloud dynamics and thermodynamics, as well as aerosol-cloud interactions, can potentially improve model performance for long-term climate simulations.

Influence of meteorological variables on rainfall partitioning for deciduous and coniferous tree species in urban area

NASA Astrophysics Data System (ADS)

Zabret, Katarina; Rakovec, Jože; Šraj, Mojca

2018-03-01

Rainfall partitioning is an important part of the ecohydrological cycle, influenced by numerous variables. Rainfall partitioning for pine (Pinus nigra Arnold) and birch (Betula pendula Roth.) trees was measured from January 2014 to June 2017 in an urban area of Ljubljana, Slovenia. 180 events from more than three years of observations were analyzed, focusing on 13 meteorological variables, including the number of raindrops, their diameter, and velocity. Regression tree and boosted regression tree analyses were performed to evaluate the influence of the variables on rainfall interception loss, throughfall, and stemflow in different phenoseasons. The amount of rainfall was recognized as the most influential variable, followed by rainfall intensity and the number of raindrops. Higher rainfall amount, intensity, and the number of drops decreased percentage of rainfall interception loss. Rainfall amount and intensity were the most influential on interception loss by birch and pine trees during the leafed and leafless periods, respectively. Lower wind speed was found to increase throughfall, whereas wind direction had no significant influence. Consideration of drop size spectrum properties proved to be important, since the number of drops, drop diameter, and median volume diameter were often recognized as important influential variables.

Meteorological Automatic Weather Station (MAWS) Instrument Handbook

DOE Office of Scientific and Technical Information (OSTI.GOV)

Holdridge, Donna J; Kyrouac, Jenni A

The Meteorological Automatic Weather Station (MAWS) is a surface meteorological station, manufactured by Vaisala, Inc., dedicated to the balloon-borne sounding system (BBSS), providing surface measurements of the thermodynamic state of the atmosphere and the wind speed and direction for each radiosonde profile. These data are automatically provided to the BBSS during the launch procedure and included in the radiosonde profile as the surface measurements of record for the sounding. The MAWS core set of measurements is: Barometric Pressure (hPa), Temperature (°C), Relative Humidity (%), Arithmetic-Averaged Wind Speed (m/s), and Vector-Averaged Wind Direction (deg). The sensors that collect the core variablesmore » are mounted at the standard heights defined for each variable.« less

Modelling the association of dengue fever cases with temperature and relative humidity in Jeddah, Saudi Arabia-A generalised linear model with break-point analysis.

PubMed

Alkhaldy, Ibrahim

2017-04-01

The aim of this study was to examine the role of environmental factors in the temporal distribution of dengue fever in Jeddah, Saudi Arabia. The relationship between dengue fever cases and climatic factors such as relative humidity and temperature was investigated during 2006-2009 to determine whether there is any relationship between dengue fever cases and climatic parameters in Jeddah City, Saudi Arabia. A generalised linear model (GLM) with a break-point was used to determine how different levels of temperature and relative humidity affected the distribution of the number of cases of dengue fever. Break-point analysis was performed to modelled the effect before and after a break-point (change point) in the explanatory parameters under various scenarios. Akaike information criterion (AIC) and cross validation (CV) were used to assess the performance of the models. The results showed that maximum temperature and mean relative humidity are most probably the better predictors of the number of dengue fever cases in Jeddah. In this study three scenarios were modelled: no time lag, 1-week lag and 2-weeks lag. Among these scenarios, the 1-week lag model using mean relative humidity as an explanatory variable showed better performance. This study showed a clear relationship between the meteorological variables and the number of dengue fever cases in Jeddah. The results also demonstrated that meteorological variables can be successfully used to estimate the number of dengue fever cases for a given period of time. Break-point analysis provides further insight into the association between meteorological parameters and dengue fever cases by dividing the meteorological parameters into certain break-points. Copyright © 2016 Elsevier B.V. All rights reserved.

Downscaling of Remotely Sensed Land Surface Temperature with multi-sensor based products

NASA Astrophysics Data System (ADS)

Jeong, J.; Baik, J.; Choi, M.

2016-12-01

Remotely sensed satellite data provides a bird's eye view, which allows us to understand spatiotemporal behavior of hydrologic variables at global scale. Especially, geostationary satellite continuously observing specific regions is useful to monitor the fluctuations of hydrologic variables as well as meteorological factors. However, there are still problems regarding spatial resolution whether the fine scale land cover can be represented with the spatial resolution of the satellite sensor, especially in the area of complex topography. To solve these problems, many researchers have been trying to establish the relationship among various hydrological factors and combine images from multi-sensor to downscale land surface products. One of geostationary satellite, Communication, Ocean and Meteorological Satellite (COMS), has Meteorological Imager (MI) and Geostationary Ocean Color Imager (GOCI). MI performing the meteorological mission produce Rainfall Intensity (RI), Land Surface Temperature (LST), and many others every 15 minutes. Even though it has high temporal resolution, low spatial resolution of MI data is treated as major research problem in many studies. This study suggests a methodology to downscale 4 km LST datasets derived from MI in finer resolution (500m) by using GOCI datasets in Northeast Asia. Normalized Difference Vegetation Index (NDVI) recognized as variable which has significant relationship with LST are chosen to estimate LST in finer resolution. Each pixels of NDVI and LST are separated according to land cover provided from MODerate resolution Imaging Spectroradiometer (MODIS) to achieve more accurate relationship. Downscaled LST are compared with LST observed from Automated Synoptic Observing System (ASOS) for assessing its accuracy. The downscaled LST results of this study, coupled with advantage of geostationary satellite, can be applied to observe hydrologic process efficiently.

The Aggregate Description of Semi-Arid Vegetation with Precipitation-Generated Soil Moisture Heterogeneity

NASA Technical Reports Server (NTRS)

White, Cary B.; Houser, Paul R.; Arain, Altaf M.; Yang, Zong-Liang; Syed, Kamran; Shuttleworth, W. James

1997-01-01

Meteorological measurements in the Walnut Gulch catchment in Arizona were used to synthesize a distributed, hourly-average time series of data across a 26.9 by 12.5 km area with a grid resolution of 480 m for a continuous 18-month period which included two seasons of monsoonal rainfall. Coupled surface-atmosphere model runs established the acceptability (for modelling purposes) of assuming uniformity in all meteorological variables other than rainfall. Rainfall was interpolated onto the grid from an array of 82 recording rain gauges. These meteorological data were used as forcing variables for an equivalent array of stand-alone Biosphere-Atmosphere Transfer Scheme (BATS) models to describe the evolution of soil moisture and surface energy fluxes in response to the prevalent, heterogeneous pattern of convective precipitation. The calculated area-average behaviour was compared with that given by a single aggregate BATS simulation forced with area-average meteorological data. Heterogeneous rainfall gives rise to significant but partly compensating differences in the transpiration and the intercepted rainfall components of total evaporation during rain storms. However, the calculated area-average surface energy fluxes given by the two simulations in rain-free conditions with strong heterogeneity in soil moisture were always close to identical, a result which is independent of whether default or site-specific vegetation and soil parameters were used. Because the spatial variability in soil moisture throughout the catchment has the same order of magnitude as the amount of rain failing in a typical convective storm (commonly 10% of the vegetation's root zone saturation) in a semi-arid environment, non-linearitv in the relationship between transpiration and the soil moisture available to the vegetation has limited influence on area-average surface fluxes.

Trends and variability of daily precipitation extremes during 1960-2012 in the Yangtze River Basin, China

USDA-ARS?s Scientific Manuscript database

Trends and variability of extreme precipitation events are important for water-related disaster prevention and mitigation as well as water resource management. Based on daily precipitation dataset from 143 meteorological stations in the Yangtze River Basin (YRB), a suite of precipitation indices rec...

Figure 3

EPA Pesticide Factsheets

The Figure.tar.gz contains a directory for each WRF ensemble run. In these directories are *.csv files for each meteorology variable examined. These are comma delimited text files that contain statistics for each observation site. Also provided is an R script that reads these files (user would need to change directory pointers) and computes the variability of error and bias of the ensemble at each site and plots these for reproduction of figure 3.This dataset is associated with the following publication:Gilliam , R., C. Hogrefe , J. Godowitch, S. Napelenok , R. Mathur , and S.T. Rao. Impact of inherent meteorology uncertainty on air quality model predictions. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES. American Geophysical Union, Washington, DC, USA, 120(23): 12,259–12,280, (2015).

Sampling errors in the estimation of empirical orthogonal functions. [for climatology studies

NASA Technical Reports Server (NTRS)

North, G. R.; Bell, T. L.; Cahalan, R. F.; Moeng, F. J.

1982-01-01

Empirical Orthogonal Functions (EOF's), eigenvectors of the spatial cross-covariance matrix of a meteorological field, are reviewed with special attention given to the necessary weighting factors for gridded data and the sampling errors incurred when too small a sample is available. The geographical shape of an EOF shows large intersample variability when its associated eigenvalue is 'close' to a neighboring one. A rule of thumb indicating when an EOF is likely to be subject to large sampling fluctuations is presented. An explicit example, based on the statistics of the 500 mb geopotential height field, displays large intersample variability in the EOF's for sample sizes of a few hundred independent realizations, a size seldom exceeded by meteorological data sets.

Flight responses by a migratory soaring raptor to changing meteorological conditions.

PubMed

Lanzone, Michael J; Miller, Tricia A; Turk, Philip; Brandes, David; Halverson, Casey; Maisonneuve, Charles; Tremblay, Junior; Cooper, Jeff; O'Malley, Kieran; Brooks, Robert P; Katzner, Todd

2012-10-23

Soaring birds that undertake long-distance migration should develop strategies to minimize the energetic costs of endurance flight. This is relevant because condition upon completion of migration has direct consequences for fecundity, fitness and thus, demography. Therefore, strong evolutionary pressures are expected for energy minimization tactics linked to weather and topography. Importantly, the minute-by-minute mechanisms birds use to subsidize migration in variable weather are largely unknown, in large part because of the technological limitations in studying detailed long-distance bird flight. Here, we show golden eagle (Aquila chrysaetos) migratory response to changing meteorological conditions as monitored by high-resolution telemetry. In contrast to expectations, responses to meteorological variability were stereotyped across the 10 individuals studied. Eagles reacted to increased wind speed by using more orographic lift and less thermal lift. Concomitantly, as use of thermals decreased, variation in flight speed and altitude also decreased. These results demonstrate how soaring migrant birds can minimize energetic expenditures, they show the context for avian decisions and choices of specific instantaneous flight mechanisms and they have important implications for design of bird-friendly wind energy.

Climate Trends and Farmers' Perceptions of Climate Change in Zambia.

PubMed

Mulenga, Brian P; Wineman, Ayala; Sitko, Nicholas J

2017-02-01

A number of studies use meteorological records to analyze climate trends and assess the impact of climate change on agricultural yields. While these provide quantitative evidence on climate trends and the likely effects thereof, they incorporate limited qualitative analysis of farmers' perceptions of climate change and/or variability. The present study builds on the quantitative methods used elsewhere to analyze climate trends, and in addition compares local narratives of climate change with evidence found in meteorological records in Zambia. Farmers offer remarkably consistent reports of a rainy season that is growing shorter and less predictable. For some climate parameters-notably, rising average temperature-there is a clear overlap between farmers' observations and patterns found in the meteorological records. However, the data do not support the perception that the rainy season used to begin earlier, and we generally do not detect a reported increase in the frequency of dry spells. Several explanations for these discrepancies are offered. Further, we provide policy recommendations to help farmers adapt to climate change/variability, as well as suggestions to shape future climate change policies, programs, and research in developing countries.

Meteorological variables associated with deep slab avalanches on persistent weak layers

USGS Publications Warehouse

Marienthal, Alex; Hendrikx, Jordy; Birkeland, Karl; Irvine, Kathryn M.

2014-01-01

Deep slab avalanches are a particularly challenging avalanche forecasting problem. These avalanches are typically difficult to trigger, yet when they are triggered they tend to propagate far and result in large and destructive avalanches. For this work we define deep slab avalanches as those that fail on persistent weak layers deeper than 0.9m (3 feet), and that occur after February 1st. We utilized a 44-year record of avalanche control and meteorological data from Bridger Bowl Ski Area to test the usefulness of meteorological variables for predicting deep slab avalanches. As in previous studies, we used data from the days preceding deep slab cycles, but we also considered meteorological metrics over the early months of the season. We utilized classification trees for our analyses. Our results showed warmer temperatures in the prior twenty-four hours and more loading over the seven days before days with deep slab avalanches on persistent weak layers. In line with previous research, extended periods of above freezing temperatures led to days with deep wet slab avalanches on persistent weak layers. Seasons with either dry or wet avalanches on deep persistent weak layers typically had drier early months, and often had some significant snow depth prior to those dry months. This paper provides insights for ski patrollers, guides, and avalanche forecasters who struggle to forecast deep slab avalanches on persistent weak layers late in the season.

Assessing the Performance of a Network of Low Cost Particulate Matter Sensors Deployed in Sacramento, California

NASA Astrophysics Data System (ADS)

Mukherjee, A. D.; Brown, S. G.; McCarthy, M. C.

2017-12-01

A new generation of low cost air quality sensors have the potential to provide valuable information on the spatial-temporal variability of air pollution - if the measurements have sufficient quality. This study examined the performance of a particulate matter sensor model, the AirBeam (HabitatMap Inc., Brooklyn, NY), over a three month period in the urban environment of Sacramento, California. Nineteen AirBeam sensors were deployed at a regulatory air monitoring site collocated with meteorology measurements and as a local network over an 80 km2 domain in Sacramento, CA. This study presents the methodology to evaluate the precision, accuracy, and reliability of the sensors over a range of meteorological and aerosol conditions. The sensors demonstrated a robust degree of precision during collocated measurement periods (R2 = 0.98 - 0.99) and a moderate degree of correlation against a Beta Attenuation Monitor PM2.5 monitor (R2 0.6). A normalization correction is applied during the study period so that each AirBeam sensor in the network reports a comparable value. The role of the meteorological environment on the accuracy of the sensor measurements is investigated, along with the possibility of improving the measurements through a meteorology weighted correction. The data quality of the network of sensors is examined, and the spatial variability of particulate matter through the study domain derived from the sensor network is presented.

Abstraction the public from scientific - applied meteorological-climatologic data

NASA Astrophysics Data System (ADS)

Trajanoska, L.

2010-09-01

Mathematical and meteorological statistic processing of meteorological-climatologic data, which includes assessment of the exactness, level of confidence of the average and extreme values, frequencies (probabilities) of the occurrence of each meteorological phenomenon and element e.t.c. helps to describe the impacts climate may have on different social and economic activities (transportation, heat& power generation), as well as on human health. Having in mind the new technology and the commercial world, during the work with meteorological-climatologic data we have meet many different challenges. Priority in all of this is the quality of the meteorological-climatologic set of data. First, we need compatible modern, sophisticated measurement and informatics solution for data. Results of this measurement through applied processing and analyze is the second branch which is very important also. Should we all (country) need that? Today we have many unpleasant events connected with meteorology, many questions which are not answered and all of this has too long lasting. We must give the answers and solve the real and basic issue. In this paper the data issue will be presented. We have too much of data but so little of real and quality applied of them, Why? There is a data for: -public applied -for jurisdiction needs -for getting fast decision-solutions (meteorological-dangerous phenomenon's) -for getting decisions for long-lasting plans -for explore in different sphere of human living So, it is very important for what kind of data we are talking. Does the data we are talking are with public or scientific-applied character? So,we have two groups. The first group which work with the data direct from the measurement place and instrument. They are store a quality data base and are on extra help to the journalists, medical workers, human civil engineers, electromechanical engineers, agro meteorological and forestry engineer e.g. The second group do work with all scientific methods for the needed purposes. Hours, days, years and periods with characteristic meanings are separated for the purposes of the comprehensive analyze and application.

NDVI dynamics of the taiga zone in connection with modern climate changes

NASA Astrophysics Data System (ADS)

Bobkov, A.; Panidi, E.; Torlopova, N.; Tsepelev, V.

2015-04-01

This research is dedicated to the investigation of the relations between the XXI century climate changes and Normalized Difference Vegetation Index (NDVI) variability of the taiga zone. For this purposes was used the observations of vegetation variability on the test area located nearby Syktyvkar city (Komi Republic, Russia), 16-day averages of NDVI data derived from TERRA/MODIS space imagery (spatial resolution is about 250 meters), and the air temperature and precipitation observations from Syktyvkar meteorological station. The research results confirmed the statistically significant positive correlation between NDVI and air temperature for all vegetation types of the test area, for both spring and autumn seasons. The weakest correlation was found for coniferous forest, namely, pine forest on poor soils, and the strongest correlation was found for meadows and bogs. Additionally the map of NDVI trends of the test area shows that the sectors of greatest positive trend located on the territories with non-forest cover, and as a result, the positive trend of air temperature is indicated most brightly on vegetation of non-forest lands. Thereby these lands can serve as climate changes indicator in the investigated region. The study was partially supported by Russian Foundation for Basic Research (RFBR), research project No. 14-05-00858 a.

Intraseasonal and Interannual Variability of Mars Present Climate

NASA Astrophysics Data System (ADS)

Hollingsworth, Jeffery L.; Bridger, Alison F. C.; Haberle, Robert M.

1996-01-01

This is a Final Report for a Joint Research Interchange (JRI) between NASA Ames Research Center and San Jose State University, Department of Meteorology. The focus of this JRI has been to investigate the nature of intraseasonal and interannual variability of Mars'present climate. We have applied a three-dimensional climate model based on the full hydrostatic primitive equations to determine the spatial, but primarily, the temporal structures of the planet's large-scale circulation as it evolves during a given seasonal advance, and, over multi-annual cycles. The particular climate model applies simplified physical parameterizations and is computationally efficient. It could thus easily be integrated in a perpetual season or advancing season configuration, as well as over many Mars years. We have assessed both high and low-frequency components of the circulation (i.e., motions having periods of Omicron(2-10 days) or greater than Omicron(10 days), respectively). Results from this investigation have explored the basic issue whether Mars' climate system is naturally 'chaotic' associated with nonlinear interactions of the large-scale circulation-regardless of any allowance for year-to-year variations in external forcing mechanisms. Titles of papers presented at scientific conferences and a manuscript to be submitted to the scientific literature are provided. An overview of a areas for further investigation is also presented.

Explaining the inter-annual variability in the ecosystem fluxes of the Brasschaat Scots pine forest: 20 years of eddy flux and pollution monitoring

NASA Astrophysics Data System (ADS)

Horemans, Joanna; Roland, Marilyn; Janssens, Ivan; Ceulemans, Reinhart

2017-04-01

Because of their ecological and recreational value, the health of forest ecosystems and their response to global change and pollution are of high importance. At a number of EuroFlux and ICOS ecosystem sites in Europe - as the Brasschaat forest site - the measurements of ecosystem fluxes of carbon and other gases are combined with vertical profiles of air pollution within the framework of the ICP-Forest monitoring program. The Brasschaat forest is dominated by 80-year old Scots pines (Pinus sylvestris L.), and has a total area of about 150 ha. It is situated near an urban area in the Campine region of Flanders, Belgium and is characterized by a mean annual temperature of 9.8 °C and an annual rainfall of 830 mm. In this contribution we report on a long-term analysis (1996-2016) of the ecosystem carbon and water fluxes, the energy exchanges and the pollutant concentrations (ozone, NOx, NH3, SO2). Particular interest goes to the inter-annual variation of the carbon fluxes and the carbon allocation patterns. The impact of the long-term (aggregated) and the short-term variability in both the meteorological drivers and in the main tropospheric pollutants on the carbon fluxes is examined, as well as their mutual interactive effects and their potential memory effect. The effect of variability in the drivers during the phenological phases (seasonality) on the inter-annual variability of the fluxes is also examined. Basic statistical techniques as well as spectral analyses and data mining techniques are being used.

MATISSE: a meteorological aviation supporting system developed in a GIS environment

NASA Astrophysics Data System (ADS)

Rillo, Valeria; Mercogliano, Paola

2014-05-01

Awareness of weather conditions plays an increasing role in different societal and economic sectors, in particular the aviation one which is very sensitive to the meteorological conditions. In fact, adverse meteorological conditions are among the most important causes of accidents causing human and economic losses. For these reasons it is crucial to monitor and nowcast such events and avoid risks during all the flight phases. In this framework CIRA (Italian Aerospace Research Center) has implemented MATISSE (Meteorological AviaTIon Supporting SystEm), an ArcGIS Desktop Plug in, in order to detect and forecast meteorological aviation hazards over the main European airports, by using different sources of meteorological data (synoptic information, satellite data, numerical weather prediction models outputs). Such functionalities are realized after a preprocessing of raw data achieving more complex information, useful for the detection and the forecast of aviation hazards. After that, the data are stored in a database used by ArcGIS and further processed in order to provide maps, graphs and statistics. MATISSE presents a dockable toolbar in a GIS environment, allowing the user to easily select and visualize the desired information. In particular, the user can access to real time functionalities and visualize, on a map, the chosen meteorological hazard or variable (such as visibility conditions, cumulonimbi, wind speeds and directions, present weather, pressure, relative humidity, past weather, cloud cover, height of base of clouds, cloud type, geopotential, altimeter settings, three hour pressure change) over an airport or an area of interest (Europe, Italy). Such variables are represented in a user friendly way, by using simple icons easy to understand and reporting the risk level for aviation in order to provide pilots information about the meteorological conditions during the flight and the following hours. MATISSE, in fact, is able to handle the output of COSMO LM model (NetCDF files) and visualize such information. Moreover it is interfaced to an innovative tool based on MSG-2 satellite data, able to forecast the evolution of cumulonimbi, clouds responsible of thunderstorms, wind shear, icing and turbulence phenomena. MATISSE includes also tool for the statistical characterization of the typical weather bad conditions on the airport of interest, for example percentage of fog events on particular time windows.

Modelling Spatial Dependence Structures Between Climate Variables by Combining Mixture Models with Copula Models

NASA Astrophysics Data System (ADS)

Khan, F.; Pilz, J.; Spöck, G.

2017-12-01

Spatio-temporal dependence structures play a pivotal role in understanding the meteorological characteristics of a basin or sub-basin. This further affects the hydrological conditions and consequently will provide misleading results if these structures are not taken into account properly. In this study we modeled the spatial dependence structure between climate variables including maximum, minimum temperature and precipitation in the Monsoon dominated region of Pakistan. For temperature, six, and for precipitation four meteorological stations have been considered. For modelling the dependence structure between temperature and precipitation at multiple sites, we utilized C-Vine, D-Vine and Student t-copula models. For temperature, multivariate mixture normal distributions and for precipitation gamma distributions have been used as marginals under the copula models. A comparison was made between C-Vine, D-Vine and Student t-copula by observational and simulated spatial dependence structure to choose an appropriate model for the climate data. The results show that all copula models performed well, however, there are subtle differences in their performances. The copula models captured the patterns of spatial dependence structures between climate variables at multiple meteorological sites, however, the t-copula showed poor performance in reproducing the dependence structure with respect to magnitude. It was observed that important statistics of observed data have been closely approximated except of maximum values for temperature and minimum values for minimum temperature. Probability density functions of simulated data closely follow the probability density functions of observational data for all variables. C and D-Vines are better tools when it comes to modelling the dependence between variables, however, Student t-copulas compete closely for precipitation. Keywords: Copula model, C-Vine, D-Vine, Spatial dependence structure, Monsoon dominated region of Pakistan, Mixture models, EM algorithm.

Local-scale topoclimate effects on treeline elevations: a country-wide investigation of New Zealand's southern beech treelines.

PubMed

Case, Bradley S; Buckley, Hannah L

2015-01-01

Although treeline elevations are limited globally by growing season temperature, at regional scales treelines frequently deviate below their climatic limit. The cause of these deviations relate to a host of climatic, disturbance, and geomorphic factors that operate at multiple scales. The ability to disentangle the relative effects of these factors is currently hampered by the lack of reliable topoclimatic data, which describe how regional climatic characteristics are modified by topographic effects in mountain areas. In this study we present an analysis of the combined effects of local- and regional-scale factors on southern beech treeline elevation variability at 28 study areas across New Zealand. We apply a mesoscale atmospheric model to generate local-scale (200 m) meteorological data at these treelines and, from these data, we derive a set of topoclimatic indices that reflect possible detrimental and ameliorative influences on tree physiological functioning. Principal components analysis of meteorological data revealed geographic structure in how study areas were situated in multivariate space along gradients of topoclimate. Random forest and conditional inference tree modelling enabled us to tease apart the relative effects of 17 explanatory factors on local-scale treeline elevation variability. Overall, modelling explained about 50% of the variation in treeline elevation variability across the 28 study areas, with local landform and topoclimatic effects generally outweighing those from regional-scale factors across the 28 study areas. Further, the nature of the relationships between treeline elevation variability and the explanatory variables were complex, frequently non-linear, and consistent with the treeline literature. To our knowledge, this is the first study where model-generated meteorological data, and derived topoclimatic indices, have been developed and applied to explain treeline variation. Our results demonstrate the potential of such an approach for ecological research in mountainous environments.

Local-scale topoclimate effects on treeline elevations: a country-wide investigation of New Zealand’s southern beech treelines

PubMed Central

Buckley, Hannah L.

2015-01-01

Although treeline elevations are limited globally by growing season temperature, at regional scales treelines frequently deviate below their climatic limit. The cause of these deviations relate to a host of climatic, disturbance, and geomorphic factors that operate at multiple scales. The ability to disentangle the relative effects of these factors is currently hampered by the lack of reliable topoclimatic data, which describe how regional climatic characteristics are modified by topographic effects in mountain areas. In this study we present an analysis of the combined effects of local- and regional-scale factors on southern beech treeline elevation variability at 28 study areas across New Zealand. We apply a mesoscale atmospheric model to generate local-scale (200 m) meteorological data at these treelines and, from these data, we derive a set of topoclimatic indices that reflect possible detrimental and ameliorative influences on tree physiological functioning. Principal components analysis of meteorological data revealed geographic structure in how study areas were situated in multivariate space along gradients of topoclimate. Random forest and conditional inference tree modelling enabled us to tease apart the relative effects of 17 explanatory factors on local-scale treeline elevation variability. Overall, modelling explained about 50% of the variation in treeline elevation variability across the 28 study areas, with local landform and topoclimatic effects generally outweighing those from regional-scale factors across the 28 study areas. Further, the nature of the relationships between treeline elevation variability and the explanatory variables were complex, frequently non-linear, and consistent with the treeline literature. To our knowledge, this is the first study where model-generated meteorological data, and derived topoclimatic indices, have been developed and applied to explain treeline variation. Our results demonstrate the potential of such an approach for ecological research in mountainous environments. PMID:26528407

Airborne fungal spores of Alternaria, meteorological parameters and predicting variables

NASA Astrophysics Data System (ADS)

Filali Ben Sidel, Farah; Bouziane, Hassan; del Mar Trigo, Maria; El Haskouri, Fatima; Bardei, Fadoua; Redouane, Abdelbari; Kadiri, Mohamed; Riadi, Hassane; Kazzaz, Mohamed

2015-03-01

Alternaria is frequently found as airborne fungal spores and is recognized as an important cause of respiratory allergies. The aerobiological monitoring of fungal spores was performed using a Burkard volumetric spore traps. To establish predicting variables for daily and weakly spore counts, a stepwise multiple regression between spore concentrations and independent variables (meteorological parameters and lagged values from the series of spore concentrations: previous day or week concentration (Alt t - 1) and mean concentration of the same day or week in other years ( C mean)) was made with data obtained during 2009-2011. Alternaria conidia are present throughout the year in the atmosphere of Tetouan, although they show important seasonal fluctuations. The highest levels of Alternaria spores were recorded during the spring and summer or autumn. Alternaria showed maximum daily values in April, May or October depending on year. When the spore variables of Alternaria, namely C mean and Alt t - 1, and meteorological parameters were included in the equation, the resulting R 2 satisfactorily predict future concentrations for 55.5 to 81.6 % during the main spore season and the pre-peak 2. In the predictive model using weekly values, the adjusted R 2 varied from 0.655 to 0.676. The Wilcoxon test was used to compare the results from the expected values and the pre-peak spore data or weekly values for 2012, indicating that there were no significant differences between series compared. This test showed the C mean, Alt t - 1, frequency of the wind third quadrant, maximum wind speed and minimum relative humidity as the most efficient independent variables to forecast the overall trend of this spore in the air.

Classification and Space-Time Analysis of Precipitation Events in Manizales, Caldas, Colombia.

NASA Astrophysics Data System (ADS)

Suarez Hincapie, J. N.; Vélez, J.; Romo Melo, L.; Chang, P.

2015-12-01

Manizales is a mid-mountain Andean city located near the Nevado del Ruiz volcano in west-central Colombia, this location exposes it to earthquakes, floods, landslides and volcanic eruptions. It is located in the intertropical convergence zone (ITCZ) and presents a climate with a bimodal rainfall regime (Cortés, 2010). Its mean annual rainfall is 2000 mm, one may observe precipitation 70% of the days over a year. This rain which favors the formation of large masses of clouds and the presence of macroclimatic phenomenon as "El Niño South Oscillation", has historically caused great impacts in the region (Vélez et al, 2012). For example the geographical location coupled with rain events results in a high risk of landslides in the city. Manizales has a hydrometeorological network of 40 stations that measure and transmit data of up to eight climate variables. Some of these stations keep 10 years of historical data. However, until now this information has not been used for space-time classification of precipitation events, nor has the meteorological variables that influence them been thoroughly researched. The purpose of this study was to classify historical events of rain in an urban area of Manizales and investigate patterns of atmospheric behavior that influence or trigger such events. Classification of events was performed by calculating the "n" index of the heavy rainfall, describing the behavior of precipitation as a function of time throughout the event (Monjo, 2009). The analysis of meteorological variables was performed using statistical quantification over variable time periods before each event. The proposed classification allowed for an analysis of the evolution of rainfall events. Specially, it helped to look for the influence of different meteorological variables triggering rainfall events in hazardous areas as the city of Manizales.

Spatial interpolation of GPS PWV and meteorological variables over the west coast of Peninsular Malaysia during 2013 Klang Valley Flash Flood

NASA Astrophysics Data System (ADS)

Suparta, Wayan; Rahman, Rosnani

2016-02-01

Global Positioning System (GPS) receivers are widely installed throughout the Peninsular Malaysia, but the implementation for monitoring weather hazard system such as flash flood is still not optimal. To increase the benefit for meteorological applications, the GPS system should be installed in collocation with meteorological sensors so the precipitable water vapor (PWV) can be measured. The distribution of PWV is a key element to the Earth's climate for quantitative precipitation improvement as well as flash flood forecasts. The accuracy of this parameter depends on a large extent on the number of GPS receiver installations and meteorological sensors in the targeted area. Due to cost constraints, a spatial interpolation method is proposed to address these issues. In this paper, we investigated spatial distribution of GPS PWV and meteorological variables (surface temperature, relative humidity, and rainfall) by using thin plate spline (tps) and ordinary kriging (Krig) interpolation techniques over the Klang Valley in Peninsular Malaysia (longitude: 99.5°-102.5°E and latitude: 2.0°-6.5°N). Three flash flood cases in September, October, and December 2013 were studied. The analysis was performed using mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) to determine the accuracy and reliability of the interpolation techniques. Results at different phases (pre, onset, and post) that were evaluated showed that tps interpolation technique is more accurate, reliable, and highly correlated in estimating GPS PWV and relative humidity, whereas Krig is more reliable for predicting temperature and rainfall during pre-flash flood events. During the onset of flash flood events, both methods showed good interpolation in estimating all meteorological parameters with high accuracy and reliability. The finding suggests that the proposed method of spatial interpolation techniques are capable of handling limited data sources with high accuracy, which in turn can be used to predict future floods.

Urban and regional land use analysis: CARETS and census cities experiment package

NASA Technical Reports Server (NTRS)

Alexander, R. (Principal Investigator); Lins, H. F., Jr.; Gallagher, D. B.

1975-01-01

The author has identified the following significant results. Temperatures in degrees Celsius were derived from PCM counts using the Pease's modified gray window technique. The Outcalt simulator was setup on the USGS computer. The input data to the model are basically meteorological and geographical in nature. The output data is presented in three matrices.

Central American Flying Weather

DTIC Science & Technology

1985-12-01

CEILING; VISIBILITY; WIND, PRECIPITATIDNc’--." HAZE, SMOKE, TEMPORALE ; MOUNTAIN WAVE; MILITARY METEOROLOGY. 4k- / ’A. bstract; Asummary of~ing weather...1 The " Temporale " ....................................1 Mountain Waves ......................I...............1 Severe Thunderstorms...charts. The for any part of Central America lies in having: Tactical Pilota.e Chart series , produced by the Df -.nse Mapping Agency, is * A good, basic

Marine Science Technician Second Class, 15-2. Military Curriculum Materials for Vocational and Technical Education.

ERIC Educational Resources Information Center

Ohio State Univ., Columbus. National Center for Research in Vocational Education.

This course, adapted from military curriculum materials for use in vocational and technical education, was designed to provide the theory portion of the Marine Science Technician Program. It includes a review of basic subjects, marine biology, oceanography, as well as meteorologic observations and recording. The course consists of a lesson book…

A Comparison of Five Numerical Weather Prediction Analysis Climatologies in Southern High Latitudes.

NASA Astrophysics Data System (ADS)

Connolley, William M.; Harangozo, Stephen A.

2001-01-01

In this paper, numerical weather prediction analyses from four major centers are compared-the Australian Bureau of Meteorology (ABM), the European Centre for Medium-Range Weather Forecasts (ECMWF), the U.S. National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR), and The Met. Office (UKMO). Two of the series-ECMWF reanalysis (ERA) and NCEP-NCAR reanalysis (NNR)-are `reanalyses'; that is, the data have recently been processed through a consistent, modern analysis system. The other three-ABM, ECMWF operational (EOP), and UKMO-are archived from operational analyses.The primary focus in this paper is on the period of 1979-93, the period used for the reanalyses, and on climatology. However, ABM and NNR are also compared for the period before 1979, for which the evidence tends to favor NNR. The authors are concerned with basic variables-mean sea level pressure, height of the 500-hPa surface, and near-surface temperature-that are available from the basic analysis step, rather than more derived quantities (such as precipitation), which are available only from the forecast step.Direct comparisons against station observations, intercomparisons of the spatial pattern of the analyses, and intercomparisons of the temporal variation indicate that ERA, EOP, and UKMO are best for sea level pressure;that UKMO and EOP are best for 500-hPa height; and that none of the analyses perform well for near-surface temperature.

Exploring the full natural variability of eruption sizes within probabilistic hazard assessment of tephra dispersal

NASA Astrophysics Data System (ADS)

Selva, Jacopo; Sandri, Laura; Costa, Antonio; Tonini, Roberto; Folch, Arnau; Macedonio, Giovanni

2014-05-01

The intrinsic uncertainty and variability associated to the size of next eruption strongly affects short to long-term tephra hazard assessment. Often, emergency plans are established accounting for the effects of one or a few representative scenarios (meant as a specific combination of eruptive size and vent position), selected with subjective criteria. On the other hand, probabilistic hazard assessments (PHA) consistently explore the natural variability of such scenarios. PHA for tephra dispersal needs the definition of eruptive scenarios (usually by grouping possible eruption sizes and vent positions in classes) with associated probabilities, a meteorological dataset covering a representative time period, and a tephra dispersal model. PHA results from combining simulations considering different volcanological and meteorological conditions through a weight given by their specific probability of occurrence. However, volcanological parameters, such as erupted mass, eruption column height and duration, bulk granulometry, fraction of aggregates, typically encompass a wide range of values. Because of such a variability, single representative scenarios or size classes cannot be adequately defined using single values for the volcanological inputs. Here we propose a method that accounts for this within-size-class variability in the framework of Event Trees. The variability of each parameter is modeled with specific Probability Density Functions, and meteorological and volcanological inputs are chosen by using a stratified sampling method. This procedure allows avoiding the bias introduced by selecting single representative scenarios and thus neglecting most of the intrinsic eruptive variability. When considering within-size-class variability, attention must be paid to appropriately weight events falling within the same size class. While a uniform weight to all the events belonging to a size class is the most straightforward idea, this implies a strong dependence on the thresholds dividing classes: under this choice, the largest event of a size class has a much larger weight than the smallest event of the subsequent size class. In order to overcome this problem, in this study, we propose an innovative solution able to smoothly link the weight variability within each size class to the variability among the size classes through a common power law, and, simultaneously, respect the probability of different size classes conditional to the occurrence of an eruption. Embedding this procedure into the Bayesian Event Tree scheme enables for tephra fall PHA, quantified through hazard curves and maps representing readable results applicable in planning risk mitigation actions, and for the quantification of its epistemic uncertainties. As examples, we analyze long-term tephra fall PHA at Vesuvius and Campi Flegrei. We integrate two tephra dispersal models (the analytical HAZMAP and the numerical FALL3D) into BET_VH. The ECMWF reanalysis dataset are used for exploring different meteorological conditions. The results obtained clearly show that PHA accounting for the whole natural variability significantly differs from that based on a representative scenarios, as in volcanic hazard common practice.

Meteorological factors, air pollutants, and emergency department visits for otitis media: a time series study.

