Climate dynamics: a network-based approach for the analysis of global precipitation.

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Stefania Scarsoglio

Full Text Available Precipitation is one of the most important meteorological variables for defining the climate dynamics, but the spatial patterns of precipitation have not been fully investigated yet. The complex network theory, which provides a robust tool to investigate the statistical interdependence of many interacting elements, is used here to analyze the spatial dynamics of annual precipitation over seventy years (1941-2010. The precipitation network is built associating a node to a geographical region, which has a temporal distribution of precipitation, and identifying possible links among nodes through the correlation function. The precipitation network reveals significant spatial variability with barely connected regions, as Eastern China and Japan, and highly connected regions, such as the African Sahel, Eastern Australia and, to a lesser extent, Northern Europe. Sahel and Eastern Australia are remarkably dry regions, where low amounts of rainfall are uniformly distributed on continental scales and small-scale extreme events are rare. As a consequence, the precipitation gradient is low, making these regions well connected on a large spatial scale. On the contrary, the Asiatic South-East is often reached by extreme events such as monsoons, tropical cyclones and heat waves, which can all contribute to reduce the correlation to the short-range scale only. Some patterns emerging between mid-latitude and tropical regions suggest a possible impact of the propagation of planetary waves on precipitation at a global scale. Other links can be qualitatively associated to the atmospheric and oceanic circulation. To analyze the sensitivity of the network to the physical closeness of the nodes, short-term connections are broken. The African Sahel, Eastern Australia and Northern Europe regions again appear as the supernodes of the network, confirming furthermore their long-range connection structure. Almost all North-American and Asian nodes vanish, revealing that

Functional Connectivity of Precipitation Networks in the Brazilian Rainforest-Savanna Transition Zone

Science.gov (United States)

Adera, S.; Larsen, L.; Levy, M. C.; Thompson, S. E.

2016-12-01

In the Brazilian rainforest-savanna transition zone, vegetation change has the potential to significantly affect precipitation patterns. Deforestation, in particular, can affect precipitation patterns by increasing land surface albedo, increasing aerosol loading to the atmosphere, changing land surface roughness, and reducing transpiration. Understanding land surface-precipitation couplings in this region is important not only for sustaining Amazon and Cerrado ecosystems, but also for cattle ranching and agriculture, hydropower generation, and drinking water management. Simulations suggest complex, scale-dependent interactions between precipitation and land cover. For example, the size and distribution of deforested patches has been found to affect precipitation patterns. We take an empirical approach to ask: (1) what are the dominant spatial and temporal length scales of precipitation coupling in the Brazilian rainforest-savanna transition zone? (2) How do these length scales change over time? (3) How does the connectivity of precipitation change over time? The answers to these questions will help address fundamental questions about the impacts of deforestation on precipitation. We use rain gauge data from 1100 rain gauges intermittently covering the period 1980 - 2013, a period of intensive land cover change in the region. The dominant spatial and temporal length scales of precipitation coupling are resolved using transfer entropy, a metric from information theory. Connectivity of the emergent network of couplings is quantified using network statistics. Analyses using transfer entropy and network statistics reveal the spatial and temporal interdependencies of rainfall events occurring in different parts of the study domain.

DeepRain: ConvLSTM Network for Precipitation Prediction using Multichannel Radar Data

OpenAIRE

Kim, Seongchan; Hong, Seungkyun; Joh, Minsu; Song, Sa-kwang

2017-01-01

Accurate rainfall forecasting is critical because it has a great impact on people's social and economic activities. Recent trends on various literatures show that Deep Learning (Neural Network) is a promising methodology to tackle many challenging tasks. In this study, we introduce a brand-new data-driven precipitation prediction model called DeepRain. This model predicts the amount of rainfall from weather radar data, which is three-dimensional and four-channel data, using convolutional LSTM...

Modeling winter precipitation over the Juneau Icefield, Alaska, using a linear model of orographic precipitation

Science.gov (United States)

Roth, Aurora; Hock, Regine; Schuler, Thomas V.; Bieniek, Peter A.; Pelto, Mauri; Aschwanden, Andy

2018-03-01

Assessing and modeling precipitation in mountainous areas remains a major challenge in glacier mass balance modeling. Observations are typically scarce and reanalysis data and similar climate products are too coarse to accurately capture orographic effects. Here we use the linear theory of orographic precipitation model (LT model) to downscale winter precipitation from a regional climate model over the Juneau Icefield, one of the largest ice masses in North America (>4000 km2), for the period 1979-2013. The LT model is physically-based yet computationally efficient, combining airflow dynamics and simple cloud microphysics. The resulting 1 km resolution precipitation fields show substantially reduced precipitation on the northeastern portion of the icefield compared to the southwestern side, a pattern that is not well captured in the coarse resolution (20 km) WRF data. Net snow accumulation derived from the LT model precipitation agrees well with point observations across the icefield. To investigate the robustness of the LT model results, we perform a series of sensitivity experiments varying hydrometeor fall speeds, the horizontal resolution of the underlying grid, and the source of the meteorological forcing data. The resulting normalized spatial precipitation pattern is similar for all sensitivity experiments, but local precipitation amounts vary strongly, with greatest sensitivity to variations in snow fall speed. Results indicate that the LT model has great potential to provide improved spatial patterns of winter precipitation for glacier mass balance modeling purposes in complex terrain, but ground observations are necessary to constrain model parameters to match total amounts.

Downsizing a long-term precipitation network: Using a quantitative approach to inform difficult decisions

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Mark B. Green; John L. Campbell; Ruth D. Yanai; Scott W. Bailey; Amey S. Bailey; Nicholas Grant; Ian Halm; Eric P. Kelsey; Lindsey E. Rustad

2018-01-01

The design of a precipitation monitoring network must balance the demand for accurate estimates with the resources needed to build and maintain the network. If there are changes in the objectives of the monitoring or the availability of resources, network designs should be adjusted. At the Hubbard Brook Experimental Forest in New Hampshire, USA, precipitation has been...

Method for modeling the deposition of sulfur by precipitation over regional scales

International Nuclear Information System (INIS)

Hicks, B.B.; Shannon, J.D.

1979-01-01

Radioactive fallout data suggest that the concentration of pollutants in rainfall, while highly variable, might be described on the average by about an inverse half-power dependence on the amount of precipitation. Recent measurements of sulfur concentrations in summer rainfall collected at Argonne National Laboratory tend to support this contention, as do preliminary results derived from operations of the DOE precipitation chemistry network. The concept is extended to develop a bulk removal rate for airborne total sulfur by precipitation for use in regional dispersion modeling

Insights into mountain precipitation and snowpack from a basin-scale wireless-sensor network

Science.gov (United States)

Zhang, Z.; Glaser, S.; Bales, R.; Conklin, M.; Rice, R.; Marks, D.

2017-08-01

A spatially distributed wireless-sensor network, installed across the 2154 km2 portion of the 5311 km2 American River basin above 1500 m elevation, provided spatial measurements of temperature, relative humidity, and snow depth in the Sierra Nevada, California. The network consisted of 10 sensor clusters, each with 10 measurement nodes, distributed to capture the variability in topography and vegetation cover. The sensor network captured significant spatial heterogeneity in rain versus snow precipitation for water-year 2014, variability that was not apparent in the more limited operational data. Using daily dew-point temperature to track temporal elevational changes in the rain-snow transition, the amount of snow accumulation at each node was used to estimate the fraction of rain versus snow. This resulted in an underestimate of total precipitation below the 0°C dew-point elevation, which averaged 1730 m across 10 precipitation events, indicating that measuring snow does not capture total precipitation. We suggest blending lower elevation rain gauge data with higher-elevation sensor-node data for each event to estimate total precipitation. Blended estimates were on average 15-30% higher than using either set of measurements alone. Using data from the current operational snow-pillow sites gives even lower estimates of basin-wide precipitation. Given the increasing importance of liquid precipitation in a warming climate, a strategy that blends distributed measurements of both liquid and solid precipitation will provide more accurate basin-wide precipitation estimates, plus spatial and temporal patters of snow accumulation and melt in a basin.

Downsizing a long-term precipitation network: Using a quantitative approach to inform difficult decisions.

Science.gov (United States)

Green, Mark B; Campbell, John L; Yanai, Ruth D; Bailey, Scott W; Bailey, Amey S; Grant, Nicholas; Halm, Ian; Kelsey, Eric P; Rustad, Lindsey E

2018-01-01

The design of a precipitation monitoring network must balance the demand for accurate estimates with the resources needed to build and maintain the network. If there are changes in the objectives of the monitoring or the availability of resources, network designs should be adjusted. At the Hubbard Brook Experimental Forest in New Hampshire, USA, precipitation has been monitored with a network established in 1955 that has grown to 23 gauges distributed across nine small catchments. This high sampling intensity allowed us to simulate reduced sampling schemes and thereby evaluate the effect of decommissioning gauges on the quality of precipitation estimates. We considered all possible scenarios of sampling intensity for the catchments on the south-facing slope (2047 combinations) and the north-facing slope (4095 combinations), from the current scenario with 11 or 12 gauges to only 1 gauge remaining. Gauge scenarios differed by as much as 6.0% from the best estimate (based on all the gauges), depending on the catchment, but 95% of the scenarios gave estimates within 2% of the long-term average annual precipitation. The insensitivity of precipitation estimates and the catchment fluxes that depend on them under many reduced monitoring scenarios allowed us to base our reduction decision on other factors such as technician safety, the time required for monitoring, and co-location with other hydrometeorological measurements (snow, air temperature). At Hubbard Brook, precipitation gauges could be reduced from 23 to 10 with a change of <2% in the long-term precipitation estimates. The decision-making approach illustrated in this case study is applicable to the redesign of monitoring networks when reduction of effort seems warranted.

Improved daily precipitation nitrate and ammonium concentration models for the Chesapeake Bay Watershed

International Nuclear Information System (INIS)

Grimm, J.W.; Lynch, J.A.

2005-01-01

Daily precipitation nitrate and ammonium concentration models were developed for the Chesapeake Bay Watershed (USA) using a linear least-squares regression approach and precipitation chemistry data from 29 National Atmospheric Deposition Program/National Trends Network (NADP/NTN) sites. Only weekly samples that comprised a single precipitation event were used in model development. The most significant variables in both ammonium and nitrate models included: precipitation volume, the number of days since the last event, a measure of seasonality, latitude, and the proportion of land within 8 km covered by forest or devoted to industry and transportation. Additional variables included in the nitrate model were the proportion of land within 0.8 km covered by water and/or forest. Local and regional ammonia and nitrogen oxide emissions were not as well correlated as land cover. Modeled concentrations compared very well with event chemistry data collected at six NADP/AirMoN sites within the Chesapeake Bay Watershed. Wet deposition estimates were also consistent with observed deposition at selected sites. Accurately describing the spatial distribution of precipitation volume throughout the watershed is important in providing critical estimates of wet-fall deposition of ammonium and nitrate. - A linear least-squares regression approach was used to develop daily precipitation nitrate and ammonium concentration models for the Chesapeake Bay Watershed

Predictive modelling of Fe(III) precipitation in iron removal process for bioleaching circuits.