PubMed

Gestro, Massimo; Condemi, Vincenzo; Bardi, Luisella; Fantino, Claudio; Solimene, Umberto

2017-10-01

AbstractOtitis media (OM) is a very common disease in children, which results in a significant economic burden to the healthcare system for hospital-based outpatient departments, emergency departments (EDs), unscheduled medical examinations, and antibiotic prescriptions. The aim of this retrospective observational study is to investigate the association between climate variables, air pollutants, and OM visits observed in the 2007-2010 period at the ED of Cuneo, Italy. Measures of meteorological parameters (temperature, humidity, atmospheric pressure, wind) and outdoor air pollutants (particulate matter, ozone, nitrous dioxide) were analyzed at two statistical stages and in several specific steps (crude and adjusted models) according to Poisson's regression. Response variables included daily examinations for age groups 0-3, 0-6, and 0-18. Control variables included upper respiratory infections (URI), flu (FLU), and several calendar factors. A statistical procedure was implemented to capture any delayed effects. Results show a moderate association for temperature (T), age 0-3, and 0-6 with P < 0.05, as well as nitrous dioxide (NO 2 ) with P < 0.005 at age 0-18. Results of subsequent models point out to URI as an important control variable. No statistical association was observed for other pollutants and meteorological variables. The dose-response models (DLNM-final stage) implemented separately on a daily and hourly basis point out to an association between temperature (daily model) and RR 1.44 at age 0-3, CI 1.11-1.88 (lag time 0-1 days) and RR 1.43, CI 1.05-1.94 (lag time 0-3 days). The hourly model confirms a specific dose-response effect for T with RR 1.20, CI 1.04-1.38 (lag time range from 0 to 11 to 0-15 h) and for NO 2 with RR 1.03, CI 1.01-1.05 (lag time range from 0 to 8 to 0-15 h). These results support the hypothesis that the clinical context of URI may be an important risk factor in the onset of OM diagnosed at ED level. The study highlights the relevance of URI as a control variable to be included in the statistical analysis in association with meteorological factors and air pollutants. The study also points out to a moderate association of OM with low temperatures and NO 2 , with specific risk factors for this variable early in life. Further studies are needed to confirm these findings, particularly with respect to air pollutants in larger urban environments.

Meteorological factors, air pollutants, and emergency department visits for otitis media: a time series study

NASA Astrophysics Data System (ADS)

Gestro, Massimo; Condemi, Vincenzo; Bardi, Luisella; Fantino, Claudio; Solimene, Umberto

2017-10-01

Abstract Otitis media (OM) is a very common disease in children, which results in a significant economic burden to the healthcare system for hospital-based outpatient departments, emergency departments (EDs), unscheduled medical examinations, and antibiotic prescriptions. The aim of this retrospective observational study is to investigate the association between climate variables, air pollutants, and OM visits observed in the 2007-2010 period at the ED of Cuneo, Italy. Measures of meteorological parameters (temperature, humidity, atmospheric pressure, wind) and outdoor air pollutants (particulate matter, ozone, nitrous dioxide) were analyzed at two statistical stages and in several specific steps (crude and adjusted models) according to Poisson's regression. Response variables included daily examinations for age groups 0-3, 0-6, and 0-18. Control variables included upper respiratory infections (URI), flu (FLU), and several calendar factors. A statistical procedure was implemented to capture any delayed effects. Results show a moderate association for temperature ( T), age 0-3, and 0-6 with P < 0.05, as well as nitrous dioxide (NO2) with P < 0.005 at age 0-18. Results of subsequent models point out to URI as an important control variable. No statistical association was observed for other pollutants and meteorological variables. The dose-response models (DLNM—final stage) implemented separately on a daily and hourly basis point out to an association between temperature (daily model) and RR 1.44 at age 0-3, CI 1.11-1.88 (lag time 0-1 days) and RR 1.43, CI 1.05-1.94 (lag time 0-3 days). The hourly model confirms a specific dose-response effect for T with RR 1.20, CI 1.04-1.38 (lag time range from 0 to 11 to 0-15 h) and for NO2 with RR 1.03, CI 1.01-1.05 (lag time range from 0 to 8 to 0-15 h). These results support the hypothesis that the clinical context of URI may be an important risk factor in the onset of OM diagnosed at ED level. The study highlights the relevance of URI as a control variable to be included in the statistical analysis in association with meteorological factors and air pollutants. The study also points out to a moderate association of OM with low temperatures and NO2, with specific risk factors for this variable early in life. Further studies are needed to confirm these findings, particularly with respect to air pollutants in larger urban environments.

Introduction of the in-orbit test and its performance for the first meteorological imager of the Communication, Ocean, and Meteorological Satellite

NASA Astrophysics Data System (ADS)

Kim, D. H.; Ahn, M. H.

2014-08-01

The first geostationary Earth observation satellite of Korea - the Communication, Ocean, and Meteorological Satellite (COMS) - was successfully launched on 27 June 2010. After arrival at its operational orbit, the satellite underwent an in-orbit test (IOT) that lasted for about 8 months. During the IOT period, the main payload for the weather application, the meteorological imager, went through successful tests for demonstrating its function and performance, and the test results are introduced here. The radiometric performance of the meteorological imager (MI) is tested by means of signal-to-noise ratio (SNR) for the visible channel, noise-equivalent differential temperature (NEdT) for the infrared channels, and pixel-to-pixel nonuniformity for both the visible and infrared channels. In the case of the visible channel, the SNR of all eight detectors is obtained using the ground-measured parameters with the background signals obtained in orbit. The overall performance shows a value larger than 26 at 5% albedo, exceeding the user requirement of 10 by a significant margin. Also, the relative variability of detector responsivity among the eight visible channels meets the user requirement, showing values within 10% of the user requirement. For the infrared channels, the NEdT of each detector is well within the user requirement and is comparable with or better than the legacy instruments, except for the water vapor channel, which is slightly noisier than the legacy instruments. The variability of detector responsivity of infrared channels is also below the user requirement, within 40% of the requirement, except for the shortwave infrared channel. The improved performance result is partly due to the stable and low detector temperature obtained due to spacecraft design, i.e., by installing a single solar panel on the opposite side of the MI.

May tropospheric noise in satellite radar data affect decision making results?

NASA Astrophysics Data System (ADS)

Bloutsos, Aristeidis; Bekri, Eleni; Moschas, Fanis; Saltogianni, Vasso; Stiros, Stathis; Yannopoulos, Panayotis

2015-04-01

Meteorological and air pollution conditions affect the satellite positioning signals. To investigate the uncertainty introduced in these signals in various meteorological and air pollution conditions, an array of GPS/GNSS stations and another of meteorological and air pollution stations has been established. The study area is expanded next to Patraikos and Corinth Gulf (NW Peloponnisos, Greece), which is characterized by high variability sequences from hot to cold weather, low to high relative humidity and clear to cloudy or/and Sahara dusty atmosphere, as a result of the particular geographical and topographical features of the study area. The GNSS recordings from several stations with very high vertical separation (with altitude up to 1600m and with a gradient of up to 20%) are analyzed in order to control in some extend both the vertical and the horizontal variability of the atmospheric effects, as well as the noise of geodetic recordings. Then, the GPS results will be combined with meteorological and atmospheric pollution data, as well as satellite radar data, in order to evaluate the enhanced troposphere noise in satellite radar and to estimate the magnitude of uncertainty that may cause alterations to decision making results in the management of water and other natural resources. This project takes advantage of GPS stations established in wider study area in the framework of the Corinth Rift Laboratory (http://crlab.eu/) in conjunction to the air pollution and meteorological monitoring stations of the Environmental Engineering Laboratory of the Department of Civil Engineering of the University of Patras. Regarding GPS stations, the project has been partly funded by the PLATO Project of the Greek Secretariat for Research and Technology.

Respiratory viral infections and effects of meteorological parameters and air pollution in adults with respiratory symptoms admitted to the emergency room

PubMed Central

Silva, Denise R; Viana, Vinícius P; Müller, Alice M; Livi, Fernando P; Dalcin, Paulo de Tarso R

2014-01-01

Background Respiratory viral infections (RVIs) are the most common causes of respiratory infections. The prevalence of respiratory viruses in adults is underestimated. Meteorological variations and air pollution are likely to play a role in these infections. Objectives The objectives of this study were to determine the number of emergency visits for influenza-like illness (ILI) and severe acute respiratory infection (SARI) and to evaluate the association between ILI/SARI, RVI prevalence, and meteorological factors/air pollution, in the city of Porto Alegre, Brazil, from November 2008 to October 2010. Methods Eleven thousand nine hundred and fifty-three hospitalizations (adults and children) for respiratory symptoms were correlated with meteorological parameters and air pollutants. In a subset of adults, nasopharyngeal aspirates were collected and analyzed through IFI test. The data were analyzed using time-series analysis. Results Influenza-like illness and SARI were diagnosed in 3698 (30·9%) and 2063 (17·7%) patients, respectively. Thirty-seven (9·0%) samples were positive by IFI and 93 of 410 (22·7%) were IFI and/or PCR positive. In a multivariate logistic regression model, IFI positivity was statistically associated with absolute humidity, use of air conditioning, and presence of mold in home. Sunshine duration was significantly associated with the frequency of ILI cases. For SARI cases, the variables mean temperature, sunshine duration, relative humidity, and mean concentration of pollutants were singnificant. Conclusions At least 22% of infections in adult patients admitted to ER with respiratory complaints were caused by RVI. The correlations among meteorological variables, air pollution, ILI/SARI cases, and respiratory viruses demonstrated the relevance of climate factors as significant underlying contributors to the prevalence of RVI. PMID:24034701

New early instrumental series since the beginning of the 19th century in eastern Iberia (Valencia, Spain)

NASA Astrophysics Data System (ADS)

Sanchez-Lorenzo, Arturo; Barriendos, Mariano; Guinaldo, Elena; Lopez-Bustins, Joan A.

2010-05-01

Early instrumental series are the main source for climate information in the 18th and the first part of the 19th century, which is when systematic meteorological observations started in most national meteorological services. The first continuous series in Spain starts in 1780 in Barcelona due to meteorological observations made by the medical doctor Francisco Salvá Campillo. Moreover, only two other series have been recovered at the present in Spain: Madrid and Cádiz/San Fernando. Until present, in Spain the major part of the meteorological observations detected in early instrumental periods were made by medical doctors, who started to pay attention to the environmental factors influencing population health under the Hippocrates oath, although also there are military institutions and academic university staff (e.g. physicists, mathematicians, etc.). Due to the high spatial and temporal climate variability in the Iberian Peninsula, it is important to recover and digitize more climatic series, and this is one of the main goals of the Salvá-Sinobas project (http://salva-sinobas.uvigo.es/) funded by the Spanish Ministry of Environment, and Rural and Marine Affairs for the 2009-2011 period. The first new series with systematic observations was detected in the city of Valencia, in the eastern façade of the Iberian Peninsula. The meteorological observations were daily published in the newspapers Diario de Valencia (1804-1834) and Diario Mercantil de Valencia (1837-1863) until official meteorological observations started in 1858 at the University of Valencia. Each day 3-daily observations (morning, midday, afternoon) were published with five climatic variables: temperature, air pressure, humidity, wind direction and the sky state. Only during the 1804-1808 period daily rainfall data is available. We checked the observer comments published in the newspapers to obtain metadata about the instruments and meteorological station information. Unfortunately, temperature data was recorded indoor and unknown hygrometer was used during the first decades until 1841. One curious detail of the Valencia early instrumental series is that the records were initiated by a local clockmaker, a new profession interested in meteorological observations in Spain during this period. A great effort has been made to detect original manuscripts, but the archive revision did not provide encouraging results. We started to digitalize daily air pressure records, to improve atmospheric circulation reconstruction in the Mediterranean region, and the sky observations (defined as cloud free, cloudy or overcast conditions), since we are interested into reconstruct cloud cover variability since early 19th century in Valencia. Finally, due to the lack of metadata about wind direction, we tried to assess the reliability of these measurements using the daily Western Mediterranean Oscillation index (WeMOi), a regional circulation pattern in the western Mediterranean basin. Wind direction records in Valencia were registered in 32 class intervals. The negative phase of the WeMOi is linked to those intervals associated to easterly humid flows.

“Skill of Generalized Additive Model to Detect PM2.5 Health ...

EPA Pesticide Factsheets

Summary. Measures of health outcomes are collinear with meteorology and air quality, making analysis of connections between human health and air quality difficult. The purpose of this analysis was to determine time scales and periods shared by the variables of interest (and by implication scales and periods that are not shared). Hospital admissions, meteorology (temperature and relative humidity), and air quality (PM2.5 and daily maximum ozone) for New York City during the period 2000-2006 were decomposed into temporal scales ranging from 2 days to greater than two years using a complex wavelet transform. Health effects were modeled as functions of the wavelet components of meteorology and air quality using the generalized additive model (GAM) framework. This simulation study showed that GAM is extremely successful at extracting and estimating a health effect embedded in a dataset. It also shows that, if the objective in mind is to estimate the health signal but not to fully explain this signal, a simple GAM model with a single confounder (calendar time) whose smooth representation includes a sufficient number of constraints is as good as a more complex model.Introduction. In the context of wavelet regression, confounding occurs when two or more independent variables interact with the dependent variable at the same frequency. Confounding also acts on a variety of time scales, changing the PM2.5 coefficient (magnitude and sign) and its significance ac

Spatiotemporal variability of biogenic terpenoid emissions in Pearl River Delta, China, with high-resolution land-cover and meteorological data

NASA Astrophysics Data System (ADS)

Wang, Xuemei; Situ, Shuping; Guenther, Alex; Chen, Fei; Wu, Zhiyong; Xia, Beicheng; Wang, Tijian

2011-04-01

This study intended to provide 4-km gridded, hourly, year-long, regional estimates of terpenoid emissions in the Pearl River Delta (PRD), China. It combined Thematic Mapper images and local-survey data to characterize plant functional types, and used observed emission potential of biogenic volatile organic compounds (BVOC) from local plant species and high-resolution meteorological outputs from the MM5 model to constrain the MEGAN BVOC-emission model. The estimated annual emissions for isoprene, monoterpene and sesquiterpene are 95.55 × 106 kg C, 117.35 × 106 kg C and 9.77 × 106 kg C, respectively. The results show strong variabilities of terpenoid emissions spanning diurnal and seasonal time scales, which are mainly distributed in the remote areas (with more vegetation and less economic development) in PRD. Using MODIS PFTs data reduced terpenoid emissions by 27% in remote areas. Using MEGAN-model default emission factors led to a 24% increase in BVOC emission. The model errors of temperature and radiation in MM5 output were used to assess impacts of uncertainties in meteorological forcing on emissions: increasing (decreasing) temperature and downward shortwave radiation produces more (less) terpenoid emissions for July and January. Strong temporal variability of terpenoid emissions leads to enhanced ozone formation during midday in rural areas where the anthropogenic VOC emissions are limited.

Impact of Soil Moisture Dynamics on ASAR Observed Backscatters and its Spatial Variability over the Upstream of the Heihe River Basin, China

NASA Astrophysics Data System (ADS)

Wang, Shuguo

2013-01-01

The so called change detection method is a promising way to acquire soil moisture (SM) dynamics dependent on time series of radar backscatter (σ0) observations. The current study is a preceded step for using this method to carry out SM inversion at basin scale, in order to investigate the applicability of the change detection method in the Heihe River Basin, and to inspect the sensitivity of SAR signals to soil moisture variations. At the meantime, a prior knowledge of SM dynamics and land heterogeneities that may contribute to backscatter observations can be obtained. The impact of land surface states on spatial and temporal σ0 variability measured by ASAR has been evaluated in the upstream of the Heihe River Basin, which was one of the foci experimental areas (FEAs) in Watershed Allied Telemetry Experimental Research (WATER). Based on the in situ measurements provided by an automatic meteorological station (AMS) established at the A’rou site and time series of ASAR observations focused on a 1 km2 area, the relationships between the temporal dynamics of σ0 with in situ SM variations, and land heterogeneities of the study area according to the characteristics of spatial variability of σ0, were identified. The in situ measurements of soil moisture and temperature show a very clear seasonal freeze/thaw cycle in the study site. The temporal σ0 evolvement is basically coherent with ground measurements.

Urban Biometeorology: analysis of the air pollution and climate change on cognition and physical abilities of geriatric population of São Paulo City

NASA Astrophysics Data System (ADS)

Teixeira Gonçalves, Fabio Luiz; Jacob, Wilson; Alucci, Marcia; Busse, Alexandre; Duarte, Denise; Monteiro, Leonardo; Trezza, Beatriz; Tribess, Arlindo; Batista, Rafael; Ambrizzi, Tercip

2013-04-01

This is a multidisciplinary Project, which emphasizes geriatric population impacts, i. e., over 65 years old, of meteorological variables and air pollutants (such as particulate matter) associated to human health, and concerning to the real climatology and climate change in the Metropolitan Region of São Paulo. This is a biometeorological study, human subdivision, based on ISB (International Society of Biometeorology). According to the society, the environmental effects are considered meteorotropics where one or more environmental variables (meteorological or climatic even air pollution) affect one or more individuals of a population. Atmospheric pollution will be analyzed using a personal particulate matter multi-collector, concerning the impact of unfavorable meteorological conditions where the impacts will be evaluated comparing the test results during dry season (high air pollutant concentrations) and wet season (low pollutant concentrations). Therefore, the aim of this study will be to evaluate the cognitive and physical performance of a geriatric population in a pre-selected group of aged people which are considered as capable (healthy). This performance is affected by environmental conditions which thermal comfort (where meteorological variables act together) and air pollution are the meteorotropic ones. Consequently, one of the aims of the study is to establish a human thermal comfort index for geriatric populations. Architectural premises (thermal performance and ergonomics) will be also developed. An acclimatized chamber will be used to simulate the extremes of São Paulo climate and to propose a thermal comfort index. Indoors (chamber) and outdoors will be used in order to compare the impact on the selected aged people. Finally, the climate change will be based on GCM's global models which show the meteorological variations in order to calculate their impact on a comfort index. The physical and cognitive performances and architectural premises (thermal performance and ergonomics) will be analyzed inside of the climatic chamber. The preliminary results for future (climate change for 2070-2100) comfort indexes present a reasonable impact for heat discomfort during the summer and less cold discomfort during wintertime.

Different meteorological parameters influence metapneumovirus and respiratory syncytial virus activity.

PubMed

Darniot, Magali; Pitoiset, Cécile; Millière, Laurine; Aho-Glélé, Ludwig Serge; Florentin, Emmanuel; Bour, Jean-Baptiste; Manoha, Catherine

2018-05-05

Both human metapneumovirus (hMPV) and respiratory syncytial virus (RSV) cause epidemics during the cold season in temperate climates. The purpose of this study was to find out whether climatic factors are associated with RSV and hMPV epidemics. Our study was based on data from 4300 patients admitted to the Dijon University Hospital for acute respiratory infection (ARI) over three winter seasons chosen for their dissimilar meteorological and virological patterns. Cases of hMPV and RSV were correlated with meteorological parameters recorded in the Dijon area. The relationship between virus data and local meteorological conditions was analyzed by univariate and multivariate negative binomial regression analysis. RSV detection was inversely associated with temperature and positively with relative humidity and air pressure, whereas hMPV was inversely associated with temperature and positively with wind speed. The association among meteorological variables and weekly ARIs cases due to RSV and hMPV demonstrated the relevance of climate factors as contributors to both hMPV and RSV activities. Meteorological drivers of RSV and hMPV epidemics are different. Low temperatures influence both hMPV and RSV activity. Relative humidity is an important predictor of RSV activity, but it does not influence hMPV activity. Copyright © 2018 Elsevier B.V. All rights reserved.

Meteorological and associated data collected over agricultural fields in Pinal County, Arizona, 1989 and 1990

USGS Publications Warehouse

Owen-Joyce, Sandra J.; Brown, Paul W.

1995-01-01

Data were collected at temporary meteorological stations installed in agricultural fields in Pinal County, Arizona, to evaluate the spatial and temporal variability of point data and to examine how station location affects ground-based meteorological data and the resulting values of evapotranspiration calculated using remotely sensed multispectral data from satellites. Time-specific data were collected to correspond with satellite overpasses from April to October 1989, and June 27-28, 1990. Meteorological data consisting of air temperature, relative humidity, wind speed, solar radiation, and net radiation were collected at each station during all periods of the project. Supplementary measurements of soil temperature, soil heat flux density, and surface or canopy temperature were obtained at some locations during certain periods of the project. Additional data include information on data-collection periods, station positions, instrumentation, sensor heights, and field dimensions. Other data, which correspond to the extensive field measurements made in con- junction with satellite overpasses in 1989 and 1990, include crop type, canopy cover, canopy height, irrigation, cultivation, and orientation of rows. Field boundaries and crop types were mapped in a 2- to 3-square-kilometer area surrounding each meteorological station. Field data are presented in tabular and graphic form. Meteorological and supplementary data are available, upon request, in digital form.

Trends and variability of daily temperature extremes during 1960-2012 in the Yangtze River Basin, China

USDA-ARS?s Scientific Manuscript database

The variability of temperature extremes has been the focus of attention during the past few decades, and may exert a great influence on the global hydrologic cycle and energy balance through thermal forcing. Based on daily minimum and maximum temperature observed by the China Meteorological Administ...

Solving the African Climate Observation Puzzle, and Concurrently Building Capacity

NASA Astrophysics Data System (ADS)

Selker, J. S.; Van De Giesen, N.; Annor, F. O.; Hochreutener, R.; Jachens, E. R.

2017-12-01

The Trans-African Hydro-Meteorological Observatory (TAHMO.org) is directly addressing basic issues of climate observation, climate science, and education through a novel public-private partnership. With 500 stations now reporting from over 20 African countries, TAHMO is the largest single source of continental-scale weather and climate data for Africa. Working directly with national meteorological agencies, TAHMO first builds local human capacity and real-time data to the host country. TAHMO also provides all of these data free of charge to all researchers and teams seeking to develop peer-reviewed scientific contributions. This will be the basis of a whole new level of observation-informed science for the African continent. Most TAHMO stations are housed at African schools, with a local host-teacher who attends to basic day-to-day cleaning. These schools also receive free curricular support providing geographic, mathematical, statistical, hydrologic, and meteorological lessons that connect student to their environment and creates climate-aware citizens, which we believe is the most fundamental element of developing a climate-resilient society. Installation of these stations have been made possible through the support of private companies like IBM and development programmes through the Global Resilience Partnership, World Bank, USAID among others. The availability of these new data sets will help generate more accurate weather forecasts which will be made freely available across the African continent. TAHMO leverages low-cost cell phone data transmission with solid-state sensor technology (provided by the METER corporation) to provide a cost-effective, sustainable, and transformative solution to the climate observation gap in Africa.

Forecasting zoonotic cutaneous leishmaniasis using meteorological factors in eastern Fars province, Iran: a SARIMA analysis.

PubMed

Tohidinik, Hamid Reza; Mohebali, Mehdi; Mansournia, Mohammad Ali; Niakan Kalhori, Sharareh R; Ali-Akbarpour, Mohsen; Yazdani, Kamran

2018-05-22

To predict the occurrence of zoonotic cutaneous leishmaniasis (ZCL) and evaluate the effect of climatic variables on disease incidence in the east of Fars province, Iran using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model. The Box-Jenkins approach was applied to fit the SARIMA model for ZCL incidence from 2004 to 2015. Then the model was used to predict the number of ZCL cases for the year 2016. Finally, we assessed the relation of meteorological variables (rainfall, rainy days, temperature, hours of sunshine and relative humidity) with ZCL incidence. SARIMA(2,0,0) (2,1,0)12 was the preferred model for predicting ZCL incidence in the east of Fars province (validation Root Mean Square Error, RMSE = 0.27). It showed that ZCL incidence in a given month can be estimated by the number of cases occurring 1 and 2 months, as well as 12 and 24 months earlier. The predictive power of SARIMA models was improved by the inclusion of rainfall at a lag of 2 months (β = -0.02), rainy days at a lag of 2 months (β = -0.09) and relative humidity at a lag of 8 months (β = 0.13) as external regressors (P-values < 0.05). The latter was the best climatic variable for predicting ZCL cases (validation RMSE = 0.26). Time series models can be useful tools to predict the trend of ZCL in Fars province, Iran; thus, they can be used in the planning of public health programmes. Introducing meteorological variables into the models may improve their precision. © 2018 John Wiley & Sons Ltd.

The use of random forests in modelling short-term air pollution effects based on traffic and meteorological conditions: A case study in Wrocław.

PubMed

Kamińska, Joanna A

2018-07-01

Random forests, an advanced data mining method, are used here to model the regression relationships between concentrations of the pollutants NO 2 , NO x and PM 2.5 , and nine variables describing meteorological conditions, temporal conditions and traffic flow. The study was based on hourly values of wind speed, wind direction, temperature, air pressure and relative humidity, temporal variables, and finally traffic flow, in the two years 2015 and 2016. An air quality measurement station was selected on a main road, located a short distance (40 m) from a large intersection equipped with a traffic flow measurement system. Nine different time subsets were defined, based among other things on the climatic conditions in Wrocław. An analysis was made of the fit of models created for those subsets, and of the importance of the predictors. Both the fit and the importance of particular predictors were found to be dependent on season. The best fit was obtained for models created for the six-month warm season (April-September) and for the summer season (June-August). The most important explanatory variable in the models of concentrations of nitrogen oxides was traffic flow, while in the case of PM 2.5 the most important were meteorological conditions, in particular temperature, wind speed and wind direction. Temporal variables (except for month in the case of PM 2.5 ) were found to have no significant effect on the concentrations of the studied pollutants. Copyright © 2018 Elsevier Ltd. All rights reserved.

Factors associated with NO2 and NOX concentration gradients near a highway

NASA Astrophysics Data System (ADS)

Richmond-Bryant, J.; Snyder, M. G.; Owen, R. C.; Kimbrough, S.

2018-02-01

The objective of this research is to learn how the near-road gradient, in which NO2 and NOX (NO + NO2) concentrations are elevated, varies with changes in meteorological and traffic variables. Measurements of NO2 and NOX were obtained east of I-15 in Las Vegas and fit to functions whose slopes (dCNO2/dx and dCNOX/dx, respectively) characterize the size of the near-road zone where NO2 and NOX concentrations from mobile sources on the highway are elevated. These metrics were used to learn about the near-road gradient by modeling dCNO2/dx and dCNOX/dx as functions of meteorological variables (e.g., wind direction, wind speed), traffic (vehicle count), NOX concentration upwind of the road, and O3 concentration at two fixed-site ambient monitors. Generalized additive models (GAM) were used to model dCNO2/dx and dCNOX/dx versus the independent variables because they allowed for nonlinearity of the variables being compared. When data from all wind directions were included in the analysis, variability in O3 concentration comprised the largest proportion of variability in dCNO2/dx, followed by variability in wind direction. In a second analysis constrained to winds from the west, variability in O3 concentration remained the largest contributor to variability in dCNO2/dx, but the relative contribution of variability in wind speed to variability in dCNO2/dx increased relative to its contribution for the all-wind analysis. When data from all wind directions were analyzed, variability in wind direction was by far the largest contributor to variability in dCNOX/dx, with smaller contributions from hour of day and upwind NOX concentration. When only winds from the west were analyzed, variability in upwind NOX concentration, wind speed, hour of day, and traffic count all were associated with variability in dCNOX/dx. Increases in O3 concentration were associated with increased magnitude near-road dCNO2/dx, possibly shrinking the zone of elevated concentrations occurring near roads. Wind direction parallel to the highway was also related to an increased magnitude of both dCNO2/dx and dCNOX/dx, again likely shrinking the zone of elevated concentrations occurring near roads. Wind direction perpendicular to the road decreased the magnitude of dCNO2/dx and dCNOX/dx and likely contributed to growth of the zone of elevated concentrations occurring near roads. Thus, variability in near-road concentrations is influenced by local meteorology and ambient O3 concentration.

Studies of Arctic Tropospheric Ozone Depletion Events Through Buoy-Borne Observations and Laboratory Studies

NASA Astrophysics Data System (ADS)

Halfacre, John W.

The photochemically-induced destruction of ground-level Arctic ozone in the Arctic occurs at the onset of spring, in concert with polar sunrise. Solar radiation is believed to stimulate a series of reactions that cause the production and release of molecular halogens from frozen, salty surfaces, though this mechanism is not yet well understood. The subsequent photolysis of molecular halogens produces reactive halogen atoms that remove ozone from the atmosphere in these so-called "Ozone Depletion Events" (ODEs). Given that much of the Arctic region is sunlit, meteorologically stable, and covered by saline ice and snow, it is expected that ODEs could be a phenomenon that occurs across the entire Arctic region. Indeed, an ever-growing body of evidence from coastal sites indicates that Arctic air masses devoid of O3 most often pass over sea ice-covered regions before arriving at an observation site, suggesting ODE chemistry occurs upwind over the frozen Arctic Ocean. However, outside of coastal observations, there exist very few long-term observations from the Arctic Ocean from which quantitative assessments of basic ODE characteristics can be made. This work presents the interpretation of ODEs through unique chemical and meteorological observations from several ice-tethered buoys deployed around the Arctic Ocean. These observations include detection of ozone, bromine monoxide, and measurements of temperature, relative humidity, atmospheric pressure, wind speed, and wind direction. To assess whether the O-Buoys were observing locally based depletion chemistry or the transport of ozone-poor air masses, periods of ozone decay were interpreted based on current understanding of ozone depletion kinetics, which are believed to follow a pseudo-first order rate law. In addition, the spatial extents of ODEs were estimated using air mass trajectory modeling to assess whether they are a localized or synoptic phenomenon. Results indicate that current understanding of the responsible chemical mechanisms are lacking, ODEs are observed primarily due to air mass transport (even in the Arctic Ocean), or some combination of both. Air mass trajectory modeling was also used in tandem with remote sensing observations of sea ice to determine the types of surfaces air masses were exposed to before arriving at O-Buoys. The impact of surface exposure was subsequently compared with local meteorology to assess which variables had the most effect on O 3 variability. For two observation sites, the impact of local meteorology was significantly stronger than air mass history, while a third was inconclusive. Finally, this work tests the viability of the hypothesis that initial production of molecular halogens from frozen saline surfaces results from photolytic production of the hydroxyl radical, and could be enhanced in the presence of O3. This investigation was enabled by a custom frozen-walled flow reactor coupled with chemical ionization spectrometry. It was found that hydroxyl radical could indeed promote the production and release of iodine, bromine, and chlorine, and that this production could be enhanced in the presence of ozone.

Multi-year application of WRF-CAM5 over East Asia-Part II: Interannual variability, trend analysis, and aerosol indirect effects

NASA Astrophysics Data System (ADS)

Zhang, Yang; Wang, Kai; He, Jian

2017-09-01

Following a comprehensive evaluation of WRF-CAM5 in Part I, Part II describes analyses of interannual variability, multi-year variation trends, and the direct, indirect, and total effects of anthropogenic aerosols. The interannual variations of chemical column and surface concentrations, and ozone (O3)/particulate matter (PM) indicators are strongly correlated to anthropogenic emission changes. Despite model biases, the model captures well the observed interannual variations of temperature at 2-m, cloud fraction, shortwave cloud forcing, downwelling shortwave radiation, cloud droplet number concentration, column O3, and column formaldehyde (HCHO) for the whole domain. While the model reproduces the volatile organic compound (VOC)-limited regimes of O3 chemistry at sites in Hong Kong, Taiwan, Japan, South Korea, and from the Acid Deposition Monitoring Network in East Asia (EANET) and the degree of sulfate neutralization at the EANET sites, it has limited capability in capturing the interannual variations of the ratio of O3 and nitrogen dioxide (O3/NO2) and PM chemical regime indicators, due to uncertainties in the emissions of precursors for O3 and secondary PM, the model assumption for ammonium bisulfate (NH4HSO4) as well as lack of gas/particle partitioning of total ammonia and total nitrate. While the variation trends in multi-year periods in aerosol optical depth and column concentrations of carbon monoxide, sulfur dioxide, and NO2 are mainly caused by anthropogenic emissions, those of major meteorological and cloud variables partly reflect feedbacks of chemistry to meteorological variables. The impacts of anthropogenic aerosol indirect effects either dominate or play an important role in the aerosol total effects for most cloud and chemical predictions, whereas anthropogenic aerosol direct effects influence most meteorological and radiation variables. The direct, indirect, and total effects of anthropogenic aerosols exhibit a strong interannual variability in 2001, 2006, and 2011.

Meteorological, environmental remote sensing and neural network analysis of the epidemiology of malaria transmission in Thailand.

PubMed

Kiang, Richard; Adimi, Farida; Soika, Valerii; Nigro, Joseph; Singhasivanon, Pratap; Sirichaisinthop, Jeeraphat; Leemingsawat, Somjai; Apiwathnasorn, Chamnarn; Looareesuwan, Sornchai

2006-11-01

In many malarious regions malaria transmission roughly coincides with rainy seasons, which provide for more abundant larval habitats. In addition to precipitation, other meteorological and environmental factors may also influence malaria transmission. These factors can be remotely sensed using earth observing environmental satellites and estimated with seasonal climate forecasts. The use of remote sensing usage as an early warning tool for malaria epidemics have been broadly studied in recent years, especially for Africa, where the majority of the world's malaria occurs. Although the Greater Mekong Subregion (GMS), which includes Thailand and the surrounding countries, is an epicenter of multidrug resistant falciparum malaria, the meteorological and environmental factors affecting malaria transmissions in the GMS have not been examined in detail. In this study, the parasitological data used consisted of the monthly malaria epidemiology data at the provincial level compiled by the Thai Ministry of Public Health. Precipitation, temperature, relative humidity, and vegetation index obtained from both climate time series and satellite measurements were used as independent variables to model malaria. We used neural network methods, an artificial-intelligence technique, to model the dependency of malaria transmission on these variables. The average training accuracy of the neural network analysis for three provinces (Kanchanaburi, Mae Hong Son, and Tak) which are among the provinces most endemic for malaria, is 72.8% and the average testing accuracy is 62.9% based on the 1994-1999 data. A more complex neural network architecture resulted in higher training accuracy but also lower testing accuracy. Taking into account of the uncertainty regarding reported malaria cases, we divided the malaria cases into bands (classes) to compute training accuracy. Using the same neural network architecture on the 19 most endemic provinces for years 1994 to 2000, the mean training accuracy weighted by provincial malaria cases was 73%. Prediction of malaria cases for 2001 using neural networks trained for 1994-2000 gave a weighted accuracy of 53%. Because there was a significant decrease (31%) in the number of malaria cases in the 19 provinces from 2000 to 2001, the networks overestimated malaria transmissions. The decrease in transmission was not due to climatic or environmental changes. Thailand is a country with long borders. Migrant populations from the neighboring countries enlarge the human malaria reservoir because these populations have more limited access to health care. This issue also confounds the complexity of modeling malaria based on meteorological and environmental variables alone. In spite of the relatively low resolution of the data and the impact of migrant populations, we have uncovered a reasonably clear dependency of malaria on meteorological and environmental remote sensing variables. When other contextual determinants do not vary significantly, using neural network analysis along with remote sensing variables to predict malaria endemicity should be feasible.

Regional yield predictions of malting barley by remote sensing and ancillary data

NASA Astrophysics Data System (ADS)

Weissteiner, Christof J.; Braun, Matthias; Kuehbauch, Walter

2004-02-01

Yield forecasts are of high interest to the malting and brewing industry in order to allow the most convenient purchasing policy of raw materials. Within this investigation, malting barley yield forecasts (Hordeum vulgare L.) were performed for typical growing regions in South-Western Germany. Multisensoral and multitemporal Remote Sensing data on one hand and ancillary meteorological, agrostatistical, topographical and pedological data on the other hand were used as input data for prediction models, which were based on an empirical-statistical modeling approach. Since spring barley production is depending on acreage and on the yield per area, classification is needed, which was performed by a supervised multitemporal classification algorithm, utilizing optical Remote Sensing data (LANDSAT TM/ETM+). Comparison between a pixel-based and an object-oriented classification algorithm was carried out. The basic version of the yield estimation model was conducted by means of linear correlation of Remote Sensing data (NOAA-AVHRR NDVI), CORINE land cover data and agrostatistical data. In an extended version meteorological data (temperature, precipitation, etc.) and soil data was incorporated. Both, basic and extended prediction systems, led to feasible results, depending on the selection of the time span for NDVI accumulation.

Deriving meteorological variables across Africa for the study and control of vector-borne disease: a comparison of remote sensing and spatial interpolation of climate

PubMed Central

Hay, S. I.; Lennon, J. J.

2012-01-01

Summary This paper presents the results of an investigation into the utility of remote sensing (RS) using meteorological satellites sensors and spatial interpolation (SI) of data from meteorological stations, for the prediction of spatial variation in monthly climate across continental Africa in 1990. Information from the Advanced Very High Resolution Radiometer (AVHRR) of the National Oceanic and Atmospheric Administration’s (NOAA) polar-orbiting meteorological satellites was used to estimate land surface temperature (LST) and atmospheric moisture. Cold cloud duration (CCD) data derived from the High Resolution Radiometer (HRR) on-board the European Meteorological Satellite programme’s (EUMETSAT) Meteosat satellite series were also used as a RS proxy measurement of rainfall. Temperature, atmospheric moisture and rainfall surfaces were independently derived from SI of measurements from the World Meteorological Organization (WMO) member stations of Africa. These meteorological station data were then used to test the accuracy of each methodology, so that the appropriateness of the two techniques for epidemiological research could be compared. SI was a more accurate predictor of temperature, whereas RS provided a better surrogate for rainfall; both were equally accurate at predicting atmospheric moisture. The implications of these results for mapping short and long-term climate change and hence their potential for the study and control of disease vectors are considered. Taking into account logistic and analytical problems, there were no clear conclusions regarding the optimality of either technique, but there was considerable potential for synergy. PMID:10203175

Deriving meteorological variables across Africa for the study and control of vector-borne disease: a comparison of remote sensing and spatial interpolation of climate.