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Nurmi, Pauliina; Ozkaya, Bestamin; Kaksonen, Anna H; Tuovinen, Olli H; Puhakka, Jaakko A

2010-05-01

In this study, the applicability of three modelling approaches was determined in an effort to describe complex relationships between process parameters and to predict the performance of an integrated process, which consisted of a fluidized bed bioreactor for Fe(3+) regeneration and a gravity settler for precipitative iron removal. Self-organizing maps were used to visually evaluate the associations between variables prior to the comparison of two different modelling methods, the multiple regression modelling and artificial neural network (ANN) modelling, for predicting Fe(III) precipitation. With the ANN model, an excellent match between the predicted and measured data was obtained (R (2) = 0.97). The best-fitting regression model also gave a good fit (R (2) = 0.87). This study demonstrates that ANNs and regression models are robust tools for predicting iron precipitation in the integrated process and can thus be used in the management of such systems.

Spanish Network for Isotopes in Precipitation: Isotope Spatial distribution and contribution to the knowledge of the hydrological cycle

International Nuclear Information System (INIS)

Diaz-Teijeiro, M. F.; Rodriguez-Arevalo, J.; Castano, S.

2009-01-01

The results of seven years of operation of the Spanish Network for Isotopes ( 2 H, 1 8O y 3 H) in Precipitation (REVIP) are shown. this Network is managed since 2000 by the Centro de Estudios de Tecnicas Aplicadas of the Centro de Estudios y Experimentacion de Obras Publicas (CEDEX) in collaboration with the Agencia Estatal de Meteorologia (AEMET). The results of REVIP are sent to the International Atomic Energy Agency (IAEA) in order to be integrated in the Global Network for Isotopes in Precipitation (GNIP). The spatial distribution of stable isotopes ( 1 8O h 2 H) in precipitation in Spain follows a multiple regression model, based on two geographic factors: latitude and elevation, which is strongly correlated with temperature, an important factor controlling isotope fractionation. This information on 1 8O and 2 H is useful to trace surface and ground waters and, combined with the information, about the spatial and temporal distribution of the Tritium ( 3 H) concentration in precipitation, allows to date these waters in order to estimate flow directions and velocities, and to evaluate the residence time of water resources and aquifer vulnerability. (Author)

Improved daily precipitation nitrate and ammonium concentration models for the Chesapeake Bay Watershed.

Science.gov (United States)

Grimm, J W; Lynch, J A

2005-06-01

Daily precipitation nitrate and ammonium concentration models were developed for the Chesapeake Bay Watershed (USA) using a linear least-squares regression approach and precipitation chemistry data from 29 National Atmospheric Deposition Program/National Trends Network (NADP/NTN) sites. Only weekly samples that comprised a single precipitation event were used in model development. The most significant variables in both ammonium and nitrate models included: precipitation volume, the number of days since the last event, a measure of seasonality, latitude, and the proportion of land within 8km covered by forest or devoted to industry and transportation. Additional variables included in the nitrate model were the proportion of land within 0.8km covered by water and/or forest. Local and regional ammonia and nitrogen oxide emissions were not as well correlated as land cover. Modeled concentrations compared very well with event chemistry data collected at six NADP/AirMoN sites within the Chesapeake Bay Watershed. Wet deposition estimates were also consistent with observed deposition at selected sites. Accurately describing the spatial distribution of precipitation volume throughout the watershed is important in providing critical estimates of wet-fall deposition of ammonium and nitrate.

Precipitation data for water years 1992 and 1993 from a network of nonrecording gages at Yucca Mountain, Nevada

International Nuclear Information System (INIS)

Ambos, D.S.; Flint, A.L.; Hevesi, J.A.

1995-01-01

This report presents precipitation data collected in a storage gage network at Yucca Mountain, Nevada, from October 1, 1991, to September 30, 1993. The measured values indicate total accumulated precipitation for specified time intervals approximately corresponding to separate storm events. Installation of a precipitation monitoring network was initiated in January 1990, and was continually expanded and upgraded throughout the period ending in September 1993. The final network included 3 different gage types for a total of 133 gages at 108 locations within the three drainages overlying the potential repository site. Measured precipitation indicated above average accumulations for water years 1992 and 1993 relative to the most recent estimate of 6.7 inches for long-term average annual precipitation over the area of the network. The total precipitation averaged over the network in 1992 was about 8.2 inches with a maximum of about 11.2 inches measured at borehole USW GA-1. The total precipitation averaged over the network in 1993 was about 10.3 inches with a maximum of about 12.1 inches at neutron-access borehole UE-25 UZN number-sign 4

Spatial interpolation of precipitation in a dense gauge network for monsoon storm events in the southwestern United States

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Garcia, Matthew; Peters-Lidard, Christa D.; Goodrich, David C.

2008-05-01

Inaccuracy in spatially distributed precipitation fields can contribute significantly to the uncertainty of hydrological states and fluxes estimated from land surface models. This paper examines the results of selected interpolation methods for both convective and mixed/stratiform events that occurred during the North American monsoon season over a dense gauge network at the U.S. Department of Agriculture Agricultural Research Service Walnut Gulch Experimental Watershed in the southwestern United States. The spatial coefficient of variation for the precipitation field is employed as an indicator of event morphology, and a gauge clustering factor CF is formulated as a new, scale-independent measure of network organization. We consider that CF 0 (clustering in the gauge network) will produce errors because of reduced areal representation of the precipitation field. Spatial interpolation is performed using both inverse-distance-weighted (IDW) and multiquadric-biharmonic (MQB) methods. We employ ensembles of randomly selected network subsets for the statistical evaluation of interpolation errors in comparison with the observed precipitation. The magnitude of interpolation errors and differences in accuracy between interpolation methods depend on both the density and the geometrical organization of the gauge network. Generally, MQB methods outperform IDW methods in terms of interpolation accuracy under all conditions, but it is found that the order of the IDW method is important to the results and may, under some conditions, be just as accurate as the MQB method. In almost all results it is demonstrated that the inverse-distance-squared method for spatial interpolation, commonly employed in operational analyses and for engineering assessments, is inferior to the ID-cubed method, which is also more computationally efficient than the MQB method in studies of large networks.

Projection of future climate change conditions using IPCC simulations, neural networks and Bayesian statistics. Part 2: Precipitation mean state and seasonal cycle in South America

Energy Technology Data Exchange (ETDEWEB)

Boulanger, Jean-Philippe [LODYC, UMR CNRS/IRD/UPMC, Tour 45-55/Etage 4/Case 100, UPMC, Paris Cedex 05 (France); University of Buenos Aires, Departamento de Ciencias de la Atmosfera y los Oceanos, Facultad de Ciencias Exactas y Naturales, Buenos Aires (Argentina); Martinez, Fernando; Segura, Enrique C. [University of Buenos Aires, Departamento de Computacion, Facultad de Ciencias Exactas y Naturales, Buenos Aires (Argentina)

2007-02-15

Evaluating the response of climate to greenhouse gas forcing is a major objective of the climate community, and the use of large ensemble of simulations is considered as a significant step toward that goal. The present paper thus discusses a new methodology based on neural network to mix ensemble of climate model simulations. Our analysis consists of one simulation of seven Atmosphere-Ocean Global Climate Models, which participated in the IPCC Project and provided at least one simulation for the twentieth century (20c3m) and one simulation for each of three SRES scenarios: A2, A1B and B1. Our statistical method based on neural networks and Bayesian statistics computes a transfer function between models and observations. Such a transfer function was then used to project future conditions and to derive what we would call the optimal ensemble combination for twenty-first century climate change projections. Our approach is therefore based on one statement and one hypothesis. The statement is that an optimal ensemble projection should be built by giving larger weights to models, which have more skill in representing present climate conditions. The hypothesis is that our method based on neural network is actually weighting the models that way. While the statement is actually an open question, which answer may vary according to the region or climate signal under study, our results demonstrate that the neural network approach indeed allows to weighting models according to their skills. As such, our method is an improvement of existing Bayesian methods developed to mix ensembles of simulations. However, the general low skill of climate models in simulating precipitation mean climatology implies that the final projection maps (whatever the method used to compute them) may significantly change in the future as models improve. Therefore, the projection results for late twenty-first century conditions are presented as possible projections based on the &apos

Analysis of precipitation teleconnections in CMIP models as a measure of model fidelity in simulating precipitation

Science.gov (United States)

Langenbrunner, B.; Neelin, J.; Meyerson, J.

2011-12-01

The accurate representation of precipitation is a recurring issue in global climate models, especially in the tropics. Poor skill in modeling the variability and climate teleconnections associated with El Niño/Southern Oscillation (ENSO) also persisted in the latest Climate Model Intercomparison Project (CMIP) campaigns. Observed ENSO precipitation teleconnections provide a standard by which we can judge a given model's ability to reproduce precipitation and dynamic feedback processes originating in the tropical Pacific. Using CMIP3 Atmospheric Model Intercomparison Project (AMIP) runs as a baseline, we compare precipitation teleconnections between models and observations, and we evaluate these results against available CMIP5 historical and AMIP runs. Using AMIP simulations restricts evaluation to the atmospheric response, as sea surface temperatures (SSTs) in AMIP are prescribed by observations. We use a rank correlation between ENSO SST indices and precipitation to define teleconnections, since this method is robust to outliers and appropriate for non-Gaussian data. Spatial correlations of the modeled and observed teleconnections are then evaluated. We look at these correlations in regions of strong precipitation teleconnections, including equatorial S. America, the "horseshoe" region in the western tropical Pacific, and southern N. America. For each region and season, we create a "normalized projection" of a given model's teleconnection pattern onto that of the observations, a metric that assesses the quality of regional pattern simulations while rewarding signals of correct sign over the region. Comparing this to an area-averaged (i.e., more generous) metric suggests models do better when restrictions on exact spatial dependence are loosened and conservation constraints apply. Model fidelity in regional measures remains far from perfect, suggesting intrinsic issues with the models' regional sensitivities in moist processes.

Reproducibility of summertime diurnal precipitation over northern Eurasia simulated by CMIP5 climate models

Science.gov (United States)

Hirota, N.; Takayabu, Y. N.

2015-12-01

Reproducibility of diurnal precipitation over northern Eurasia simulated by CMIP5 climate models in their historical runs were evaluated, in comparison with station data (NCDC-9813) and satellite data (GSMaP-V5). We first calculated diurnal cycles by averaging precipitation at each local solar time (LST) in June-July-August during 1981-2000 over the continent of northern Eurasia (0-180E, 45-90N). Then we examined occurrence time of maximum precipitation and a contribution of diurnally varying precipitation to the total precipitation.The contribution of diurnal precipitation was about 21% in both NCDC-9813 and GSMaP-V5. The maximum precipitation occurred at 18LST in NCDC-9813 but 16LST in GSMaP-V5, indicating some uncertainties even in the observational datasets. The diurnal contribution of the CMIP5 models varied largely from 11% to 62%, and their timing of the precipitation maximum ranged from 11LST to 20LST. Interestingly, the contribution and the timing had strong negative correlation of -0.65. The models with larger diurnal precipitation showed precipitation maximum earlier around noon. Next, we compared sensitivity of precipitation to surface temperature and tropospheric humidity between 5 models with large diurnal precipitation (LDMs) and 5 models with small diurnal precipitation (SDMs). Precipitation in LDMs showed high sensitivity to surface temperature, indicating its close relationship with local instability. On the other hand, synoptic disturbances were more active in SDMs with a dominant role of the large scale condensation, and precipitation in SDMs was more related with tropospheric moisture. Therefore, the relative importance of the local instability and the synoptic disturbances was suggested to be an important factor in determining the contribution and timing of the diurnal precipitation. Acknowledgment: This study is supported by Green Network of Excellence (GRENE) Program by the Ministry of Education, Culture, Sports, Science and Technology

Continuum Model for River Networks

Science.gov (United States)

Giacometti, Achille; Maritan, Amos; Banavar, Jayanth R.