PubMed

Hay, S I; Lennon, J J

1999-01-01

This paper presents the results of an investigation into the utility of remote sensing (RS) using meteorological satellites sensors and spatial interpolation (SI) of data from meteorological stations, for the prediction of spatial variation in monthly climate across continental Africa in 1990. Information from the Advanced Very High Resolution Radiometer (AVHRR) of the National Oceanic and Atmospheric Administration's (NOAA) polar-orbiting meteorological satellites was used to estimate land surface temperature (LST) and atmospheric moisture. Cold cloud duration (CCD) data derived from the High Resolution Radiometer (HRR) on-board the European Meteorological Satellite programme's (EUMETSAT) Meteosat satellite series were also used as a RS proxy measurement of rainfall. Temperature, atmospheric moisture and rainfall surfaces were independently derived from SI of measurements from the World Meteorological Organization (WMO) member stations of Africa. These meteorological station data were then used to test the accuracy of each methodology, so that the appropriateness of the two techniques for epidemiological research could be compared. SI was a more accurate predictor of temperature, whereas RS provided a better surrogate for rainfall; both were equally accurate at predicting atmospheric moisture. The implications of these results for mapping short and long-term climate change and hence their potential for the study and control of disease vectors are considered. Taking into account logistic and analytical problems, there were no clear conclusions regarding the optimality of either technique, but there was considerable potential for synergy.

Relative performance of empirical and physical models in assessing the seasonal and annual glacier surface mass balance of Saint-Sorlin Glacier (French Alps)

NASA Astrophysics Data System (ADS)

Réveillet, Marion; Six, Delphine; Vincent, Christian; Rabatel, Antoine; Dumont, Marie; Lafaysse, Matthieu; Morin, Samuel; Vionnet, Vincent; Litt, Maxime

2018-04-01

This study focuses on simulations of the seasonal and annual surface mass balance (SMB) of Saint-Sorlin Glacier (French Alps) for the period 1996-2015 using the detailed SURFEX/ISBA-Crocus snowpack model. The model is forced by SAFRAN meteorological reanalysis data, adjusted with automatic weather station (AWS) measurements to ensure that simulations of all the energy balance components, in particular turbulent fluxes, are accurately represented with respect to the measured energy balance. Results indicate good model performance for the simulation of summer SMB when using meteorological forcing adjusted with in situ measurements. Model performance however strongly decreases without in situ meteorological measurements. The sensitivity of the model to meteorological forcing indicates a strong sensitivity to wind speed, higher than the sensitivity to ice albedo. Compared to an empirical approach, the model exhibited better performance for simulations of snow and firn melting in the accumulation area and similar performance in the ablation area when forced with meteorological data adjusted with nearby AWS measurements. When such measurements were not available close to the glacier, the empirical model performed better. Our results suggest that simulations of the evolution of future mass balance using an energy balance model require very accurate meteorological data. Given the uncertainties in the temporal evolution of the relevant meteorological variables and glacier surface properties in the future, empirical approaches based on temperature and precipitation could be more appropriate for simulations of glaciers in the future.

The relationship between Arabian Sea upwelling and Indian monsoon revisited

NASA Astrophysics Data System (ADS)

Yi, X.; Hünicke, B.; Tim, N.; Zorita, E.

2015-11-01

Studies based on upwelling indices (sediment records, sea-surface temperature and wind) suggest that upwelling along the western coast of Arabian Sea is strongly affected by the Indian summer monsoon (ISM). In order to examine this relationship directly, we employ the vertical water mass transport produced by the eddy-resolving global ocean simulation STORM driven by meteorological reanalysis over the last 61 years. With its very high spatial resolution (10 km), STORM allows us to identify characteristics of the upwelling system. We analyze the co-variability between upwelling and meteorological and oceanic variables from 1950 to 2010. The analyses reveal high interannual correlations between coastal upwelling and along-shore wind-stress (r=0.73) as well as with sea-surface temperature (r0.83). However, the correlation between the upwelling and the ISM is small and other factors might contribute to the upwelling variability. In addition, no long-term trend is detected in our modeled upwelling time series.

Early meteorological records from Latin-America and the Caribbean during the 18th and 19th centuries

PubMed Central

Domínguez-Castro, Fernando; Vaquero, José Manuel; Gallego, María Cruz; Farrona, Ana María Marín; Antuña-Marrero, Juan Carlos; Cevallos, Erika Elizabeth; Herrera, Ricardo García; de la Guía, Cristina; Mejía, Raúl David; Naranjo, José Manuel; del Rosario Prieto, María; Ramos Guadalupe, Luis Enrique; Seiner, Lizardo; Trigo, Ricardo Machado; Villacís, Marcos

2017-01-01

This paper provides early instrumental data recovered for 20 countries of Latin-America and the Caribbean (Argentina, Bahamas, Belize, Brazil, British Guiana, Chile, Colombia, Costa Rica, Cuba, Ecuador, France (Martinique and Guadalupe), Guatemala, Jamaica, Mexico, Nicaragua, Panama, Peru, Puerto Rico, El Salvador and Suriname) during the 18th and 19th centuries. The main meteorological variables retrieved were air temperature, atmospheric pressure, and precipitation, but other variables, such as humidity, wind direction, and state of the sky were retrieved when possible. In total, more than 300,000 early instrumental data were rescued (96% with daily resolution). Especial effort was made to document all the available metadata in order to allow further post-processing. The compilation is far from being exhaustive, but the dataset will contribute to a better understanding of climate variability in the region, and to enlarging the period of overlap between instrumental data and natural/documentary proxies. PMID:29135974

Crop weather models of corn and soybeans for Agrophysical Units (APU's) in Iowa using monthly meteorological predictors

NASA Technical Reports Server (NTRS)

Leduc, S. (Principal Investigator)

1982-01-01

Models based on multiple regression were developed to estimate corn and soybean yield from weather data for agrophysical units (APU) in Iowa. The predictor variables are derived from monthly average temperature and monthly total precipitation data at meteorological stations in the cooperative network. The models are similar in form to the previous models developed for crop reporting districts (CRD). The trends and derived variables were the same and the approach to select the significant predictors was similar to that used in developing the CRD models. The APU's were selected to be more homogeneous with respect crop to production than the CRDs. The APU models are quite similar to the CRD models, similar explained variation and number of predictor variables. The APU models are to be independently evaluated and compared to the previously evaluated CRD models. That comparison should indicate the preferred model area for this application, i.e., APU or CRD.

How do scientists respond to anomalies? Different strategies used in basic and applied science.

PubMed

Trickett, Susan Bell; Trafton, J Gregory; Schunn, Christian D

2009-10-01

We conducted two in vivo studies to explore how scientists respond to anomalies. Based on prior research, we identify three candidate strategies: mental simulation, mental manipulation of an image, and comparison between images. In Study 1, we compared experts in basic and applied domains (physics and meteorology). We found that the basic scientists used mental simulation to resolve an anomaly, whereas applied science practitioners mentally manipulated the image. In Study 2, we compared novice and expert meteorologists. We found that unlike experts, novices used comparison to address anomalies. We discuss the nature of expertise in the two kinds of science, the relationship between the type of science and the task performed, and the relationship of the strategies investigated to scientific creativity. Copyright © 2009 Cognitive Science Society, Inc.

Mesoscale Convective Complex versus Non-Mesoscale Convective Complex Thunderstorms: A Comparison of Selected Meteorological Variables.

DTIC Science & Technology

1986-08-01

mean square errors for selected variables . . 34 8. Variable range and mean value for MCC and non-MCC cases . . 36 9. Alpha ( a ) levels at which the...Table 9. For each variable, the a level is listed at which the two mean values are determined to be significantly 38 Table 9. Alpha ( a ) levels at...vorticity advection None 700 mb vertical velocity forecast .20 different. These a levels express the probability of erroneously con- cluding that the

Connections between meteorological and hydrological droughts in a semi-arid basin of the middle Yellow River

NASA Astrophysics Data System (ADS)

Li, Binquan; Zhu, Changchang; Liang, Zhongmin; Wang, Guoqing; Zhang, Yu

2018-06-01

Differences between meteorological and hydrological droughts could reflect the regional water consumption by both natural elements and human water-use. The connections between these two drought types were analyzed using the Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Streamflow Index (SSI), respectively. In a typical semi-arid basin of the middle Yellow River (Qingjianhe River basin), annual precipitation and air temperature showed significantly downward and upward trends, respectively, with the rates of -2.37 mm yr-1 and 0.03 °C yr-1 (1961-2007). Under their synthetic effects, water balance variable (represented by SPEI) showed obviously downward (drying) trend at both upstream and whole basin areas. For the spatial variability of precipitation, air temperature and the calculated SPEI, both upstream and downstream areas experienced very similar change characteristics. Results also suggested that the Qingjianhe River basin experienced near normal condition during the study period. As a whole, this semi-arid basin mainly had the meteorological drought episodes in the mid-1960s, late-1990s and the 2000s depicted by 12-month SPEI. The drying trend could also be depicted by the hydrological drought index (12-month SSI) at both upstream and downstream stations (Zichang and Yanchuan), but the decreasing trends were not significant. A correlation analysis showed that hydrological system responds rapidly to the change of meteorological conditions in this semi-arid region. This finding could be an useful implication to drought research for those semi-arid basins with intensive human activities.

Decadal variations in atmospheric water vapor time series estimated using GNSS, ERA-Interim, and synoptic data

NASA Astrophysics Data System (ADS)

Alshawaf, Fadwa; Dick, Galina; Heise, Stefan; Balidakis, Kyriakos; Schmidt, Torsten; Wickert, Jens

2017-04-01

Ground-based GNSS (Global Navigation Satellite Systems) have efficiently been used since the 1990s as a meteorological observing system. Recently scientists used GNSS time series of precipitable water vapor (PWV) for climate research although they may not be sufficiently long. In this work, we compare the trend estimated from GNSS time series with that estimated from European Center for Medium-RangeWeather Forecasts Reanalysis (ERA-Interim) data and meteorological measurements.We aim at evaluating climate evolution in Central Europe by monitoring different atmospheric variables such as temperature and PWV. PWV time series were obtained by three methods: 1) estimated from ground-based GNSS observations using the method of precise point positioning, 2) inferred from ERA-Interim data, and 3) determined based on daily surface measurements of temperature and relative humidity. The other variables are available from surface meteorological stations or received from ERA-Interim. The PWV trend component estimated from GNSS data strongly correlates (>70%) with that estimated from the other data sets. The linear trend is estimated by straight line fitting over 30 years of seasonally-adjusted PWV time series obtained using the meteorological measurements. The results show a positive trend in the PWV time series with an increase of 0.2-0.7 mm/decade with a mean standard deviations of 0.016 mm/decade. In this paper, we present the results at three GNSS stations. The temporal increment of the PWV correlates with the temporal increase in the temperature levels.

Japan Meteorological Agency/Meteorological Research Institute-Coupled Prediction System version 2 (JMA/MRI-CPS2): atmosphere-land-ocean-sea ice coupled prediction system for operational seasonal forecasting

NASA Astrophysics Data System (ADS)

Takaya, Yuhei; Hirahara, Shoji; Yasuda, Tamaki; Matsueda, Satoko; Toyoda, Takahiro; Fujii, Yosuke; Sugimoto, Hiroyuki; Matsukawa, Chihiro; Ishikawa, Ichiro; Mori, Hirotoshi; Nagasawa, Ryoji; Kubo, Yutaro; Adachi, Noriyuki; Yamanaka, Goro; Kuragano, Tsurane; Shimpo, Akihiko; Maeda, Shuhei; Ose, Tomoaki

2018-02-01

This paper describes the Japan Meteorological Agency/Meteorological Research Institute-Coupled Prediction System version 2 (JMA/MRI-CPS2), which was put into operation in June 2015 for the purpose of performing seasonal predictions. JMA/MRI-CPS2 has various upgrades from its predecessor, JMA/MRI-CPS1, including improved resolution and physics in its atmospheric and oceanic components, introduction of an interactive sea-ice model and realistic initialization of its land component. Verification of extensive re-forecasts covering a 30-year period (1981-2010) demonstrates that JMA/MRI-CPS2 possesses improved seasonal predictive skills for both atmospheric and oceanic interannual variability as well as key coupled variability such as the El Niño-Southern Oscillation (ENSO). For ENSO prediction, the new system better represents the forecast uncertainty and transition/duration of ENSO phases. Our analysis suggests that the enhanced predictive skills are attributable to incremental improvements resulting from all of the changes, as is apparent in the beneficial effects of sea-ice coupling and land initialization on 2-m temperature predictions. JMA/MRI-CPS2 is capable of reasonably representing the seasonal cycle and secular trends of sea ice. The sea-ice coupling remarkably enhances the predictive capability for the Arctic 2-m temperature, indicating the importance of this factor, particularly for seasonal predictions in the Arctic region.

Interannual Variability of Ammonia Concentrations over the United States: Sources and Implications for Inorganic Particulate Matter

NASA Astrophysics Data System (ADS)

Schiferl, L. D.; Heald, C. L.; Van Damme, M.; Pierre-Francois, C.; Clerbaux, C.

2015-12-01

Modern agricultural practices have greatly increased the emission of ammonia (NH3) to the atmosphere. Recent controls to reduce the emissions of sulfur and nitrogen oxides (SOX and NOX) have increased the importance of understanding the role ammonia plays in the formation of surface fine inorganic particulate matter (PM2.5) in the United States. In this study, we identify the interannual variability in ammonia concentration, explore the sources of this variability and determine their contribution to the variability in surface PM2.5 concentration. Over the summers of 2008-2012, measurements from the Ammonia Monitoring Network (AMoN) and the Infrared Atmospheric Sounding Interferometer (IASI) satellite instrument show considerable variability in both surface and column ammonia concentrations (+/- 29% and 28% of the mean), respectively. This observed variability is larger than that simulated by the GEOS-Chem chemical transport model, where meteorology dominates the variability in ammonia and PM2.5 concentrations compared to the changes caused by SOX and NOX reductions. Our initial simulation does not include year-to-year changes in ammonia agricultural emissions. We use county-wide information on fertilizer sales and livestock populations, as well as meteorological variations to account for the interannual variability in agricultural activity and ammonia volatilization. These sources of ammonia emission variability are important for replicating observed variations in ammonia and PM2.5, highlighting how accurate ammonia emissions characterization is central to PM air quality prediction.

Evaluation of Different MODIS AOD Retrieval Algorithms for PM (sub 2.5) Estimation in the Western, Midwestern and Southeastern United States with Implications for Public Health

NASA Technical Reports Server (NTRS)

Al-Hamdan, Mohammad; Crosson, William; Burrows, Erica; Coffield, Shane; Crane, Breanna

2016-01-01

This study was part of the research activities of the Center for Applied Atmospheric Research and Education (CAARE) funded by the NASA MUREP (Minority University Research and Education Project) Institutional Research Opportunity (MIRO) Program. Satellite measurements of Aerosol Optical Depth (AOD) have been shown to be correlated with ground measurements of fine particulate matter less than 2.5 microns PM (sub 2.5), which in turn has been linked to respiratory and heart diseases. The strength of the correlation between AOD and PM (sub 2.5) varies for different AOD retrieval algorithms and geographic regions. We evaluated several Moderate Resolution Imaging Spectrometer (MODIS) AOD products from different satellites (Aqua vs. Terra), retrieval algorithms (Dark Target versus Deep Blue), Collections (5.1 versus 6) and spatial resolutions (10-kilometers versus 3-kilometers) for cities in the Western, Midwestern and Southeastern U.S. We developed and validated PM (sub 2.5) prediction models using remotely-sensed AOD data, which were improved by incorporating meteorological variables (temperature, relative humidity, precipitation, wind speed, and wind direction) from the North American Land Data Assimilation System Phase 2 (NLDAS-2). Adding these meteorological data significantly improved the predictive power of all the PM (sub 2.5) models, especially in the Western U.S. Temperature, relative humidity and wind speed were the most significant meteorological variables throughout the year in the Western U.S. Wind speed was the most significant meteorological variable for the cold season while temperature was the most significant variable for the warm season in the Midwestern and Southeastern U.S. Our study re-establishes the connection between PM (sub 2.5) and public health concerns including respiratory and cardiovascular diseases (asthma, high blood pressure, coronary heart disease, heart attack, and stroke). Using PM (sub 2.5) data and health data from the Centers for Disease Control and Prevention (CDC)'s Behavioral Risk Factor Surveillance System (BRFSS), our statistical analysis showed that heart attack and stroke occurrences had the strongest correlations with PM (sub 2.5).

Evaluation of Different MODIS AOD Retrieval Algorithms for PM2.5 Estimation in the Western, Midwestern and Southeastern United States with Implications for Public Health

NASA Astrophysics Data System (ADS)

Al-Hamdan, M. Z.; Crosson, W. L.; Burrows, E. C.; Coffield, S.; Crane, B.

2016-12-01

This study was part of the research activities of the Center for Applied Atmospheric Research and Education (CAARE) funded by the NASA MUREP Institutional Research Opportunity (MIRO) Program. Satellite measurements of Aerosol Optical Depth (AOD) have been shown to be correlated with ground measurements of fine particulate matter less than 2.5 microns (PM2.5), which in turn has been linked to respiratory and heart diseases. The strength of the correlation between AOD and PM2.5 varies for different AOD retrieval algorithms and geographic regions. We evaluated several Moderate Resolution Imaging Spectrometer (MODIS) AOD products from different satellites (Aqua vs. Terra), retrieval algorithms (Dark Target vs. Deep Blue), Collections (5.1 vs. 6) and spatial resolutions (10-km vs. 3-km) for cities in the Western, Midwestern and Southeastern United States. We developed and validated PM2.5 prediction models using remotely sensed AOD data, which were improved by incorporating meteorological variables (temperature, relative humidity, precipitation, wind speed, and wind direction) from the North American Land Data Assimilation System Phase 2 (NLDAS-2). Adding these meteorological data significantly improved the predictive power of all the PM2.5 models, and especially in the Western U.S. Temperature, relative humidity and wind speed were the most significant meteorological variables throughout the year in the Western U.S. Wind speed was the most significant meteorological variable for the cold season while temperature was the most significant variable for the warm season in the Midwestern and Southeastern U.S. Finally, our study re-establishes the connection between PM2.5 and public health concerns including respiratory and cardiovascular diseases (asthma, high blood pressure, coronary heart disease, heart attack, and stroke). Using PM2.5 data and health data from the Centers for Disease Control and Prevention (CDC)'s Behavioral Risk Factor Surveillance System (BRFSS), our statistical analysis showed that heart attack and stroke occurrences had the strongest correlations with PM2.5.

Hydro-meteorological functioning of the Eastern Andean Tropical Montane Cloud Forests: Insight from a paired catchment study in the Orinoco river basin highlands

NASA Astrophysics Data System (ADS)

Ramirez, Beatriz; Teuling, Adriaan J.; Ganzeveld, Laurens; Leemans, Rik

2016-04-01

Tropical forests regulate large scale precipitation patterns and catchment-scale streamflow, while tropical mountains influence runoff by orographic effects and snowmelt. Along tropical elevation gradients, these climate/ecosystem/hydrological interactions are specific and heterogeneous. These interactions are poorly understood and represented in hydro-meteorological monitoring networks and regional or global earth system models. A typical case are the South American Tropical Montane Cloud Forests (TMCF), whose water balance is strongly driven by fog persistence. This also depends on local and up wind temperature and moisture, and changes in this balance alter the impacts of changes in land use and climate on hydrology. These TMCFs were until 2010 only investigated up to 350km from the coast. Continental TMCFs are largely ignored. This gap is covered by our study area, which is part of the Orinoco river basin highlands and located on the northern Eastern Andes at an altitudinal range of 1550 to 2300m a.s.l. The upwind part of our study area is dominated by lowland savannahs that are flooded seasonally. Because meteorological stations are absent in our study area, we first describe the spatial and seasonal meteorological variability and analyse the corresponding catchment hydrology. Our hydro-meteorological data set is collected at three gauged neighbouring catchments with contrasting TMCF/grassland cover from June 2013 to May 2014 and includes hourly solar radiation, temperature, relative humidity, wind speed, precipitation, soil moisture and runoff measurements. We compare our results with recent TCMF studies in the eastern Andean highlands in the Amazon basin. The studied elevational range always shows wetter conditions at higher elevations. This indicates a positive relation between elevation and fog or rainfall persistence. Lower elevations are more seasonally variable. Soil moisture data indicate that TMCFs do not use persistently more water than grasslands. Runoff data from our three catchments reflect the interaction between ecosystems and elevation. The less-forested catchment at lower elevations has a more seasonally variable runoff and present the lowest base flows during the dry season. In this season, soil water storage and the wetter conditions at higher elevations are crucial to sustain their base flow. The hydro-meteorological patterns of our study area are similar to those at the eastern Andean TMCF sites, but differences in the elevation of fog and rainfall persistence suggest that specific upwind ecosystem conditions and distance to the coast are important to explain and understand regional seasonal differences.

Meteorological services annual data report for 2017

DOE Office of Scientific and Technical Information (OSTI.GOV)

Heiser, John

This document presents the meteorological data collected at Brookhaven National Laboratory (BNL) by Meteorological Services (Met Services) for the calendar year 2017. The purpose is to publicize the data sets available to emergency personnel, researchers and facility operations. Met services has been collecting data at BNL since 1949. Data from 1994 to the present is available in digital format. Data is presented in monthly plots of one-minute data. This allows the reader the ability to peruse the data for trends or anomalies that may be of interest to them. Full data sets are available to BNL personnel and to amore » limited degree outside researchers. The full data sets allow plotting the data on expanded time scales to obtain greater details (e.g., daily solar variability, inversions, etc.).« less

Meteorological services annual data report for 2015

DOE Office of Scientific and Technical Information (OSTI.GOV)

Heiser, John; Smith, Scott

This document presents the meteorological data collected at Brookhaven National Laboratory (BNL) by Meteorological Services (Met Services) for the calendar year 2015. The purpose is to publicize the data sets available to emergency personnel, researchers and facility operations. Met services has been collecting data at BNL since 1949. Data from 1994 to the present is available in digital format. Data is presented in monthly plots of one-minute data. This allows the reader the ability to peruse the data for trends or anomalies that may be of interest to them. Full data sets are available to BNL personnel and to amore » limited degree outside researchers. The full data sets allow plotting the data on expanded time scales to obtain greater details (e.g., daily solar variability, inversions, etc.).« less

Meteorological services annual data report for 2016

DOE Office of Scientific and Technical Information (OSTI.GOV)

Heiser, John; Smith, S.

This document presents the meteorological data collected at Brookhaven National Laboratory (BNL) by Meteorological Services (Met Services) for the calendar year 2016. The purpose is to publicize the data sets available to emergency personnel, researchers and facility operations. Met services has been collecting data at BNL since 1949. Data from 1994 to the present is available in digital format. Data is presented in monthly plots of one-minute data. This allows the reader the ability to peruse the data for trends or anomalies that may be of interest to them. Full data sets are available to BNL personnel and to amore » limited degree outside researchers. The full data sets allow plotting the data on expanded time scales to obtain greater details (e.g., daily solar variability, inversions, etc.).« less

Hydrological Responses of Andean Lakes and Tropical Floodplains to Climate Variability and Human Intervention: an Integrative Modelling Framework

NASA Astrophysics Data System (ADS)

Hoyos, I. C.; González Morales, C.; Serna López, J. P.; Duque, C. L.; Canon Barriga, J. E.; Dominguez, F.

2013-12-01

Andean water bodies in tropical regions are significantly influenced by fluctuations associated with climatic and anthropogenic drivers, which implies long term changes in mountain snow peaks, land covers and ecosystems, among others. Our work aims at providing an integrative framework to realistically assess the possible future of natural water bodies with different degrees of human intervention. We are studying in particular the evolution of three water bodies in Colombia: two Andean lakes and a floodplain wetland. These natural reservoirs represent the accumulated effect of hydrological processes in their respective basins, which exhibit different patterns of climate variability and distinct human intervention and environmental histories. Modelling the hydrological responses of these local water bodies to climate variability and human intervention require an understanding of the strong linkage between geophysical and social factors. From the geophysical perspective, the challenge is how to downscale global climate projections in the local context: complex orography and relative lack of data. To overcome this challenge we combine the correlational and physically based analysis of several sources of spatially distributed biophysical and meteorological information to accurately determine aspects such as moisture sources and sinks and past, present and future local precipitation and temperature regimes. From the social perspective, the challenge is how to adequately represent and incorporate into the models the likely response of social agents whose water-related interests are diverse and usually conflictive. To deal with the complexity of these systems we develop interaction matrices, which are useful tools to holistically discuss and represent each environment as a complex system. Our goal is to assess partially the uncertainties of the hydrological balances in these intervened water bodies we establish climate/social scenarios, using hybrid models that combine the computational power of numerical simulations (of both physical and social components) with interactive responses given by users who define strategies and make decisions in real time, providing valuable information about people's attitudes and choices regarding future climate perspectives. Part of our interest with this project is to effectively transfer the knowledge and scientific information gathered to the communities in a way that is useful and propositive. To this end we developed a website (http://peerlagoscolombia.udea.edu.co) that includes relevant information about the project outcomes. We also developed and installed telemetric hydrologic stations in each site, whose data on water storage levels and basic meteorological variables can be accessed online. Acknowledgement: this project is funded by the USAID-NSF PEER program (First cycle, project 31).

Fine-scale application of WRF-CAM5 during a dust storm episode over East Asia: Sensitivity to grid resolutions and aerosol activation parameterizations

NASA Astrophysics Data System (ADS)

Wang, Kai; Zhang, Yang; Zhang, Xin; Fan, Jiwen; Leung, L. Ruby; Zheng, Bo; Zhang, Qiang; He, Kebin

2018-03-01

An advanced online-coupled meteorology and chemistry model WRF-CAM5 has been applied to East Asia using triple-nested domains at different grid resolutions (i.e., 36-, 12-, and 4-km) to simulate a severe dust storm period in spring 2010. Analyses are performed to evaluate the model performance and investigate model sensitivity to different horizontal grid sizes and aerosol activation parameterizations and to examine aerosol-cloud interactions and their impacts on the air quality. A comprehensive model evaluation of the baseline simulations using the default Abdul-Razzak and Ghan (AG) aerosol activation scheme shows that the model can well predict major meteorological variables such as 2-m temperature (T2), water vapor mixing ratio (Q2), 10-m wind speed (WS10) and wind direction (WD10), and shortwave and longwave radiation across different resolutions with domain-average normalized mean biases typically within ±15%. The baseline simulations also show moderate biases for precipitation and moderate-to-large underpredictions for other major variables associated with aerosol-cloud interactions such as cloud droplet number concentration (CDNC), cloud optical thickness (COT), and cloud liquid water path (LWP) due to uncertainties or limitations in the aerosol-cloud treatments. The model performance is sensitive to grid resolutions, especially for surface meteorological variables such as T2, Q2, WS10, and WD10, with the performance generally improving at finer grid resolutions for those variables. Comparison of the sensitivity simulations with an alternative (i.e., the Fountoukis and Nenes (FN) series scheme) and the default (i.e., AG scheme) aerosol activation scheme shows that the former predicts larger values for cloud variables such as CDNC and COT across all grid resolutions and improves the overall domain-average model performance for many cloud/radiation variables and precipitation. Sensitivity simulations using the FN series scheme also have large impacts on radiations, T2, precipitation, and air quality (e.g., decreasing O3) through complex aerosol-radiation-cloud-chemistry feedbacks. The inclusion of adsorptive activation of dust particles in the FN series scheme has similar impacts on the meteorology and air quality but to lesser extent as compared to differences between the FN series and AG schemes. Compared to the overall differences between the FN series and AG schemes, impacts of adsorptive activation of dust particles can contribute significantly to the increase of total CDNC (∼45%) during dust storm events and indicate their importance in modulating regional climate over East Asia.

One multi-media environmental system with linkage between meteorology/ hydrology/ air quality models and water quality model

NASA Astrophysics Data System (ADS)

Tang, C.; Lynch, J. A.; Dennis, R. L.

2016-12-01

The biogeochemical processing of nitrogen and associated pollutants is driven by meteorological and hydrological processes in conjunction with pollutant loading. There are feedbacks between meteorology and hydrology that will be affected by land-use change and climate change. Changes in meteorology will affect pollutant deposition. It is important to account for those feedbacks and produce internally consistent simulations of meteorology, hydrology, and pollutant loading to drive the (watershed/water quality) biogeochemical models. In this study, the ecological response to emission reductions in streams in the Potomac watershed was evaluated. Firstly, we simulated the deposition by using the fully coupled Weather Research & Forecasting (WRF) model and the Community Multiscale Air Quality (CAMQ) model; secondly, we created the hydrological data by the offline linked Variable Infiltration Capacity (VIC) model and the WRF model. Lastly, we investigated the water quality by one comprehensive/environment model, namely the linkage of CMAQ, WRF, VIC and the Model of Acidification of Groundwater In Catchment (MAGIC) model from 2002 to 2010.The simulated results (such as NO3, SO4, and SBC) fit well to the observed values. The linkage provides a generally accurate, well-tested tool for evaluating sensitivities to varying meteorology and environmental changes on acidification and other biogeochemical processes, with capability to comprehensively explore strategic policy and management design.

Weather patterns as a downscaling tool - evaluating their skill in stratifying local climate variables

NASA Astrophysics Data System (ADS)

Murawski, Aline; Bürger, Gerd; Vorogushyn, Sergiy; Merz, Bruno

2016-04-01

The use of a weather pattern based approach for downscaling of coarse, gridded atmospheric data, as usually obtained from the output of general circulation models (GCM), allows for investigating the impact of anthropogenic greenhouse gas emissions on fluxes and state variables of the hydrological cycle such as e.g. on runoff in large river catchments. Here we aim at attributing changes in high flows in the Rhine catchment to anthropogenic climate change. Therefore we run an objective classification scheme (simulated annealing and diversified randomisation - SANDRA, available from the cost733 classification software) on ERA20C reanalyses data and apply the established classification to GCMs from the CMIP5 project. After deriving weather pattern time series from GCM runs using forcing from all greenhouse gases (All-Hist) and using natural greenhouse gas forcing only (Nat-Hist), a weather generator will be employed to obtain climate data time series for the hydrological model. The parameters of the weather pattern classification (i.e. spatial extent, number of patterns, classification variables) need to be selected in a way that allows for good stratification of the meteorological variables that are of interest for the hydrological modelling. We evaluate the skill of the classification in stratifying meteorological data using a multi-variable approach. This allows for estimating the stratification skill for all meteorological variables together, not separately as usually done in existing similar work. The advantage of the multi-variable approach is to properly account for situations where e.g. two patterns are associated with similar mean daily temperature, but one pattern is dry while the other one is related to considerable amounts of precipitation. Thus, the separation of these two patterns would not be justified when considering temperature only, but is perfectly reasonable when accounting for precipitation as well. Besides that, the weather patterns derived from reanalyses data should be well represented in the All-Hist GCM runs in terms of e.g. frequency, seasonality, and persistence. In this contribution we show how to select the most appropriate weather pattern classification and how the classes derived from it are reflected in the GCMs.

Probabilistic discrimination between liquid rainfall events, hailstorms, biomass burning and industrial fires from C-Band Radar Polarimetric Variables

NASA Astrophysics Data System (ADS)

Valencia, J. M.; Sepúlveda, J.; Hoyos, C.; Herrera, L.

2017-12-01

Characterization and identification of fire and hailstorm events using weather radar data in a tropical complex topography region is an important task in risk management and agriculture. Polarimetric variables from a C-Band Dual polarization weather radar have potential uses in particle classification, due to the relationship their sensitivity to shape, spatial orientation, size and fall behavior of particles. In this sense, three forest fires and two chemical fires were identified for the Áburra Valley regions. Measurements were compared between each fire event type and with typical data radar retrievals for liquid precipitation events. Results of this analysis show different probability density functions for each type of event according to the particles present in them. This is very important and useful result for early warning systems to avoid precipitation false alarms during fire events within the study region, as well as for the early detection of fires using radar retrievals in remote cases. The comparative methodology is extended to hailstorm cases. Complementary sensors like laser precipitation sensors (LPM) disdrometers and meteorological stations were used to select dates of solid precipitation occurrence. Then, in this dates weather radar data variables were taken in pixels surrounding the stations and solid precipitation polar values were statistically compared with liquid precipitation values. Spectrum precipitation measured by LPM disdrometer helps to define typical features like particles number, fall velocities and diameters for both precipitation types. In addition, to achieve a complete hailstorm characterization, other meteorological variables were analyzed: wind field from meteorological stations and radar wind profiler, profiling data from Micro Rain Radar (MRR), and thermodynamic data from a microwave radiometer.

Characterizing multiscale variability of zero intermittency in spatial rainfall

NASA Technical Reports Server (NTRS)

Kumar, Praveen; Foufoula-Georgiou, Efi

1994-01-01

In this paper the authors study how zero intermittency in spatial rainfall, as described by the fraction of area covered by rainfall, changes with spatial scale of rainfall measurement or representation. A statistical measure of intermittency that describes the size distribution of 'voids' (nonrainy areas imbedded inside rainy areas) as a function of scale is also introduced. Morphological algorithms are proposed for reconstructing rainfall intermittency at fine scales given the intermittency at coarser scales. These algorithms are envisioned to be useful in hydroclimatological studies where the rainfall spatial variability at the subgrid scale needs to be reconstructed from the results of synoptic- or mesoscale meteorological numerical models. The developed methodologies are demsonstrated and tested using data from a severe springtime midlatitude squall line and a mild midlatitude winter storm monitored by a meteorological radar in Norman, Oklahoma.

Prevalence of asthma symptoms in schoolchildren, and climate in west European countries: an ecologic study.

PubMed

Arnedo-Pena, Alberto; García-Marcos, Luis; Bercedo-Sanz, Alberto; Aguinaga-Ontoso, Inés; González-Díaz, Carlos; García-Merino, Agueda; Busquets-Monge, Rosa; Suárez-Varela, Maria Morales; Batlles-Garrido, Juan; Blanco-Quirós, Alfredo A; López-Silvarrey, Angel; García-Hernández, Gloria; Fuertes, Jorge

2013-09-01

The aim of the present study was to estimate the associations between the prevalence of asthma symptoms in schoolchildren and meteorological variables in west European countries that participated in the International Study of Asthma and Allergies in Children (ISAAC), Phase III 1997-2003. An ecologic study was carried out. The prevalence of asthma was obtained from this study from 48 centers in 14 countries, and meteorological variables from those stations closest to ISAAC centers, together with other socioeconomic and health care variables. Multilevel mixed-effects linear regression models were used. For schoolchildren aged 6-7 years, the prevalence rate of asthma decreased with an increase in mean annual sunshine hours, showed a positive association with rainy weather, and warm temperature, and a negative one with relative humidity and physician density (PD). Current wheeze prevalence was stronger in autumn/winter seasons and decreased with increasing PD. Severe current wheeze decreased with PD. For schoolchildren aged 13-14 years, the prevalence rates of asthma and current wheeze increased with rainy weather, and these rates decreased with increased PD. Current wheeze, as measured by a video questionnaire, was inversely associated with sunny weather, and nurse density. Severe current wheeze prevalence was stronger during autumn/winter seasons, decreased with PD, and indoor chlorinated public swimming pool density, and increased with rainy weather. Meteorological factors, including sunny and rainy weather, and PD may have some effect on the prevalence rates of asthma symptoms in children from west European countries.

Prevalence of asthma symptoms in schoolchildren, and climate in west European countries: an ecologic study

NASA Astrophysics Data System (ADS)

Arnedo-Pena, Alberto; García-Marcos, Luis; Bercedo-Sanz, Alberto; Aguinaga-Ontoso, Inés; González-Díaz, Carlos; García-Merino, Águeda; Busquets-Monge, Rosa; Suárez-Varela, Maria Morales; Batlles-Garrido, Juan; Blanco-Quirós, Alfredo A.; López-Silvarrey, Angel; García-Hernández, Gloria; Fuertes, Jorge

2013-09-01

The aim of the present study was to estimate the associations between the prevalence of asthma symptoms in schoolchildren and meteorological variables in west European countries that participated in the International Study of Asthma and Allergies in Children (ISAAC), Phase III 1997-2003. An ecologic study was carried out. The prevalence of asthma was obtained from this study from 48 centers in 14 countries, and meteorological variables from those stations closest to ISAAC centers, together with other socioeconomic and health care variables. Multilevel mixed-effects linear regression models were used. For schoolchildren aged 6-7 years, the prevalence rate of asthma decreased with an increase in mean annual sunshine hours, showed a positive association with rainy weather, and warm temperature, and a negative one with relative humidity and physician density (PD). Current wheeze prevalence was stronger in autumn/winter seasons and decreased with increasing PD. Severe current wheeze decreased with PD. For schoolchildren aged 13-14 years, the prevalence rates of asthma and current wheeze increased with rainy weather, and these rates decreased with increased PD. Current wheeze, as measured by a video questionnaire, was inversely associated with sunny weather, and nurse density. Severe current wheeze prevalence was stronger during autumn/winter seasons, decreased with PD, and indoor chlorinated public swimming pool density, and increased with rainy weather. Meteorological factors, including sunny and rainy weather, and PD may have some effect on the prevalence rates of asthma symptoms in children from west European countries.

Meteorological conditions during the summer 1986 CITE 2 flight series

NASA Technical Reports Server (NTRS)

Shipham, Mark C.; Cahoon, Donald R.; Bachmeier, A. Scott

1990-01-01

An overview of meteorological conditions during the NASA Global Tropospheric Experiment/Chemical Instrumentation Testing and Evaluation (GTE/CITE 2) summer 1986 flight series is presented. Computer-generated isentropic trajectories are used to trace the history of air masses encountered along each aircraft flight path. The synoptic-scale wind fields are depicted based on Montgomery stream function analyses. Time series of aircraft-measured temperature, dew point, ozone, and altitude are shown to depict air mass variability. Observed differences between maritime tropical and maritime polar air masses are discussed.