1995-07-01

The effects of erosion, avalanching, and random precipitation are captured in a simple stochastic partial differential equation for modeling the evolution of river networks. Our model leads to a self-organized structured landscape and to abstraction and piracy of the smaller tributaries as the evolution proceeds. An algebraic distribution of the average basin areas and a power law relationship between the drainage basin area and the river length are found.

Precipitates/Salts Model Sensitivity Calculation

International Nuclear Information System (INIS)

Mariner, P.

2001-01-01

The objective and scope of this calculation is to assist Performance Assessment Operations and the Engineered Barrier System (EBS) Department in modeling the geochemical effects of evaporation on potential seepage waters within a potential repository drift. This work is developed and documented using procedure AP-3.12Q, ''Calculations'', in support of ''Technical Work Plan For Engineered Barrier System Department Modeling and Testing FY 02 Work Activities'' (BSC 2001a). The specific objective of this calculation is to examine the sensitivity and uncertainties of the Precipitates/Salts model. The Precipitates/Salts model is documented in an Analysis/Model Report (AMR), ''In-Drift Precipitates/Salts Analysis'' (BSC 2001b). The calculation in the current document examines the effects of starting water composition, mineral suppressions, and the fugacity of carbon dioxide (CO 2 ) on the chemical evolution of water in the drift

Environmental controls on stable isotopes of precipitation in Lanzhou, China: An enhanced network at city scale.

Science.gov (United States)

Chen, Fenli; Zhang, Mingjun; Wang, Shengjie; Qiu, Xue; Du, Mingxia

2017-12-31

Stable hydrogen and oxygen isotopes in precipitation are very sensitive to environmental changes, and can record evolution of water cycle. The Lanzhou city in northwestern China is jointly influenced by the monsoon and westerlies, which is considered as a vital platform to investigate the moisture regime for this region. Since 2011, an observation network of stable isotopes in precipitation was established across the city, and four stations were included in the network. In 2013, six more sampling stations were added, and the enhanced network might provide more meaningful information on spatial incoherence and synoptic process. This study focused on the variations of stable isotopes (δ 18 O and δD) in precipitation and the environmental controls based on the 1432 samples in this enhanced network from April 2011 to October 2014. The results showed that the precipitation isotopes had great spatial diversity, and the neighboring stations may present large difference in δD and δ 18 O. Based on the observation at ten sampling sites, an isoscape in precipitation was calculated, and the method is useful to produce isoscape for small domains. The temperature effect and amount effect was reconsidered based on the dataset. Taking meteorological parameters (temperature, precipitation amount, relative humidity, water vapor pressure and dew point temperature) as variables in a multi-linear regression, the result of coefficients for these meteorological parameters were calculated. Some cases were also involved in this study, and the isotopic characteristics during one event or continuous days were used to understand the environmental controls on precipitation isotopes. Copyright © 2017 Elsevier B.V. All rights reserved.

Evaluation of Satellite and Model Precipitation Products Over Turkey

Science.gov (United States)

Yilmaz, M. T.; Amjad, M.

2017-12-01

Satellite-based remote sensing, gauge stations, and models are the three major platforms to acquire precipitation dataset. Among them satellites and models have the advantage of retrieving spatially and temporally continuous and consistent datasets, while the uncertainty estimates of these retrievals are often required for many hydrological studies to understand the source and the magnitude of the uncertainty in hydrological response parameters. In this study, satellite and model precipitation data products are validated over various temporal scales (daily, 3-daily, 7-daily, 10-daily and monthly) using in-situ measured precipitation observations from a network of 733 gauges from all over the Turkey. Tropical Rainfall Measurement Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42 version 7 and European Center of Medium-Range Weather Forecast (ECMWF) model estimates (daily, 3-daily, 7-daily and 10-daily accumulated forecast) are used in this study. Retrievals are evaluated for their mean and standard deviation and their accuracies are evaluated via bias, root mean square error, error standard deviation and correlation coefficient statistics. Intensity vs frequency analysis and some contingency table statistics like percent correct, probability of detection, false alarm ratio and critical success index are determined using daily time-series. Both ECMWF forecasts and TRMM observations, on average, overestimate the precipitation compared to gauge estimates; wet biases are 10.26 mm/month and 8.65 mm/month, respectively for ECMWF and TRMM. RMSE values of ECMWF forecasts and TRMM estimates are 39.69 mm/month and 41.55 mm/month, respectively. Monthly correlations between Gauges-ECMWF, Gauges-TRMM and ECMWF-TRMM are 0.76, 0.73 and 0.81, respectively. The model and the satellite error statistics are further compared against the gauges error statistics based on inverse distance weighting (IWD) analysis. Both the model and satellite data have less IWD errors (14

Precipitates/Salts Model Sensitivity Calculation

Energy Technology Data Exchange (ETDEWEB)

P. Mariner

2001-12-20

The objective and scope of this calculation is to assist Performance Assessment Operations and the Engineered Barrier System (EBS) Department in modeling the geochemical effects of evaporation on potential seepage waters within a potential repository drift. This work is developed and documented using procedure AP-3.12Q, ''Calculations'', in support of ''Technical Work Plan For Engineered Barrier System Department Modeling and Testing FY 02 Work Activities'' (BSC 2001a). The specific objective of this calculation is to examine the sensitivity and uncertainties of the Precipitates/Salts model. The Precipitates/Salts model is documented in an Analysis/Model Report (AMR), ''In-Drift Precipitates/Salts Analysis'' (BSC 2001b). The calculation in the current document examines the effects of starting water composition, mineral suppressions, and the fugacity of carbon dioxide (CO{sub 2}) on the chemical evolution of water in the drift.

A Survey of Precipitation Data for Environmental Modeling

Science.gov (United States)

This report explores the types of precipitation data available for environmental modeling. Precipitation is the main driver in the hydrological cycle and modelers use this information to understand water quality and water availability. Models use observed precipitation informatio...

A global gridded dataset of daily precipitation going back to 1950, ideal for analysing precipitation extremes

Science.gov (United States)

Contractor, S.; Donat, M.; Alexander, L. V.

2017-12-01

Reliable observations of precipitation are necessary to determine past changes in precipitation and validate models, allowing for reliable future projections. Existing gauge based gridded datasets of daily precipitation and satellite based observations contain artefacts and have a short length of record, making them unsuitable to analyse precipitation extremes. The largest limiting factor for the gauge based datasets is a dense and reliable station network. Currently, there are two major data archives of global in situ daily rainfall data, first is Global Historical Station Network (GHCN-Daily) hosted by National Oceanic and Atmospheric Administration (NOAA) and the other by Global Precipitation Climatology Centre (GPCC) part of the Deutsche Wetterdienst (DWD). We combine the two data archives and use automated quality control techniques to create a reliable long term network of raw station data, which we then interpolate using block kriging to create a global gridded dataset of daily precipitation going back to 1950. We compare our interpolated dataset with existing global gridded data of daily precipitation: NOAA Climate Prediction Centre (CPC) Global V1.0 and GPCC Full Data Daily Version 1.0, as well as various regional datasets. We find that our raw station density is much higher than other datasets. To avoid artefacts due to station network variability, we provide multiple versions of our dataset based on various completeness criteria, as well as provide the standard deviation, kriging error and number of stations for each grid cell and timestep to encourage responsible use of our dataset. Despite our efforts to increase the raw data density, the in situ station network remains sparse in India after the 1960s and in Africa throughout the timespan of the dataset. Our dataset would allow for more reliable global analyses of rainfall including its extremes and pave the way for better global precipitation observations with lower and more transparent uncertainties.

Evaluation of the WRF model for precipitation downscaling on orographic complex islands

Science.gov (United States)

Díaz, Juan P.; González, Albano; Expósito, Francisco; Pérez, Juan C.

2010-05-01

General Circulation Models (GCMs) have proven to be an effective tool to simulate many aspects of large-scale and global climate. However, their applicability to climate impact studies is limited by their capabilities to resolve regional scale situations. In this sense, dynamical downscaling techniques are an appropriate alternative to estimate high resolution regional climatologies. In this work, the Weather Research and Forecasting model (WRF) has been used to simulate precipitations over the Canary Islands region during 2009. The precipitation patterns over Canary Islands, located at North Atlantic region, show large gradients over a relatively small geographical area due to large scale factors such as Trade Winds regime predominant in the area and mesoscale factors mainly due to the complex terrain. Sensitivity study of simulated WRF precipitations to variations in model setup and parameterizations was carried out. Thus, WRF experiments were performed using two way nesting at 3 km horizontal grid spacing and 28 vertical levels in the Canaries inner domain. The initial and lateral and lower boundary conditions for the outer domain were provided at 6 hourly intervals by NCEP FNL (Final) Operational Global Analysis data on 1.0x1.0 degree resolution interpolated onto the WRF model grid. Numerous model options have been tested, including different microphysics schemes, cumulus parameterizations and nudging configuration Positive-definite moisture advection condition was also checked. Two integration approaches were analyzed: a 1-year continuous long-term integration and a consecutive short-term monthly reinitialized integration. To assess the accuracy of our simulations, model results are compared against observational datasets obtained from a network of meteorological stations in the region. In general, we can observe that the regional model is able to reproduce the spatial distribution of precipitation, but overestimates rainfall, mainly during strong

Using stochastic space-time models to map extreme precipitation in southern Portugal

Directory of Open Access Journals (Sweden)

A. C. Costa

2008-07-01

Full Text Available The topographic characteristics and spatial climatic diversity are significant in the South of continental Portugal where the rainfall regime is typically Mediterranean. Direct sequential cosimulation is proposed for mapping an extreme precipitation index in southern Portugal using elevation as auxiliary information. The analysed index (R5D can be considered a flood indicator because it provides a measure of medium-term precipitation total. The methodology accounts for local data variability and incorporates space-time models that allow capturing long-term trends of extreme precipitation, and local changes in the relationship between elevation and extreme precipitation through time. Annual gridded datasets of the flood indicator are produced from 1940 to 1999 on 800 m×800 m grids by using the space-time relationship between elevation and the index. Uncertainty evaluations of the proposed scenarios are also produced for each year. The results indicate that the relationship between elevation and extreme precipitation varies locally and has decreased through time over the study region. In wetter years the flood indicator exhibits the highest values in mountainous regions of the South, while in drier years the spatial pattern of extreme precipitation has much less variability over the study region. The uncertainty of extreme precipitation estimates also varies in time and space, and in earlier decades is strongly dependent on the density of the monitoring stations network. The produced maps will be useful in regional and local studies related to climate change, desertification, land and water resources management, hydrological modelling, and flood mitigation planning.