Introduction to meteorological measurements and data handling for solar energy applications. Task IV. Development of an isolation handbook and instrument package

DOE Office of Scientific and Technical Information (OSTI.GOV)

None

The following are covered: the Sun and its radiation, solar radiation and atmospheric interaction, solar radiation measurement methods, spectral irradiance measurements of natural sources, the measurement of infrared radiation, the measurement of circumsolar radiation, some empirical properties of solar radiation and related parameters, duration of sunshine, and meteorological variables related to solar energy. Included in appendices are manufacturers and distributors of solar radiation measuring instruments and an approximate method for quality control of solar radiation instruments. (MHR)

Meteorologic factors and subjective sleep continuity: a preliminary evaluation

NASA Astrophysics Data System (ADS)

Pandey, Juhi; Grandner, Michael; Crittenden, Crista; Smith, Michael T.; Perlis, Michael L.

2005-01-01

Little research has been undertaken to evaluate whether environmental factors other than bright light influence the individual’s ability to initiate and maintain sleep. In the present analyses, nine meteorologic variables were evaluated for their possible relationship to self-reported sleep continuity in a sample of 43 subjects over a period of 105 days. In this preliminary analysis, high barometric pressure, low precipitation, and lower temperatures were significantly correlated with good sleep continuity. Interestingly, ambient light and lunar phase were not found to be strongly associated sleep diary measures.

Meterology-driven Prediction of RSV/RHV Incidence in Rural Nepal

PubMed Central

Scott, Anna; Englund, Janet; Chu, Helen; Tielsch, James; Tielsch, James; Khatry, Subarna; Leclerq, Steven C; Shrestha, Laxman; Kuypers, Jane; Steinhoff, Mark C; Katz, Joanne

2017-01-01

Abstract Background Incidence of respiratory syncytial virus (RSV) and rhinovirus (RHV) varies throughout the year. We aim to quantify the relationship between weather variables (temperature, humidity, precipitation, and aerosol concentration) and disease incidence in order to quantify how outbreaks of RSV and RHV are related to seasonal or sub-seasonal meteorology, and if these relationships can predict viral outbreaks of RSV and RHV. Methods Health data were collected in a community-based, prospective randomized trial of maternal influenza immunization of pregnant women and their infants conducted in rural Nepal from 2011–2014. Adult illness episodes were defined as fever plus cough, sore throat, runny nose, and/or myalgia, with infant illness defined similarly but without fever requirement. Cases were identified through longitudinal household-based weekly surveillance. Temperature, humidity, precipitation, and fine particulate matter (PM 2.5) data come from reanalysis data products NCEP, Era-Interim, and Merra-2, which are produced by assimilating historical in-situ and satellite-based observations into a weather model. Results RSV exhibits a relationship with temperature after removing the seasonal cycle (r = -0.16, N = 208, P = 0.02), and RHV exhibits a strong relationship to daily temperature (r =-0.14, N =208, P = 0.05). When lagging meteorology by up to 15 weeks, correlations with disease count and weather improve (RSV: r_max = 0.45, P < 0.05; RHV: r_max = 0.15, P = 0.05). We use an SIR model forced by lagged meteorological variables to predict RSV and RHV, suggesting that disease burden can be predicted at lead times of weeks to months. Conclusion Meteorological variables are associated with RSV and RHV incidence in rural Nepal and can be used to drive predictive models with a lead time of several months. Disclosures J. Englund, Gilead: Consultant and Investigator, Research support Chimerix: Investigator, Research support Alios: Investigator, Research support Novavax: Investigator, Research support MedImmune: Investigator, Research support GlaxoSmithKline: Investigator, Research support

Comparative analysis of atmosphere temperature variability for Northern Eurasia based on the Reanalysis and in-situ observed data

NASA Astrophysics Data System (ADS)

Shulgina, T.; Genina, E.; Gordov, E.; Nikitchuk, K.

2009-04-01

At present numerous data archives which include meteorological observations as well as climate processes modeling data are available for Earth Science specialists. Methods of mathematical statistics are widely used for their processing and analysis. In many cases they represent the only way of quantitative assessment of the meteorological and climatic information. Unified set of analysis methods allows us to compare climatic characteristics calculated on the basis of different datasets with the purpose of performing more detailed analysis of climate dynamics for both regional and global levels. The report presents the results of comparative analysis of atmosphere temperature behavior for the Northern Eurasia territory for the period from 1979 to 2004 based on the NCEP/NCAR Reanalysis, NCEP/DOE Reanalysis AMIP II, JMA/CRIEPI JRA-25 Reanalysis, ECMWF ERA-40 Reanalysis data and observation data obtained from meteorological stations of the former Soviet Union. Statistical processing of atmosphere temperature data included analysis of time series homogeneity of climate indices approved by WMO, such as "Number of frost days", "Number of summer days", "Number of icing days", "Number of tropical nights", etc. by means of parametric methods of mathematical statistics (Fisher and Student tests). That allowed conducting comprehensive research of spatio-temporal features of the atmosphere temperature. Analysis of the atmosphere temperature dynamics revealed inhomogeneity of the data obtained for large observation intervals. Particularly, analysis performed for the period 1979 - 2004 showed the significant increase of the number of frost and icing days approximately by 1 day for every 2 years and decrease roughly by 1 day for 2 years for the number of summer days. Also it should be mentioned that the growth period mean temperature have increased by 1.5 - 2° C for the time period being considered. The usage of different Reanalysis datasets in conjunction with in-situ observed data allowed comparing of climate indices values calculated on the basis of different datasets that improves the reliability of the results obtained. Partial support of SB RAS Basic Research Program 4.5.2 (Project 2) is acknowledged.

The optical slit sensor as a standard sensor for spacecraft attitude determination

NASA Technical Reports Server (NTRS)

Wertz, J.

1975-01-01

The basic concept of an optical slit sensor as a standard altitude sensor is considered for any missions using a spinning spacecraft or where rotating sensors or mirrors could be used. Information available from a single sensor or from two sensors is analyzed. A standard slit sensor package is compared with the altitude package flown on the first synchronous meteorological satellite.

Changes in the zonal mean flow, temperature, and planetary waves observed in the Northern Hemisphere mid-winter months during the last decades

NASA Astrophysics Data System (ADS)

Rakushina, E. V.; Ermakova, T. S.; Pogoreltsev, A. I.

2018-06-01

Four sets of data: the UK Met Office, Modern Era Retrospective-analysis for Research and Applications (MERRA), Japanese 55-year Reanalysis data (JRA-55), and ERA-Interim data (ERA) have been used to estimate the climatic variability of the zonal mean flow, temperature, and Stationary Planetary Waves (SPW1, SPW2) from the troposphere up to the lower mesosphere levels. The composites of the meteorological fields during mid-winter month have been averaged over the first (1995-2005) and second (2006-2016) 11 years intervals and have been compared mainly paying attention to interannual and intraseasonal variability. Results show that changes in the mean fields and SPW2 are weaker and statistical significance of these changes is lower in comparison with the changes observed in the intraseasonal variability of these characteristics. All data sets demonstrate a decrease of SPW1 amplitude at the higher-middle latitudes in the lower stratosphere and opposite effect in the upper stratosphere. However, there is an increase of the intraseasonal variability for all meteorological parameters and this rise is statistically significant. The results obtained show that UK Met Office data demonstrate stronger changes and increase of the intraseasonal variability in comparison with other data sets.

Effects of rainfall partitioning in the seasonal and spatial variability of soil water content in a Mediterranean downy oak forest

NASA Astrophysics Data System (ADS)

Garcia-Estringana, P.; Latron, J.; Molina, A. J.; Llorens, P.

2012-04-01

Rainfall partitioning fluxes (throughfall and stemflow) have a large degree of temporal and spatial variability and may consequently lead to significant changes in the volume and composition of water that reach the understory and the soil. The objective of this work is to study the effect of rainfall partitioning on the seasonal and spatial variability of the soil water content in a Mediterranean downy oak forest (Quercus pubescens), located in the Vallcebre research catchments (42° 12'N, 1° 49'E). The monitoring design, started on July 2011, consists of a set of 20 automatic rain recorders and 40 automatic soil moisture probes located below the canopy. One hundred hemispheric photographs of the canopy were used to place the instruments at representative locations (in terms of canopy cover) within the plot. Bulk rainfall, stemflow and meteorological conditions above the forest cover are also automatically recorded. Canopy cover, in leaf and leafless periods, as well as biometric characteristics of the plot, are also regularly measured. This work presents the first results describing throughfall and soil moisture spatial variability during both the leaf and leafless periods. The main drivers of throughfall variability, as canopy structure and meteorological conditions are also analysed.

Factors associated with NO2 and NOX concentration gradients near a highway.

PubMed

Richmond-Bryant, J; Snyder, M G; Owen, R C; Kimbrough, S

2017-11-21

The objective of this research is to learn how the near-road gradient, in which NO 2 and NO X (NO + NO 2 ) concentrations are elevated, varies with changes in meteorological and traffic variables. Measurements of NO 2 and NO X were obtained east of I-15 in Las Vegas and fit to functions whose slopes (dC NO 2 /dx and dC NO X /dx, respectively) characterize the size of the near-road zone where NO 2 and NO X concentrations from mobile sources on the highway are elevated. These metrics were used to learn about the near-road gradient by modeling dC NO 2 /dx and dC NO X /dx as functions of meteorological variables (e.g., wind direction, wind speed), traffic (vehicle count), NO X concentration upwind of the road, and O 3 concentration at two fixed-site ambient monitors. Generalized additive models (GAM) were used to model dC NO 2 /dx and dC NO X /dx versus the independent variables because they allowed for nonlinearity of the variables being compared. When data from all wind directions were included in the analysis, variability in O 3 concentration comprised the largest proportion of variability in dC NO 2 /dx, followed by variability in wind direction. In a second analysis constrained to winds from the west, variability in O 3 concentration remained the largest contributor to variability in dC NO 2 /dx, but the relative contribution of variability in wind speed to variability in dC NO 2 /dx increased relative to its contribution for the all-wind analysis. When data from all wind directions were analyzed, variability in wind direction was by far the largest contributor to variability in dC NO X /dx, with smaller contributions from hour of day and upwind NO X concentration. When only winds from the west were analyzed, variability in upwind NO X concentration, wind speed, hour of day, and traffic count all were associated with variability in dC NO X /dx. Increases in O 3 concentration were associated with increased magnitude near-road dC NO 2 /dx, possibly shrinking the zone of elevated concentrations occurring near roads. Wind direction parallel to the highway was also related to an increased magnitude of both dC NO 2 /dx and dC NO X /dx, again likely shrinking the zone of elevated concentrations occurring near roads. Wind direction perpendicular to the road decreased the magnitude of dC NO 2 /dx and dC NO X /dx and likely contributed to growth of the zone of elevated concentrations occurring near roads. Thus, variability in near-road concentrations is influenced by local meteorology and ambient O 3 concentration.

Simulating and Predicting Cereal Crop Yields in Ethiopia: Model Calibration and Verification

NASA Astrophysics Data System (ADS)

Yang, M.; Wang, G.; Ahmed, K. F.; Eggen, M.; Adugna, B.; Anagnostou, E. N.

2017-12-01

Agriculture in developing countries are extremely vulnerable to climate variability and changes. In East Africa, most people live in the rural areas with outdated agriculture techniques and infrastructure. Smallholder agriculture continues to play a key role in this area, and the rate of irrigation is among the lowest of the world. As a result, seasonal and inter-annual weather patterns play an important role in the spatiotemporal variability of crop yields. This study investigates how various climate variables (e.g., temperature, precipitation, sunshine) and agricultural practice (e.g., fertilization, irrigation, planting date) influence cereal crop yields using a process-based model (DSSAT) and statistical analysis, and focuses on the Blue Nile Basin of Ethiopia. The DSSAT model is driven with meteorological forcing from the ECMWF's latest reanalysis product that cover the past 35 years; the statistical model will be developed by linking the same meteorological reanalysis data with harvest data at the woreda level from the Ethiopian national dataset. Results from this study will set the stage for the development of a seasonal prediction system for weather and crop yields in Ethiopia, which will serve multiple sectors in coping with the agricultural impact of climate variability.

Trends and variability in the hydrological regime of the Mackenzie River Basin

NASA Astrophysics Data System (ADS)

Abdul Aziz, Omar I.; Burn, Donald H.

2006-03-01

Trends and variability in the hydrological regime were analyzed for the Mackenzie River Basin in northern Canada. The procedure utilized the Mann-Kendall non-parametric test to detect trends, the Trend Free Pre-Whitening (TFPW) approach for correcting time-series data for autocorrelation and a bootstrap resampling method to account for the cross-correlation structure of the data. A total of 19 hydrological and six meteorological variables were selected for the study. Analysis was conducted on hydrological data from a network of 54 hydrometric stations and meteorological data from a network of 10 stations. The results indicated that several hydrological variables exhibit a greater number of significant trends than are expected to occur by chance. Noteworthy were strong increasing trends over the winter month flows of December to April as well as in the annual minimum flow and weak decreasing trends in the early summer and late fall flows as well as in the annual mean flow. An earlier onset of the spring freshet is noted over the basin. The results are expected to assist water resources managers and policy makers in making better planning decisions in the Mackenzie River Basin.

Designing a better weather display

NASA Astrophysics Data System (ADS)

Ware, Colin; Plumlee, Matthew

2012-01-01

The variables most commonly displayed on weather maps are atmospheric pressure, wind speed and direction, and surface temperature. But they are usually shown separately, not together on a single map. As a design exercise, we set the goal of finding out if it is possible to show all three variables (two 2D scalar fields and a 2D vector field) simultaneously such that values can be accurately read using keys for all variables, a reasonable level of detail is shown, and important meteorological features stand out clearly. Our solution involves employing three perceptual "channels", a color channel, a texture channel, and a motion channel in order to perceptually separate the variables and make them independently readable. We conducted an experiment to evaluate our new design both against a conventional solution, and against a glyph-based solution. The evaluation tested the abilities of novice subjects both to read values using a key, and to see meteorological patterns in the data. Our new scheme was superior especially in the representation of wind patterns using the motion channel, and it also performed well enough in the representation of pressure using the texture channel to suggest it as a viable design alternative.

A respiratory alert model for the Shenandoah Valley, Virginia, USA

NASA Astrophysics Data System (ADS)

Hondula, David M.; Davis, Robert E.; Knight, David B.; Sitka, Luke J.; Enfield, Kyle; Gawtry, Stephen B.; Stenger, Phillip J.; Deaton, Michael L.; Normile, Caroline P.; Lee, Temple R.

2013-01-01

Respiratory morbidity (particularly COPD and asthma) can be influenced by short-term weather fluctuations that affect air quality and lung function. We developed a model to evaluate meteorological conditions associated with respiratory hospital admissions in the Shenandoah Valley of Virginia, USA. We generated ensembles of classification trees based on six years of respiratory-related hospital admissions (64,620 cases) and a suite of 83 potential environmental predictor variables. As our goal was to identify short-term weather linkages to high admission periods, the dependent variable was formulated as a binary classification of five-day moving average respiratory admission departures from the seasonal mean value. Accounting for seasonality removed the long-term apparent inverse relationship between temperature and admissions. We generated eight total models specific to the northern and southern portions of the valley for each season. All eight models demonstrate predictive skill (mean odds ratio = 3.635) when evaluated using a randomization procedure. The predictor variables selected by the ensembling algorithm vary across models, and both meteorological and air quality variables are included. In general, the models indicate complex linkages between respiratory health and environmental conditions that may be difficult to identify using more traditional approaches.

Large-scale Meteorological Patterns Associated with Extreme Precipitation Events over Portland, OR

NASA Astrophysics Data System (ADS)

Aragon, C.; Loikith, P. C.; Lintner, B. R.; Pike, M.

2017-12-01

Extreme precipitation events can have profound impacts on human life and infrastructure, with broad implications across a range of stakeholders. Changes to extreme precipitation events are a projected outcome of climate change that warrants further study, especially at regional- to local-scales. While global climate models are generally capable of simulating mean climate at global-to-regional scales with reasonable skill, resiliency and adaptation decisions are made at local-scales where most state-of-the-art climate models are limited by coarse resolution. Characterization of large-scale meteorological patterns associated with extreme precipitation events at local-scales can provide climatic information without this scale limitation, thus facilitating stakeholder decision-making. This research will use synoptic climatology as a tool by which to characterize the key large-scale meteorological patterns associated with extreme precipitation events in the Portland, Oregon metro region. Composite analysis of meteorological patterns associated with extreme precipitation days, and associated watershed-specific flooding, is employed to enhance understanding of the climatic drivers behind such events. The self-organizing maps approach is then used to characterize the within-composite variability of the large-scale meteorological patterns associated with extreme precipitation events, allowing us to better understand the different types of meteorological conditions that lead to high-impact precipitation events and associated hydrologic impacts. A more comprehensive understanding of the meteorological drivers of extremes will aid in evaluation of the ability of climate models to capture key patterns associated with extreme precipitation over Portland and to better interpret projections of future climate at impact-relevant scales.

The USWRP Workshop on the Weather Research Needs of the Private Sector.

NASA Astrophysics Data System (ADS)

Pielke, Roger A., Jr.; Abraham, Jim; Abrams, Elliot; Block, Jim; Carbone, Richard; Chang, David; Droegemeier, Kelvin; Emanuel, Kerry; Friday, Elbert W. Joe, Jr.; Gall, Robert; Gaynor, John; Getz, Rodger R.; Glickman, Todd; Hoggatt, Bradley; Hooke, William H.; Johnson, Edward R.; Kalnay, Eugenia; Kimpel, James Jeff; Kocin, Paul; Marler, Byron; Morss, Rebecca; Nathan, Ravi; Nelson, Steve; Pielke, Roger, Sr.; Pirone, Maria; Prater, Erwin; Qualley, Warren; Simmons, Kevin; Smith, Michael; Thomson, John; Wilson, Greg

2003-07-01

Private sector meteorology is a rapidly growing enterprise. It has been estimated that the provision of weather information has, by some estimates, a global market totaling in the billions of dollars. Further, the decisions based on such information could easily total trillions of dollars in the U.S. economy alone. The private sector clearly plays an important, and growing, role at the interface of weather research and the weather information needs of society. To date, little information has been paid to the connections of the meteorological research community and the scientific needs of the private sector. Thus, the time is ripe to stimulate a more active dialogue between what is generally considered the "basic" research community of physical and social scientists and those individuals and businesses that provide weather information to myriad customers across the U.S. economy. In December 2000, the U.S. Weather Research Program (supported by NSF, NOAA, NASA, and the U.S. Navy) sponsored a workshop in Palm Springs, California, to bring together weather researchers and representatives of private sector meteorology to discuss needs, wants, opportunities, and challenges and how to enhance the linkages between the two relatively detached communities. The workshop focused on developing a better understanding of the relations of research and private sector meteorology, which ultimately means a better understanding of one of the important connections of research and societal needs.

Making the Introductory Meteorology Class Relevant in a Minority Serving Community College

NASA Astrophysics Data System (ADS)

Marchese, P. J.; Tremberger, G.; Bluestone, C.

2008-12-01

Queensborough Community College (QCC), a constituent campus of the City University of New York (CUNY), has modified the introductory Meteorology Class lecture and lab to include active learning activities and discovery based learning. The modules were developed at QCC and other 4 year colleges and designed to introduce basic physical concepts important in meteorology. The modules consisted of either interactive lecture demonstrations or discovery-based activities. The discovery based activities are intended to have students become familiar with scientific investigation. Students engage in formulating hypotheses, developing and carrying out experiments, and analyzing scientific data. These activities differ from traditional lab experiments in that they avoid "cookbook" procedures and emphasize having the students learn about physical concepts by applying the scientific method. During the interactive lecture demonstrations the instructor describes an experiment/phenomenon that is to be demonstrated in class. Students discuss the phenomenon based on their experiences and make a prediction about the outcome. The class then runs the experiment, makes observations, and compares the expected results to the actual outcome. As a result of these activities students in the introductory Meteorology class scored higher in exams questions measuring conceptual understanding, as well as factual knowledge. Lower scoring students demonstrated the greatest benefit, while the better students had little (or no) changes. All students also had higher self-efficacy scores after the intervention, compared to an unmodified class.

Sources of variability of evapotranspiration in California

USGS Publications Warehouse

Hidalgo, H.G.; Cayan, D.R.; Dettinger, M.D.

2005-01-01

The variability (1990-2002) of potential evapotranspiration estimates (ETo) and related meteorological variables from a set of stations from the California Irrigation Management System (CIMIS) is studied. Data from the National Climatic Data Center (NCDC) and from the Department of Energy from 1950 to 2001 were used to validate the results. The objective is to determine the characteristics of climatological ETo and to identify factors controlling its variability (including associated atmospheric circulations). Daily ETo anomalies are strongly correlated with net radiation (Rn) anomalies, relative humidity (RH), and cloud cover, and less with average daily temperature (Tavg). The highest intraseasonal variability of ETo daily anomalies occurs during the spring, mainly caused by anomalies below the high ETo seasonal values during cloudy days. A characteristic circulation pattern is associated with anomalies of ETo and its driving meteorological inputs, Rn, RH, and Tavg, at daily to seasonal time scales. This circulation pattern is dominated by 700-hPa geopotential height (Z700) anomalies over a region off the west coast of North America, approximately between 32?? and 44?? latitude, referred to as the California Pressure Anomaly (CPA). High cloudiness and lower than normal ETo are associated with the lowheight (pressure) phase of the CPA pattern. Higher than normal ETo anomalies are associated with clear skies maintained through anomalously high Z700 anomalies offshore of the North American coast. Spring CPA, cloudiness, maximum temperature (Tmax), pan evaporation (Epan), and ETo conditions have not trended significantly or consistently during the second half of the twentieth century in California. Because it is not known how cloud cover and humidity will respond to climate change, the response of ETo in California to increased greenhouse-gas concentrations is essentially unknown; however, to retain the levels of ETo in the current climate, a decline of Rn by about 6% would be required to compensate for a warming of +3??C. ?? 2005 American Meteorological Society.

A GIS Procedure to Monitor PWV During Severe Meteorological Events

NASA Astrophysics Data System (ADS)

Ferrando, I.; Federici, B.; Sguerso, D.

2016-12-01

As widely known, the observation of GNSS signal's delay can improve the knowledge of meteorological phenomena. The local Precipitable Water Vapour (PWV), which can be easily derived from Zenith Total Delay (ZTD), Pressure (P) and Temperature (T) (Bevis et al., 1994), is not a satisfactory parameter to evaluate the occurrence of severe meteorological events. Hence, a GIS procedure, called G4M (GNSS for Meteorology), has been conceived to produce 2D PWV maps with high spatial and temporal resolution (1 km and 6 minutes respectively). The input data are GNSS, P and T observations not necessarily co-located coming from existing infrastructures, combined with a simplified physical model, owned by the research group.On spite of the low density and the different configurations of GNSS, P and T networks, the procedure is capable to detect severe meteorological events with reliable results. The procedure has already been applied in a wide and orographically complex area covering approximately the north-west of Italy and the French-Italian border region, to study two severe meteorological events occurred in Genoa (Italy) and other meteorological alert cases. The P, T and PWV 2D maps obtained by the procedure have been compared with the ones coming from meteorological re-analysis models, used as reference to obtain statistics on the goodness of the procedure in representing these fields. Additionally, the spatial variability of PWV was taken into account as indicator for representing potential critical situations; this index seems promising in highlighting remarkable features that precede intense precipitations. The strength and originality of the procedure lie into the employment of existing infrastructures, the independence from meteorological models, the high adaptability to different networks configurations, and the ability to produce high-resolution 2D PWV maps even from sparse input data. In the next future, the procedure could also be set up for near real-time applications.

On The Development of One-way Nesting of Air-pollution Model Smog Into Numerical Weather Prediction Model Eta

NASA Astrophysics Data System (ADS)

Halenka, T.; Bednar, J.; Brechler, J.

The spatial distribution of air pollution on the regional scale (Bohemian region) is simulated by means of Charles University puff model SMOG. The results are used for the assessment of the concentration fields of ozone, nitrogen oxides and other ozone precursors. Current improved version of the model covers up to 16 groups of basic compounds and it is based on trajectory computation and puff interaction both by means of Gaussian diffusion mixing and chemical reactions of basic species. Gener- ally, the method used for trajectory computation is valuable mainly for episodes sim- ulation, nevertheless, climatological study can be solved as well by means of average wind rose. For the study being presented huge database of real emission sources was incorporated with all kind of sources included. Some problem with the background values of concentrations was removed. The model SMOG has been nested into the forecast model ETA to obtain appropriate meteorological data input. We can estimate air pollution characteristics both for episodes analysis and the prediction of future air quality conditions. Necessary prognostic variables from the numerical weather pre- diction model are taken for the region of the central Bohemia, where the original puff model was tested. We used mainly 850 hPa wind field for computation of prognos- tic trajectories, the influence of surface temperature as a parameter of photochemistry reactions as well as the effect of cloudness has been tested.

Modelling the future distribution of ammonium nitrate concentrations in The Netherlands for 2020: The sensitivity to meteorological parameters

NASA Astrophysics Data System (ADS)

Williams, J. E.; van der Swaluw, E.; de Vries, W. J.; Sauter, F. J.; van Pul, W. A. J.; Hoogerbrugge, R.

2015-08-01

We present a parameterization developed to simulate Ammonium particle (NH4+) concentrations in the Operational Priority Substances (OPS) source-receptor model, without the necessity of using a detailed chemical scheme. By using the ratios of the main pre-cursor gases SO2, NO2 and NH3, and utilising calculations performed using a chemical box-model, we show that the parameterization can simulate annual mean NH4+ concentration fields to within ∼15% of measured values at locations throughout the Netherlands. Performing simulations for different decades, we find a strong correlation of simulated NH4+ distributions for both past (1993-1995) and present (2009-2012) time periods. Although the total concentration of NH4+ has decreased over the period, we find that the fraction of NH4+ transported into the Netherlands has increased from around 40% in the past to 50% for present-day. This is due to the variable efficiency of mitigation practises across economic sectors. Performing simulations for the year 2020 using associated emission estimates, we show that there are generally decreases of ∼8-25% compared to present day concentrations. By altering the meteorological fields applied in the future simulations, we show that a significant uncertainty of between ∼50 and 100% exists on this estimated NH4+ distribution as a result of variability in the temperature dependent emission terms and relative humidity. Therefore, any projections of future NH4+ distributions should be performed using well chosen meteorological fields representing recent meteorological situations.

A hydro-meteorological model chain to assess the influence of natural variability and impacts of climate change on extreme events and propose optimal water management

NASA Astrophysics Data System (ADS)

von Trentini, F.; Willkofer, F.; Wood, R. R.; Schmid, F. J.; Ludwig, R.

2017-12-01

The ClimEx project (Climate change and hydrological extreme events - risks and perspectives for water management in Bavaria and Québec) focuses on the effects of climate change on hydro-meteorological extreme events and their implications for water management in Bavaria and Québec. Therefore, a hydro-meteorological model chain is applied. It employs high performance computing capacity of the Leibniz Supercomputing Centre facility SuperMUC to dynamically downscale 50 members of the Global Circulation Model CanESM2 over European and Eastern North American domains using the Canadian Regional Climate Model (RCM) CRCM5. Over Europe, the unique single model ensemble is conjointly analyzed with the latest information provided through the CORDEX-initiative, to better assess the influence of natural climate variability and climatic change in the dynamics of extreme events. Furthermore, these 50 members of a single RCM will enhance extreme value statistics (extreme return periods) by exploiting the available 1500 model years for the reference period from 1981 to 2010. Hence, the RCM output is applied to drive the process based, fully distributed, and deterministic hydrological model WaSiM in high temporal (3h) and spatial (500m) resolution. WaSiM and the large ensemble are further used to derive a variety of hydro-meteorological patterns leading to severe flood events. A tool for virtual perfect prediction shall provide a combination of optimal lead time and management strategy to mitigate certain flood events following these patterns.

Summarising climate and air quality (ozone) data on self-organising maps: a Sydney case study.

PubMed

Jiang, Ningbo; Betts, Alan; Riley, Matt

2016-02-01

This paper explores the classification and visualisation utility of the self-organising map (SOM) method in the context of New South Wales (NSW), Australia, using gridded NCEP/NCAR geopotential height reanalysis for east Australia, together with multi-site meteorological and air quality data for Sydney from the NSW Office of Environment and Heritage Air Quality Monitoring Network. A twice-daily synoptic classification has been derived for east Australia for the period of 1958-2012. The classification has not only reproduced the typical synoptic patterns previously identified in the literature but also provided an opportunity to visualise the subtle, non-linear change in the eastward-migrating synoptic systems influencing NSW (including Sydney). The summarisation of long-term, multi-site air quality/meteorological data from the Sydney basin on the SOM plane has identified a set of typical air pollution/meteorological spatial patterns in the region. Importantly, the examination of these patterns in relation to synoptic weather types has provided important visual insights into how local and synoptic meteorological conditions interact with each other and affect the variability of air quality in tandem. The study illustrates that while synoptic circulation types are influential, the within-type variability in mesoscale flows plays a critical role in determining local ozone levels in Sydney. These results indicate that the SOM can be a useful tool for assessing the impact of weather and climatic conditions on air quality in the regional airshed. This study further promotes the use of the SOM method in environmental research.

Evaposublimation from the snow in the Mediterranean mountains of Sierra Nevada (Spain)

NASA Astrophysics Data System (ADS)

Herrero, Javier; José Polo, María

2016-12-01

In this study we quantify the evaposublimation and the energy balance of the seasonal snowpack in the Mediterranean semiarid region of Sierra Nevada, Spain (37° N). In these kinds of regions, the incidence of this return of water to the atmosphere is particularly important to the hydrology and water availability. The analysis of the evaposublimation from snow allows us to deduct the losses of water expected in the short and medium term and is critical for the efficient planning of this basic and scarce resource. To achieve this, we performed 10 field campaigns from 2009 to 2015, during which detailed measurements of mass fluxes of a controlled volume of snow were recorded using a modified version of an evaporation pan with lysimeter. Meteorological data at the site of the snow control volume were extensively monitored during the tests. With these data, a point energy balance snowmelt model was validated for the area. This model, fed with the complete meteorological data set available at the Refugio Poqueira Station (2500 m a.s.l.), let us estimate that evaposublimation losses for this site can range from 24 to 33 % of total annual ablation. This ratio is very variable throughout the year and between years, depending on the particular occurrence of snowfall and mild weather events, which is generally quite erratic in this semiarid region. Evaposublimation proceeds at maximum rates of up to 0.49 mm h-1, an order of magnitude less than maximum melt rates. However, evaposublimation occurs during 60 % of the time that snow lies, while snowmelt only takes up 10 % of this time. Hence, both processes remain close in magnitude on the annual scale.

Actual vs. Perceived Climate Variability among Smallholding Rice Farmers

NASA Astrophysics Data System (ADS)

Carrico, A.; Gilligan, J. M.; Truelove, H. B.

2016-12-01

It is recognized that those engaged in resource-dependent livelihoods often hold extensive knowledge of their surrounding environment that, in some cases, facilitates sustainable practices and adaptation to environmental shocks. However, there remain significant gaps in our understanding of how actors at this scale perceive, understand, and respond to climate variability, particularly in the absence of good information. There are further unanswered questions about how these perceptions translate into livelihood decisions. In this paper, we use data collected in 2015 from 607 paddy farmers living in 12 villages throughout the heavily agricultural dry zone of Sri Lanka. Farmers were asked to report their perceptions of decadal scale changes in temperature and rainfall along a number of dimensions (e.g., annual rainfall, onset of monsoon rains, frequency of droughts, temperature). These data are compared to local meteorological data collected over the same time period to examine the perceptions of meteorological trends. Furthermore, we examine heterogeneity in perceptions as a function of demographic factors, reliance on irrigation, use of agricultural technology, and other socioeconomic characteristics of the farmer. The impact of perceptions on agricultural practices such as crop selection and water management, and resultant yields, will also be examined. Preliminary results based on five communities suggest a strong negativity bias in perceptions, with widespread agreement that meteorological conditions have become less hospitable for farming. Perceptions of temperature changes largely corresponded to meteorological records; however, perceptions of rainfall changes did not. There was some evidence that length of time spent in a village and the presence of elders in the household was associated with perceptions that more closely corresponded to the observed meteorological data. Updated analyses based on the complete data set will be presented. We will discuss the implications of these findings on the projected agricultural impacts of climate change, as well as for policies and programs designed to support adaptation among smallholding farmers.

Distinct wind convergence patterns in the Mexico City basin due to the interaction of the gap winds with the synoptic flow

NASA Astrophysics Data System (ADS)

de Foy, B.; Clappier, A.; Molina, L. T.; Molina, M. J.

2006-04-01

Mexico City lies in a high altitude basin where air quality and pollutant fate is strongly influenced by local winds. The combination of high terrain with weak synoptic forcing leads to weak and variable winds with complex circulation patterns. A gap wind entering the basin in the afternoon leads to very different wind convergence lines over the city depending on the meteorological conditions. Surface and upper-air meteorological observations are analysed during the MCMA-2003 field campaign to establish the meteorological conditions and obtain an index of the strength and timing of the gap wind. A mesoscale meteorological model (MM5) is used in combination with high-resolution satellite data for the land surface parameters and soil moisture maps derived from diurnal ground temperature range. A simple method to map the lines of wind convergence both in the basin and on the regional scale is used to show the different convergence patterns according to episode types. The gap wind is found to occur on most days of the campaign and is the result of a temperature gradient across the southern basin rim which is very similar from day to day. Momentum mixing from winds aloft into the surface layer is much more variable and can determine both the strength of the flow and the pattern of the convergence zones. Northerly flows aloft lead to a weak jet with an east-west convergence line that progresses northwards in the late afternoon and early evening. Westerlies aloft lead to both stronger gap flows due to channelling and winds over the southern and western basin rim. This results in a north-south convergence line through the middle of the basin starting in the early afternoon. Improved understanding of basin meteorology will lead to better air quality forecasts for the city and better understanding of the chemical regimes in the urban atmosphere.

Assessment of the natural sources of particulate matter on the opencast mines air quality.

PubMed

Huertas, J I; Huertas, M E; Cervantes, G; Díaz, J

2014-09-15

Particulate matter is the main air pollutant in open pit mining areas. Preferred models that simulate the dispersion of the particles have been used to assess the environmental impact of the mining activities. Results obtained through simulation have been compared with the particle concentration measured in several sites and a coefficient of determination R(2)<0.78 has been reported. This result indicates that in the open pit mining areas there may be additional sources of particulate matter that have not been considered in the modeling process. This work proposes that the unconsidered sources of emissions are of regional scope such as the re-suspension particulate matter due to the wind action over uncovered surfaces. Furthermore, this work proposes to estimate the impact of such emissions on air quality as a function of the present and past meteorological conditions. A statistical multiple regression model was implemented in one of the world's largest open pit coal mining regions which is located in northern Colombia. Data from 9 particle-concentration monitoring stations and 3 meteorological stations obtained from 2009 to 2012 were statistically compared. Results confirmed the existence of a high linear relation (R(2)>0.95) between meteorological variables and particulate matter concentration being humidity, humidity of the previous day and temperature, the meteorological variables that contributed most significantly in the variance of the particulate matter concentration measured in the mining area while the contribution of the AERMOD estimations to the short term TSP (Total Suspended Particles) measured concentrations was negligible (<5%). The multiple regression model was used to identify the meteorological condition that leads to pollution episodes. It was found that conditions drier than 54% lead to pollution episodes while humidities greater than 70% maintain safe air quality conditions in the mining region in northern Colombia. Copyright © 2014 Elsevier B.V. All rights reserved.

A New Approach in Generating Meteorological Forecasts for Ensemble Streamflow Forecasting using Multivariate Functions

NASA Astrophysics Data System (ADS)

Khajehei, S.; Madadgar, S.; Moradkhani, H.

2014-12-01

The reliability and accuracy of hydrological predictions are subject to various sources of uncertainty, including meteorological forcing, initial conditions, model parameters and model structure. To reduce the total uncertainty in hydrological applications, one approach is to reduce the uncertainty in meteorological forcing by using the statistical methods based on the conditional probability density functions (pdf). However, one of the requirements for current methods is to assume the Gaussian distribution for the marginal distribution of the observed and modeled meteorology. Here we propose a Bayesian approach based on Copula functions to develop the conditional distribution of precipitation forecast needed in deriving a hydrologic model for a sub-basin in the Columbia River Basin. Copula functions are introduced as an alternative approach in capturing the uncertainties related to meteorological forcing. Copulas are multivariate joint distribution of univariate marginal distributions, which are capable to model the joint behavior of variables with any level of correlation and dependency. The method is applied to the monthly forecast of CPC with 0.25x0.25 degree resolution to reproduce the PRISM dataset over 1970-2000. Results are compared with Ensemble Pre-Processor approach as a common procedure used by National Weather Service River forecast centers in reproducing observed climatology during a ten-year verification period (2000-2010).

Numerical Weather Prediction Models on Linux Boxes as tools in meteorological education in Hungary

NASA Astrophysics Data System (ADS)

Gyongyosi, A. Z.; Andre, K.; Salavec, P.; Horanyi, A.; Szepszo, G.; Mille, M.; Tasnadi, P.; Weidiger, T.