The Global Network of Isotopes in Precipitation after 55 years: assessing past, present and future developments

Science.gov (United States)

Terzer, Stefan; Araguas-Araguas, Luis; Wassenaar, Leonard I.; Aggarwal, Pradeep K.

2015-04-01

The Global Network of Isotopes in Precipitation (GNIP) is a global observation programme operated by the International Atomic Energy Agency (IAEA), in cooperation with the World Meteorological Organization (WMO) and more than 100 contributing institutions worldwide. GNIP has been the primary repository for baseline stable (δ18O, δ2H) and radioactive (3H) isotope data since its foundation in 1960. The impetus for GNIP was the monitoring of radioactive fallout from atmospheric thermonuclear testing and resulting tritium levels of precipitation, but tritium together with stable isotopes was recognized as a key to understanding hydrological processes. Later, new applications were developed focusing on hydrometeorology and paleoclimatic research. Increasingly, GNIP data are being used more widely in ecological and forensic investigations, e.g. for tracking of migratory animals. The GNIP database comprises more than 135,000 isotopic records (δ18O: 63,000; δ2H: 55,000; 3H: 63,000) of monthly composite precipitation samples from more than 1,000 stations worldwide. About 300 stations are currently active for stable isotopes and ca. 100 for tritium. Data for most of the active stations is available up to 2013. Several national isotopic observation networks (e.g. in Austria, Australia, China or the United States of America) exist besides GNIP, complementing precipitation isotope data at national levels. The spatially and temporally discrete nature of the GNIP dataset induces coverage gaps. Recently, highly-resolved gridded datasets were established to help overcome this deficiency through geostatistical prediction models. These 'isoscape' (isotopic landscapes) are based on combinations of multiple regression and interpolation methods, with a range of parameterization available at regional and global levels. Attempts to bridge the gap between 'one-size-fits-all' global parameterization and improved predictions at regional and local levels led to the establishment of a

The Passive Microwave Neural Network Precipitation Retrieval (PNPR) for AMSU/MHS and ATMS cross-track scanning radiometers

Science.gov (United States)

Sano', Paolo; Casella, Daniele; Panegrossi, Giulia; Cinzia Marra, Anna; Dietrich, Stefano

2016-04-01

Spaceborne microwave cross-track scanning radiometers, originally developed for temperature and humidity sounding, have shown great capabilities to provide a significant contribution in precipitation monitoring both in terms of measurement quality and spatial/temporal coverage. The Passive microwave Neural network Precipitation Retrieval (PNPR) algorithm for cross-track scanning radiometers, originally developed for the Advanced Microwave Sounding Unit/Microwave Humidity Sounder (AMSU-A/MHS) radiometers (on board the European MetOp and U.S. NOAA satellites), was recently newly designed to exploit the Advanced Technology Microwave Sounder (ATMS) on board the Suomi-NPP satellite and the future JPSS satellites. The PNPR algorithm is based on the Artificial Neural Network (ANN) approach. The main PNPR-ATMS algorithm changes with respect to PNPR-AMSU/MHS are the design and implementation of a new ANN able to manage the information derived from the additional ATMS channels (respect to the AMSU-A/MHS radiometer) and a new screening procedure for not-precipitating pixels. In order to achieve maximum consistency of the retrieved surface precipitation, both PNPR algorithms are based on the same physical foundation. The PNPR is optimized for the European and the African area. The neural network was trained using a cloud-radiation database built upon 94 cloud-resolving simulations over Europe and the Mediterranean and over the African area and radiative transfer model simulations of TB vectors consistent with the AMSU-A/MHS and ATMS channel frequencies, viewing angles, and view-angle dependent IFOV sizes along the scan projections. As opposed to other ANN precipitation retrieval algorithms, PNPR uses a unique ANN that retrieves the surface precipitation rate for all types of surface backgrounds represented in the training database, i.e., land (vegetated or arid), ocean, snow/ice or coast. This approach prevents different precipitation estimates from being inconsistent with one

Isotopic composition of precipitations in Brazil: isothermic models and the influence of evapotranspiration in the Amazonic Basin

International Nuclear Information System (INIS)

Dall'Olio, Attilio.

1976-11-01

The simplest theoretical models of the isotopic fractionation of water during equilibrium isothermical processes are analized in detail. The theoretical results are applied to the interpretation of the stable isotope concentrations in the precipitations of 11 Brazilian cities that belong to the international network of IAEA/WMO. The analysis shows that the experimental data are fairly consistent with such equilibrium models; no non-equilibrium processes need to be assumed. The study of the stable isotope content of precipitations in the Amazonic Basin suggests some modifications to the models in order that the evapotranspiration contribution to the vapour balance be taken into account [pt

Using Multiple Monthly Water Balance Models to Evaluate Gridded Precipitation Products over Peninsular Spain

Directory of Open Access Journals (Sweden)

Javier Senent-Aparicio

2018-06-01

Full Text Available The availability of precipitation data is the key driver in the application of hydrological models when simulating streamflow. Ground weather stations are regularly used to measure precipitation. However, spatial coverage is often limited in low-population areas and mountain areas. To overcome this limitation, gridded datasets from remote sensing have been widely used. This study evaluates four widely used global precipitation datasets (GPDs: The Tropical Rainfall Measuring Mission (TRMM 3B43, the Climate Forecast System Reanalysis (CFSR, the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN, and the Multi-Source Weighted-Ensemble Precipitation (MSWEP, against point gauge and gridded dataset observations using multiple monthly water balance models (MWBMs in four different meso-scale basins that cover the main climatic zones of Peninsular Spain. The volumes of precipitation obtained from the GPDs tend to be smaller than those from the gauged data. Results underscore the superiority of the national gridded dataset, although the TRMM provides satisfactory results in simulating streamflow, reaching similar Nash-Sutcliffe values, between 0.70 and 0.95, and an average total volume error of 12% when using the GR2M model. The performance of GPDs highly depends on the climate, so that the more humid the watershed is, the better results can be achieved. The procedures used can be applied in regions with similar case studies to more accurately assess the resources within a system in which there is scarcity of recorded data available.

A Thermodynamic Mixed-Solid Asphaltene Precipitation Model

DEFF Research Database (Denmark)

Lindeloff, Niels; Heidemann, R.A.; Andersen, Simon Ivar

1998-01-01

A simple model for the prediction of asphaltene precipitation is proposed. The model is based on an equation of state and uses standard thermodynamics, thus assuming that the precipitation phenomenon is a reversible process. The solid phase is treated as an ideal multicomponent mixture. An activity...

Application of physical scaling towards downscaling climate model precipitation data

Science.gov (United States)

Gaur, Abhishek; Simonovic, Slobodan P.

2018-04-01

Physical scaling (SP) method downscales climate model data to local or regional scales taking into consideration physical characteristics of the area under analysis. In this study, multiple SP method based models are tested for their effectiveness towards downscaling North American regional reanalysis (NARR) daily precipitation data. Model performance is compared with two state-of-the-art downscaling methods: statistical downscaling model (SDSM) and generalized linear modeling (GLM). The downscaled precipitation is evaluated with reference to recorded precipitation at 57 gauging stations located within the study region. The spatial and temporal robustness of the downscaling methods is evaluated using seven precipitation based indices. Results indicate that SP method-based models perform best in downscaling precipitation followed by GLM, followed by the SDSM model. Best performing models are thereafter used to downscale future precipitations made by three global circulation models (GCMs) following two emission scenarios: representative concentration pathway (RCP) 2.6 and RCP 8.5 over the twenty-first century. The downscaled future precipitation projections indicate an increase in mean and maximum precipitation intensity as well as a decrease in the total number of dry days. Further an increase in the frequency of short (1-day), moderately long (2-4 day), and long (more than 5-day) precipitation events is projected.

Dilution physics modeling: Dissolution/precipitation chemistry

International Nuclear Information System (INIS)

Onishi, Y.; Reid, H.C.; Trent, D.S.

1995-09-01

This report documents progress made to date on integrating dilution/precipitation chemistry and new physical models into the TEMPEST thermal-hydraulics computer code. Implementation of dissolution/precipitation chemistry models is necessary for predicting nonhomogeneous, time-dependent, physical/chemical behavior of tank wastes with and without a variety of possible engineered remediation and mitigation activities. Such behavior includes chemical reactions, gas retention, solids resuspension, solids dissolution and generation, solids settling/rising, and convective motion of physical and chemical species. Thus this model development is important from the standpoint of predicting the consequences of various engineered activities, such as mitigation by dilution, retrieval, or pretreatment, that can affect safe operations. The integration of a dissolution/precipitation chemistry module allows the various phase species concentrations to enter into the physical calculations that affect the TEMPEST hydrodynamic flow calculations. The yield strength model of non-Newtonian sludge correlates yield to a power function of solids concentration. Likewise, shear stress is concentration-dependent, and the dissolution/precipitation chemistry calculations develop the species concentration evolution that produces fluid flow resistance changes. Dilution of waste with pure water, molar concentrations of sodium hydroxide, and other chemical streams can be analyzed for the reactive species changes and hydrodynamic flow characteristics

The Global Climatology Network Precipitation data

International Nuclear Information System (INIS)

Peterson, T.C.; Easterling, D.R.; Eischeid, J.K.

1993-01-01

Several years ago, in response to growing concern about global climate change, the US National Climatic Data Center and the Carbon Dioxide Information Analysis Center undertook an effort to create a baseline global land surface climate data set called the Global Historical Climatology Network (GHCN, Vose et al., 1992). GHCN was created by merging several large existing climate data sets into one data base. Fifteen separate data sets went into the creation of the GHCN version 1.0. GHCN version 1.0 was released in 1992. It has 7,533 precipitation stations, but the number of stations varies with time. A slight majority (55%) have records in excess of 50 years, and a significant proportion (13%) have records in excess of 100 years. The longest period of record for any given station is 291 years (1697--1987 for Kew, United Kingdom)

Comparison of precipitation chemistry measurements obtained by the Canadian Air and Precipitation Monitoring Network and National Atmospheric Deposition Program for the period 1995-2004

Science.gov (United States)

Wetherbee, Gregory A.; Shaw, Michael J.; Latysh, Natalie E.; Lehmann, Christopher M.B.; Rothert, Jane E.

2010-01-01

Precipitation chemistry and depth measurements obtained by the Canadian Air and Precipitation Monitoring Network (CAPMoN) and the US National Atmospheric Deposition Program/National Trends Network (NADP/NTN) were compared for the 10-year period 1995–2004. Colocated sets of CAPMoN and NADP instrumentation, consisting of precipitation collectors and rain gages, were operated simultaneously per standard protocols for each network at Sutton, Ontario and Frelighsburg, Ontario, Canada and at State College, PA, USA. CAPMoN samples were collected daily, and NADP samples were collected weekly, and samples were analyzed exclusively by each network’s laboratory for pH, H + , Ca2+  , Mg2+  , Na + , K + , NH+4 , Cl − , NO−3 , and SO2−4 . Weekly and annual precipitation-weighted mean concentrations for each network were compared. This study is a follow-up to an earlier internetwork comparison for the period 1986–1993, published by Alain Sirois, Robert Vet, and Dennis Lamb in 2000. Median weekly internetwork differences for 1995–2004 data were the same to slightly lower than for data for the previous study period (1986–1993) for all analytes except NO−3 , SO2−4 , and sample depth. A 1994 NADP sampling protocol change and a 1998 change in the types of filters used to process NADP samples reversed the previously identified negative bias in NADP data for hydrogen-ion and sodium concentrations. Statistically significant biases (α = 0.10) for sodium and hydrogen-ion concentrations observed in the 1986–1993 data were not significant for 1995–2004. Weekly CAPMoN measurements generally are higher than weekly NADP measurements due to differences in sample filtration and field instrumentation, not sample evaporation, contamination, or analytical laboratory differences.

Generation of a stochastic precipitation model for the tropical climate

Science.gov (United States)

Ng, Jing Lin; Abd Aziz, Samsuzana; Huang, Yuk Feng; Wayayok, Aimrun; Rowshon, MK

2017-06-01

A tropical country like Malaysia is characterized by intense localized precipitation with temperatures remaining relatively constant throughout the year. A stochastic modeling of precipitation in the flood-prone Kelantan River Basin is particularly challenging due to the high intermittency of precipitation events of the northeast monsoons. There is an urgent need to have long series of precipitation in modeling the hydrological responses. A single-site stochastic precipitation model that includes precipitation occurrence and an intensity model was developed, calibrated, and validated for the Kelantan River Basin. The simulation process was carried out separately for each station without considering the spatial correlation of precipitation. The Markov chains up to the fifth-order and six distributions were considered. The daily precipitation data of 17 rainfall stations for the study period of 1954-2013 were selected. The results suggested that second- and third-order Markov chains were suitable for simulating monthly and yearly precipitation occurrences, respectively. The fifth-order Markov chain resulted in overestimation of precipitation occurrences. For the mean, distribution, and standard deviation of precipitation amounts, the exponential, gamma, log-normal, skew normal, mixed exponential, and generalized Pareto distributions performed superiorly. However, for the extremes of precipitation, the exponential and log-normal distributions were better while the skew normal and generalized Pareto distributions tend to show underestimations. The log-normal distribution was chosen as the best distribution to simulate precipitation amounts. Overall, the stochastic precipitation model developed is considered a convenient tool to simulate the characteristics of precipitation in the Kelantan River Basin.

High resolution modelling of extreme precipitation events in urban areas

Science.gov (United States)

Siemerink, Martijn; Volp, Nicolette; Schuurmans, Wytze; Deckers, Dave

2015-04-01

The present day society needs to adjust to the effects of climate change. More extreme weather conditions are expected, which can lead to longer periods of drought, but also to more extreme precipitation events. Urban water systems are not designed for such extreme events. Most sewer systems are not able to drain the excessive storm water, causing urban flooding. This leads to high economic damage. In order to take appropriate measures against extreme urban storms, detailed knowledge about the behaviour of the urban water system above and below the streets is required. To investigate the behaviour of urban water systems during extreme precipitation events new assessment tools are necessary. These tools should provide a detailed and integral description of the flow in the full domain of overland runoff, sewer flow, surface water flow and groundwater flow. We developed a new assessment tool, called 3Di, which provides detailed insight in the urban water system. This tool is based on a new numerical methodology that can accurately deal with the interaction between overland runoff, sewer flow and surface water flow. A one-dimensional model for the sewer system and open channel flow is fully coupled to a two-dimensional depth-averaged model that simulates the overland flow. The tool uses a subgrid-based approach in order to take high resolution information of the sewer system and of the terrain into account [1, 2]. The combination of using the high resolution information and the subgrid based approach results in an accurate and efficient modelling tool. It is now possible to simulate entire urban water systems using extreme high resolution (0.5m x 0.5m) terrain data in combination with a detailed sewer and surface water network representation. The new tool has been tested in several Dutch cities, such as Rotterdam, Amsterdam and The Hague. We will present the results of an extreme precipitation event in the city of Schiedam (The Netherlands). This city deals with

STAMMEX high resolution gridded daily precipitation dataset over Germany: a new potential for regional precipitation climate research

Science.gov (United States)

Zolina, Olga; Simmer, Clemens; Kapala, Alice; Mächel, Hermann; Gulev, Sergey; Groisman, Pavel

2014-05-01

We present new high resolution precipitation daily grids developed at Meteorological Institute, University of Bonn and German Weather Service (DWD) under the STAMMEX project (Spatial and Temporal Scales and Mechanisms of Extreme Precipitation Events over Central Europe). Daily precipitation grids have been developed from the daily-observing precipitation network of DWD, which runs one of the World's densest rain gauge networks comprising more than 7500 stations. Several quality-controlled daily gridded products with homogenized sampling were developed covering the periods 1931-onwards (with 0.5 degree resolution), 1951-onwards (0.25 degree and 0.5 degree), and 1971-2000 (0.1 degree). Different methods were tested to select the best gridding methodology that minimizes errors of integral grid estimates over hilly terrain. Besides daily precipitation values with uncertainty estimates (which include standard estimates of the kriging uncertainty as well as error estimates derived by a bootstrapping algorithm), the STAMMEX data sets include a variety of statistics that characterize temporal and spatial dynamics of the precipitation distribution (quantiles, extremes, wet/dry spells, etc.). Comparisons with existing continental-scale daily precipitation grids (e.g., CRU, ECA E-OBS, GCOS) which include considerably less observations compared to those used in STAMMEX, demonstrate the added value of high-resolution grids for extreme rainfall analyses. These data exhibit spatial variability pattern and trends in precipitation extremes, which are missed or incorrectly reproduced over Central Europe from coarser resolution grids based on sparser networks. The STAMMEX dataset can be used for high-quality climate diagnostics of precipitation variability, as a reference for reanalyses and remotely-sensed precipitation products (including the upcoming Global Precipitation Mission products), and for input into regional climate and operational weather forecast models. We will present

California Wintertime Precipitation in Regional and Global Climate Models

Energy Technology Data Exchange (ETDEWEB)

Caldwell, P M

2009-04-27

In this paper, wintertime precipitation from a variety of observational datasets, regional climate models (RCMs), and general circulation models (GCMs) is averaged over the state of California (CA) and compared. Several averaging methodologies are considered and all are found to give similar values when model grid spacing is less than 3{sup o}. This suggests that CA is a reasonable size for regional intercomparisons using modern GCMs. Results show that reanalysis-forced RCMs tend to significantly overpredict CA precipitation. This appears to be due mainly to overprediction of extreme events; RCM precipitation frequency is generally underpredicted. Overprediction is also reflected in wintertime precipitation variability, which tends to be too high for RCMs on both daily and interannual scales. Wintertime precipitation in most (but not all) GCMs is underestimated. This is in contrast to previous studies based on global blended gauge/satellite observations which are shown here to underestimate precipitation relative to higher-resolution gauge-only datasets. Several GCMs provide reasonable daily precipitation distributions, a trait which doesn't seem tied to model resolution. GCM daily and interannual variability is generally underpredicted.

Storms over the METER--ORNL Precipitation Network: the first six months

International Nuclear Information System (INIS)

Miller, R.L.; Patrinos, A.A.N.; Saylor, R.E.

1979-06-01

This report presents the first set of data collected by the METER--ORNL Precipitation Network. This network of 49 recording raingages and 5 recording windsets was installed in February 1978, around the Bowen Electric Generating Plant in northwest Georgia for the purpose of investigating the potential effect of the plant's cooling towers on rainfall. This study is conducted on behalf of the DOE Program on Meteorological Effects of Thermal Energy Releases (METER). Included in this report are the complete descriptions of 98 rainfall events which occurred over the METER--ORNL network during the period February 22--August 31, 1978. These descriptions are augmented by information and data supplied by the National Weather Service (NWS). Several stratifications of the rainfall events are performed for reference purposes

Documentation of a daily mean stream temperature module—An enhancement to the Precipitation-Runoff Modeling System

Science.gov (United States)

Sanders, Michael J.; Markstrom, Steven L.; Regan, R. Steven; Atkinson, R. Dwight

2017-09-15

A module for simulation of daily mean water temperature in a network of stream segments has been developed as an enhancement to the U.S. Geological Survey Precipitation Runoff Modeling System (PRMS). This new module is based on the U.S. Fish and Wildlife Service Stream Network Temperature model, a mechanistic, one-dimensional heat transport model. The new module is integrated in PRMS. Stream-water temperature simulation is activated by selection of the appropriate input flags in the PRMS Control File and by providing the necessary additional inputs in standard PRMS input files.This report includes a comprehensive discussion of the methods relevant to the stream temperature calculations and detailed instructions for model input preparation.

Multiresolution comparison of precipitation datasets for large-scale models

Science.gov (United States)

Chun, K. P.; Sapriza Azuri, G.; Davison, B.; DeBeer, C. M.; Wheater, H. S.

2014-12-01

Gridded precipitation datasets are crucial for driving large-scale models which are related to weather forecast and climate research. However, the quality of precipitation products is usually validated individually. Comparisons between gridded precipitation products along with ground observations provide another avenue for investigating how the precipitation uncertainty would affect the performance of large-scale models. In this study, using data from a set of precipitation gauges over British Columbia and Alberta, we evaluate several widely used North America gridded products including the Canadian Gridded Precipitation Anomalies (CANGRD), the National Center for Environmental Prediction (NCEP) reanalysis, the Water and Global Change (WATCH) project, the thin plate spline smoothing algorithms (ANUSPLIN) and Canadian Precipitation Analysis (CaPA). Based on verification criteria for various temporal and spatial scales, results provide an assessment of possible applications for various precipitation datasets. For long-term climate variation studies (~100 years), CANGRD, NCEP, WATCH and ANUSPLIN have different comparative advantages in terms of their resolution and accuracy. For synoptic and mesoscale precipitation patterns, CaPA provides appealing performance of spatial coherence. In addition to the products comparison, various downscaling methods are also surveyed to explore new verification and bias-reduction methods for improving gridded precipitation outputs for large-scale models.

MAP3S precipitation chemistry network: fourth periodic summary report (1980)

Energy Technology Data Exchange (ETDEWEB)

1981-12-01

This, the fourth in a series of summary reports, contains complete field and chemical data from the MAP3S/RAINE (Multistate Atmospheric Power Production Pollution Studies) Precipitation Chemistry Network for the year 1980. The 1980 data were added to the previous data base, and an update of the previous statistical summary completed. Included are basic statistics, time trend analyses, and monthly averages.