2012-04-01

Education of Meteorologist in Hungary - according to the Bologna Process - has three stages: BSc, MSc and PhD, and students graduating at each stage get the respective degree (BSc, MSc and PhD). The three year long base BSc course in Meteorology can be chosen by undergraduate students in the fields of Geosciences, Environmental Sciences and Physics. BasicsFundamentals in Mathematics (Calculus), Physics (General and Theoretical) Physics and Informatics are emphasized during their elementary education. The two year long MSc course - in which about 15 to 25 students are admitted each year - can be studied only at our the Eötvös Loránd uUniversity in the our country. Our aim is to give a basic education in all fields of Meteorology. Main topics are: Climatology, Atmospheric Physics, Atmospheric Chemistry, Dynamic and Synoptic Meteorology, Numerical Weather Prediction, modeling Modeling of surfaceSurface-atmosphere Iinteractions and Cclimate change. Education is performed in two branches: Climate Researcher and Forecaster. Education of Meteorologist in Hungary - according to the Bologna Process - has three stages: BSc, MSc and PhD, and students graduating at each stage get the respective degree. The three year long BSc course in Meteorology can be chosen by undergraduate students in the fields of Geosciences, Environmental Sciences and Physics. Fundamentals in Mathematics (Calculus), (General and Theoretical) Physics and Informatics are emphasized during their elementary education. The two year long MSc course - in which about 15 to 25 students are admitted each year - can be studied only at the Eötvös Loránd University in our country. Our aim is to give a basic education in all fields of Meteorology: Climatology, Atmospheric Physics, Atmospheric Chemistry, Dynamic and Synoptic Meteorology, Numerical Weather Prediction, Modeling of Surface-atmosphere Interactions and Climate change. Education is performed in two branches: Climate Researcher and Forecaster. Numerical modeling became a common tool in the daily practice of weather experts forecasters due to the i) increasing user demands for weather data by the costumers, ii) the growth in computer resources, iii) numerical weather prediction systems available for integration on affordable, off the shelf computers and iv) available input data (from ECMWF or NCEP) for model integrations. Beside learning the theoretical basis, since the last year. Students in their MSc or BSc Thesis Research or in Student's Research ProjectsStudent's Research Projects h have the opportunity to run numerical models and to analyze the outputs for different purposes including wind energy estimation, simulation of the dynamics of a polar low, and subtropical cyclones, analysis of the isentropic potential vorticity field, examination of coupled atmospheric dispersion models, etc. A special course in the application of numerical modeling has been held (is being announced for the upcoming semester) (is being announced for the upcoming semester) for our students in order to improve their skills on this field. Several numerical model (NRIPR ETA and WRF) systems have been adapted in the University and integrated WRF have been tested and used for the geographical region of the Carpathian Basin (NRIPR, ETA and WRF). Recently ALADIN/CHAPEAU the academic version of the ARPEGE ALADIN cy33t1 meso-scale numerical weather prediction model system (which is the operational forecasting tool of our National Weather Service) has been installed at our Institute. ALADIN is the operational forecasting model of the Hungarian Meteorological Service and developed in the framework of the international ALADIN co-operation. Our main objectives are i) the analysis of different typical weather situations, ii) fine tuning of parameterization schemes and the iii) comparison of the ALADIN/CHAPEAU and WRF model outputs based on case studies. The necessary hardware and software innovations has have been done. In the presentation the computer resources needed for the integration of both WRF and ALADIN/CHAPEAU models will be briefly described. The software developments performed for the evaluation and comparison of the different modeling systems will be demonstrated. The main objectives of the education program on the practical numerical weather modeling will be introduced, as well as its detailed thematics and the structure of the labs.

Improving of local ozone forecasting by integrated models.

PubMed

Gradišar, Dejan; Grašič, Boštjan; Božnar, Marija Zlata; Mlakar, Primož; Kocijan, Juš

2016-09-01

This paper discuss the problem of forecasting the maximum ozone concentrations in urban microlocations, where reliable alerting of the local population when thresholds have been surpassed is necessary. To improve the forecast, the methodology of integrated models is proposed. The model is based on multilayer perceptron neural networks that use as inputs all available information from QualeAria air-quality model, WRF numerical weather prediction model and onsite measurements of meteorology and air pollution. While air-quality and meteorological models cover large geographical 3-dimensional space, their local resolution is often not satisfactory. On the other hand, empirical methods have the advantage of good local forecasts. In this paper, integrated models are used for improved 1-day-ahead forecasting of the maximum hourly value of ozone within each day for representative locations in Slovenia. The WRF meteorological model is used for forecasting meteorological variables and the QualeAria air-quality model for gas concentrations. Their predictions, together with measurements from ground stations, are used as inputs to a neural network. The model validation results show that integrated models noticeably improve ozone forecasts and provide better alert systems.

Linear Multivariable Regression Models for Prediction of Eddy Dissipation Rate from Available Meteorological Data

NASA Technical Reports Server (NTRS)

MCKissick, Burnell T. (Technical Monitor); Plassman, Gerald E.; Mall, Gerald H.; Quagliano, John R.

2005-01-01

Linear multivariable regression models for predicting day and night Eddy Dissipation Rate (EDR) from available meteorological data sources are defined and validated. Model definition is based on a combination of 1997-2000 Dallas/Fort Worth (DFW) data sources, EDR from Aircraft Vortex Spacing System (AVOSS) deployment data, and regression variables primarily from corresponding Automated Surface Observation System (ASOS) data. Model validation is accomplished through EDR predictions on a similar combination of 1994-1995 Memphis (MEM) AVOSS and ASOS data. Model forms include an intercept plus a single term of fixed optimal power for each of these regression variables; 30-minute forward averaged mean and variance of near-surface wind speed and temperature, variance of wind direction, and a discrete cloud cover metric. Distinct day and night models, regressing on EDR and the natural log of EDR respectively, yield best performance and avoid model discontinuity over day/night data boundaries.

Southern hemisphere low level wind circulation statistics from the Seasat scatterometer

NASA Technical Reports Server (NTRS)

Levy, Gad

1994-01-01

Analyses of remotely sensed low-level wind vector data over the Southern Ocean are performed. Five-day averages and monthly means are created and the month-to-month variability during the winter (July-September) of 1978 is investigated. The remotely sensed winds are compared to the Australian Bureau of Meteorology (ABM) and the National Meteorological Center (NMC) surface analyses. In southern latitudes the remotely sensed winds are stronger than what the weather services' analyses suggest, indicating under-estimation by ABM and NMC in these regions. The evolution of the low-level jet and the major stormtracks during the season are studied and different flow regimes are identified. The large-scale variability of the meridional flow is studied with the aid of empirical orthogonal function (EOF) analysis. The dominance of quasi-stationary wave numbers 3,4, and 5 in the winter flows is evident in both the EOF analysis and the mean flow. The signature of an exceptionally strong blocking situation is evident in July and the special conditions leading to it are discussed. A very large intraseasonal variability with different flow regimes at different months is documented.

Atmospheric mold spore counts in relation to meteorological parameters

NASA Astrophysics Data System (ADS)

Katial, R. K.; Zhang, Yiming; Jones, Richard H.; Dyer, Philip D.

Fungal spore counts of Cladosporium, Alternaria, and Epicoccum were studied during 8 years in Denver, Colorado. Fungal spore counts were obtained daily during the pollinating season by a Rotorod sampler. Weather data were obtained from the National Climatic Data Center. Daily averages of temperature, relative humidity, daily precipitation, barometric pressure, and wind speed were studied. A time series analysis was performed on the data to mathematically model the spore counts in relation to weather parameters. Using SAS PROC ARIMA software, a regression analysis was performed, regressing the spore counts on the weather variables assuming an autoregressive moving average (ARMA) error structure. Cladosporium was found to be positively correlated (P<0.02) with average daily temperature, relative humidity, and negatively correlated with precipitation. Alternaria and Epicoccum did not show increased predictability with weather variables. A mathematical model was derived for Cladosporium spore counts using the annual seasonal cycle and significant weather variables. The model for Alternaria and Epicoccum incorporated the annual seasonal cycle. Fungal spore counts can be modeled by time series analysis and related to meteorological parameters controlling for seasonallity; this modeling can provide estimates of exposure to fungal aeroallergens.

The role of local urban traffic and meteorological conditions in air pollution: A data-based case study in Madrid, Spain

NASA Astrophysics Data System (ADS)

Laña, Ibai; Del Ser, Javier; Padró, Ales; Vélez, Manuel; Casanova-Mateo, Carlos

2016-11-01

Urban air pollution is a matter of growing concern for both public administrations and citizens. Road traffic is one of the main sources of air pollutants, though topography characteristics and meteorological conditions can make pollution levels increase or diminish dramatically. In this context an upsurge of research has been conducted towards functionally linking variables of such domains to measured pollution data, with studies dealing with up to one-hour resolution meteorological data. However, the majority of such reported contributions do not deal with traffic data or, at most, simulate traffic conditions jointly with the consideration of different topographical features. The aim of this study is to further explore this relationship by using high-resolution real traffic data. This paper describes a methodology based on the construction of regression models to predict levels of different pollutants (i.e. CO, NO, NO2, O3 and PM10) based on traffic data and meteorological conditions, from which an estimation of the predictive relevance (importance) of each utilized feature can be estimated by virtue of their particular training procedure. The study was made with one hour resolution meteorological, traffic and pollution historic data in roadside and background locations of the city of Madrid (Spain) captured over 2015. The obtained results reveal that the impact of vehicular emissions on the pollution levels is overshadowed by the effects of stable meteorological conditions of this city.

Effect of Meteorological Conditions and Geographical Factors in the Onset of Enterovirus 71

NASA Astrophysics Data System (ADS)

Chen, Yu-An; Yu, Hwa-Lung

2015-04-01

Since it was first recognized in California in 1969, enterovirus 71 (EV71) infection has been a significant cause of neurological disorder and death in children worldwide. In 1998 a historic epidemic of EV71 infection caused hand-foot-and-mouth disease and herpangina in thousands of people in Taiwan. The impact of EV71 infection is greatest during the summer months in Asia, and epidemics recur with a seasonal pattern. It was reported that seasonal patterns of EV71 differed by geographical localities. Previous studies have also showed significant relationships between meteorological variables, in particular, temperature and relative humidity, and the seasonal epidemic patterns of EV71. However, important issues that remain unclear include the spatiotemporal pattern of the EV71 outbreaks in Taiwan, and what role of favorable meteorological conditions in the transmission of the disease in the space-time domain. Thus, this study used a semiparametric generalized additive model (GAM) to understand the association between EV71 and meteorological factors across space and time. This study utilized a population-based database containing space-time data for clinic and hospital visits (i.e., hospital location and appointment times) of EV71 occurring in children less than 18 years old in Taipei from 1998 to 2008. Meteorological data (i.e., temperature, rainfall, and relative humidity) for the study period were provided by the Taiwan Central Weather Bureau. This study expect to find out an important meteorological factor and threshold.

A note on the correlation between circular and linear variables with an application to wind direction and air temperature data in a Mediterranean climate

NASA Astrophysics Data System (ADS)

Lototzis, M.; Papadopoulos, G. K.; Droulia, F.; Tseliou, A.; Tsiros, I. X.

2018-04-01

There are several cases where a circular variable is associated with a linear one. A typical example is wind direction that is often associated with linear quantities such as air temperature and air humidity. The analysis of a statistical relationship of this kind can be tested by the use of parametric and non-parametric methods, each of which has its own advantages and drawbacks. This work deals with correlation analysis using both the parametric and the non-parametric procedure on a small set of meteorological data of air temperature and wind direction during a summer period in a Mediterranean climate. Correlations were examined between hourly, daily and maximum-prevailing values, under typical and non-typical meteorological conditions. Both tests indicated a strong correlation between mean hourly wind directions and mean hourly air temperature, whereas mean daily wind direction and mean daily air temperature do not seem to be correlated. In some cases, however, the two procedures were found to give quite dissimilar levels of significance on the rejection or not of the null hypothesis of no correlation. The simple statistical analysis presented in this study, appropriately extended in large sets of meteorological data, may be a useful tool for estimating effects of wind on local climate studies.

The Greenland Sea Odden: Intra- and inter-annual variability

USGS Publications Warehouse

Russell, C.A.; Fischer, K.W.; Shuchman, R.A.; Josberger, E.G.

1997-01-01

The "Odden" is a large sea ice feature that forms in the East Greenland Sea which generally forms at the beginning of the winter season and can cover 300,000 km2. Throughout the winter, the outer edge of the Odden may advance and retreat by several hundred kilometers on time scales of a few days to weeks. Satellite passive microwave observations from 1978 through 1995 provide a continuous record of the spatial and temporal variations of this extremely dynamic phenomenon. The 17 year record shows both strong inter- and intra-annual variations in Odden extent and temporal behavior. An analysis of the satellite passive microwave derived ice area and extent time series along with meteorological data from the Arctic Drifting Buoy Network determined the meteorological forcing required for Odden growth, maintenance and decay. The key meteorological parameters which cause the rapid ice formation and decay associated with the Odden are, in order of importance, air temperature, wind speed, and wind direction. Atmospheric pressure was found not to play a significant role in the Odden events. Air temperature and wind direction are the dominant variables with temperatures below -9.5??C and winds from the west required to trigger significant Odden ice formation events. ??2004 Copyright SPIE - The International Society for Optical Engineering.

[Influence of weather in the incidence of acute myocardial infarction in Galicia (Spain)].

PubMed

Fernández-García, José Manuel; Dosil Díaz, Olga; Taboada Hidalgo, Juan José; Fernández, José Ramón; Sánchez-Santos, Luis

2015-08-07

To assess the interactions between weather and the impact of each individual meteorological parameters in the incidence of acute myocardial infarctions (AMI) in Galicia. Retrospective study analyzing the number of AMI diagnosed and transferred to the hospital by the Emergencies Sanitary System of Galicia between 2002 and 2009. We included patients with clinical and ECG findings of AMI. The correlation between 10-minute meteorological variables (temperature, humidity, pressure, accumulated rainfall and wind speed) recorded by MeteoGalicia and the incidence of AMI was assessed. A total of 4,717 AMI were registered (72.8% men, 27.2% women). No seasonal variations were found. No significant correlations were detected with regard to average daily temperature (P=.683) or wind speed (P=.895). Correlation between atmospheric pressure and incidence of AMI was significant (P<.005), as well as with the daily relative humidity average (P=.005). Our study showed a statistical significant association with atmospheric pressure and with the daily relative humidity average. Since the local conditions of weather are widely variable, future studies should establish the relationship between weather patterns (including combinations of meteorological parameters), rather than seasonal variations, and the incidence of AMI. Copyright © 2013 Elsevier España, S.L.U. All rights reserved.

Assessing the impact of humidex on HFMD in Guangdong Province and its variability across social-economic status and age groups

NASA Astrophysics Data System (ADS)

Zhang, Wangjian; Du, Zhicheng; Zhang, Dingmei; Yu, Shicheng; Huang, Yong; Hao, Yuantao

2016-01-01

Humidex is a meteorological index that combines the impacts of temperature and humidity, and is directly comparable with dry temperature in degrees Celsius. However, to date, no research has focused on the effect of humidex on hand, foot and mouth disease (HFMD). The current study was designed to address this research need. Case-based HFMD surveillance data and daily meteorological data collected between 2010 and 2012 was obtained from the China CDC and the National Meteorological Information Center, respectively. Distributed lag nonlinear models were applied to assess the impact of humidex on HFMD among children under 15 years oldin Guangdong, and its variability across social-economic status and age groups. We found that relative risk (RR) largely increased with humidex. Lag-specific and cumulative humidex-RR curves for children from the Pearl-River Delta Region as well as older children were more likely to show two-peak distribution patterns. One RR peak occurred at a humidex of between 15 and 20, and the other occurred between 30 and 35. This study provides a comprehensive picture of the impact of humidex on HFMD incidence in Guangdong Province. Results from the present study should be important in the development of area-and-age-targeted control programs.

Application of the Golden Software Surfer mapping software for automation of visualisation of meteorological and oceanographic data in IMGW Maritime Branch.

NASA Astrophysics Data System (ADS)

Piliczewski, B.

2003-04-01

The Golden Software Surfer has been used in IMGW Maritime Branch for more than ten years. This tool provides ActiveX Automation objects, which allow scripts to control practically every feature of Surfer. These objects can be accessed from any Automation-enabled environment, such as Visual Basic or Excel. Several applications based on Surfer has been developed in IMGW. The first example is an on-line oceanographic service, which presents forecasts of the water temperature, sea level and currents originating from the HIROMB model and is automatically updated every day. Surfer was also utilised in MERMAID, an international project supported by EC under the 5th Framework Programme. The main aim of this project was to create a prototype of the Internet-based data brokerage system, which would enable to search, extract, buy and download datasets containing meteorological or oceanographic data. During the project IMGW developed an online application, called Mermaid Viewer, which enables communication with the data broker and automatic visualisation of the downloaded data using Surfer. Both the above mentioned applications were developed in Visual Basic. Currently it is considered to adopt Surfer for the monitoring service, which provides access to the data collected in the monitoring of the Baltic Sea environment.

Analysis of Large Scale Spatial Variability of Soil Moisture Using a Geostatistical Method

DTIC Science & Technology

2010-01-25

2010 / Accepted: 19 January 2010 / Published: 25 January 2010 Abstract: Spatial and temporal soil moisture dynamics are critically needed to...scale observed and simulated estimates of soil moisture under pre- and post-precipitation event conditions. This large scale variability is a crucial... dynamics is essential in the hydrological and meteorological modeling, improves our understanding of land surface–atmosphere interactions. Spatial and

Estimation of Wild Fire Risk Area based on Climate and Maximum Entropy in Korean Peninsular

NASA Astrophysics Data System (ADS)

Kim, T.; Lim, C. H.; Song, C.; Lee, W. K.

2015-12-01

The number of forest fires and accompanying human injuries and physical damages has been increased by frequent drought. In this study, forest fire danger zone of Korea is estimated to predict and prepare for future forest fire hazard regions. The MaxEnt (Maximum Entropy) model is used to estimate the forest fire hazard region which estimates the probability distribution of the status. The MaxEnt model is primarily for the analysis of species distribution, but its applicability for various natural disasters is getting recognition. The detailed forest fire occurrence data collected by the MODIS for past 5 years (2010-2014) is used as occurrence data for the model. Also meteorology, topography, vegetation data are used as environmental variable. In particular, various meteorological variables are used to check impact of climate such as annual average temperature, annual precipitation, precipitation of dry season, annual effective humidity, effective humidity of dry season, aridity index. Consequently, the result was valid based on the AUC(Area Under the Curve) value (= 0.805) which is used to predict accuracy in the MaxEnt model. Also predicted forest fire locations were practically corresponded with the actual forest fire distribution map. Meteorological variables such as effective humidity showed the greatest contribution, and topography variables such as TWI (Topographic Wetness Index) and slope also contributed on the forest fire. As a result, the east coast and the south part of Korea peninsula were predicted to have high risk on the forest fire. In contrast, high-altitude mountain area and the west coast appeared to be safe with the forest fire. The result of this study is similar with former studies, which indicates high risks of forest fire in accessible area and reflects climatic characteristics of east and south part in dry season. To sum up, we estimated the forest fire hazard zone with existing forest fire locations and environment variables and had meaningful result with artificial and natural effect. It is expected to predict future forest fire risk with future climate variables as the climate changes.

Overview of Dust Model Inter-comparison (DMIP) in East Asia

NASA Astrophysics Data System (ADS)

Uno, I.

2004-12-01

Dust transport modeling plays an important role in understanding the recent increase of Asian Dust episodes and its impact to the regional climate system. Several dust models have been developed in several research institutes and government agencies independently since 1990s. Their numerical results either look very similar or different. Those disagreements are caused by difference in dust modules (concepts and basic mechanisms) and atmospheric models (meteorological and transport models). Therefore common understanding of performance and uncertainty of dust erosion and transport models in the Asian region becomes very important. To have a better understanding of dust model application, we proposed the dust model intercomparison under the international cooperation networks as a part of activity of ADEC (Aeolian Dust Experiment on Climate Impact) project research. Current participants are Kyusyu Univ. (Japan), Meteorological Research Institute (Japan), Hong-Kong City Univ. (China), Korean Meteorological Agency METRI (Korea), US Naval Research Laboratory (USA), Chinese Meteorological Agency (China), Institute of Atmospheric Physics (China), Insular Coastal Dynamics (Malta) and Meteorological Service of Canada (Canada). As a case study episode, we set two huge dust storms occurred in March and April 2002. Results from the dust transport model from all the participants are compiled on the same methods and examined the model characteristics against the ground and airborne measurement data. We will also examine the dust model results from the horizontal distribution at specified levels, vertical profiles, concentration at special check point and emission flux at source region, and show the important parameters for dust modeling. In this paper, we will introduce the general overview of this DMIP activity and several important conclusions from this activity.

The closure problem for turbulence in meteorology and oceanography

NASA Technical Reports Server (NTRS)

Pierson, W. J., Jr.

1985-01-01

The dependent variables used for computer based meteorological predictions and in plans for oceanographic predictions are wave number and frequency filtered values that retain only scales resolvable by the model. Scales unresolvable by the grid in use become 'turbulence'. Whether or not properly processed data are used for initial values is important, especially for sparce data. Fickian diffusion with a constant eddy diffusion is used as a closure for many of the present models. A physically realistic closure based on more modern turbulence concepts, especially one with a reverse cascade at the right times and places, could help improve predictions.

The Calculation of the Heat Required for Wing Thermal Ice Prevention in Specified Icing Conditions

NASA Technical Reports Server (NTRS)

Bergrun, Norman R.; Jukoff, David; Schlaff, Bernard A.; Neel, Carr B., Jr.

1947-01-01

Flight tests were made in natural icing conditions with two 8-ft-chord heated airfoils of different sections. Measurements of meteorological variables conducive to ice formation were made simultaneously with the procurement of airfoil thermal data. The extent of knowledge on the meteorology of icing, the impingement of water drops on airfoil surfaces, and the processes of heat transfer and evaporation from a wetted airfoil surface have been increased to a point where the design of heated wings on a fundamental, wet-air basis now can be undertaken with reasonable certainty.

Describing the direct and indirect radiative effects of atmospheric aerosols over Europe by using coupled meteorology-chemistry simulations: a contribution from the AQMEII-Phase II exercise

NASA Astrophysics Data System (ADS)

Jimenez-Guerrero, Pedro; Balzarini, Alessandra; Baró, Rocío; Curci, Gabriele; Forkel, Renate; Hirtl, Marcus; Honzak, Luka; Langer, Matthias; Pérez, Juan L.; Pirovano, Guido; San José, Roberto; Tuccella, Paolo; Werhahn, Johannes; Zabkar, Rahela

2014-05-01

The study of the response of the aerosol levels in the atmosphere to a changing climate and how this affects the radiative budget of the Earth (direct, semi-direct and indirect effects) is an essential topic to build confidence on climate science, since these feedbacks involve the largest uncertainties nowadays. Air quality-climate interactions (AQCI) are, therefore, a key, but uncertain contributor to the anthropogenic forcing that remains poorly understood. To build confidence in the AQCI studies, regional-scale integrated meteorology-atmospheric chemistry models (i.e., models with on-line chemistry) that include detailed treatment of aerosol life cycle and aerosol impacts on radiation (direct effects) and clouds (indirect effects) are in demand. In this context, the main objective of this contribution is the study and definition of the uncertainties in the climate-chemistry-aerosol-cloud-radiation system associated to the direct radiative forcing and the indirect effect caused by aerosols over Europe, using an ensemble of fully-coupled meteorology-chemistry model simulations with the WRF-Chem model run under the umbrella of AQMEII-Phase 2 international initiative. Simulations were performed for Europe for the entire year 2010. According to the common simulation strategy, the year was simulated as a sequence of 2-day time slices. For better comparability, the seven groups applied the same grid spacing of 23 km and shared common processing of initial and boundary conditions as well as anthropogenic and fire emissions. With exception of a simulation with different cloud microphysics, identical physics options were chosen while the chemistry options were varied. Two model set-ups will be considered here: one sub-ensemble of simulations not taking into account any aerosol feedbacks (the baseline case) and another sub-ensemble of simulations which differs from the former by the inclusion of aerosol-radiation feedback. The existing differences for meteorological variables (mainly 2-m temperature and precipitation) and air quality levels (mainly ozone an PM10) between both sub-ensembles of WRF-Chem simulations have been characterized. In the case of ozone and PM10, an increase in solar radiation and temperature has generally resulted in an enhanced photochemical activity and therefore a negative feedback (areas with low aerosol concentrations present more than 50 W m-2 higher global radiation for cloudy conditions). However, simulated feedback effects between aerosol concentrations and meteorological variables and on pollutant distributions strongly depend on the model configuration and the meteorological situation. These results will help providing improved science-based foundations to better assess the impacts of climate variability, support the development of effective climate change policies and optimize private decision-making.

Anomalous CO2 Emissions in Different Ecosystems Around the World

NASA Astrophysics Data System (ADS)

Sanchez-Canete, E. P.; Moya Jiménez, M. R.; Kowalski, A. S.; Serrano-Ortiz, P.; López-Ballesteros, A.; Oyonarte, C.; Domingo, F.

2016-12-01

As an important tool for understanding and monitoring ecosystem dynamics at ecosystem level, the eddy covariance (EC) technique allows the assessment of the diurnal and seasonal variation of the net ecosystem exchange (NEE). Despite the high temporal resolution data available, there are still many processes (in addition to photosynthesis and respiration) that, although they are being monitored, have been neglected. Only a few authors have studied anomalous CO2 emissions (non biological), and have related them to soil ventilation, photodegradation or geochemical processes. The aim of this study is: 1) to identify anomalous short term CO2 emissions in different ecosystems distributed around the world, 2) to determine the meteorological variables that are influencing these emissions, and 3) to explore the potential processes that can be involved. We have studied EC data together with other meteorological ancillary variables obtained from the FLUXNET database (version 2015) and have found more than 50 sites with anomalous CO2 emissions in different ecosystem types such as grasslands, croplands or savannas. Data were filtered according to the FLUXNET quality control flags (only data with quality control flag equal to 0 was used) and correlation analysis were performed with NEE and ancillary data. Preliminary results showed strong and highly significant correlations between meteorological variables and anomalous CO2 emissions. Correlation results showed clear differing behaviors between ecosystems types, which could be related to the different processes involved in the anomalous CO2 emissions. We suggest that anomalous CO2 emissions are happening globally and therefore, their contribution to the global net ecosystem carbon balance requires further investigation in order to better understand its drivers.

Estimation of Secondary Compounds Concentrations Contributed by Biogenic VOC With Chemical Transport Model in the Central Area of Japan

NASA Astrophysics Data System (ADS)

Yamamoto, K.; Kanemaru, A.; Okumura, M.; Tohno, S.

2008-12-01

Biogenic VOC (BVOC) has comparably large contribution to generation of secondary air pollutants, such as photochemical oxidant or urban aerosol. In this study a BVOC emission inventory in the Kansai area, which is located in the central part of Japan, based on the field observation was developed. Some validations of the inventory were conducted by estimating the concentration distribution of oxidants with this developed and an existing BVOC emission inventory in Kansai area by meteorological model MM5 and atmospheric chemical transport model CMAQ. In the development of BVOC emission, the vegetation map by the Biodiversity Center of Japan which had been arranged as basic information on natural environmental preservation in a regional standard mesh (the third mesh) in 1999 was used. In this study isoprene and the mono-terpene were taken up as BVOC. Quercus crispula and Quercus serrata were selected as the source of isoprene, and Cryptomeria japonica, Chamaecyparis obtuse, Quercus phillyraeoides, Pinus densiflora, and Pinus thunbergii were selected as sources of mono-terpene. The parameter of the basic emission rate included in the model was decided by arranging the result of the observation in Kansai Research Center of Forestry and Forest Products Research Institute in each season. This emission flux from each species were calculated by G93 model by Guenther et al. and meteorological fields for the model, such as temperatures and sunlight intensities, were renewed hour by hour, therefore, this emission inventory has a high time resolution according to the season and time. In calculating meteorological fields, meteorological model MM5 Ver.3.7 was conducted in Japanese standard mesh in the selected five days of April, July, and October in 2004, and January 2005 respectively, and taking out the result of wind velocities and temperatures for substituting to the G93 model. Then atmospheric chemical transport model CMAQ Ver.4.6 with the emission inventories and meteorological fields was used for estimating secondary produced compounds concentration in the Kansai region. While the emission amount data of BVOC is also included in the EAGrid-Japan database, constructed by A. Kannari et al., another simulation with this existing BVOC emission inventory was conducted. As for other emission inventories of precursors, EAGrid-Japan was also used in both simulations. According to the result of estimation of BVOC emission, the total amount of BVOC is almost same as that of EAGrid-Japan, however, the ratio of isoprene to total BVOC emission is quite low in our estimation, due to the used vegetation map in this study, and the configuration of basic emission parameter in Autumn and Winter which is set to zero. According to the result of atmospheric chemical transport simulation with this developed BVOC inventory, oxidant concentrations are lower than observed values. This result suggests that the amount of isoprene emission strongly affected on the concentrations of oxidants, therefore, more accurate vegetation map data as a basis of BVOC emissions should be developed.

Decadal trend of precipitation and temperature patterns and impacts on snow-related variables in a semiarid region, Sierra Nevada, Spain.

NASA Astrophysics Data System (ADS)

José Pérez-Palazón, María; Pimentel, Rafael; Herrero, Javier; José Polo, María

2016-04-01

In the current context of global change, mountainous areas constitute singular locations in which these changes can be traced. Early detection of significant shifts of snow state variables in semiarid regions can help assess climate variability impacts and future snow dynamics in northern latitudes. The Sierra Nevada mountain range, in southern Spain, is a representative example of snow areas in Mediterranean-climate regions and both monitoring and modelling efforts have been performed to assess this variability and its significant scales. This work presents a decadal trend analysis throughout the 50-yr period 1960-2010 performed on some snow-related variables over Sierra Nevada, in Spain, which is included in the global climate change observatories network around the world. The study area comprises 4583 km2 distributed throughout the five head basins influenced by these mountains, with altitude values ranging from 140 to 3479 m.a.s.l., just 40 km from the Mediterranean coastline. Meteorological variables obtained from 44 weather stations from the National Meteorological Agency were studied and further used as input to the distributed hydrological model WiMMed (Polo et al., 2010), operational at the study area, to obtain selected snow variables. Decadal trends were obtained, together with their statistical significance, over the following variables, averaged over the whole study area: (1) annual precipitation; (2) annual snowfall; annual (3) mean, (4) maximum and (5) minimum daily temperature; annual (6) mean and (7) maximum daily fraction of snow covered areas; (8) annual number of days with snow cover; (9) mean and (10) maximum daily snow water equivalent; (11) annual number of extreme precipitation events; and (12) mean intensity of the annual extreme precipitation events. These variables were also studied over each of the five regions associated to each basin in the range. Globally decreasing decadal trends were obtained for all the meteorological variables, with the exception of the average annual mean and maximum daily temperature. In the case of the snow-related variables, no significant trends are observed at this time scale; nonetheless, a global decreasing rate is predominant in most of the variables. The torrential events are more frequent in the last decades of the study period, with an apparently increasing associated dispersion. This study constitutes a first sound analysis of the long-term observed trends of the snow regime in this area under the context of increasing temperature and decreasing precipitation regimes. The results highlight the complexity of non-linearity in environmental processes in Mediterranean regions, and point out to a significant shift in the precipitation and temperature regime, and thus on the snow-affected hydrological variables in the study area.

Random forest meteorological normalisation models for Swiss PM10 trend analysis

NASA Astrophysics Data System (ADS)

Grange, Stuart K.; Carslaw, David C.; Lewis, Alastair C.; Boleti, Eirini; Hueglin, Christoph

2018-05-01

Meteorological normalisation is a technique which accounts for changes in meteorology over time in an air quality time series. Controlling for such changes helps support robust trend analysis because there is more certainty that the observed trends are due to changes in emissions or chemistry, not changes in meteorology. Predictive random forest models (RF; a decision tree machine learning technique) were grown for 31 air quality monitoring sites in Switzerland using surface meteorological, synoptic scale, boundary layer height, and time variables to explain daily PM10 concentrations. The RF models were used to calculate meteorologically normalised trends which were formally tested and evaluated using the Theil-Sen estimator. Between 1997 and 2016, significantly decreasing normalised PM10 trends ranged between -0.09 and -1.16 µg m-3 yr-1 with urban traffic sites experiencing the greatest mean decrease in PM10 concentrations at -0.77 µg m-3 yr-1. Similar magnitudes have been reported for normalised PM10 trends for earlier time periods in Switzerland which indicates PM10 concentrations are continuing to decrease at similar rates as in the past. The ability for RF models to be interpreted was leveraged using partial dependence plots to explain the observed trends and relevant physical and chemical processes influencing PM10 concentrations. Notably, two regimes were suggested by the models which cause elevated PM10 concentrations in Switzerland: one related to poor dispersion conditions and a second resulting from high rates of secondary PM generation in deep, photochemically active boundary layers. The RF meteorological normalisation process was found to be robust, user friendly and simple to implement, and readily interpretable which suggests the technique could be useful in many air quality exploratory data analysis situations.

Hydro-geomorphologic events in Portugal and its association with Circulation weather types

NASA Astrophysics Data System (ADS)

Pereira, Susana; Ramos, Alexandre M.; Rebelo, Luís; Trigo, Ricardo M.; Zêzere, José L.

2017-04-01

Floods and landslides correspond to the most hazardous weather driven natural disasters in Portugal. A recent improvement on their characterization has been achieved with the gathering of basic information on past floods and landslides that caused social consequences in Portugal for the period 1865-2015 through the DISASTER database (Zêzere et al., 2014). This database was built under the assumption that strong social impacts of floods and landslides are sufficient relevant to be reported consistently by national and regional newspapers. The DISASTER database contains detailed information on the location, date of occurrence and social impacts (fatalities, injuries, missing people, evacuated and homeless people) of each individual hydro-geomorphologic case (1677 flood cases and 292 landslide cases). These hydro-geomorphologic disaster cases are grouped in a restrict number of DISASTER events that were selected according to the following criteria: a set of at least 3 DISASTER cases sharing the same trigger in time (with no more than 3 days without cases), which have a widespread spatial extension related to the triggering mechanism and a certain magnitude. In total, the DISASTER database includes 134 events (3.7 average days of duration) that generated high social impacts in Portugal (962 fatalities and 40878 homeless people). Each DISASTER event was characterized with the following attributes: hydro-geomorphologic event type (e.g landslides, floods, flash floods, urban floods); date of occurrence (year, month and days); duration in days; spatial location in GIS; number of fatalities, injured, evacuated and homeless people; and weather type responsible for triggering the event. The atmospheric forcing at different time scales is the main trigger for the hydro-meteorological DISASTER events occurred in Portugal. In this regard there is an urge for a more systematic assessment of the weather types associated to flood and landslide damaging events to correctly characterize the climatic forcing of hydro-geomorphologic risk in Portugal. The weather type classification used herein is an automated version of the Lamb weather type procedure, initially developed for the United Kingdom and often named circulation weather types (CWT) and latter adapted for Portugal. We computed the daily CWT for the 1865-2015 period by means of the daily SLP retrieved from the 20 Century Reanalysis dataset. The relationship between the CWTs and the hydro-meteorological events in Portugal shows that the cyclonic, westerly and southwesterly are CWTs frequently associated with major socio-economic impacts of DISASTER events. In addition, CWT basic variables (flow strength, vorticity and direction) were used to better understand the impacts of the meteorological conditions in the hydro-meteorological events in Portugal. Reference: Zêzere, J. L., Pereira, S., Tavares, A. O., Bateira, C., Trigo, R. M., Quaresma, I., Santos, P. P., Santos, M. and Verde, J.: DISASTER: a GIS database on hydro-geomorphologic disasters in Portugal, Nat. Hazards, 72(2), 503-532, doi:10.1007/s11069-013-1018-y, 2014. This work was supported by the project FORLAND - Hydrogeomorphologic risk in Portugal: driving forces and application for land use planning [grant number PTDC/ATPGEO/1660/2014] funded by the Portuguese Foundation for Science and Technology (FCT), Portugal. A. M. Ramos was also supported by a FCT postdoctoral grant (FCT/DFRH/ SFRH/BPD/84328/2012).

Predicting Low Flow Conditions from Climatic Indices - Indicator-Based Modeling for Climate Change Impact Assessment

NASA Astrophysics Data System (ADS)

Fangmann, Anne; Haberlandt, Uwe

2014-05-01

In the face of climate change, the assessment of future hydrological regimes has become indispensable in the field of water resources management. Investigation of potential change is vital for proper planning, especially with regard to hydrological extremes. Commonly, projection of future streamflow is done applying process-based hydrological models, using climate model data as input, whose complex model structures generally require excessive amounts of time and effort for set-up and computation. This study aims at identifying practical alternatives to the employment of sophisticated models by considering simpler, yet sufficiently accurate methods for modeling rainfall-runoff relations with regard to hydrological extremes. The focus is thereby put on the prediction of low flow periods, which are, in contrast to flood events, characterized by extended durations and spatial dimensions. The models to be established in this study base on indicators, which characterize both meteorological and hydrological conditions within dry periods. This approach makes direct use of the coupling between atmospheric driving forces and streamflow response with the underlying presumption that low-precipitation and high-evaporation periods result in diminished flow, implying that relationships exist between the properties of both meteorological and hydrological events (duration, volume, severity etc.). Eventually, optimal combinations of meteorological indicators are sought that are suitable to predict various low flow characteristics with satisfactory accuracy. Two approaches for model specification are tested: a) multiple linear regression, and b) Fuzzy logic. The data used for this study are daily time series of mean discharge obtained from 294 gauges with variable record length situated in the federal state of Lower Saxony, Germany, as well as interpolated climate variables available for a period from 1951 to 2011. After extraction of a variety of indicators from the available discharge and climate time series on a bi-annual basis, regression and Fuzzy models are fit. Fitting is done in two variations: locally at each of the watersheds in the study area, and regionally, yielding one specific model expression for the entire study area. Models for the individual stations perform well using only the meteorological indicators as predictor variables, while the regional models require the additional input of catchment descriptors to account for the variability of the rainfall-runoff translation processes between the catchments.

Weather types and strokes in the Augsburg region (Southern Germany)

NASA Astrophysics Data System (ADS)

Beck, Christoph; Ertl, Michael; Giemsa, Esther; Jacobeit, Jucundus; Naumann, Markus; Seubert, Stefanie

2017-04-01

Strokes are one of the leading causes of morbidity and mortality worldwide and the main reason for longterm care dependency in Germany. Concerning the economical impact on patients and healthcare systems it is of particular importance to prevent this disease as well as to improve the outcome of the affected persons. Beside the primary well-known risk factors like hypertension, cigarette smoking, physical inactivity and others, also weather seems to have pronounced influence on the occurrence and frequency of strokes. Previous studies most often focused on effects of singular meteorological variables like ambient air temperature, air pressure or humidity. An advanced approach is to link the entire suite of daily weather elements classified to air mass- or weather types to cerebrovascular morbidity or mortality. In a joint pilot study bringing together climatologists, environmental scientists and physicians from the University of Augsburg and the clinical centre Augsburg, we analysed relationships between singular meteorological parameters as well as combined weather effects (e.g. weather types) and strokes in the urban area of Augsburg and the surrounding rural region. A total of 17.501 stroke admissions to Neurological Clinic and Clinical Neurophysiology at Klinikum Augsburg between 2006 and 2015 are classified to either "ischaemic" (16.354) or "haemorrhagic" (1.147) subtype according to etiology (based on the International Classification of Diseases - 10th Revision). Spearman correlations between daily frequencies of ischaemic and haemorrhagic strokes and singular atmospheric parameters (T, Tmin, Tmax, air pressure, humidity etc.) measured at the DWD (German weather service) meteorological station at Augsburg Muehlhausen are rather low. However, higher correlations are achieved when considering sub-samples of "homogenous weather conditions" derived from synoptic circulation classifications: e.g. within almost all of 10 types arising from a classification of central European mean sea level pressure fields into "Großwettertypes" (Beck 2000) the relationships between meteorological variables and stroke frequencies are increasing. Mainly temperature variables (Tmin, Tmax, Tmean) appear to be important particularly in winter and summer. Moreover distinct correlations of similar magnitude are obtained with other variables like wind speed or precipitation for specific weather types (e.g. westerly type). In how far these initial findings do really point to additional health impacts beyond temperature effects is subject of ongoing work.

Supporting data for hydrologic studies in San Francisco Bay, California : meteorological measurements at the Port of Redwood City during 1998-2001

USGS Publications Warehouse

Schemel, Laurence E.

2002-01-01

Meteorological data were collected during 1998-2001 at the Port of Redwood City, California, to support hydrologic studies in South San Francisco Bay. The measured meteorological variables were air temperature, atmospheric pressure, quantum flux (insolation), and four parameters of wind speed and direction: scalar mean horizontal wind speed, (vector) resultant horizontal wind speed, resultant wind direction, and standard deviation of the wind direction. Hourly mean values based on measurements at five-minute intervals were logged at the site. Daily mean values were computed for temperature, infolation, pressure, and scalar wind speed. Daily mean values for 1998-2001 are described in this report, and a short record of hourly mean values is compared to data from another near-by station. Data (hourly and daily mean) from the entire period of record (starting in April 1992) and reports describing data prior to 1998 are provided.

Lagrangian Turbulence and Transport in Semi-enclosed Basins and Coastal Regions

DTIC Science & Technology

2009-01-01

enclosed Basins and Coastal Regions Annalisa Griffa Division of Meteorology and Physical Oceanography Rosenstiel School of Marine and Atmospheric...enclosed Basins and Coastal Regions 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e. TASK NUMBER...variables. A set of diagnostics is then performed, including hydrological sections, transport, mean circulation and variability, aimed at quantifying

A Method for Evaluation of Model-Generated Vertical Profiles of Meteorological Variables

DTIC Science & Technology

2016-03-01

3 2.1 RAOB Soundings and WRF Output for Profile Generation 3 2.2 Height-Based Profiles 5 2.3 Pressure-Based Profiles 5 3. Comparisons 8 4...downward arrow. The blue lines represent sublayers with sublayer means indicated by red triangles. Circles indicate the observations or WRF output...9 Table 3 Sample of differences in listed variables derived from WRF and RAOB data

Optimal Interpolation scheme to generate reference crop evapotranspiration

NASA Astrophysics Data System (ADS)

Tomas-Burguera, Miquel; Beguería, Santiago; Vicente-Serrano, Sergio; Maneta, Marco

2018-05-01

We used an Optimal Interpolation (OI) scheme to generate a reference crop evapotranspiration (ETo) grid, forcing meteorological variables, and their respective error variance in the Iberian Peninsula for the period 1989-2011. To perform the OI we used observational data from the Spanish Meteorological Agency (AEMET) and outputs from a physically-based climate model. To compute ETo we used five OI schemes to generate grids for the five observed climate variables necessary to compute ETo using the FAO-recommended form of the Penman-Monteith equation (FAO-PM). The granularity of the resulting grids are less sensitive to variations in the density and distribution of the observational network than those generated by other interpolation methods. This is because our implementation of the OI method uses a physically-based climate model as prior background information about the spatial distribution of the climatic variables, which is critical for under-observed regions. This provides temporal consistency in the spatial variability of the climatic fields. We also show that increases in the density and improvements in the distribution of the observational network reduces substantially the uncertainty of the climatic and ETo estimates. Finally, a sensitivity analysis of observational uncertainties and network densification suggests the existence of a trade-off between quantity and quality of observations.

An advanced stochastic weather generator for simulating 2-D high-resolution climate variables

NASA Astrophysics Data System (ADS)

Peleg, Nadav; Fatichi, Simone; Paschalis, Athanasios; Molnar, Peter; Burlando, Paolo

2017-07-01

A new stochastic weather generator, Advanced WEather GENerator for a two-dimensional grid (AWE-GEN-2d) is presented. The model combines physical and stochastic approaches to simulate key meteorological variables at high spatial and temporal resolution: 2 km × 2 km and 5 min for precipitation and cloud cover and 100 m × 100 m and 1 h for near-surface air temperature, solar radiation, vapor pressure, atmospheric pressure, and near-surface wind. The model requires spatially distributed data for the calibration process, which can nowadays be obtained by remote sensing devices (weather radar and satellites), reanalysis data sets and ground stations. AWE-GEN-2d is parsimonious in terms of computational demand and therefore is particularly suitable for studies where exploring internal climatic variability at multiple spatial and temporal scales is fundamental. Applications of the model include models of environmental systems, such as hydrological and geomorphological models, where high-resolution spatial and temporal meteorological forcing is crucial. The weather generator was calibrated and validated for the Engelberg region, an area with complex topography in the Swiss Alps. Model test shows that the climate variables are generated by AWE-GEN-2d with a level of accuracy that is sufficient for many practical applications.

Hourly predictive Levenberg-Marquardt ANN and multi linear regression models for predicting of dew point temperature

NASA Astrophysics Data System (ADS)

Zounemat-Kermani, Mohammad

2012-08-01

In this study, the ability of two models of multi linear regression (MLR) and Levenberg-Marquardt (LM) feed-forward neural network was examined to estimate the hourly dew point temperature. Dew point temperature is the temperature at which water vapor in the air condenses into liquid. This temperature can be useful in estimating meteorological variables such as fog, rain, snow, dew, and evapotranspiration and in investigating agronomical issues as stomatal closure in plants. The availability of hourly records of climatic data (air temperature, relative humidity and pressure) which could be used to predict dew point temperature initiated the practice of modeling. Additionally, the wind vector (wind speed magnitude and direction) and conceptual input of weather condition were employed as other input variables. The three quantitative standard statistical performance evaluation measures, i.e. the root mean squared error, mean absolute error, and absolute logarithmic Nash-Sutcliffe efficiency coefficient ( {| {{{Log}}({{NS}})} |} ) were employed to evaluate the performances of the developed models. The results showed that applying wind vector and weather condition as input vectors along with meteorological variables could slightly increase the ANN and MLR predictive accuracy. The results also revealed that LM-NN was superior to MLR model and the best performance was obtained by considering all potential input variables in terms of different evaluation criteria.

Watershed-scale response of groundwater recharge to inter-annual and inter-decadal variability in precipitation (Alberta, Canada)

NASA Astrophysics Data System (ADS)

Hayashi, Masaki; Farrow, Christopher R.

2014-12-01

Groundwater recharge sets a constraint on aquifer water balance in the context of water management. Historical data on groundwater and other relevant hydrological processes can be used to understand the effects of climatic variability on recharge, but such data sets are rare. The climate of the Canadian prairies is characterized by large inter-annual and inter-decadal variability in precipitation, which provides opportunities to examine the response of groundwater recharge to changes in meteorological conditions. A decadal study was conducted in a small (250 km2) prairie watershed in Alberta, Canada. Relative magnitude of annual recharge, indicated by water-level rise, was significantly correlated with a combination of growing-season precipitation and snowmelt runoff, which drives depression-focussed infiltration of meltwater. Annual precipitation was greater than vapour flux at an experimental site in some years and smaller in other years. On average precipitation minus vapour flux was 10 mm y-1, which was comparable to the magnitude of watershed-scale groundwater recharge estimated from creek baseflow. Average baseflow showed a distinct shift from a low value (4 mm y-1) in 1982-1995 to a high value (15 mm y-1) in 2003-2013, indicating the sensitivity of groundwater recharge to a decadal-scale variability of meteorological conditions.

Developing Predictive Models for Algal Bloom Occurrence and Identifying Factors Controlling their Occurrence in the Charlotte County and Surroundings

NASA Astrophysics Data System (ADS)

Karki, S.; Sultan, M.; Elkadiri, R.; Chouinard, K.

2017-12-01

Numerous occurrences of harmful algal blooms (Karenia Brevis) were reported from Southwest Florida along the coast of Charlotte County, Florida. We are developing data-driven (remote sensing, field, and meteorological data) models to accomplish the following: (1) identify the factors controlling bloom development, (2) forecast bloom occurrences, and (3) make recommendations for monitoring variables that are found to be most indicative of algal bloom occurrences and for identifying optimum locations for monitoring stations. To accomplish these three tasks we completed/are working on the following steps. Firstly, we developed an automatic system for downloading and processing of ocean color data acquired through MODIS Terra and MODIS Aqua products using SeaDAS ocean color processing software. Examples of extracted variables include: chlorophyll a (OC3M), chlorophyll a Generalized Inherent Optical Property (GIOP), chlorophyll a Garver-Siegel- Maritorena (GSM), sea surface temperature (SST), Secchi disk depth, euphotic depth, turbidity index, wind direction and speed, colored dissolved organic material (CDOM). Secondly we are developing a GIS database and a web-based GIS to host the generated remote sensing-based products in addition to relevant meteorological and field data. Examples of the meteorological and field inputs include: precipitation amount and rates, concentrations of nitrogen, phosphorous, fecal coliform and Dissolved Oxygen (DO). Thirdly, we are constructing and validating a multivariate regression model and an artificial neural network model to simulate past algal bloom occurrences using the compiled archival remote sensing, meteorological, and field data. The validated model will then be used to predict the timing and location of algal bloom occurrences. The developed system, upon completion, could enhance the decision making process, improve the citizen's quality of life, and strengthen the local economy.

Local environmental and meteorological conditions influencing the invasive mosquito Ae. albopictus and arbovirus transmission risk in New York City.

PubMed

Little, Eliza; Bajwa, Waheed; Shaman, Jeffrey

2017-08-01

Ae. albopictus, an invasive mosquito vector now endemic to much of the northeastern US, is a significant public health threat both as a nuisance biter and vector of disease (e.g. chikungunya virus). Here, we aim to quantify the relationships between local environmental and meteorological conditions and the abundance of Ae. albopictus mosquitoes in New York City. Using statistical modeling, we create a fine-scale spatially explicit risk map of Ae. albopictus abundance and validate the accuracy of spatiotemporal model predictions using observational data from 2016. We find that the spatial variability of annual Ae. albopictus abundance is greater than its temporal variability in New York City but that both local environmental and meteorological conditions are associated with Ae. albopictus numbers. Specifically, key land use characteristics, including open spaces, residential areas, and vacant lots, and spring and early summer meteorological conditions are associated with annual Ae. albopictus abundance. In addition, we investigate the distribution of imported chikungunya cases during 2014 and use these data to delineate areas with the highest rates of arboviral importation. We show that the spatial distribution of imported arboviral cases has been mostly discordant with mosquito production and thus, to date, has provided a check on local arboviral transmission in New York City. We do, however, find concordant areas where high Ae. albopictus abundance and chikungunya importation co-occur. Public health and vector control officials should prioritize control efforts to these areas and thus more cost effectively reduce the risk of local arboviral transmission. The methods applied here can be used to monitor and identify areas of risk for other imported vector-borne diseases.

Local environmental and meteorological conditions influencing the invasive mosquito Ae. albopictus and arbovirus transmission risk in New York City

PubMed Central

Bajwa, Waheed; Shaman, Jeffrey

2017-01-01

Ae. albopictus, an invasive mosquito vector now endemic to much of the northeastern US, is a significant public health threat both as a nuisance biter and vector of disease (e.g. chikungunya virus). Here, we aim to quantify the relationships between local environmental and meteorological conditions and the abundance of Ae. albopictus mosquitoes in New York City. Using statistical modeling, we create a fine-scale spatially explicit risk map of Ae. albopictus abundance and validate the accuracy of spatiotemporal model predictions using observational data from 2016. We find that the spatial variability of annual Ae. albopictus abundance is greater than its temporal variability in New York City but that both local environmental and meteorological conditions are associated with Ae. albopictus numbers. Specifically, key land use characteristics, including open spaces, residential areas, and vacant lots, and spring and early summer meteorological conditions are associated with annual Ae. albopictus abundance. In addition, we investigate the distribution of imported chikungunya cases during 2014 and use these data to delineate areas with the highest rates of arboviral importation. We show that the spatial distribution of imported arboviral cases has been mostly discordant with mosquito production and thus, to date, has provided a check on local arboviral transmission in New York City. We do, however, find concordant areas where high Ae. albopictus abundance and chikungunya importation co-occur. Public health and vector control officials should prioritize control efforts to these areas and thus more cost effectively reduce the risk of local arboviral transmission. The methods applied here can be used to monitor and identify areas of risk for other imported vector-borne diseases. PMID:28832586

Seasonal climate variability in historical and prehistorical times deduced from varved lake sediments: Calibration of records from Lakes Woseriner See and Tiefer See

NASA Astrophysics Data System (ADS)

Czymzik, Markus; Kienel, Ulrike; Dreibrodt, Stefan; Brauer, Achim

2013-04-01

Societies are susceptible to the effects of even short-term climate variations on water supply, health, and agricultural productivity. However, understanding of human-climate interactions is limited due to the lack of high-resolution climate records in space and time. Varved lake sediments provide long time-series of seasonal climate variability directly from populated areas that can be compared to historical and archeological records. Calibration against meteorological data enables process-based insights into sediment deposition within the lake that can be extrapolated into the past using transfer functions. Lakes Woseriner See (53°40'N/12°2'E; 37 m asl.) and Tiefer See (53°23'N/13°97'E, 65 m asl.) in northeastern Germany are located only 35 km apart. Situated within the former settlement areas, the lakes are well suited for studying climate influences on society related to the Neolithic Funnelbeaker culture or the Slavic colonization. Sub-recent annual laminations allow to establish climate proxy data-series at seasonal resolution that can be calibrated against the long meteorological record from the nearby City of Schwerin. Seasonal climate proxy data-series covering the last 90 years have been obtained from short sediment cores applying a combination of microfacies analyses, X-ray fluorescence scanning (µ-XRF), and varve counting. Main sediment microfacies in both lakes are endogenic calcite varves comprising calcite and organic layer couplets of varying thickness, diatom layers, and dispersed detrital grains. Calibration against meteorological data indicates that variations in sediment layer thickness and composition are not stationary through time but influenced by inter-annual variations in meteorological conditions.

Present and Future Projections of Habitat Suitability of the Asian Tiger Mosquito, a Vector of Viral Pathogens, from Global Climate Simulations.

NASA Astrophysics Data System (ADS)

Proestos, Y.; Christophides, G.; Erguler, K.; Tanarhte, M.; Waldock, J.; Lelieveld, J.

2014-12-01

Climate change can influence the transmission of vector borne diseases (VBDs) through altering the habitat suitability of insect vectors. Here we present global climate model simulations and evaluate the associated uncertainties in view of the main meteorological factors that may affect the distribution of the Asian Tiger mosquito (Aedes albopictus), which can transmit pathogens that cause Chikungunya, Dengue fever, yellow fever and various encephalitides. Using a general circulation model (GCM) at 50 km horizontal resolution to simulate mosquito survival variables including temperature, precipitation and relative humidity, we present both global and regional projections of the habitat suitability up to the middle of the 21st century. The model resolution of 50 km allows evaluation against previous projections for Europe and provides a basis for comparative analyses with other regions. Model uncertainties and performance are addressed in light of the recent CMIP5 ensemble climate model simulations for the RCP8.5 concentration pathway and using meteorological re-analysis data (ERA-Interim/ECMWF) for the recent past. Uncertainty ranges associated with the thresholds of meteorological variables that may affect the distribution of Ae. albopictus are diagnosed using fuzzy-logic methodology, notably to assess the influence of selected meteorological criteria and combinations of criteria that influence mosquito habitat suitability. From the climate projections for 2050, and adopting a habitat suitability index larger than 70%, we estimate that about 2.4 billion individuals in a land area of nearly 20 million square kilometres will potentially be exposed to Ae. albopictus. The synthesis of fuzzy-logic based on mosquito biology and climate change analysis provides new insights into the regional and global spreading of VBDs to support disease control and policy making.

Towards seasonal forecasting of malaria in India.

PubMed

Lauderdale, Jonathan M; Caminade, Cyril; Heath, Andrew E; Jones, Anne E; MacLeod, David A; Gouda, Krushna C; Murty, Upadhyayula Suryanarayana; Goswami, Prashant; Mutheneni, Srinivasa R; Morse, Andrew P

2014-08-10

Malaria presents public health challenge despite extensive intervention campaigns. A 30-year hindcast of the climatic suitability for malaria transmission in India is presented, using meteorological variables from a state of the art seasonal forecast model to drive a process-based, dynamic disease model. The spatial distribution and seasonal cycles of temperature and precipitation from the forecast model are compared to three observationally-based meteorological datasets. These time series are then used to drive the disease model, producing a simulated forecast of malaria and three synthetic malaria time series that are qualitatively compared to contemporary and pre-intervention malaria estimates. The area under the Relative Operator Characteristic (ROC) curve is calculated as a quantitative metric of forecast skill, comparing the forecast to the meteorologically-driven synthetic malaria time series. The forecast shows probabilistic skill in predicting the spatial distribution of Plasmodium falciparum incidence when compared to the simulated meteorologically-driven malaria time series, particularly where modelled incidence shows high seasonal and interannual variability such as in Orissa, West Bengal, and Jharkhand (North-east India), and Gujarat, Rajastan, Madhya Pradesh and Maharashtra (North-west India). Focusing on these two regions, the malaria forecast is able to distinguish between years of "high", "above average" and "low" malaria incidence in the peak malaria transmission seasons, with more than 70% sensitivity and a statistically significant area under the ROC curve. These results are encouraging given that the three month forecast lead time used is well in excess of the target for early warning systems adopted by the World Health Organization. This approach could form the basis of an operational system to identify the probability of regional malaria epidemics, allowing advanced and targeted allocation of resources for combatting malaria in India.

Selection of meteorological parameters affecting rainfall estimation using neuro-fuzzy computing methodology

NASA Astrophysics Data System (ADS)

Hashim, Roslan; Roy, Chandrabhushan; Motamedi, Shervin; Shamshirband, Shahaboddin; Petković, Dalibor; Gocic, Milan; Lee, Siew Cheng

2016-05-01

Rainfall is a complex atmospheric process that varies over time and space. Researchers have used various empirical and numerical methods to enhance estimation of rainfall intensity. We developed a novel prediction model in this study, with the emphasis on accuracy to identify the most significant meteorological parameters having effect on rainfall. For this, we used five input parameters: wet day frequency (dwet), vapor pressure (e̅a), and maximum and minimum air temperatures (Tmax and Tmin) as well as cloud cover (cc). The data were obtained from the Indian Meteorological Department for the Patna city, Bihar, India. Further, a type of soft-computing method, known as the adaptive-neuro-fuzzy inference system (ANFIS), was applied to the available data. In this respect, the observation data from 1901 to 2000 were employed for testing, validating, and estimating monthly rainfall via the simulated model. In addition, the ANFIS process for variable selection was implemented to detect the predominant variables affecting the rainfall prediction. Finally, the performance of the model was compared to other soft-computing approaches, including the artificial neural network (ANN), support vector machine (SVM), extreme learning machine (ELM), and genetic programming (GP). The results revealed that ANN, ELM, ANFIS, SVM, and GP had R2 of 0.9531, 0.9572, 0.9764, 0.9525, and 0.9526, respectively. Therefore, we conclude that the ANFIS is the best method among all to predict monthly rainfall. Moreover, dwet was found to be the most influential parameter for rainfall prediction, and the best predictor of accuracy. This study also identified sets of two and three meteorological parameters that show the best predictions.

Introduction to the in orbit test and its performance of the first meteorological imager of the Communication, Ocean, and Meteorological Satellite

NASA Astrophysics Data System (ADS)

Kim, D.; Ahn, M. H.

2013-12-01

The first geostationary earth observation satellite of Korea, named Communication, Ocean, and Meteorological Satellite (COMS), is successfully launched on 27 June 2010 in Korea Standard Time. After arrival of its operational orbit, the satellite underwent in orbit test (IOT) lasting for about 8 months. During the IOT period, the meteorological imager went through tests for its functional and performance demonstration. With the successful acquisition of the first visible channel image, signal chain from the payload to satellite bus and to the ground is also verified. While waiting for the outgassing operation, several functional tests for the payload are also performed. By taking an observation of different sizes of image, of various object targets such as the Sun, moon, and internal calibration target, it has been demonstrated that the payload performs as commanded, satisfying its functional requirements. After successful operation of outgassing which lasted about 40 days, the first set of infrared images is also successfully acquired and the full performance test started. The radiometric performance of the meteorological imager is tested by signal to noise ratio (SNR) for the visible channel, noise equivalent differential temperature (NEdT) for the infrared channels, and pixel to pixel non-uniformity. In case of the visible channel, SNR of all 8 detectors are obtained using the ground measured parameters and background signals obtained in orbit and are larger than 26 at 5% albedo, exceeding the user requirement value of 10 with a significant margin. The values at 100% albedo also meet the user requirements. Also, the relative variability of detector responsivity among the 8 visible channels meets the user requirement, showing values of about 10% of the user requrirement. For the infrared channels, the NEdT of each detector is well within the user requirement and is comparable with or better than the legacy instruments, except the water vapor channel which is slightly noisier than the legacy instruments. The variability of detector responsivity of infrared channels is also below the user requirement, within 40% of the requirement except shortwave infrared channel. The improved performance result is partly due to the stable and low detector temperature obtained with the spacecraft design, by installing a single solar panel to the opposite side of the meteorological imager.

Methodologies for evaluating performance and assessing uncertainty of atmospheric dispersion models

NASA Astrophysics Data System (ADS)

Chang, Joseph C.

This thesis describes methodologies to evaluate the performance and to assess the uncertainty of atmospheric dispersion models, tools that predict the fate of gases and aerosols upon their release into the atmosphere. Because of the large economic and public-health impacts often associated with the use of the dispersion model results, these models should be properly evaluated, and their uncertainty should be properly accounted for and understood. The CALPUFF, HPAC, and VLSTRACK dispersion modeling systems were applied to the Dipole Pride (DP26) field data (˜20 km in scale), in order to demonstrate the evaluation and uncertainty assessment methodologies. Dispersion model performance was found to be strongly dependent on the wind models used to generate gridded wind fields from observed station data. This is because, despite the fact that the test site was a flat area, the observed surface wind fields still showed considerable spatial variability, partly because of the surrounding mountains. It was found that the two components were comparable for the DP26 field data, with variability more important than uncertainty closer to the source, and less important farther away from the source. Therefore, reducing data errors for input meteorology may not necessarily increase model accuracy due to random turbulence. DP26 was a research-grade field experiment, where the source, meteorological, and concentration data were all well-measured. Another typical application of dispersion modeling is a forensic study where the data are usually quite scarce. An example would be the modeling of the alleged releases of chemical warfare agents during the 1991 Persian Gulf War, where the source data had to rely on intelligence reports, and where Iraq had stopped reporting weather data to the World Meteorological Organization since the 1981 Iran-Iraq-war. Therefore the meteorological fields inside Iraq must be estimated by models such as prognostic mesoscale meteorological models, based on observational data from areas outside of Iraq, and using the global fields simulated by the global meteorological models as the initial and boundary conditions for the mesoscale models. It was found that while comparing model predictions to observations in areas outside of Iraq, the predicted surface wind directions had errors between 30 to 90 deg, but the inter-model differences (or uncertainties) in the predicted surface wind directions inside Iraq, where there were no onsite data, were fairly constant at about 70 deg. (Abstract shortened by UMI.)

Impact of inherent meteorology uncertainty on air quality ...

EPA Pesticide Factsheets

It is well established that there are a number of different classifications and sources of uncertainties in environmental modeling systems. Air quality models rely on two key inputs, namely, meteorology and emissions. When using air quality models for decision making, it is important to understand how uncertainties in these inputs affect the simulated concentrations. Ensembles are one method to explore how uncertainty in meteorology affects air pollution concentrations. Most studies explore this uncertainty by running different meteorological models or the same model with different physics options and in some cases combinations of different meteorological and air quality models. While these have been shown to be useful techniques in some cases, we present a technique that leverages the initial condition perturbations of a weather forecast ensemble, namely, the Short-Range Ensemble Forecast system to drive the four-dimensional data assimilation in the Weather Research and Forecasting (WRF)-Community Multiscale Air Quality (CMAQ) model with a key focus being the response of ozone chemistry and transport. Results confirm that a sizable spread in WRF solutions, including common weather variables of temperature, wind, boundary layer depth, clouds, and radiation, can cause a relatively large range of ozone-mixing ratios. Pollutant transport can be altered by hundreds of kilometers over several days. Ozone-mixing ratios of the ensemble can vary as much as 10–20 ppb

Is suicide mortality associated with meteorological and socio-economic factors? An ecological study in a city in Taiwan with a high suicide rate.

PubMed

Wu, Ya Wen; Chen, Chih Ken; Wang, Liang Jen

2014-06-01

Keelung City has the highest suicide rate in Taiwan. This study aimed to determine whether meteorological and socio-economic factors are associated with suicide mortality in Keelung City, by gender and by means of suicide. Data on suicides between January 2006 and December 2010 were provided by the Department of Health, Keelung City Government. The suicide victims were categorized into non-violent and violent groups, based on the International Classification of Disease, Ninth Revision. Meteorological data were obtained from the Central Weather Bureau of Taiwan. Socio-economic data were gathered from the Accounting and Statistics Office, Keelung City Government. Multiple linear regression analysis with backward elimination was performed to determine the model that was most effective in predicting dependent variables. During the 5-year study period, the overall suicide mortality rate was negatively associated with ambient temperature. Male suicide mortality was positively correlated with unemployment, and negatively correlated with ambient temperature, barometric pressure, rainy days, family income and number of holidays. Female suicide mortality and violent suicide mortality were not significantly correlated with any meteorological or socio-economic factors. Non-violent suicide mortality was positively correlated with unemployment, and negatively correlated with ambient temperature, barometric pressure and family income. Suicide is a complex psychopathological phenomenon. Further studies with individual data are warranted to confirm how meteorological and socio-economic conditions influence ones' suicidal behaviour.

An Observational Study of the Relationship between Cloud, Aerosol and Meteorology in Broken Low-Level Cloud Conditions

NASA Technical Reports Server (NTRS)

Loeb, Norman G.; Schuster, Gregory L.

2008-01-01

Global satellite analyses showing strong correlations between aerosol optical depth and 3 cloud cover have stirred much debate recently. While it is tempting to interpret the results as evidence of aerosol enhancement of cloud cover, other factors such as the influence of meteorology on both the aerosol and cloud distributions can also play a role, as both aerosols and clouds depend upon local meteorology. This study uses satellite observations to examine aerosol-cloud relationships for broken low-level cloud regions off the coast of Africa. The analysis approach minimizes the influence of large-scale meteorology by restricting the spatial and temporal domains in which the aerosol and cloud properties are compared. While distributions of several meteorological variables within 5deg 5deg latitude-longitude regions are nearly identical under low and high aerosol optical depth, the corresponding distributions of single-layer low cloud properties and top-of-atmosphere radiative fluxes differ markedly, consistent with earlier studies showing increased cloud cover with aerosol optical depth. Furthermore, fine-mode fraction and Angstrom Exponent are also larger in conditions of higher aerosol optical depth, even though no evidence of systematic latitudinal or longitudinal gradients between the low and high aerosol optical depth populations are observed. When the analysis is repeated for all 5deg 5deg latitude-longitude regions over the global oceans (after removing cases in which significant meteorological differences are found between the low and high aerosol populations), results are qualitatively similar to those off the coast of Africa.

Enviro-HIRLAM in Studies of Urban and Aerosol Impacts on Metropolitan Areas: Science-Education Approach

NASA Astrophysics Data System (ADS)

Mahura, Alexander; Nuterman, Roman; Mazeikis, Adomas; Gonzalez-Aparicio, Iratxe; Ivanov, Sergey; Palamarchuk, Julia

2014-05-01

To attract more perspective young scientists (and especially, MSc and PhD students) for advanced research and development of complex and modern modelling systems, a specific approach is required. It should allow within a short period of time to evaluate personal background levels, skills, capabilities, etc. To learn more about new potential science-oriented developers of the models, it is often not enough to look into the personal resume. Thus, a special event such as Young Scientist Summer School (YSSS) can be organized, where young researchers could have an opportunity to attend not only relevant lectures, but also participate in practical exercises allowing to solidify lecture materials. Here, the practical exercises are presented as independent small-scale (having duration of up to a week) research projects or studies oriented on specific topics of YSSS. Developed approach was tested and realized during 2008 and 2011 YSSS events held and organized in Zelenogorsk, Russia (by NetFAM et al.; http://netfam.fmi.fi/YSSS08) and Odessa, Ukraine (by MUSCATEN et al.; http://atmos.physic.ut.ee/~muscaten/YSSS/1info.html), respectively. It has been refined for the new YSSS (Jul 2014) to be organized by the COST Action EuMetChem. The main focus of all these YSSSs was/is on the integrated modelling of meteorological and chemical transport processes and impact of chemical weather on numerical weather prediction and climate modelling. During previous YSSSs some of such projects - "URBAN: The Influence of Metropolitan Areas on Meteorology", "AEROSOL: The Impact of Aerosols Effects on Meteorology", and "COASTAL: The Coastal & Cities Effects on Meteorology" - were focused on evaluation of influence of metropolitan areas on formation of meteorological and chemical fields above urban areas (such as Paris, France; Copenhagen, Denmark, and Bilbao, Spain) and surroundings. The Environment - HIgh Resolution Limited Area Model (Enviro-HIRLAM) was used and modifications were made taking into account urban (anthropogenic heat flux, roughness, buildings and their characteristics), chemical species/ aerosol (feedback mechanisms) effects with further analysis of temporal and spatial variability of diurnal cycle for meteorological variables of key importance. Main items of listed above YSSS small-scale research projects include the following: • Introduction with background discussions (with brainstorming to outline research and technical tasks planned including main goal, specific objectives, etc.) in groups; • Analysis of meteorological situations (selecting specific cases/ dates using surface maps, diagrams of vertical sounding, and surface meteorological measurements); • Learning practical technical steps (in order to make necessary changes in the model and implementing urban and aerosol effects, compiling executables, making test runs); • Performing model runs/simulations at different options (dates, control vs. modified urban and aerosol runs, forecast lengths, spatial and temporal resolutions, etc.); • Visualization/ plotting of results obtained (in a form of graphs, tables, animations); • Evaluation of possible impact on urban areas (estimating differences between the control and modified runs through temporal and spatial variability of simulated meteorological (air temperature, wind speed, relative humidity, sensible and latent heat fluxes, etc.) and chemical pollutants (concentration and deposition) fields/ patterns; • Team's oral presentation of the project about results and findings and following guidelines (including aim and specific objectives, methodology and approaches, results and discussions with examples, conclusions, acknowledgements, references). Outline and detailed description of the developed approach, key items of the research projects and their schedules, preparatory steps including team of students' familiarization with general information on planned exercises and literature list (composed of required, recommended, and additional readings), requirements for successful completion and defense of the project, team independent work as well as under supervision are presented and discussed.

The classification of wind shears from the point of view of aerodynamics and flight mechanics

NASA Technical Reports Server (NTRS)

Seidler, Fritz; Hensel, Gunter

1987-01-01

A study of international statistical data shows that in about three quarters of all serious accidents which occurred with jet propelled airliners wind shear was either one of the main causes of the accident or represented a major contributory cause. Wind shear related problems are examined. The necessity of a use of different concepts, definitions, and divisions is explained, and the concepts and definitions required for the division of wind and wind shear into different categories is discussed. A description of the context between meteorological and aerodynamics-flight mechanics concepts, definitions, and divisions is also provided. Attention is given to wind and wind components, general characteristics of wind shear and the meteorological terms, the basic types of wind shear for aerodynamics-flight mechanics investigations, special types of wind shear for aerodynamics-flight mechanics investigations, and possibilities regarding a change of the wind component.

NASA GISS Surface Temperature (GISTEMP) Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schmidt, G.; Ruedy, R.; Persin, A

The NASA GISS Surface Temperature (GISTEMP) analysis provides a measure of the changing global surface temperature with monthly resolution for the period since 1880, when a reasonably global distribution of meteorological stations was established. The input data that the GISTEMP Team use for the analysis, collected by many national meteorological services around the world, are the adjusted data of the Global Historical Climatology Network (GHCN) Vs. 3 (this represents a change from prior use of unadjusted Vs. 2 data) (Peterson and Vose, 1997 and 1998), United States Historical Climatology Network (USHCN) data, and SCAR (Scientific Committee on Antarctic Research) datamore » from Antarctic stations. Documentation of the basic analysis method is provided by Hansen et al. (1999), with several modifications described by Hansen et al. (2001). The GISS analysis is updated monthly, however CDIAC's presentation of the data here is updated annually.« less

Virtually-Enhanced Fluid Laboratories for Teaching Meteorology

NASA Astrophysics Data System (ADS)

Marshall, J.; Illari, L.

2015-12-01

The Weather in a Tank (WIAT) project aims to offer instructors a repertoire of rotating tank experiments, and a curriculum in fluid dynamics, to better assist students in learning how to move between phenomena in the real world and basic principles of rotating fluid dynamics which play a central role in determining the climate of the planet. Despite the increasing use of laboratory experiments in teaching meteorology, however, we are aware that many teachers and students do not have access to suitable apparatus and so cannot benefit from them. Here we describe a 'virtually-enhanced' laboratory that we hope could be very effective in getting across a flavor of the experiments and bring them to a wider audience. In the pedagogical spirit of WIAT we focus on how simple underlying principles, illustrated through laboratory experiments, shape the observed structure of the large-scale atmospheric circulation.

Building resilience to weather-related hazards through better preparedness

NASA Astrophysics Data System (ADS)

Keller, Julia; Golding, Brian; Johnston, David; Ruti, Paolo

2017-04-01

Recent developments in weather forecasting have transformed our ability to predict weather-related hazards, while mobile communication is radically changing the way that people receive information. At the same time, vulnerability to weather-related hazards is growing through urban expansion, population growth and climate change. This talk will address issues facing the science community in responding to the Sendai Framework objective to "substantially increase the availability of and access to multi-hazard early warning systems" in the context of weather-related hazards. It will also provide an overview of activities and approaches developed in the World Meteorological Organisation's High Impact Weather (HIWeather) project. HIWeather has identified and is promoting research in key multi-disciplinary gaps in our knowledge, including in basic meteorology, risk prediction, communication and decision making, that affect our ability to provide effective warnings. The results will be pulled together in demonstration projects that will both showcase leading edge capability and build developing country capacity.

Modelling of Black and Organic Carbon Variability in the Northern Hemisphere

NASA Astrophysics Data System (ADS)

Kurganskiy, Alexander; Nuterman, Roman; Mahura, Alexander; Kaas, Eigil; Baklanov, Alexander; Hansen Sass, Bent

2016-04-01

Black and organic carbon as short-lived climate forcers have influence on air quality and climate in Northern Europe and Arctic. Atmospheric dispersion, deposition and transport of these climate forcers from remote sources is especially difficult to model in Arctic regions due to complexity of meteorological and chemical processes and uncertainties of emissions. In our study, the online integrated meteorology-chemistry/aerosols model Enviro-HIRLAM (Environment - High Resolution Limited Area Model) was employed for evaluating spatio-temporal variability of black and organic carbon aerosols in atmospheric composition in the Northern Hemisphere regions. The model setup included horizontal resolution of 0.72 deg, time step of 450 sec, 6 h meteorological surface data assimilation, 1 month spin-up; and model was run for the full year of 2010. Emissions included anthropogenic (ECLIPSE), shipping (AU_RCP&FMI), wildfires (IS4FIRES), and interactive sea salt, dust and DMS. Meteorological (from IFS at 0.75 deg) and chemical (from MACC Reanalysis at 1.125 deg) boundary conditions were obtained from ECMWF. Annual and month-to-month variability of mean concentration, accumulated dry/wet and total deposition fluxes is analyzed for the model domain and selected European and Arctic observation sites. Modelled and observed BC daily mean concentrations during January and July showed fair-good correlation (0.31-0.64) for stations in Germany, UK and Italy; however, for Arctic stations (Tiksi, Russia and Zeppelin, Norway) the correlations were negative in January, but higher correlations and positive (0.2-0.7) in July. For OC, it varied 0.45-0.67 in January and 0.19-0.57 in July. On seasonal scale, during both summer and winter seasons the BC and OC correlations are positive and higher for European stations compared with Arctic. On annual scale, both BC and OC correlations are positive and vary between 0.4-0.6 for European stations, and these are smoothed to negligible values for Arctic stations. Results of simulations showed that in general the model tends to underestimate both black and organic carbon concentrations for the Arctic and European stations.