Effect of the precipitation interpolation method on the performance of a snowmelt runoff model

Science.gov (United States)

Jacquin, Alexandra

2014-05-01

Uncertainties on the spatial distribution of precipitation seriously affect the reliability of the discharge estimates produced by watershed models. Although there is abundant research evaluating the goodness of fit of precipitation estimates obtained with different gauge interpolation methods, few studies have focused on the influence of the interpolation strategy on the response of watershed models. The relevance of this choice may be even greater in the case of mountain catchments, because of the influence of orography on precipitation. This study evaluates the effect of the precipitation interpolation method on the performance of conceptual type snowmelt runoff models. The HBV Light model version 4.0.0.2, operating at daily time steps, is used as a case study. The model is applied in Aconcagua at Chacabuquito catchment, located in the Andes Mountains of Central Chile. The catchment's area is 2110[Km2] and elevation ranges from 950[m.a.s.l.] to 5930[m.a.s.l.] The local meteorological network is sparse, with all precipitation gauges located below 3000[m.a.s.l.] Precipitation amounts corresponding to different elevation zones are estimated through areal averaging of precipitation fields interpolated from gauge data. Interpolation methods applied include kriging with external drift (KED), optimal interpolation method (OIM), Thiessen polygons (TP), multiquadratic functions fitting (MFF) and inverse distance weighting (IDW). Both KED and OIM are able to account for the existence of a spatial trend in the expectation of precipitation. By contrast, TP, MFF and IDW, traditional methods widely used in engineering hydrology, cannot explicitly incorporate this information. Preliminary analysis confirmed that these methods notably underestimate precipitation in the study catchment, while KED and OIM are able to reduce the bias; this analysis also revealed that OIM provides more reliable estimations than KED in this region. Using input precipitation obtained by each method

Improving Frozen Precipitation Density Estimation in Land Surface Modeling

Science.gov (United States)

Sparrow, K.; Fall, G. M.

2017-12-01

The Office of Water Prediction (OWP) produces high-value water supply and flood risk planning information through the use of operational land surface modeling. Improvements in diagnosing frozen precipitation density will benefit the NWS's meteorological and hydrological services by refining estimates of a significant and vital input into land surface models. A current common practice for handling the density of snow accumulation in a land surface model is to use a standard 10:1 snow-to-liquid-equivalent ratio (SLR). Our research findings suggest the possibility of a more skillful approach for assessing the spatial variability of precipitation density. We developed a 30-year SLR climatology for the coterminous US from version 3.22 of the Daily Global Historical Climatology Network - Daily (GHCN-D) dataset. Our methods followed the approach described by Baxter (2005) to estimate mean climatological SLR values at GHCN-D sites in the US, Canada, and Mexico for the years 1986-2015. In addition to the Baxter criteria, the following refinements were made: tests were performed to eliminate SLR outliers and frequent reports of SLR = 10, a linear SLR vs. elevation trend was fitted to station SLR mean values to remove the elevation trend from the data, and detrended SLR residuals were interpolated using ordinary kriging with a spherical semivariogram model. The elevation values of each station were based on the GMTED 2010 digital elevation model and the elevation trend in the data was established via linear least squares approximation. The ordinary kriging procedure was used to interpolate the data into gridded climatological SLR estimates for each calendar month at a 0.125 degree resolution. To assess the skill of this climatology, we compared estimates from our SLR climatology with observations from the GHCN-D dataset to consider the potential use of this climatology as a first guess of frozen precipitation density in an operational land surface model. The difference in

Applying an orographic precipitation model to improve mass balance modeling of the Juneau Icefield, AK

Science.gov (United States)

Roth, A. C.; Hock, R.; Schuler, T.; Bieniek, P.; Aschwanden, A.

2017-12-01

Mass loss from glaciers in Southeast Alaska is expected to alter downstream ecological systems as runoff patterns change. To investigate these potential changes under future climate scenarios, distributed glacier mass balance modeling is required. However, the spatial resolution gap between global or regional climate models and the requirements for glacier mass balance modeling studies must be addressed first. We have used a linear theory of orographic precipitation model to downscale precipitation from both the Weather Research and Forecasting (WRF) model and ERA-Interim to the Juneau Icefield region over the period 1979-2013. This implementation of the LT model is a unique parameterization that relies on the specification of snow fall speed and rain fall speed as tuning parameters to calculate the cloud time delay, τ. We assessed the LT model results by considering winter precipitation so the effect of melt was minimized. The downscaled precipitation pattern produced by the LT model captures the orographic precipitation pattern absent from the coarse resolution WRF and ERA-Interim precipitation fields. Observational data constraints limited our ability to determine a unique parameter combination and calibrate the LT model to glaciological observations. We established a reference run of parameter values based on literature and performed a sensitivity analysis of the LT model parameters, horizontal resolution, and climate input data on the average winter precipitation. The results of the reference run showed reasonable agreement with the available glaciological measurements. The precipitation pattern produced by the LT model was consistent regardless of parameter combination, horizontal resolution, and climate input data, but the precipitation amount varied strongly with these factors. Due to the consistency of the winter precipitation pattern and the uncertainty in precipitation amount, we suggest a precipitation index map approach to be used in combination with

The Alaska Water Isotope Network (AKWIN): Precipitation, lake, river and stream dynamics

Science.gov (United States)

Rogers, M.; Welker, J. M.; Toohey, R.

2011-12-01

The hydrologic cycle is central to the structure and function of northern landscapes. The movement of water creates interactions between terrestrial, aquatic, marine and atmospheric processes. Understanding the processes and the spatial patterns that govern the isotopic (δ18O & δD) characteristics of the hydrologic cycle is especially important today as: a) modern climate/weather-isotope relations allow for more accurate interpretation of climate proxies and the calibration of atmospheric models, b) water isotopes facilitate understanding the role of storm tracks in regulating precipitation isotopic variability, c) water isotopes allow for estimates of glacial melt water inputs into aquatic systems, d) water isotopes allow for quantification of surface and groundwater interactions, e) water isotopes allow for quantification of permafrost meltwater use by plant communities, f) water isotopes aid in migratory bird forensics, g) water isotopes are critical to estimating field metabolic rates, h) water isotopes allow for crop and diet forensics and i) water isotopes can provide insight into evaporation and transpiration processes. As part of a new NSF MRI project at the Environment and Natural Resources Institute (ENRI) at the University of Alaska Anchorage and as an extension of the US Network for Isotopes in Precipitation (USNIP); we are forming AKWIN. The network will utilize long-term weekly sampling at Denali National Park and Caribou Poker Creek Watershed (USNIP sites-1989 to present), regular sampling across Alaska involving land management agencies (USGS, NPS, USFWS, EPA), educators, volunteers and citizen scientists, UA extended campuses, individual research projects, opportunistic sampling and published data to construct isoscapes and time series databases and information packages. We will be using a suite of spatial and temporal analysis methods to characterize water isotopes across Alaska and will provide web portals for data products. Our network is

Short-range quantitative precipitation forecasting using Deep Learning approaches

Science.gov (United States)

Akbari Asanjan, A.; Yang, T.; Gao, X.; Hsu, K. L.; Sorooshian, S.

2017-12-01

Predicting short-range quantitative precipitation is very important for flood forecasting, early flood warning and other hydrometeorological purposes. This study aims to improve the precipitation forecasting skills using a recently developed and advanced machine learning technique named Long Short-Term Memory (LSTM). The proposed LSTM learns the changing patterns of clouds from Cloud-Top Brightness Temperature (CTBT) images, retrieved from the infrared channel of Geostationary Operational Environmental Satellite (GOES), using a sophisticated and effective learning method. After learning the dynamics of clouds, the LSTM model predicts the upcoming rainy CTBT events. The proposed model is then merged with a precipitation estimation algorithm termed Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) to provide precipitation forecasts. The results of merged LSTM with PERSIANN are compared to the results of an Elman-type Recurrent Neural Network (RNN) merged with PERSIANN and Final Analysis of Global Forecast System model over the states of Oklahoma, Florida and Oregon. The performance of each model is investigated during 3 storm events each located over one of the study regions. The results indicate the outperformance of merged LSTM forecasts comparing to the numerical and statistical baselines in terms of Probability of Detection (POD), False Alarm Ratio (FAR), Critical Success Index (CSI), RMSE and correlation coefficient especially in convective systems. The proposed method shows superior capabilities in short-term forecasting over compared methods.

Fracture network modeling and GoldSim simulation support

International Nuclear Information System (INIS)

Sugita, Kenichiro; Dershowitz, William

2004-01-01

During Heisei-15, Golder Associates provided support for JNC Tokai through discrete fracture network data analysis and simulation of the MIU Underground Rock Laboratory, participation in Task 6 of the Aespoe Task Force on Modelling of Groundwater Flow and Transport, and development of methodologies for analysis of repository site characterization strategies and safety assessment. MIU Underground Rock Laboratory support during H-15 involved development of new discrete fracture network (DFN) models for the MIU Shoba-sama Site, in the region of shaft development. Golder developed three DFN models for the site using discrete fracture network, equivalent porous medium (EPM), and nested DFN/EPM approaches. Each of these models were compared based upon criteria established for the multiple modeling project (MMP). Golder supported JNC participation in Task 6AB, 6D and 6E of the Aespoe Task Force on Modelling of Groundwater Flow and Transport during H-15. For Task 6AB, Golder implemented an updated microstructural model in GoldSim, and used this updated model to simulate the propagation of uncertainty from experimental to safety assessment time scales, for 5 m scale transport path lengths. Task 6D and 6E compared safety assessment (PA) and experimental time scale simulations in a 200 m scale discrete fracture network. For Task 6D, Golder implemented a DFN model using FracMan/PA Works, and determined the sensitivity of solute transport to a range of material property and geometric assumptions. For Task 6E, Golder carried out demonstration FracMan/PA Works transport calculations at a 1 million year time scale, to ensure that task specifications are realistic. The majority of work for Task 6E will be carried out during H-16. During H-15, Golder supported JNC's Total System Performance Assessment (TSPO) strategy by developing technologies for the analysis of precipitant concentration. These approaches were based on the GoldSim precipitant data management features, and were

Patterns of precipitation and soil moisture extremes in Texas, US: A complex network analysis

Science.gov (United States)

Sun, Alexander Y.; Xia, Youlong; Caldwell, Todd G.; Hao, Zengchao

2018-02-01

Understanding of the spatial and temporal dynamics of extreme precipitation not only improves prediction skills, but also helps to prioritize hazard mitigation efforts. This study seeks to enhance the understanding of spatiotemporal covariation patterns embedded in precipitation (P) and soil moisture (SM) by using an event-based, complex-network-theoretic approach. Events concurrences are quantified using a nonparametric event synchronization measure, and spatial patterns of hydroclimate variables are analyzed by using several network measures and a community detection algorithm. SM-P coupling is examined using a directional event coincidence analysis measure that takes the order of event occurrences into account. The complex network approach is demonstrated for Texas, US, a region possessing a rich set of hydroclimate features and is frequented by catastrophic flooding. Gridded daily observed P data and simulated SM data are used to create complex networks of P and SM extremes. The uncovered high degree centrality regions and community structures are qualitatively in agreement with the overall existing knowledge of hydroclimate extremes in the study region. Our analyses provide new visual insights on the propagation, connectivity, and synchronicity of P extremes, as well as the SM-P coupling, in this flood-prone region, and can be readily used as a basis for event-driven predictive analytics for other regions.

A statistical intercomparison between "urban" and "rural" precipitation chemistry data from greater Manchester and two nearby secondary national network sites in the United Kingdom

Science.gov (United States)

Lee, David S.; Longhurst, James W. S.

Precipitation chemistry data from a dense urban monitoring network in Greater Manchester, northwest England, were compared with interpolated values from the U.K. secondary national acid deposition monitoring network for the year 1988. Differences were found to be small. However, when data from individual sites from the Greater Manchester network were compared with data from the two nearest secondary national network sites, significant differences were found using simple and complex statistical analyses. Precipitation chemistry at rural sites could be similar to that at urban sites, but the sources of some ions were thought to be different. The synoptic-scale gradients of precipitation chemistry, as shown by the secondary national network, also accounted for some of the differences.

PbO networks composed of single crystalline nanosheets synthesized by a facile chemical precipitation method

Energy Technology Data Exchange (ETDEWEB)

Samberg, Joshua P. [Department of Materials Science and Engineering, North Carolina State University, 911 Partners Way, Engineering Building I, Raleigh, NC 27695-7907 (United States); Kajbafvala, Amir, E-mail: amir.kajbafvala@gmail.com [Department of Materials Science and Engineering, North Carolina State University, 911 Partners Way, Engineering Building I, Raleigh, NC 27695-7907 (United States); Koolivand, Amir [Department of Chemistry, North Carolina State University, 2620 Yarbrough Drive, Raleigh, NC 27695 (United States)

2014-03-01

Graphical abstract: - Highlights: • Synthesis of PbO networks through a simple chemical precipitation route. • The synthesis method is rapid and low-cost. • Each network is composed of single crystalline PbO nanosheets. • A possible growth mechanism is proposed for synthesized PbO networks. - Abstract: For the field of energy storage, nanostructured lead oxide (PbO) shows immense potential for increased specific energy and deep discharge for lead acid battery technologies. In this work, PbO networks composed of single crystalline nanosheets were synthesized utilizing a simple, low cost and rapid chemical precipitation method. The PbO networks were prepared in a single reaction vessel from starting reagents of lead acetate dehydrate, ammonium hydroxide and deionized water. Lead acetate dehydrate was chosen as a reagent, as opposed to lead nitrate, to eliminate the possibility of nitrate contamination of the final product. X-ray diffraction (XRD) analysis, high resolution scanning electron microscopy (HRSEM) and high resolution transmission electron microscopy (HRTEM) analysis were used to characterize the synthesized PbO networks. The reproducible method described herein synthesized pure β-PbO (massicot) powders, with no byproducts. A possible formation mechanism for these PbO networks is proposed. The growth is found to proceed predominately in the 〈1 1 1〉 and 〈2 0 0〉 directions while being limited in the 〈0 1 1〉 direction.

PbO networks composed of single crystalline nanosheets synthesized by a facile chemical precipitation method

International Nuclear Information System (INIS)

Samberg, Joshua P.; Kajbafvala, Amir; Koolivand, Amir

2014-01-01

Graphical abstract: - Highlights: • Synthesis of PbO networks through a simple chemical precipitation route. • The synthesis method is rapid and low-cost. • Each network is composed of single crystalline PbO nanosheets. • A possible growth mechanism is proposed for synthesized PbO networks. - Abstract: For the field of energy storage, nanostructured lead oxide (PbO) shows immense potential for increased specific energy and deep discharge for lead acid battery technologies. In this work, PbO networks composed of single crystalline nanosheets were synthesized utilizing a simple, low cost and rapid chemical precipitation method. The PbO networks were prepared in a single reaction vessel from starting reagents of lead acetate dehydrate, ammonium hydroxide and deionized water. Lead acetate dehydrate was chosen as a reagent, as opposed to lead nitrate, to eliminate the possibility of nitrate contamination of the final product. X-ray diffraction (XRD) analysis, high resolution scanning electron microscopy (HRSEM) and high resolution transmission electron microscopy (HRTEM) analysis were used to characterize the synthesized PbO networks. The reproducible method described herein synthesized pure β-PbO (massicot) powders, with no byproducts. A possible formation mechanism for these PbO networks is proposed. The growth is found to proceed predominately in the 〈1 1 1〉 and 〈2 0 0〉 directions while being limited in the 〈0 1 1〉 direction

Climate network analysis of regional precipitation extremes: The true story told by event synchronization

Science.gov (United States)

Odenweller, Adrian; Donner, Reik V.

2017-04-01

Over the last decade, complex network methods have been frequently used for characterizing spatio-temporal patterns of climate variability from a complex systems perspective, yielding new insights into time-dependent teleconnectivity patterns and couplings between different components of the Earth climate. Among the foremost results reported, network analyses of the synchronicity of extreme events as captured by the so-called event synchronization have been proposed to be powerful tools for disentangling the spatio-temporal organization of particularly extreme rainfall events and anticipating the timing of monsoon onsets or extreme floodings. Rooted in the analysis of spike train synchrony analysis in the neurosciences, event synchronization has the great advantage of automatically classifying pairs of events arising at two distinct spatial locations as temporally close (and, thus, possibly statistically - or even dynamically - interrelated) or not without the necessity of selecting an additional parameter in terms of a maximally tolerable delay between these events. This consideration is conceptually justified in case of the original application to spike trains in electroencephalogram (EEG) recordings, where the inter-spike intervals show relatively narrow distributions at high temporal sampling rates. However, in case of climate studies, precipitation extremes defined by daily precipitation sums exceeding a certain empirical percentile of their local distribution exhibit a distinctively different type of distribution of waiting times between subsequent events. This raises conceptual concerns if event synchronization is still appropriate for detecting interlinkages between spatially distributed precipitation extremes. In order to study this problem in more detail, we employ event synchronization together with an alternative similarity measure for event sequences, event coincidence rates, which requires a manual setting of the tolerable maximum delay between two

Interpolation of Missing Precipitation Data Using Kernel Estimations for Hydrologic Modeling

Directory of Open Access Journals (Sweden)

Hyojin Lee

2015-01-01

Full Text Available Precipitation is the main factor that drives hydrologic modeling; therefore, missing precipitation data can cause malfunctions in hydrologic modeling. Although interpolation of missing precipitation data is recognized as an important research topic, only a few methods follow a regression approach. In this study, daily precipitation data were interpolated using five different kernel functions, namely, Epanechnikov, Quartic, Triweight, Tricube, and Cosine, to estimate missing precipitation data. This study also presents an assessment that compares estimation of missing precipitation data through Kth nearest neighborhood (KNN regression to the five different kernel estimations and their performance in simulating streamflow using the Soil Water Assessment Tool (SWAT hydrologic model. The results show that the kernel approaches provide higher quality interpolation of precipitation data compared with the KNN regression approach, in terms of both statistical data assessment and hydrologic modeling performance.

Scaling of Precipitation Extremes Modelled by Generalized Pareto Distribution

Science.gov (United States)

Rajulapati, C. R.; Mujumdar, P. P.

2017-12-01

Precipitation extremes are often modelled with data from annual maximum series or peaks over threshold series. The Generalized Pareto Distribution (GPD) is commonly used to fit the peaks over threshold series. Scaling of precipitation extremes from larger time scales to smaller time scales when the extremes are modelled with the GPD is burdened with difficulties arising from varying thresholds for different durations. In this study, the scale invariance theory is used to develop a disaggregation model for precipitation extremes exceeding specified thresholds. A scaling relationship is developed for a range of thresholds obtained from a set of quantiles of non-zero precipitation of different durations. The GPD parameters and exceedance rate parameters are modelled by the Bayesian approach and the uncertainty in scaling exponent is quantified. A quantile based modification in the scaling relationship is proposed for obtaining the varying thresholds and exceedance rate parameters for shorter durations. The disaggregation model is applied to precipitation datasets of Berlin City, Germany and Bangalore City, India. From both the applications, it is observed that the uncertainty in the scaling exponent has a considerable effect on uncertainty in scaled parameters and return levels of shorter durations.

A spatial approach to the modelling and estimation of areal precipitation

Energy Technology Data Exchange (ETDEWEB)

Skaugen, T

1996-12-31

In hydroelectric power technology it is important that the mean precipitation that falls in an area can be calculated. This doctoral thesis studies how the morphology of rainfall, described by the spatial statistical parameters, can be used to improve interpolation and estimation procedures. It attempts to formulate a theory which includes the relations between the size of the catchment and the size of the precipitation events in the modelling of areal precipitation. The problem of estimating and modelling areal precipitation can be formulated as the problem of estimating an inhomogeneously distributed flux of a certain spatial extent being measured at points in a randomly placed domain. The information contained in the different morphology of precipitation types is used to improve estimation procedures of areal precipitation, by interpolation (kriging) or by constructing areal reduction factors. A new approach to precipitation modelling is introduced where the analysis of the spatial coverage of precipitation at different intensities plays a key role in the formulation of a stochastic model for extreme areal precipitation and in deriving the probability density function of areal precipitation. 127 refs., 30 figs., 13 tabs.

Dynamic process model of a plutonium oxalate precipitator

International Nuclear Information System (INIS)

Borgonovi, G.M.; Hammelman, J.E.; Miller, C.L.

1980-01-01

A dynamic model of a plutonium oxalate precipitator is developed to provide a means of predicting plutonium inventory on a continuous basis. The model is based on state-of-the-art crystallization equations, which describe nucleation and growth phenomena. The model parameters were obtained through the use of batch experimental data. The model has been used to study the approach to steady state, to investigate the response to input transients, and to simulate the control of the precipitation process. 12 refs

United States Historical Climatology Network Daily Temperature and Precipitation Data (1871-1997)

Energy Technology Data Exchange (ETDEWEB)

Easterling, D.R.

2002-10-28

This document describes a database containing daily observations of maximum and minimum temperature, precipitation amount, snowfall amount, and snow depth from 1062 observing stations across the contiguous US. This database is an expansion and update of the original 138-station database previously released by the Carbon Dioxide Information Analysis Center (CDIAC) as CDIAC numeric data package NDP-042. These 1062 stations are a subset of the 1221-station US Historical Climatology Network (HCN), a monthly database compiled by the National Climatic Data Center (Asheville, North Carolina) that has been widely used in analyzing US climate. Data from 1050 of these daily records extend into the 1990s, while 990 of these extend through 1997. Most station records are essentially complete for at least 40 years; the latest beginning year of record is 1948. Records from 158 stations begin prior to 1900, with that of Charleston, South Carolina beginning the earliest (1871). The daily resolution of these data makes them extremely valuable for studies attempting to detect and monitor long-term climatic changes on a regional scale. Studies using daily data may be able to detect changes in regional climate that would not be apparent from analysis of monthly temperature and precipitation data. Such studies may include analyses of trends in maximum and minimum temperatures, temperature extremes, daily temperature range, precipitation ''event size'' frequency, and the magnitude and duration of wet and dry periods. The data are also valuable in areas such as regional climate model validation and climate change impact assessment. This database is available free of charge from CDIAC as a numeric data package (NDP).