A 60-year reconstructed high-resolution local meteorological data set in Central Sahel (1950-2009): evaluation, analysis and application to land surface modelling

NASA Astrophysics Data System (ADS)

Leauthaud, Crystele; Cappelaere, Bernard; Demarty, Jérôme; Guichard, Françoise; Velluet, Cécile; Kergoat, Laurent; Vischel, Théo; Grippa, Manuela; Mouhaimouni, Mohammed; Bouzou Moussa, Ibrahim; Mainassara, Ibrahim; Sultan, Benjamin

2017-04-01

The Sahel has experienced strong climate variability in the past decades. Understanding its implications for natural and cultivated ecosystems is pivotal in a context of high population growth and mainly agriculture-based livelihoods. However, efforts to model processes at the land-atmosphere interface are hindered, particularly when the multi-decadal timescale is targeted, as climatic data are scarce, largely incomplete and often unreliable. This study presents the generation of a long-term, high-temporal resolution, multivariate local climatic data set for Niamey, Central Sahel. The continuous series spans the period 1950-2009 at a 30-min timescale and includes ground station-based meteorological variables (precipitation, air temperature, relative and specific humidity, air pressure, wind speed, downwelling long- and short-wave radiation) as well as process-modelled surface fluxes (upwelling long- and short-wave radiation,latent, sensible and soil heat fluxes and surface temperature). A combination of complementary techniques (linear/spline regressions, a multivariate analogue method, artificial neural networks and recursive gap filling) was used to reconstruct missing meteorological data. The complete surface energy budget was then obtained for two dominant land cover types, fallow bush and millet, by applying the meteorological forcing data set to a finely field-calibrated land surface model. Uncertainty in reconstructed data was expressed by means of a stochastic ensemble of plausible historical time series. Climatological statistics were computed at sub-daily to decadal timescales and compared with local, regional and global data sets such as CRU and ERA-Interim. The reconstructed precipitation statistics, ˜1°C increase in mean annual temperature from 1950 to 2009, and mean diurnal and annual cycles for all variables were in good agreement with previous studies. The new data set, denoted NAD (Niamey Airport-derived set) and publicly available, can be used to investigate the water and energy cycles in Central Sahel, while the methodology can be applied to reconstruct series at other stations. The study has been published in Int. J. Climatol. (2016), DOI: 10.1002/joc.4874

Analysis of a long-term measurement of air pollutants (2007-2011) in North China Plain (NCP); Impact of emission reduction during the Beijing Olympic Games.

PubMed

Xu, Ruiguang; Tang, Guiqian; Wang, Yuesi; Tie, Xuexi

2016-09-01

Five years measurements were used to evaluate the effect of emission controls on the changes of air pollutants in Beijing and its surroundings in the NCP during 2008 Olympic Games (2008OG). The major challenge of this study was to filter out the effect of variability of meteorological conditions, when compared the air pollutants during the game to non-game period. We used four-year (2007, 2009-2011) average as the Non-2008OG to smooth the temporal variability caused by meteorological parameters. To study the spatial variability and regional transport, 6 sites (urban, rural, a mega city, a heavy industrial city, and a remote site) were selected. The result showed that the annually meteorological variability was significantly reduced. Such as, in BJ the differences between 2008OG and 5-years averaged values were 2.7% for relative humidity and 0.6% for wind speed. As a result, the anomaly of air pollutants between 2008OG and Non-2008OG can largely attribute to the emission control. The comparison showed that the major pollutants (PM10, PM2.5, NO, NOx) at the 6 sites in 2008OG were consistently lowered. For example, PM2.5 in BJ decreased from 75 to 45 μg/m(3) (40% reduction). However, the emission controls had minor effect on O3 concentrations (1% reduction). In contrast, the O3 precursor (NOx) reduced from 19.7 to 13.2 ppb (33% reduction). The in-sensitivity between NOx and O3 suggested that the O3 formation was under VOCs control condition in NCP, showing that strong VOC emission control is needed in order to significantly reduce O3 concentration in the region. Copyright © 2016 Elsevier Ltd. All rights reserved.

Relationships between northern Adriatic Sea mucilage events and climate variability.

PubMed

Deserti, Marco; Cacciamani, Carlo; Chiggiato, Jacopo; Rinaldi, Attilio; Ferrari, Carla R

2005-12-15

A long term analysis (1865-2002) of meteorological data collected in the Po Valley and Northern Adriatic Basin have been analysed to find possible links between variability in the climatic parameters and the phenomenon of mucilage. Seasonal anomalies of temperature, calculated as spatial mean over the Po Valley area, and anomalies of North Atlantic Oscillation were compared with the historical record of mucilage episodes. Both climatic indices were found to be positively correlated with mucilage events, suggesting a possible relationship between climatic variability and the increased appearance of mucilage aggregates.

figure1.nc

EPA Pesticide Factsheets

NetCDF file of the SREF standard deviation of wind speed and direction that was used to inject variability in the FDDA input.variable U_NDG_OLD contains standard deviation of wind speed (m/s)variable V_NDG_OLD contains the standard deviation of wind direction (deg)This dataset is associated with the following publication:Gilliam , R., C. Hogrefe , J. Godowitch, S. Napelenok , R. Mathur , and S.T. Rao. Impact of inherent meteorology uncertainty on air quality model predictions. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES. American Geophysical Union, Washington, DC, USA, 120(23): 12,259–12,280, (2015).

[Influence of meteorological factors on the activity of the adult taiga tick (Ixodes persulcatus Sch., Ixodinae) in St. Petersburg and its environs].

PubMed

Osipova, T N; Grigoryeva, L A; Samoylova, E P; Shapar, A O; Bychkova, E M

2017-01-01

The article deals with influence of meteorolical factors on the activity of the taiga tick Ixodes persulvatus Sch. in St. Petersburg and its environs. The results of correlation analysis of meteorological data (21 index) and data ticks collected in 1980-2012 allowed determining linear dependence between 11 meteorological indices an average amount of ticks. Factor analysis reduced dimentionality down to 3 indices: sum of temperatures higher than +5.0 °C, sum of precipitation higher than 5 mm per year, and Selyaninov hydrothermal coefficient. It was demonstrated that, at the background of the general tendency for the decrease of the average number of active ticks in the studied territories, correlation between the amount of ticks and meteorological indices can significantly vary as in the correlation density, so in the character and in dependence of microclimatic features of the collecting site. When variability of the mean abundance of ticks during years of investigation is low, the methods of collecting can significantly affect the results of the statistical analysis. This fact must be taken in consideration during prognosis of both dates of the beginning of epidemiological season and its intensity.

Identifying Meteorological Controls on Open and Closed Mesoscale Cellular Convection as Associated with Marine Cold Air Outbreaks

NASA Astrophysics Data System (ADS)

McCoy, Isabel; Wood, Robert; Fletcher, Jennifer

Marine low clouds are key influencers of the climate and contribute significantly to uncertainty in model climate sensitivity due to their small scale and complex processes. Many low clouds occur in large-scale cellular patterns, known as open and closed mesoscale cellular convection (MCC), which have significantly different radiative and microphysical properties. Investigating MCC development and meteorological controls will improve our understanding of their impacts on the climate. We conducted an examination of time-varying meteorological conditions associated with satellite-determined open and closed MCC. The spatial and temporal patterns of MCC clouds were compared with key meteorological control variables calculated from ERA-Interim Reanalysis to highlight dependencies and major differences. This illustrated the influence of environmental stability and surface forcing as well as the role of marine cold air outbreaks (MCAO, the movement of cold air from polar-regions across warmer waters) in MCC cloud formation. Such outbreaks are important to open MCC development and may also influence the transition from open to closed MCC. Our results may lead to improvements in the parameterization of cloudiness and advance the simulation of marine low clouds. National Science Foundation Graduate Research Fellowship Grant (DGE-1256082).

The effects of meteorological factors on the occurrence of Ganoderma sp. spores in the air

NASA Astrophysics Data System (ADS)

Grinn-Gofroń, Agnieszka; Strzelczak, Agnieszka

2011-03-01

Ganoderma sp. is an airborne fungal spore type known to trigger respiratory allergy symptoms in sensitive patients. Aiming to reduce the risk for allergic individuals, we analysed fungal spore circulation in Szczecin, Poland, and its dependence on meteorological conditions. Statistical models for the airborne spore concentrations of Ganoderma sp.—one of the most abundant fungal taxa in the area—were developed. Aerobiological sampling was conducted over 2004-2008 using a volumetric Lanzoni trap. Simultaneously, the following meteorological parameters were recorded: daily level of precipitation, maximum and average wind speed, relative humidity and maximum, minimum, average and dew point temperatures. These data were used as the explaining variables. Due to the non-linearity and non-normality of the data set, the applied modelling techniques were artificial neural networks (ANN) and mutlivariate regression trees (MRT). The obtained classification and MRT models predicted threshold conditions above which Ganoderma sp. appeared in the air. It turned out that dew point temperature was the main factor influencing the presence or absence of Ganoderma sp. spores. Further analysis of spore seasons revealed that the airborne fungal spore concentration depended only slightly on meteorological factors.

On quality control procedures for solar radiation and meteorological measures, from subhourly to montly average time periods

NASA Astrophysics Data System (ADS)

Espinar, B.; Blanc, P.; Wald, L.; Hoyer-Klick, C.; Schroedter-Homscheidt, M.; Wanderer, T.

2012-04-01

Meteorological data measured by ground stations are often a key element in the development and validation of methods exploiting satellite images. These data are considered as a reference against which satellite-derived estimates are compared. Long-term radiation and meteorological measurements are available from a large number of measuring stations. However, close examination of the data often reveals a lack of quality, often for extended periods of time. This lack of quality has been the reason, in many cases, of the rejection of large amount of available data. The quality data must be checked before their use in order to guarantee the inputs for the methods used in modelling, monitoring, forecast, etc. To control their quality, data should be submitted to several conditions or tests. After this checking, data that are not flagged by any of the test is released as a plausible data. In this work, it has been performed a bibliographical research of quality control tests for the common meteorological variables (ambient temperature, relative humidity and wind speed) and for the usual solar radiometrical variables (horizontal global and diffuse components of the solar radiation and the beam normal component). The different tests have been grouped according to the variable and the average time period (sub-hourly, hourly, daily and monthly averages). The quality test may be classified as follows: • Range checks: test that verify values are within a specific range. There are two types of range checks, those based on extrema and those based on rare observations. • Step check: test aimed at detecting unrealistic jumps or stagnation in the time series. • Consistency checks: test that verify the relationship between two or more time series. The gathered quality tests are applicable for all latitudes as they have not been optimized regionally nor seasonably with the aim of being generic. They have been applied to ground measurements in several geographic locations, what result in the detection of some control tests that are no longer adequate, due to different reasons. After the modification of some test, based in our experience, a set of quality control tests is now presented, updated according to technology advances and classified. The presented set of quality tests allows radiation and meteorological data to be tested in order to know their plausibility to be used as inputs in theoretical or empirical methods for scientific research. The research leading to those results has partly receive funding from the European Union's Seventh Framework Programme (FP7/2007-2013) under Grant Agreement no. 262892 (ENDORSE project).

An analysis of the first two years of GASP data

NASA Technical Reports Server (NTRS)

Holdeman, J. D.; Nastrom, G. D.; Falconer, P. D.

1977-01-01

Distributions of mean ozone levels from the first two years of data from the NASA Global Atmospheric Sampling Program (GASP) show spatial and temporal variations in agreement with previous measurements. The standard deviations of these distributions reflect the large natural variability of ozone levels in the altitude range of the GASP measurements. Monthly mean levels of ozone below the tropopause show an annual cycle with a spring maximum which is believed to result from transport from the stratosphere. Correlations of ozone with independent meteorological parameters, and meteorological parameters obtained by the GASP systems show that this transport occurs primarily through cyclogenesis at mid-latitudes.

The relationship between particulate pollution levels in Australian cities, meteorology, and landscape fire activity detected from MODIS hotspots.

PubMed

Price, Owen F; Williamson, Grant J; Henderson, Sarah B; Johnston, Fay; Bowman, David M J S

2012-01-01

Smoke from bushfires is an emerging issue for fire managers because of increasing evidence for its public health effects. Development of forecasting models to predict future pollution levels based on the relationship between bushfire activity and current pollution levels would be a useful management tool. As a first step, we use daily thermal anomalies detected by the MODIS Active Fire Product (referred to as "hotspots"), pollution concentrations, and meteorological data for the years 2002 to 2008, to examine the statistical relationship between fire activity in the landscapes and pollution levels around Perth and Sydney, two large Australian cities. Resultant models were statistically significant, but differed in their goodness of fit and the distance at which the strength of the relationship was strongest. For Sydney, a univariate model for hotspot activity within 100 km explained 24% of variation in pollution levels, and the best model including atmospheric variables explained 56% of variation. For Perth, the best radius was 400 km, explaining only 7% of variation, while the model including atmospheric variables explained 31% of the variation. Pollution was higher when the atmosphere was more stable and in the presence of on-shore winds, whereas there was no effect of wind blowing from the fires toward the pollution monitors. Our analysis shows there is a good prospect for developing region-specific forecasting tools combining hotspot fire activity with meteorological data.

Climate Predictors of the Spatial Distribution of Human Plague Cases in the West Nile Region of Uganda

PubMed Central

MacMillan, Katherine; Monaghan, Andrew J.; Apangu, Titus; Griffith, Kevin S.; Mead, Paul S.; Acayo, Sarah; Acidri, Rogers; Moore, Sean M.; Mpanga, Joseph Tendo; Enscore, Russel E.; Gage, Kenneth L.; Eisen, Rebecca J.

2012-01-01

East Africa has been identified as a region where vector-borne and zoonotic diseases are most likely to emerge or re-emerge and where morbidity and mortality from these diseases is significant. Understanding when and where humans are most likely to be exposed to vector-borne and zoonotic disease agents in this region can aid in targeting limited prevention and control resources. Often, spatial and temporal distributions of vectors and vector-borne disease agents are predictable based on climatic variables. However, because of coarse meteorological observation networks, appropriately scaled and accurate climate data are often lacking for Africa. Here, we use a recently developed 10-year gridded meteorological dataset from the Advanced Weather Research and Forecasting Model to identify climatic variables predictive of the spatial distribution of human plague cases in the West Nile region of Uganda. Our logistic regression model revealed that within high elevation sites (above 1,300 m), plague risk was positively associated with rainfall during the months of February, October, and November and negatively associated with rainfall during the month of June. These findings suggest that areas that receive increased but not continuous rainfall provide ecologically conducive conditions for Yersinia pestis transmission in this region. This study serves as a foundation for similar modeling efforts of other vector-borne and zoonotic disease in regions with sparse observational meteorologic networks. PMID:22403328

An observational study of PM10 and hospital admissions for acute exacerbations of chronic respiratory disease in Tasmania, Australia 1992-2002.

PubMed

Mészáros, D; Markos, J; FitzGerald, D G; Walters, E H; Wood-Baker, R

2015-01-01

Particulate matter with a diameter below 10 µ (PM10) has been a major concern in the Tamar Valley, Launceston, where wood heaters are extensively used. We examined the relationship between PM10 levels, meteorological variables, respiratory medications and hospital admissions for respiratory disease over the decade 1992-2002. PM10 levels were provided by the Department of Primary Industry Water, Parks and Environment, and meteorological variables from the Bureau of Meteorology. We obtained hospital discharge codes for the Launceston General Hospital. Poisson regression was used for statistical analyses. Mean daily PM10 levels declined from 50.7 to 16.5 μg/m(3). Hospitalisations for asthma decreased from 29 to 21 per month, whereas chronic obstructive pulmonary disease (COPD) increased and bronchitis/bronchiolitis remained unchanged. We found a 10 μg/m(3) increase in PM10 to be associated with a 4% increase in admissions for acute bronchitis/bronchiolitis (p0.05), but no association with asthma or COPD was found. All respiratory diseases showed seasonal patterns of hospitalisation. This is the first long-term study in Australia to demonstrate an association between PM10 levels and respiratory diseases. Reducing exposure to PM10 may decrease hospital admissions for respiratory diseases. Better preventive measures, including sustained public health initiatives to combat air pollution, are required to reduce respiratory morbidity.

Interpolation of scattered temperature data measurements onto a worldwide regular grid using radial basis functions with applications to global warming

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kansa, E.J.; Axelrod, M.C.; Kercher, J.R.

1994-05-01

Our current research into the response of natural ecosystems to a hypothesized climatic change requires that we have estimates of various meteorological variables on a regularly spaced grid of points on the surface of the earth. Unfortunately, the bulk of the world`s meteorological measurement stations is located at airports that tend to be concentrated on the coastlines of the world or near populated areas. We can also see that the spatial density of the station locations is extremely non-uniform with the greatest density in the USA, followed by Western Europe. Furthermore, the density of airports is rather sparse in desertmore » regions such as the Sahara, the Arabian, Gobi, and Australian deserts; likewise the density is quite sparse in cold regions such as Antarctica Northern Canada, and interior northern Russia. The Amazon Basin in Brazil has few airports. The frequency of airports is obviously related to the population centers and the degree of industrial development of the country. We address the following problem here. Given values of meteorological variables, such as maximum monthly temperature, measured at the more than 5,500 airport stations, interpolate these values onto a regular grid of terrestrial points spaced by one degree in both latitude and longitude. This is known as the scattered data problem.« less

Spatial Analysis of Severe Fever with Thrombocytopenia Syndrome Virus in China Using a Geographically Weighted Logistic Regression Model

PubMed Central

Wu, Liang; Deng, Fei; Xie, Zhong; Hu, Sheng; Shen, Shu; Shi, Junming; Liu, Dan

2016-01-01

Severe fever with thrombocytopenia syndrome (SFTS) is caused by severe fever with thrombocytopenia syndrome virus (SFTSV), which has had a serious impact on public health in parts of Asia. There is no specific antiviral drug or vaccine for SFTSV and, therefore, it is important to determine the factors that influence the occurrence of SFTSV infections. This study aimed to explore the spatial associations between SFTSV infections and several potential determinants, and to predict the high-risk areas in mainland China. The analysis was carried out at the level of provinces in mainland China. The potential explanatory variables that were investigated consisted of meteorological factors (average temperature, average monthly precipitation and average relative humidity), the average proportion of rural population and the average proportion of primary industries over three years (2010–2012). We constructed a geographically weighted logistic regression (GWLR) model in order to explore the associations between the selected variables and confirmed cases of SFTSV. The study showed that: (1) meteorological factors have a strong influence on the SFTSV cover; (2) a GWLR model is suitable for exploring SFTSV cover in mainland China; (3) our findings can be used for predicting high-risk areas and highlighting when meteorological factors pose a risk in order to aid in the implementation of public health strategies. PMID:27845737

The impact of snow and glaciers on meteorological variables in the Khumbu Valley, Nepalese Himalaya.

NASA Astrophysics Data System (ADS)

Potter, E.; Orr, A.; Willis, I.

2017-12-01

Previous observational studies have suggested that snow and glaciers have a big impact on local meteorological variables in the Himalayas, in particular affecting near surface temperature and the localised wind system. Understanding the impact of changing surface conditions on these systems and is crucial in improving future predictions of glacier melt and precipitation in the Himalayas. However, the mechanisms that control the local meteorology remain poorly understood due to the lack of in-situ data and detailed modelling studies. To investigate these mechanisms, we run the Weather Research and Forecasting (WRF) model at kilometre scale resolution for one month during the monsoon over the Khumbu Valley, Nepalese Himalaya. The model is run with and without snow and glacier coverage at the surface. The impact of adding debris cover into the model is also investigated. In the control run with snow and ice, thermally-driven near-surface winds are found to travel up valley during the day except over the glacier slopes. When the snow and ice is removed from the model, the up valley winds extend over the entire slope. Removal of the snow and ice also results in changes to cloud cover and hydrometeors. A momentum budget approach is used to fully understand the mechanisms that maintain the localised wind system, e.g. to determine the contributions from local forcing or synoptic forcing.

Basic principles of variable speed drives

NASA Technical Reports Server (NTRS)

Loewenthal, S. H.

1973-01-01

An understanding of the principles which govern variable speed drive operation is discussed for successful drive application. The fundamental factors of torque, speed ratio, and power as they relate to drive selection are discussed. The basic types of variable speed drives, their operating characteristics and their applications are also presented.

A statistical data assimilation method for seasonal streamflow forecasting to optimize hydropower reservoir management in data-scarce regions

NASA Astrophysics Data System (ADS)

Arsenault, R.; Mai, J.; Latraverse, M.; Tolson, B.

2017-12-01

Probabilistic ensemble forecasts generated by the ensemble streamflow prediction (ESP) methodology are subject to biases due to errors in the hydrological model's initial states. In day-to-day operations, hydrologists must compensate for discrepancies between observed and simulated states such as streamflow. However, in data-scarce regions, little to no information is available to guide the streamflow assimilation process. The manual assimilation process can then lead to more uncertainty due to the numerous options available to the forecaster. Furthermore, the model's mass balance may be compromised and could affect future forecasts. In this study we propose a data-driven approach in which specific variables that may be adjusted during assimilation are defined. The underlying principle was to identify key variables that would be the most appropriate to modify during streamflow assimilation depending on the initial conditions such as the time period of the assimilation, the snow water equivalent of the snowpack and meteorological conditions. The variables to adjust were determined by performing an automatic variational data assimilation on individual (or combinations of) model state variables and meteorological forcing. The assimilation aimed to simultaneously optimize: (1) the error between the observed and simulated streamflow at the timepoint where the forecasts starts and (2) the bias between medium to long-term observed and simulated flows, which were simulated by running the model with the observed meteorological data on a hindcast period. The optimal variables were then classified according to the initial conditions at the time period where the forecast is initiated. The proposed method was evaluated by measuring the average electricity generation of a hydropower complex in Québec, Canada driven by this method. A test-bed which simulates the real-world assimilation, forecasting, water release optimization and decision-making of a hydropower cascade was developed to assess the performance of each individual process in the reservoir management chain. Here the proposed method was compared to the PF algorithm while keeping all other elements intact. Preliminary results are encouraging in terms of power generation and robustness for the proposed approach.

Associations between meteorological parameters and influenza activity in a subtropical country: Case of five sentinel sites in Yaoundé-Cameroon

PubMed Central

Monamele, Gwladys C.; Vernet, Marie-Astrid; Nsaibirni, Robert F. J.; Bigna, Jean Joel R.; Kenmoe, Sebastien; Njankouo, Mohamadou Ripa

2017-01-01

Influenza is associated with highly contagious respiratory infections. Previous research has found that influenza transmission is often associated with climate variables especially in temperate regions. This study was performed in order to fill the gap of knowledge regarding the relationship between incidence of influenza and three meteorological parameters (temperature, rainfall and humidity) in a tropical setting. This was a retrospective study performed in Yaoundé-Cameroon from January 2009 to November 2015. Weekly proportions of confirmed influenza cases from five sentinel sites were considered as dependent variables, whereas weekly values of mean temperature, average relative humidity and accumulated rainfall were considered as independent variables. A univariate linear regression model was used in determining associations between influenza activity and weather covariates. A time-series method was used to predict on future values of influenza activity. The data was divided into 2 parts; the first 71 months were used to calibrate the model, and the last 12 months to test for prediction. Overall, there were 1173 confirmed infections with influenza virus. Linear regression analysis showed that there was no statistically significant association observed between influenza activity and weather variables. Very weak relationships (-0.1 < r < 0.1) were observed. Three prediction models were obtained for the different viral types (overall positive, Influenza A and Influenza B). Model 1 (overall influenza) and model 2 (influenza A) fitted well during the estimation period; however, they did not succeed to make good forecasts for predictions. Accumulated rainfall was the only external covariate that enabled good fit of both models. Based on the stationary R2, 29.5% and 41.1% of the variation in the series can be explained by model 1 and 2, respectively. This study laid more emphasis on the fact that influenza in Cameroon is characterized by year-round activity. The meteorological variables selected in this study did not enable good forecast of future influenza activity and certainly acted as proxies to other factors not considered, such as, UV radiation, absolute humidity, air quality and wind. PMID:29088290

Probabilistic Forecasting of Surface Ozone with a Novel Statistical Approach

NASA Technical Reports Server (NTRS)

Balashov, Nikolay V.; Thompson, Anne M.; Young, George S.

2017-01-01

The recent change in the Environmental Protection Agency's surface ozone regulation, lowering the surface ozone daily maximum 8-h average (MDA8) exceedance threshold from 75 to 70 ppbv, poses significant challenges to U.S. air quality (AQ) forecasters responsible for ozone MDA8 forecasts. The forecasters, supplied by only a few AQ model products, end up relying heavily on self-developed tools. To help U.S. AQ forecasters, this study explores a surface ozone MDA8 forecasting tool that is based solely on statistical methods and standard meteorological variables from the numerical weather prediction (NWP) models. The model combines the self-organizing map (SOM), which is a clustering technique, with a step wise weighted quadratic regression using meteorological variables as predictors for ozone MDA8. The SOM method identifies different weather regimes, to distinguish between various modes of ozone variability, and groups them according to similarity. In this way, when a regression is developed for a specific regime, data from the other regimes are also used, with weights that are based on their similarity to this specific regime. This approach, regression in SOM (REGiS), yields a distinct model for each regime taking into account both the training cases for that regime and other similar training cases. To produce probabilistic MDA8 ozone forecasts, REGiS weighs and combines all of the developed regression models on the basis of the weather patterns predicted by an NWP model. REGiS is evaluated over the San Joaquin Valley in California and the northeastern plains of Colorado. The results suggest that the model performs best when trained and adjusted separately for an individual AQ station and its corresponding meteorological site.

Soil water content assessment: critical issues concerning the operational application of the triangle method.

PubMed

Maltese, Antonino; Capodici, Fulvio; Ciraolo, Giuseppe; La Loggia, Goffredo

2015-03-19

Knowledge of soil water content plays a key role in water management efforts to improve irrigation efficiency. Among the indirect estimation methods of soil water content via Earth Observation data is the triangle method, used to analyze optical and thermal features because these are primarily controlled by water content within the near-surface evaporation layer and root zone in bare and vegetated soils. Although the soil-vegetation-atmosphere transfer theory describes the ongoing processes, theoretical models reveal limits for operational use. When applying simplified empirical formulations, meteorological forcing could be replaced with alternative variables when the above-canopy temperature is unknown, to mitigate the effects of calibration inaccuracies or to account for the temporal admittance of the soil. However, if applied over a limited area, a characterization of both dry and wet edges could not be properly achieved; thus, a multi-temporal analysis can be exploited to include outer extremes in soil water content. A diachronic empirical approach introduces the need to assume a constancy of other meteorological forcing variables that control thermal features. Airborne images were acquired on a Sicilian vineyard during most of an entire irrigation period (fruit-set to ripening stages, vintage 2008), during which in situ soil water content was measured to set up the triangle method. Within this framework, we tested the triangle method by employing alternative thermal forcing. The results were inaccurate when air temperature at airborne acquisition was employed. Sonic and aerodynamic air temperatures confirmed and partially explained the limits of simultaneous meteorological forcing, and the use of proxy variables improved model accuracy. The analysis indicates that high spatial resolution does not necessarily imply higher accuracies.

Rainfall as primary driver of discharge and solute export from rock glaciers: The Col d'Olen Rock Glacier in the NW Italian Alps.

PubMed

Colombo, Nicola; Gruber, Stephan; Martin, Maria; Malandrino, Mery; Magnani, Andrea; Godone, Danilo; Freppaz, Michele; Fratianni, Simona; Salerno, Franco

2018-10-15

Three hypotheses exist to explain how meteorological variables drive the amount and concentration of solute-enriched water from rock glaciers: (1) Warm periods cause increased subsurface ice melt, which releases solutes; (2) rain periods and the melt of long-lasting snow enhance dilution of rock-glacier outflows; and (3) percolation of rain through rock glaciers facilitates the export of solutes, causing an opposite effect as that described in hypothesis (2). This lack of detailed understanding likely exists because suitable studies of meteorological variables, hydrologic processes and chemical characteristics of water bodies downstream from rock glaciers are unavailable. In this study, a rock-glacier pond in the North-Western Italian Alps was studied on a weekly basis for the ice-free seasons 2014 and 2015 by observing the meteorological variables (air temperature, snowmelt, rainfall) assumed to drive the export of solute-enriched waters from the rock glacier and the hydrochemical response of the pond (water temperature as a proxy of rock-glacier discharge, stable water isotopes, major ions and selected trace elements). An intra-seasonal pattern of increasing solute export associated with higher rock-glacier discharge was found. Specifically, rainfall, after the winter snowpack depletion and prolonged periods of atmospheric temperature above 0 °C, was found to be the primary driver of solute export from the rock glacier during the ice-free season. This occurs likely through the flushing of isotopically- and geochemically-enriched icemelt, causing concomitant increases in the rock-glacier discharge and the solute export (SO 4 2- , Mg 2+ , Ca 2+ , Ni, Mn, Co). Moreover, flushing of microbially-active sediments can cause increases in NO 3 - export. Copyright © 2018 Elsevier B.V. All rights reserved.

Assessment of a stochastic downscaling methodology in generating an ensemble of hourly future climate time series

NASA Astrophysics Data System (ADS)

Fatichi, S.; Ivanov, V. Y.; Caporali, E.

2013-04-01

This study extends a stochastic downscaling methodology to generation of an ensemble of hourly time series of meteorological variables that express possible future climate conditions at a point-scale. The stochastic downscaling uses general circulation model (GCM) realizations and an hourly weather generator, the Advanced WEather GENerator (AWE-GEN). Marginal distributions of factors of change are computed for several climate statistics using a Bayesian methodology that can weight GCM realizations based on the model relative performance with respect to a historical climate and a degree of disagreement in projecting future conditions. A Monte Carlo technique is used to sample the factors of change from their respective marginal distributions. As a comparison with traditional approaches, factors of change are also estimated by averaging GCM realizations. With either approach, the derived factors of change are applied to the climate statistics inferred from historical observations to re-evaluate parameters of the weather generator. The re-parameterized generator yields hourly time series of meteorological variables that can be considered to be representative of future climate conditions. In this study, the time series are generated in an ensemble mode to fully reflect the uncertainty of GCM projections, climate stochasticity, as well as uncertainties of the downscaling procedure. Applications of the methodology in reproducing future climate conditions for the periods of 2000-2009, 2046-2065 and 2081-2100, using the period of 1962-1992 as the historical baseline are discussed for the location of Firenze (Italy). The inferences of the methodology for the period of 2000-2009 are tested against observations to assess reliability of the stochastic downscaling procedure in reproducing statistics of meteorological variables at different time scales.

Global Meteorological Drought: A Synthesis of Current Understanding with a Focus on SST Drivers of Precipitation Deficits

NASA Technical Reports Server (NTRS)

Schubert, S.; Stewart, R.; Wang, H.; Barlow, M.; Berbery, H.; Cai, W.; Hoerling, M.; Kanikicharla, K.; Koster, R.; Lyon, B.;

Interannual, seasonal and diurnal Mars surface environmental cycles observed from Viking to Curiosity

NASA Astrophysics Data System (ADS)

Martinez, German; Vicente-Retortillo, Álvaro; Kemppinen, Osku; Fischer, Erik; Fairen, Alberto G.; Guzewich, Scott David; Haberle, Robert; Lemmon, Mark T.; Newman, Claire E.; Renno, Nilton O.; Richardson, Mark I.; Smith, Michael D.; De la Torre, Manuel; Vasavada, Ashwin R.

2016-10-01

We analyze in-situ environmental data from the Viking landers to the Curiosity rover to estimate atmospheric pressure, near-surface air and ground temperature, relative humidity, wind speed and dust opacity with the highest confidence possible. We study the interannual, seasonal and diurnal variability of these quantities at the various landing sites over a span of more than twenty Martian years to characterize the climate on Mars and its variability. Additionally, we characterize the radiative environment at the various landing sites by estimating the daily UV irradiation (also called insolation and defined as the total amount of solar UV energy received on flat surface during one sol) and by analyzing its interannual and seasonal variability.In this study we use measurements conducted by the Viking Meteorology Instrument System (VMIS) and Viking lander camera onboard the Viking landers (VL); the Atmospheric Structure Instrument/Meteorology (ASIMET) package and the Imager for Mars Pathfinder (IMP) onboard the Mars Pathfinder (MPF) lander; the Miniature Thermal Emission Spectrometer (Mini-TES) and Pancam instruments onboard the Mars Exploration Rovers (MER); the Meteorological Station (MET), Thermal Electrical Conductivity Probe (TECP) and Phoenix Surface Stereo Imager (SSI) onboard the Phoenix (PHX) lander; and the Rover Environmental Monitoring Station (REMS) and Mastcam instrument onboard the Mars Science Laboratory (MSL) rover.A thorough analysis of in-situ environmental data from past and present missions is important to aid in the selection of the Mars 2020 landing site. We plan to extend our analysis of Mars surface environmental cycles by using upcoming data from the Temperature and Wind sensors (TWINS) instrument onboard the InSight mission and the Mars Environmental Dynamics Analyzer (MEDA) instrument onboard the Mars 2020 mission.

Soil Water Content Assessment: Critical Issues Concerning the Operational Application of the Triangle Method

PubMed Central

Maltese, Antonino; Capodici, Fulvio; Ciraolo, Giuseppe; La Loggia, Goffredo

2015-01-01

Knowledge of soil water content plays a key role in water management efforts to improve irrigation efficiency. Among the indirect estimation methods of soil water content via Earth Observation data is the triangle method, used to analyze optical and thermal features because these are primarily controlled by water content within the near-surface evaporation layer and root zone in bare and vegetated soils. Although the soil-vegetation-atmosphere transfer theory describes the ongoing processes, theoretical models reveal limits for operational use. When applying simplified empirical formulations, meteorological forcing could be replaced with alternative variables when the above-canopy temperature is unknown, to mitigate the effects of calibration inaccuracies or to account for the temporal admittance of the soil. However, if applied over a limited area, a characterization of both dry and wet edges could not be properly achieved; thus, a multi-temporal analysis can be exploited to include outer extremes in soil water content. A diachronic empirical approach introduces the need to assume a constancy of other meteorological forcing variables that control thermal features. Airborne images were acquired on a Sicilian vineyard during most of an entire irrigation period (fruit-set to ripening stages, vintage 2008), during which in situ soil water content was measured to set up the triangle method. Within this framework, we tested the triangle method by employing alternative thermal forcing. The results were inaccurate when air temperature at airborne acquisition was employed. Sonic and aerodynamic air temperatures confirmed and partially explained the limits of simultaneous meteorological forcing, and the use of proxy variables improved model accuracy. The analysis indicates that high spatial resolution does not necessarily imply higher accuracies. PMID:25808771

A method for estimating the performance of photovoltaic systems

NASA Astrophysics Data System (ADS)

Clark, D. R.; Klein, S. A.; Beckman, W. A.

A method is presented for predicting the long-term average performance of photovoltaic systems having storage batteries and subject to any diurnal load profile. The monthly-average fraction of the load met by the system is estimated from array parameters and monthly-average meteorological data. The method is based on radiation statistics, and utilizability, and can account for variability in the electrical demand as well as for the variability in solar radiation.

Argentina soybean yield model

NASA Technical Reports Server (NTRS)

Callis, S. L.; Sakamoto, C.

1984-01-01

A model based on multiple regression was developed to estimate soybean yields for the country of Argentina. A meteorological data set was obtained for the country by averaging data for stations within the soybean growing area. Predictor variables for the model were derived from monthly total precipitation and monthly average temperature. A trend variable was included for the years 1969 to 1978 since an increasing trend in yields due to technology was observed between these years.

Change features and regional distribution of temperature trend and variability joint mode in mainland China

NASA Astrophysics Data System (ADS)

Chen, Xi; Li, Ning; Zhang, Zhengtao; Feng, Jieling; Wang, Ye

2018-05-01

Adaption for temperature should be suitable to local conditions for regional differences in temperature change features. This paper proposed to utilize nine temperature modes that joint the trend (increasing/decreasing/unchanged) with variability (intensifying/weakening/unchanged) to investigate features of temperature change in mainland China. Monthly temperature data over the period 1960-2013 were obtained from 522 national basic and reference meteorological stations. Here, temperature trend (TT) was reflected by the trend of mean annual temperature (MAT) and the uptrend (downtrend) of inter-monthly sliding standard deviation (SSD) series with a sliding length of 29 years (348 months) was used for representing the intensification (weakening) of temperature variability (TV). The Mann-Kendall method and the least squares method were applied to assess the significance and quantify the magnitude of trend in MAT and SSD time series, respectively. The results show that there is a consistent warming trend throughout the country except for only three stations in which a cooling trend is identified. Moreover, the overall increasing rate in the north of 35° N is the highest, over 0.4 °C/decade for most stations. TV is weakened for almost 98% of the stations, indicating the low instability of temperature at a national scale. Finally, temperature mode (TM), for more than 90% of the stations, is the combination of an increasing TT with a weakened TV (mode 8). So, it is more important for people to adapt to the increasing temperature in these regions. Compared to using annual temperature data to calculate SSD, monthly data can accurately reflect the inter-monthly change of temperature and reserve more initial characteristics of temperature.

Comparison of Measured and Numerically Simulated Turbulence Statistics in a Convective Boundary Layer Over Complex Terrain

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rai, Raj K.; Berg, Larry K.; Kosović, Branko

High resolution numerical simulation can provide insight into important physical processes that occur within the planetary boundary layer (PBL). The present work employs large eddy simulation (LES) using the Weather Forecasting and Research (WRF) model, with the LES domain nested within mesoscale simulation, to simulate real conditions in the convective PBL over an area of complex terrain. A multiple nesting approach has been used to downsize the grid spacing from 12.15 km (mesoscale) to 0.03 km (LES). A careful selection of grid spacing in the WRF Meso domain has been conducted to minimize artifacts in the WRF-LES solutions. The WRF-LESmore » results have been evaluated with in situ and remote sensing observations collected during the US Department of Energy-supported Columbia BasinWind Energy Study (CBWES). Comparison of the first- and second-order moments, turbulence spectrum, and probability density function (PDF) of wind speed shows good agreement between the simulations and data. Furthermore, the WRF-LES variables show a great deal of variability in space and time caused by the complex topography in the LES domain. The WRF-LES results show that the flow structures, such as roll vortices and convective cells, vary depending on both the location and time of day. In addition to basic studies related to boundary-layer meteorology, results from these simulations can be used in other applications, such as studying wind energy resources, atmospheric dispersion, fire weather etc.« less

Study on the medical meteorological forecast of the number of hypertension inpatient based on SVR

NASA Astrophysics Data System (ADS)

Zhai, Guangyu; Chai, Guorong; Zhang, Haifeng

2017-06-01

The purpose of this study is to build a hypertension prediction model by discussing the meteorological factors for hypertension incidence. The research method is selecting the standard data of relative humidity, air temperature, visibility, wind speed and air pressure of Lanzhou from 2010 to 2012(calculating the maximum, minimum and average value with 5 days as a unit ) as the input variables of Support Vector Regression(SVR) and the standard data of hypertension incidence of the same period as the output dependent variables to obtain the optimal prediction parameters by cross validation algorithm, then by SVR algorithm learning and training, a SVR forecast model for hypertension incidence is built. The result shows that the hypertension prediction model is composed of 15 input independent variables, the training accuracy is 0.005, the final error is 0.0026389. The forecast accuracy based on SVR model is 97.1429%, which is higher than statistical forecast equation and neural network prediction method. It is concluded that SVR model provides a new method for hypertension prediction with its simple calculation, small error as well as higher historical sample fitting and Independent sample forecast capability.

Floods and windstorms in the Czech Republic during the past millennium: synthesis of documentary and instrumental data

NASA Astrophysics Data System (ADS)

Brázdil, R.; Dobrovolný, P.; Valášek, H.; Kotyza, O.

2009-09-01

Floods and windstorms are the most disastrous natural events occurring on the territory of the Czech Republic. Study of their frequency, severity, seasonality, causes and impacts in the long-term scale is important for saving of human lives and diminishing of material losses. Information related to these phenomena from the period of instrumental hydrological and meteorological measurements can be significantly extended by using documentary evidence going back to the 12th century. Basic types of documentary evidence with information about floods and windstorms are presented and methodological problems of elaboration of such evidence are discussed. Synoptic causes of floods and windstorms in the Czech Republic are demonstrated. Series of these phenomena created for the instrumental and pre-instrumental period are finally used for compilation of synthesis series, namely for floods of the main rivers in the Czech Republic (the Vltava, the Ohře, the Elbe, the Odra and the Morava) and for windstorms divided according to the type of event, extent and character of damage. Moreover, the most disastrous events ("floods and windstorms of the century”) are particularly analyzed. Finally, floods and windstorms are discussed in the context of past long-term climate variability.

A Note on the Barotropic Response of Sea Level to Time-Dependent Wind Forcing

NASA Technical Reports Server (NTRS)

Fu, Lee-Lueng; Davidson, Roger A.

1995-01-01

This study examines the extent to which sea level variations at periods between 30 days and 1 year and spatial scales greater than 1000 km can be described by the wind- driven linear barotropic vorticity dynamics. The TOPEX/POSEIDON altimetric observations of sea level and the wind products of the National Meteorological Center are used as the database for the study. Each term of the linear barotropic vorticity equation was evaluated by averaging over regions of 10 deg x 10 deg. In most of the open ocean the result of the analysis suggests that the sea level variabilities at the scales considered cannot be fully described by the equation; the apparent net vorticity change is more than what can be explained by the local wind stress curl. In the few regions where the wind stress curl is strong enough to balance the vorticity budget, predominantly in the northeast Pacific and the southeast Pacific, the balance is basically achieved in terms of the time-dependent topographic Sverdrup relation, namely, the balance between the advection of the planetary vorticity plus the topography-induced vorticity and the forcing by the wind stress curl.

Comparing Resource Adequacy Metrics and Their Influence on Capacity Value: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ibanez, E.; Milligan, M.

2014-04-01

Traditional probabilistic methods have been used to evaluate resource adequacy. The increasing presence of variable renewable generation in power systems presents a challenge to these methods because, unlike thermal units, variable renewable generation levels change over time because they are driven by meteorological events. Thus, capacity value calculations for these resources are often performed to simple rules of thumb. This paper follows the recommendations of the North American Electric Reliability Corporation?s Integration of Variable Generation Task Force to include variable generation in the calculation of resource adequacy and compares different reliability metrics. Examples are provided using the Western Interconnection footprintmore » under different variable generation penetrations.« less

WaveNet: A Web-Based Metocean Data Access, Processing, and Analysis Tool. Part 3 - CDIP Database

DTIC Science & Technology

2014-06-01

and Analysis Tool; Part 3 – CDIP Database by Zeki Demirbilek, Lihwa Lin, and Derek Wilson PURPOSE: This Coastal and Hydraulics Engineering...Technical Note (CHETN) describes coupling of the Coastal Data Information Program ( CDIP ) database to WaveNet, the first module of MetOcnDat (Meteorological...provides a step-by-step procedure to access, process, and analyze wave and wind data from the CDIP database. BACKGROUND: WaveNet addresses a basic

An error analysis of tropical cyclone divergence and vorticity fields derived from satellite cloud winds on the Atmospheric and Oceanographic Information Processing System (AOIPS)

NASA Technical Reports Server (NTRS)

Hasler, A. F.; Rodgers, E. B.

1977-01-01

An advanced Man-Interactive image and data processing system (AOIPS) was developed to extract basic meteorological parameters from satellite data and to perform further analyses. The errors in the satellite derived cloud wind fields for tropical cyclones are investigated. The propagation of these errors through the AOIPS system and their effects on the analysis of horizontal divergence and relative vorticity are evaluated.

The planets of the Solar System

NASA Technical Reports Server (NTRS)

Marov, M. Y.

1986-01-01

This book is intended both for the lay person and the would-be scientist. The planets are discussed with a comparision of their basic natural features: mechanical characteristics and parameters of movement, surfaces, inner structure, physical properties of the atmosphere and meteorology. Also general problems of planetary cosmogony, thermal history and climatic evolution are considered briefly. The book is based on Soviet and foreign material, data from spacecraft, Earth optical and radio astronomical measurements and also data obtained from theoretical models.

Vertical profiles of wind and temperature by remote acoustical sounding

NASA Technical Reports Server (NTRS)

Fox, H. L.

1969-01-01

An acoustical method was investigated for obtaining meteorological soundings based on the refraction due to the vertical variation of wind and temperature. The method has the potential of yielding horizontally averaged measurements of the vertical variation of wind and temperature up to heights of a few kilometers; the averaging takes place over a radius of 10 to 15 km. An outline of the basic concepts and some of the results obtained with the method are presented.

Performance evaluation of the national early warning system for shallow landslides in Norway

NASA Astrophysics Data System (ADS)

Dahl, Mads-Peter; Piciullo, Luca; Devoli, Graziella; Colleuille, Hervé; Calvello, Michele

2017-04-01

As a consequence of the increased number of rainfall-and snowmelt-induced landslides (debris flows, debris slides, debris avalanches and slush flows) occurring in Norway, a national landslide early warning system (EWS) has been developed for monitoring and forecasting the hydro-meteorological conditions potentially necessary of triggering slope failures. The system, operational since 2013, is managed by the Norwegian Water Resources and Energy Directorate (NVE) and has been designed in cooperation with the Norwegian Public Road Administration (SVV), the Norwegian National Rail Administration (JBV) and the Norwegian Meteorological Institute (MET). Decision-making in the EWS is based upon hazard threshold levels, hydro-meteorological and real-time landslide observations as well as landslide inventory and susceptibility maps. Hazard threshold levels have been obtained through statistical analyses of historical landslides and modelled hydro-meteorological parameters. Daily hydro-meteorological conditions such as rainfall, snowmelt, runoff, soil saturation, groundwater level and frost depth have been derived from a distributed version of the hydrological HBV-model. Two different landslide susceptibility maps are used as supportive data in deciding daily warning levels. Daily alerts are issued throughout the country considering variable warning zones. Warnings are issued once per day for the following 3 days with an update possibility later during the day according to the information gathered by the monitoring variables. The performance of the EWS has been evaluated applying the EDuMaP method. In particular, the performance of warnings issued in Western Norway, in the period 2013-2014 has been evaluated using two different landslide datasets. The best performance is obtained for the smallest and more accurate dataset. Different performance results may be observed as a function of changing the landslide density criterion, Lden(k), (i.e., thresholds considered to differentiate among classes of landslide events) used as an input parameter within the EDuMaP method. To investigate this issue, a parametric analysis has been conducted; the results of the analysis show clear differences among computed performances when absolute or relative landslide density criteria are considered.

The "Dirty Weather" diaries of Reverend Richard Davis: insights about early Colonial-era meteorology and climate variability for Northern New Zealand, 1839-1851

NASA Astrophysics Data System (ADS)

Lorrey, A. M.; Chappell, P. R.

2015-08-01

Reverend Richard Davis (1790-1863) was a Colonial-era missionary stationed in the Far North of New Zealand who was a key figure in the early efforts of the Church Mission Society. He kept meticulous meteorological records for the early settlements of Waimate North and Kaikohe, and his observations are preserved in a two-volume set in the rare manuscripts archive at the Auckland City Library. The Davis diary volumes are significant because they constitute some of the earliest land-based meteorological measurements that were continually chronicled for New Zealand. The diary measurements cover nine years within the 1839-1851 timespan that are broken into two parts: 1839-1844 and 1848-1851. Davis' meteorological recordings include daily 9 AM and noon temperatures and mid-day pressure measurements. Qualitative comments in the diary note prevailing wind flow, wind strength, cloud cover, climate variability impacts, bio-indicators suggestive of drought, and notes on extreme weather events. "Dirty weather" comments scattered throughout the diary describe disturbed conditions with strong winds and driving rainfall. The Davis diary entries coincide with the end of the Little Ice Age (LIA) and they indicate southerly and westerly circulation influences and cooler winter temperatures were more frequent than today. A comparison of climate field reconstructions derived from the Davis diary data and tree ring-based winter temperature reconstructions are supported by tropical coral palaeotemperature evidence. Davis' pressure measurements were corroborated using ship log data from vessels associated with iconic Antarctic exploration voyages that were anchored in the Bay of Islands, and suggest the pressure series he recorded are robust and can be used as `station data'. The Reverend Davis meteorological data are expected to make a significant contribution to the Atmospheric Circulation Reconstructions across the Earth (ACRE) project, which feeds the major data requirements for the longest historical reanalysis - the 20th Century Reanalysis Project (20CR). Thus these new data will help extend surface pressure-based re-analysis reconstructions of past weather covering New Zealand within the data-sparse Southern Hemisphere.

The "dirty weather" diaries of Reverend Richard Davis: insights about early colonial-era meteorology and climate variability for northern New Zealand, 1839-1851

NASA Astrophysics Data System (ADS)

Lorrey, Andrew M.; Chappell, Petra R.

2016-03-01

Reverend Richard Davis (1790-1863) was a colonial-era missionary stationed in the Far North of New Zealand who was a key figure in the early efforts of the Church Mission Society. He kept meticulous meteorological records for the early settlements of Waimate North and Kaikohe, and his observations are preserved in a two-volume set in the Sir George Grey Special Collections in the Auckland Central Library. The Davis diary volumes are significant because they constitute some of the earliest land-based meteorological measurements that were continually chronicled for New Zealand. The diary measurements cover nine years within the 1839-1851 time span that are broken into two parts: 1839-1844 and 1848-1851. Davis' meteorological recordings include daily 9 a.m. and noon temperatures and midday pressure measurements. Qualitative comments in the diary note prevailing wind flow, wind strength, cloud cover, climate variability impacts, bio-indicators suggestive of drought, and notes on extreme weather events. "Dirty weather" comments scattered throughout the diary describe disturbed conditions with strong winds and driving rainfall. The Davis diary entries coincide with the end of the Little Ice Age (LIA) and they indicate southerly and westerly circulation influences and cooler winter temperatures were more frequent than today. A comparison of climate field reconstructions derived from the Davis diary data and tree-ring-based winter temperature reconstructions are supported by tropical coral palaeotemperature evidence. Davis' pressure measurements were corroborated using ship log data from vessels associated with iconic Antarctic exploration voyages that were anchored in the Bay of Islands, and suggest the pressure series he recorded are robust and can be used as "station data". The Reverend Davis meteorological data are expected to make a significant contribution to the Atmospheric Circulation Reconstructions across the Earth (ACRE) project, which feeds the major data requirements for the longest historical reanalysis - the 20th Century Reanalysis Project (20CR). Thus these new data will help extend surface pressure-based reanalysis reconstructions of past weather covering New Zealand within the data-sparse Southern Hemisphere.

APPLICATION OF BIAS AND ADJUSTMENT TECHNIQUES TO THE ETA-CMAQ AIR QUALITY FORECAST

EPA Science Inventory

The current air quality forecast system, based on linking NOAA's Eta meteorological model with EPA's Community Multiscale Air Quality (CMAQ) model, consistently overpredicts surface ozone concentrations, but simulates its day-to-day variability quite well. The ability of bias cor...

MONITOR THE PHOTOVOLTAIC (PV) SYSTEM ON THE NCC ROOFTOP

EPA Science Inventory

This study will investigate the pollution emission reduction and demand-side management potential of a
100 kW PV system located on the roof of the National Computer Center (NCC). Standardized instrumentation to measure meteorological and PV system performance variables will b...

Sensitivity and uncertainty of input sensor accuracy for grass-based reference evapotranspiration

USDA-ARS?s Scientific Manuscript database

Quantification of evapotranspiration (ET) in agricultural environments is becoming of increasing importance throughout the world, thus understanding input variability of relevant sensors is of paramount importance as well. The Colorado Agricultural and Meteorological Network (CoAgMet) and the Florid...

Temporal Synchronization Analysis for Improving Regression Modeling of Fecal Indicator Bacteria Levels

EPA Science Inventory

Multiple linear regression models are often used to predict levels of fecal indicator bacteria (FIB) in recreational swimming waters based on independent variables (IVs) such as meteorologic, hydrodynamic, and water-quality measures. The IVs used for these analyses are traditiona...

SEPARATING DIFFERENT SCALES OF MOTION IN TIME SERIES OF METEOROLOGICAL VARIABLES. (R825260)

EPA Science Inventory

The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Concl...

Investigation of the stochastic nature of solar radiation for renewable resources management

NASA Astrophysics Data System (ADS)

Koudouris, Giannis; Dimitriadis, Panayiotis; Iliopoulou, Theano; Mamasis, Nikos; Koutsoyiannis, Demetris

2017-04-01

A detailed investigation of the variability of solar radiation can be proven useful towards more efficient and sustainable design of renewable resources systems. This variability is mainly caused from the regular seasonal and diurnal variation, as well as its stochastic nature of the atmospheric processes, i.e. sunshine duration. In this context, we analyze numerous observations in Greece (Hellenic National Meteorological Service; http://www.hnms.gr/) and around the globe (NASA SSE - Surface meteorology and Solar Energy; http://www.soda-pro.com/web-services/radiation/nasa-sse) and we investigate the long-term behaviour and double periodicity of the solar radiation process. Also, we apply a parsimonious double-cyclostationary stochastic model to a theoretical scenario of solar energy production for an island in the Aegean Sea. Acknowledgement: This research is conducted within the frame of the undergraduate course "Stochastic Methods in Water Resources" of the National Technical University of Athens (NTUA). The School of Civil Engineering of NTUA provided moral support for the participation of the students in the Assembly.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stewart, Gordon M.; Robertson, Amy; Jonkman, Jason

A database of meteorological and ocean conditions is presented for use in offshore wind energy research and design. The original data are from 23 ocean sites around the USA and were obtained from the National Data Buoy Center run by the National Oceanic and Atmospheric Administration. The data are presented in a processed form that includes the variables of interest for offshore wind energy design: wind speed, significant wave height, wave peak-spectral period, wind direction and wave direction. For each site, a binning process is conducted to create conditional probability functions for each of these variables. The sites are thenmore » grouped according to geographic location and combined to create three representative sites, including a West Coast site, an East Coast site and a Gulf of Mexico site. Both the processed data and the probability distribution parameters for the individual and representative sites are being hosted on a publicly available domain by the National Renewable Energy Laboratory, with the intent of providing a standard basis of comparison for meteorological and ocean conditions for offshore wind energy research worldwide.« less

Exploring the background features of acidic and basic air pollutants around an industrial complex using data mining approach.

PubMed

Chen, Ho-Wen; Tsai, Ching-Tsan; She, Chin-Wen; Lin, Yo-Chen; Chiang, Chow-Feng

2010-11-01

Air pollution data around a monitored site are normally difficult to analyze due to highly inter-related meteorological and topographical factors on top of many complicated atmospheric chemical interactions occurred in local and regional wind fields. The challenge prompts this study to develop a comprehensive data-mining algorithm of cluster analysis followed by meteorological and interspecies correlations to mitigate the inherent data complexity and dissimilarity. This study investigated the background features of acidic and basic air pollutants around a high-tech industrial park in Taiwan. Monthly samplings were taken at 10 sites around the park in a year. The temporal distribution plots show a baseline with two characteristic groups of high and low peaks. Hierarchical cluster analysis confirms that high peaks were primarily associated with low speed south wind in summer for all the chemical species, except for F(-), Cl(-), NH(3) and HF. Crosschecking with the topographical map identifies several major external sources in south and southwest. Further meteorological correlation suggests that HCl is highly positively associated with humidity, while Cl(-) is highly negatively associated with temperature, both for most stations. Interestingly, HNO(3) is highly negatively associated with wind speed for most stations and the hotspot was found in summer and around the foothill of Da-Tu Mountain in the northwest, a stagnant pocket on the study site. However, F(-) is highly positively associated with wind speed at downwind stations to the prevailing north wind in winter, indicating an internal source from the north. The presence of NH(4)(+) stimulates the formation of NO(3)(-), SO(4)(-2) (R=0.7), and HNO(3), H(2)SO(4), NH(3) (R=0.3-0.4). As H(2)SO(4) could be elevated to a level as high as 40% of the regulated standard, species interactions may be a dominate mechanism responsible for the substantial increase in summer from external sources. Copyright © 2010 Elsevier Ltd. All rights reserved.

Modeling circulation patterns induced by spatial cross-shore wind variability in a small-size coastal embayment

NASA Astrophysics Data System (ADS)

Cerralbo, Pablo; Espino, Manuel; Grifoll, Manel

2016-08-01

This contribution shows the importance of the cross-shore spatial wind variability in the water circulation in a small-sized micro-tidal bay. The hydrodynamic wind response at Alfacs Bay (Ebro River delta, NW Mediterranean Sea) is investigated with a numerical model (ROMS) supported by in situ observations. The wind variability observed in meteorological measurements is characterized with meteorological model (WRF) outputs. From the hydrodynamic simulations of the bay, the water circulation response is affected by the cross-shore wind variability, leading to water current structures not observed in the homogeneous-wind case. If the wind heterogeneity response is considered, the water exchange in the longitudinal direction increases significantly, reducing the water exchange time by around 20%. Wind resolutions half the size of the bay (in our case around 9 km) inhibit cross-shore wind variability, which significantly affects the resultant circulation pattern. The characteristic response is also investigated using idealized test cases. These results show how the wind curl contributes to the hydrodynamic response in shallow areas and promotes the exchange between the bay and the open sea. Negative wind curl is related to the formation of an anti-cyclonic gyre at the bay's mouth. Our results highlight the importance of considering appropriate wind resolution even in small-scale domains (such as bays or harbors) to characterize the hydrodynamics, with relevant implications in the water exchange time and the consequent water quality and ecological parameters.

A multi-site analysis of the association between black carbon concentrations and vehicular idling, traffic, background pollution, and meteorology during school dismissals.

PubMed

Richmond-Bryant, J; Bukiewicz, L; Kalin, R; Galarraga, C; Mirer, F

2011-05-01

A study was performed to assess the relationship between black carbon (BC), passing traffic, and vehicular idling outside New York City (NYC) schools during student dismissal. Monitoring was performed at three school sites in East Harlem, the Bronx, and Brooklyn for 1month per year over a two-year period from November 2006-October 2008. Monitoring at each site was conducted before and after the Asthma Free School Zone (AFSZ) asthma reduction education program was administered. Real-time equipment with a one-minute averaging interval was used to obtain the BC data, while volume counts of idling and passing school busses, trucks, and automobiles were collected each minute by study staff. These data were matched to ambient PM(2.5) and meteorology data obtained from the New York State Department of Environmental Conservation. A generalized additive model (GAM) model was run to examine the relationship between BC concentration and each variable while accounting for site-to-site differences. F-tests were employed to assess the significance of each of the predictor variables. The model results suggested that variability in ambient PM(2.5) concentration contributed 24% of the variability in transformed BC concentration, while variability in the number of idling busses and trucks on the street during dismissal contributed 20% of the variability in transformed BC concentration. The results of this study suggest that a combination of urban scale and local traffic control approaches in combination with cessation of school bus idling will produce improved local BC concentration outside schools. Published by Elsevier B.V.

A multi-source probabilistic hazard assessment of tephra dispersal in the Neapolitan area

NASA Astrophysics Data System (ADS)

Sandri, Laura; Costa, Antonio; Selva, Jacopo; Folch, Arnau; Macedonio, Giovanni; Tonini, Roberto

2015-04-01

In this study we present the results obtained from a long-term Probabilistic Hazard Assessment (PHA) of tephra dispersal in the Neapolitan area. Usual PHA for tephra dispersal needs the definition of eruptive scenarios (usually by grouping eruption sizes and possible vent positions in a limited number of classes) with associated probabilities, a meteorological dataset covering a representative time period, and a tephra dispersal model. PHA then results from combining simulations considering different volcanological and meteorological conditions through weights associated to their specific probability of occurrence. However, volcanological parameters (i.e., erupted mass, eruption column height, eruption duration, bulk granulometry, fraction of aggregates) typically encompass a wide range of values. Because of such a natural variability, single representative scenarios or size classes cannot be adequately defined using single values for the volcanological inputs. In the present study, we use a method that accounts for this within-size-class variability in the framework of Event Trees. The variability of each parameter is modeled with specific Probability Density Functions, and meteorological and volcanological input values are chosen by using a stratified sampling method. This procedure allows for quantifying hazard without relying on the definition of scenarios, thus avoiding potential biases introduced by selecting single representative scenarios. Embedding this procedure into the Bayesian Event Tree scheme enables the tephra fall PHA and its epistemic uncertainties. We have appied this scheme to analyze long-term tephra fall PHA from Vesuvius and Campi Flegrei, in a multi-source paradigm. We integrate two tephra dispersal models (the analytical HAZMAP and the numerical FALL3D) into BET_VH. The ECMWF reanalysis dataset are used for exploring different meteorological conditions. The results obtained show that PHA accounting for the whole natural variability are consistent with previous probabilities maps elaborated for Vesuvius and Campi Flegrei on the basis of single representative scenarios, but show significant differences. In particular, the area characterized by a 300 kg/m2-load exceedance probability larger than 5%, accounting for the whole range of variability (that is, from small violent strombolian to plinian eruptions), is similar to that displayed in the maps based on the medium magnitude reference eruption, but it is of a smaller extent. This is due to the relatively higher weight of the small magnitude eruptions considered in this study, but neglected in the reference scenario maps. On the other hand, in our new maps the area characterized by a 300 kg/m2-load exceedance probability larger than 1% is much larger than that of the medium magnitude reference eruption, due to the contribution of plinian eruptions at lower probabilities, again neglected in the reference scenario maps.

Potential solar radiation and land cover contributions to digital climate surface modeling

NASA Astrophysics Data System (ADS)

Puig, Pol; Batalla, Meritxell; Pesquer, Lluís; Ninyerola, Miquel

2016-04-01

Overview: We have designed a series of ad-hoc experiments to study the role of factors that a priori have a strong weight in developing digital models of temperature and precipitation, such as solar radiation and land cover. Empirical test beds have been designed to improve climate (mean air temperature and total precipitation) digital models using statistical general techniques (multiple regression) with residual correction (interpolated with inverse weighting distance). Aim: Understand what roles these two factors (solar radiation and land cover) play to incorporate them into the process of generating mapping of temperature and rainfall. Study area: The Iberian Peninsula and supported in this, Catalonia and the Catalan Pyrenees. Data: The dependent variables used in all experiments relate to data from meteorological stations precipitation (PL), mean temperature (MT), average temperature minimum (MN) and maximum average temperature (MX). These data were obtained monthly from the AEMET (Agencia Estatal de Meteorología). Data series of stations covers the period between 1950 to 2010. Methodology: The idea is to design ad hoc, based on a sample of more equitable space statistician, to detect the role of radiation. Based on the influence of solar radiation on the temperature of the air from a quantitative point of view, the difficulty in answering this lies in the fact that there are lots of weather stations located in areas where solar radiation is similar. This suggests that the role of the radiation variable remains "off" when, instead, we intuitively think that would strongly influence the temperature. We have developed a multiple regression analysis between these meteorological variables as the dependent ones (Temperature and rainfall), and some geographical variables: altitude (ALT), latitude (LAT), continentality (CON) and solar radiation (RAD) as the independent ones. In case of the experiment with land covers, we have used the NDVI index as a proxy of land covers and added this variable in to the independents to improve the models. Results: The role of solar radiation does not improve models only under certain conditions and areas, especially in the Pyrennes. The vegetation index NDVI and therefore the land cover on which the station is located, helps improve rainfall and temperature patterns, obtaining various degrees of improvement in terms of molded variables and months.

Global trends in visibility: Implications for dust sources

USGS Publications Warehouse

Mahowald, N.M.; Ballantine, J.A.; Feddema, J.; Ramankutty, N.

2007-01-01

There is a large uncertainty in the relative roles of human land use, climate change and carbon dioxide fertilization in changing desert dust source strength over the past 100 years, and the overall sign of human impacts on dust is not known. We used visibility data from meteorological stations in dusty regions to assess the anthropogenic impact on long term trends in desert dust emissions. Visibility data are available at thousands of stations globally from 1900 to the present, but we focused on 359 stations with more than 30 years of data in regions where mineral aerosols play a dominant role in visibility observations. We evaluated the 1974 to 2003 time period because most of these stations have reliable records only during this time. We first evaluated the visibility data against AERONET aerosol optical depth data, and found that only in dusty regions are the two moderately correlated. Correlation coefficients between visibility derived variables and AERONET optical depths indicate a moderate correlation (???0.47), consistent with capturing about 20% of the variability in optical depths. Two visibility derived variables appear to compare the best with AERONET observations: the fraction of observations with visibility less than 5 km (VIS5) and the surface extinction (EXT). Regional trends show that in many dusty places, VIS5 and EXT are statistically significantly correlated with the palmer drought severity index (based on precipitation and temperature) or surface wind speeds, consistent with dust temporal variability being largely driven by meteorology. This is especially true for North African and Chinese dust sources, but less true in the Middle East, Australia or South America, where there are not consistent patterns in the correlations. Climate indices such as El Nino or the North Atlantic Oscillation are not correlated with visibility derived variables in this analysis. There are few stations where visibility measures are correlated with cultivation or grazing estimates on a temporal basis, although this may be a function of the very coarse temporal resolution of the land use datasets. On the other hand, spatial analysis of the visibility data suggests that natural topographic lows are not correlated with visibility, but land use is correlated at a moderate level. This analysis is consistent with land use being important in some regions, but meteorology driving interannual variability during 1974-2003.

Introduction to meteorological measurements and data handling for solar energy applications. Task IV-Development of an insolation handbook and instrument package

DOE Office of Scientific and Technical Information (OSTI.GOV)

None

Recognizing a need for a coordinated approach to resolve energy problems, certain members of the Organization for Economic Cooperation and Development (OECD) met in September 1974 and agreed to develop an International Energy Program. The International Energy Agency (IEA) was established within the OECD to administer, monitor and execute this International Energy Program. In July 1975, Solar Heating and Cooling was selected as one of the sixteen technology fields for multilateral cooperation. Five project areas, called tasks, were identified for cooperative activities within the IEA Program to Develop and Test Solar Heating and Cooling Systems. The objective of one taskmore » was to obtain improved basic resource information for the design and operation of solar heating and cooling systems through a better understanding of the required insolation (solar radiation) and related weather data, and through improved techniques for measurement and evaluation of such data. At the February 1976 initial experts meeting in Norrkoeping, Sweden, the participants developed the objective statement into two subtasks. (1) an insolation handbook; and (2) a portable meteorological instrument package. This handbook is the product of the first subtask. The objective of this handbook is to provide a basis for a dialogue between solar scientists and meteorologists. Introducing the solar scientist to solar radiation and related meteorological data enables him to better express his scientific and engineering needs to the meteorologist; and introducing the meteorologist to the special solar radiation and meteorological data applications of the solar scientist enables him to better meet the needs of the solar energy community.« less

Resource Selection by the California Condor (Gymnogyps californianus) Relative to Terrestrial-Based Habitats and Meteorological Conditions

PubMed Central

Rivers, James W.; Johnson, J. Matthew; Haig, Susan M.; Schwarz, Carl J.; Glendening, John W.; Burnett, L. Joseph; George, Daniel; Grantham, Jesse

2014-01-01

Condors and vultures are distinct from most other terrestrial birds because they use extensive soaring flight for their daily movements. Therefore, assessing resource selection by these avian scavengers requires quantifying the availability of terrestrial-based habitats, as well as meteorological variables that influence atmospheric conditions necessary for soaring. In this study, we undertook the first quantitative assessment of habitat- and meteorological-based resource selection in the endangered California condor (Gymnogyps californianus) within its California range and across the annual cycle. We found that condor use of terrestrial areas did not change markedly within the annual cycle, and that condor use was greatest for habitats where food resources and potential predators could be detected and where terrain was amenable for taking off from the ground in flight (e.g., sparse habitats, coastal areas). Condors originating from different release sites differed in their use of habitat, but this was likely due in part to variation in habitats surrounding release sites. Meteorological conditions were linked to condor use of ecological subregions, with thermal height, thermal velocity, and wind speed having both positive (selection) and negative (avoidance) effects on condor use in different areas. We found little evidence of systematic effects between individual characteristics (i.e., sex, age, breeding status) or components of the species management program (i.e., release site, rearing method) relative to meteorological conditions. Our findings indicate that habitat type and meteorological conditions can interact in complex ways to influence condor resource selection across landscapes, which is noteworthy given the extent of anthropogenic stressors that may impact condor populations (e.g., lead poisoning, wind energy development). Additional studies will be valuable to assess small-scale condor movements in light of these stressors to help minimize their risk to this critically endangered species. PMID:24523893

Resource selection by the California condor (Gymnogyps californianus) relative to terrestrial-based habitats and meteorological conditions

USGS Publications Warehouse

Johnson, J. Matthew; Haig, Susan M.; Schwarz, Carl J.; Glendening, John W.; Burnett, L. Joseph; George, Daniel; Grantham, Jesse

2014-01-01

Condors and vultures are distinct from most other terrestrial birds because they use extensive soaring flight for their daily movements. Therefore, assessing resource selection by these avian scavengers requires quantifying the availability of terrestrial-based habitats, as well as meteorological variables that influence atmospheric conditions necessary for soaring. In this study, we undertook the first quantitative assessment of habitat- and meteorological-based resource selection in the endangered California condor (Gymnogyps californianus) within its California range and across the annual cycle. We found that condor use of terrestrial areas did not change markedly within the annual cycle, and that condor use was greatest for habitats where food resources and potential predators could be detected and where terrain was amenable for taking off from the ground in flight (e.g., sparse habitats, coastal areas). Condors originating from different release sites differed in their use of habitat, but this was likely due in part to variation in habitats surrounding release sites. Meteorological conditions were linked to condor use of ecological subregions, with thermal height, thermal velocity, and wind speed having both positive (selection) and negative (avoidance) effects on condor use in different areas. We found little evidence of systematic effects between individual characteristics (i.e., sex, age, breeding status) or components of the species management program (i.e., release site, rearing method) relative to meteorological conditions. Our findings indicate that habitat type and meteorological conditions can interact in complex ways to influence condor resource selection across landscapes, which is noteworthy given the extent of anthropogenic stressors that may impact condor populations (e.g., lead poisoning, wind energy development). Additional studies will be valuable to assess small-scale condor movements in light of these stressors to help minimize their risk to this critically endangered species.

Analysis of traffic and meteorology on airborne particulate matter in Münster, northwest Germany.

PubMed

Gietl, Johanna K; Klemm, Otto

2009-07-01

The importance of street traffic and meteorological conditions on the concentrations of particulate matter (PM) with an aerodynamic diameter smaller than 10 microm (PM10) was studied in the city of Münster in northwest Germany. The database consisted of meteorological data, data of PM10 mass concentrations and fine particle number (6-225 nm diameter) concentrations, and traffic intensity data as counted with tally hand counters at a four- to six-lane road. On working days, a significant correlation could be found between the diurnal mean PM10 mass concentration and vehicle number. The lower number of heavy-duty vehicles compared with passenger cars contributed more to the particle number concentration on working days than on weekend days. On weekends, when the vehicle number was very low, the correlation between PM10 mass concentration and vehicle number changed completely. Other sources of PM and the meteorology dominated the PM concentration. Independent of the weekday, by decreasing the traffic by approximately 99% during late-night hours, the PM10 concentration was reduced by 12% of the daily mean value. A correlation between PM10 and the particle number concentration was found for each weekday. In this study, meteorological parameters, including the atmospheric stability of the boundary layer, were also accounted for. The authors deployed artificial neural networks to achieve more information on the influence of various meteorological parameters, traffic, and the day of the week. A multilayer perceptron network showed the best results for predicting the PM10 concentration, with the correlation coefficient being 0.72. The influence of relative humidity, temperature, and wind was strong, whereas the influence of atmospheric stability and the traffic parameters was weak. Although traffic contributes a constant amount of particles in a daily and weekly cycle, it is the meteorology that drives most of the variability.

Modelling the variation of land surface temperature as determinant of risk of heat-related health events

PubMed Central

2011-01-01

Background The evaluation of exposure to ambient temperatures in epidemiological studies has generally been based on records from meteorological stations which may not adequately represent local temperature variability. Here we propose a spatially explicit model to estimate local exposure to temperatures of large populations under various meteorological conditions based on satellite and meteorological data. Methods A general linear model was used to estimate surface temperatures using 15 LANDSAT 5 and LANDSAT 7 images for Quebec Province, Canada between 1987 and 2002 and spanning the months of June to August. The images encompassed both rural and urban landscapes and predictors included: meteorological records of temperature and wind speed, distance to major water bodies, Normalized Differential Vegetation Index (NDVI), land cover (built and bare land, water, or vegetation), latitude, longitude, and week of the year. Results The model explained 77% of the variance in surface temperature, accounting for both temporal and spatial variations. The standard error of estimates was 1.42°C. Land cover and NDVI were strong predictors of surface temperature. Conclusions This study suggests that a statistical approach to estimating surface temperature incorporating both spatially explicit satellite data and time-varying meteorological data may be relevant to assessing exposure to heat during the warm season in the Quebec. By allowing the estimation of space- and time-specific surface temperatures, this model may also be used to assess the possible impacts of land use changes under various meteorological conditions. It can be applied to assess heat exposure within a large population and at relatively fine-grained scale. It may be used to evaluate the acute health effect of heat exposure over long time frames. The method proposed here could be replicated in other areas around the globe for which satellite data and meteorological data is available. PMID:21251286

Drought propagation and its relation with catchment biophysical characteristics

NASA Astrophysics Data System (ADS)

Alvarez-Garreton, C. D.; Lara, A.; Garreaud, R. D.

2016-12-01

Droughts propagate in the hydrological cycle from meteorological to soil moisture to hydrological droughts. To understand the drivers of this process is of paramount importance since the economic and societal impacts in water resources are directly related with hydrological droughts (and not with meteorological droughts, which have been most studied). This research analyses drought characteristics over a large region and identify its main exogenous (climate forcing) and endogenous (biophysical characteristics such as land cover type and topography) explanatory factors. The study region is Chile, which covers seven major climatic subtypes according to Köppen system, it has unique geographic characteristics, very sharp topography and a wide range of landscapes and vegetation conditions. Meteorological and hydrological droughts (deficit in precipitation and streamflow, respectively) are characterized by their durations and standardized deficit volumes using a variable threshold method, over 300 representative catchments (located between 27°S and 50°S). To quantify the propagation from meteorological to hydrological drought, we propose a novel drought attenuation index (DAI), calculated as the ratio between the meteorological drought severity slope and the hydrological drought severity slope. DAI varies from zero (catchment that attenuates completely a meteorological drought) to one (the meteorological drought is fully propagated through the hydrological cycle). This novel index provides key (and comparable) information about drought propagation over a wide range of different catchments, which has been highlighted as a major research gap. Similar drought indicators across the wide range of catchments are then linked with catchment biophysical characteristics. A thorough compilation of land cover information (including the percentage of native forests, grass land, urban and industrial areas, glaciers, water bodies and no vegetated areas), catchment physical properties, and climatic conditions is done for all the catchments. Data mining techniques are applied to identify the main exogenous and endogenous factors determining drought characteristics and propagation.