Modelling the operation of precipitator with vortex effect

International Nuclear Information System (INIS)

Eysseric-Emile, C.

1994-01-01

In the Purex process which is implemented for the processing of irradiated fuels to eliminate fission products and to recover and valorise uranium and plutonium under the form of end products, a precipitation operation occurs to prepare the plutonium oxalate. This research thesis aims at analysing hydrodynamic characteristics of a specific apparatus used for this precipitation, the precipitator with vortex effect. In a first part, the author presents the problems associated with precipitation operations, their implementation in the processing of irradiated fuels, and compares the considered precipitator with other devices used for the precipitation of radioactive compounds. He proposes a review of literature on the vortex effect in agitated vessel, highlights the key parameter (the forced vortex radius), and reports some preliminary measurements performed on the precipitator. The author then reports the study of liquid phase flows in the precipitator, measurements of rate of suspension, and the study of micro-mixing with reactants. He finally reports attempts to validate trends noticed during flow analysis and a first simple modelling of the precipitator [fr

European climate change experiments on precipitation change

DEFF Research Database (Denmark)

Beier, Claus

Presentation of European activities and networks related to experiments and databases within precipitation change......Presentation of European activities and networks related to experiments and databases within precipitation change...

A systematic study of multiple minerals precipitation modelling in wastewater treatment.

Science.gov (United States)

Kazadi Mbamba, Christian; Tait, Stephan; Flores-Alsina, Xavier; Batstone, Damien J

2015-11-15

Mineral solids precipitation is important in wastewater treatment. However approaches to minerals precipitation modelling are varied, often empirical, and mostly focused on single precipitate classes. A common approach, applicable to multi-species precipitates, is needed to integrate into existing wastewater treatment models. The present study systematically tested a semi-mechanistic modelling approach, using various experimental platforms with multiple minerals precipitation. Experiments included dynamic titration with addition of sodium hydroxide to synthetic wastewater, and aeration to progressively increase pH and induce precipitation in real piggery digestate and sewage sludge digestate. The model approach consisted of an equilibrium part for aqueous phase reactions and a kinetic part for minerals precipitation. The model was fitted to dissolved calcium, magnesium, total inorganic carbon and phosphate. Results indicated that precipitation was dominated by the mineral struvite, forming together with varied and minor amounts of calcium phosphate and calcium carbonate. The model approach was noted to have the advantage of requiring a minimal number of fitted parameters, so the model was readily identifiable. Kinetic rate coefficients, which were statistically fitted, were generally in the range 0.35-11.6 h(-1) with confidence intervals of 10-80% relative. Confidence regions for the kinetic rate coefficients were often asymmetric with model-data residuals increasing more gradually with larger coefficient values. This suggests that a large kinetic coefficient could be used when actual measured data is lacking for a particular precipitate-matrix combination. Correlation between the kinetic rate coefficients of different minerals was low, indicating that parameter values for individual minerals could be independently fitted (keeping all other model parameters constant). Implementation was therefore relatively flexible, and would be readily expandable to include other

Modeling of asphaltene and wax precipitation

Energy Technology Data Exchange (ETDEWEB)

Chung, F.; Sarathi, P.; Jones, R.

1991-01-01

This research project was designed to focus on the development of a predictive technique for organic deposition during gas injection for petroleum EOR. A thermodynamic model has been developed to describe the effects of temperature, pressure, and composition on asphaltene precipitation. The proposed model combines regular solution theory with Flory-Huggins polymer solutions theory to predict maximum volume fractions of asphaltene dissolved in oil. The model requires evaluation of vapor-liquid equilibria, first using an equation of state followed by calculations of asphaltene solubility in the liquid-phase. A state-of-the-art technique for C{sub 7+} fraction characterization was employed in developing this model. The preliminary model developed in this work was able to predict qualitatively the trends of the effects of temperature, pressure, and composition. Since the mechanism of paraffinic wax deposition is different from that of asphaltene deposition, another thermodynamic model based on the solid-liquid solution theory was developed to predict the wax formation. This model is simple and can predict the wax appearance temperature with reasonable accuracy. Accompanying the modeling work, experimental studies were conducted to investigate the solubility of asphaltene in oil land solvents and to examine the effects of oil composition, CO{sub 2}, and solvent on asphaltene precipitation and its properties. This research focused on the solubility reversibility of asphaltene in oil and the precipitation caused by CO{sub 2} injection at simulated reservoir temperature and pressure conditions. These experiments have provided many observations about the properties of asphaltenes for further improvement of the model, but more detailed information about the properties of asphaltenes in solution is needed for the development of more reliable asphaltene characterization techniques. 50 refs., 8 figs., 7 tabs.

Comparing NEXRAD Operational Precipitation Estimates and Raingage Observations of Intense Precipitation in the Missouri River Basin.

Science.gov (United States)

Young, C. B.

2002-05-01

Accurate observation of precipitation is critical to the study and modeling of land surface hydrologic processes. NEXRAD radar-based precipitation estimates are increasingly used in field experiments, hydrologic modeling, and water and energy budget studies due to their high spatial and temporal resolution, national coverage, and perceived accuracy. Extensive development and testing of NEXRAD precipitation algorithms have been carried out in the Southern Plains. Previous studies (Young et al. 2000, Young et al. 1999, Smith et al. 1996) indicate that NEXRAD operational products tend to underestimate precipitation at light rain rates. This study investigates the performance of NEXRAD precipitation estimates of high-intensity rainfall, focusing on flood-producing storms in the Missouri River Basin. NEXRAD estimates for these storms are compared with data from multiple raingage networks, including NWS recording and non-recording gages and ALERT raingage data for the Kansas City metropolitan area. Analyses include comparisons of gage and radar data at a wide range of temporal and spatial scales. Particular attention is paid to the October 4th, 1998, storm that produced severe flooding in Kansas City. NOTE: The phrase `NEXRAD operational products' in this abstract includes precipitation estimates generated using the Stage III and P1 algorithms. Both of these products estimate hourly accumulations on the (approximately) 4 km HRAP grid.

Quantification of the impact of precipitation spatial distribution uncertainty on predictive uncertainty of a snowmelt runoff model

Science.gov (United States)

Jacquin, A. P.

2012-04-01

This study is intended to quantify the impact of uncertainty about precipitation spatial distribution on predictive uncertainty of a snowmelt runoff model. This problem is especially relevant in mountain catchments with a sparse precipitation observation network and relative short precipitation records. The model analysed is a conceptual watershed model operating at a monthly time step. The model divides the catchment into five elevation zones, where the fifth zone corresponds to the catchment's glaciers. Precipitation amounts at each elevation zone i are estimated as the product between observed precipitation at a station and a precipitation factor FPi. If other precipitation data are not available, these precipitation factors must be adjusted during the calibration process and are thus seen as parameters of the model. In the case of the fifth zone, glaciers are seen as an inexhaustible source of water that melts when the snow cover is depleted.The catchment case study is Aconcagua River at Chacabuquito, located in the Andean region of Central Chile. The model's predictive uncertainty is measured in terms of the output variance of the mean squared error of the Box-Cox transformed discharge, the relative volumetric error, and the weighted average of snow water equivalent in the elevation zones at the end of the simulation period. Sobol's variance decomposition (SVD) method is used for assessing the impact of precipitation spatial distribution, represented by the precipitation factors FPi, on the models' predictive uncertainty. In the SVD method, the first order effect of a parameter (or group of parameters) indicates the fraction of predictive uncertainty that could be reduced if the true value of this parameter (or group) was known. Similarly, the total effect of a parameter (or group) measures the fraction of predictive uncertainty that would remain if the true value of this parameter (or group) was unknown, but all the remaining model parameters could be fixed

Effect of land model ensemble versus coupled model ensemble on the simulation of precipitation climatology and variability

Science.gov (United States)

Wei, Jiangfeng; Dirmeyer, Paul A.; Yang, Zong-Liang; Chen, Haishan

2017-10-01

Through a series of model simulations with an atmospheric general circulation model coupled to three different land surface models, this study investigates the impacts of land model ensembles and coupled model ensemble on precipitation simulation. It is found that coupling an ensemble of land models to an atmospheric model has a very minor impact on the improvement of precipitation climatology and variability, but a simple ensemble average of the precipitation from three individually coupled land-atmosphere models produces better results, especially for precipitation variability. The generally weak impact of land processes on precipitation should be the main reason that the land model ensembles do not improve precipitation simulation. However, if there are big biases in the land surface model or land surface data set, correcting them could improve the simulated climate, especially for well-constrained regional climate simulations.

Importance of resolution and model configuration when downscaling extreme precipitation

Directory of Open Access Journals (Sweden)

Adrian J. Champion

2014-07-01

Full Text Available Dynamical downscaling is frequently used to investigate the dynamical variables of extra-tropical cyclones, for example, precipitation, using very high-resolution models nested within coarser resolution models to understand the processes that lead to intense precipitation. It is also used in climate change studies, using long timeseries to investigate trends in precipitation, or to look at the small-scale dynamical processes for specific case studies. This study investigates some of the problems associated with dynamical downscaling and looks at the optimum configuration to obtain the distribution and intensity of a precipitation field to match observations. This study uses the Met Office Unified Model run in limited area mode with grid spacings of 12, 4 and 1.5 km, driven by boundary conditions provided by the ECMWF Operational Analysis to produce high-resolution simulations for the Summer of 2007 UK flooding events. The numerical weather prediction model is initiated at varying times before the peak precipitation is observed to test the importance of the initialisation and boundary conditions, and how long the simulation can be run for. The results are compared to raingauge data as verification and show that the model intensities are most similar to observations when the model is initialised 12 hours before the peak precipitation is observed. It was also shown that using non-gridded datasets makes verification more difficult, with the density of observations also affecting the intensities observed. It is concluded that the simulations are able to produce realistic precipitation intensities when driven by the coarser resolution data.

Data Visualization and Analysis Tools for the Global Precipitation Measurement (GPM) Validation Network

Science.gov (United States)

Morris, Kenneth R.; Schwaller, Mathew

2010-01-01

The Validation Network (VN) prototype for the Global Precipitation Measurement (GPM) Mission compares data from the Tropical Rainfall Measuring Mission (TRMM) satellite Precipitation Radar (PR) to similar measurements from U.S. and international operational weather radars. This prototype is a major component of the GPM Ground Validation System (GVS). The VN provides a means for the precipitation measurement community to identify and resolve significant discrepancies between the ground radar (GR) observations and similar satellite observations. The VN prototype is based on research results and computer code described by Anagnostou et al. (2001), Bolen and Chandrasekar (2000), and Liao et al. (2001), and has previously been described by Morris, et al. (2007). Morris and Schwaller (2009) describe the PR-GR volume-matching algorithm used to create the VN match-up data set used for the comparisons. This paper describes software tools that have been developed for visualization and statistical analysis of the original and volume matched PR and GR data.

Oxidation and Precipitation of Sulfide in Sewer Networks

DEFF Research Database (Denmark)

Nielsen, A. H.

risks and corrosion of concrete and metals. Most of the problems relate to the buildup of hydrogen sulfide in the atmosphere of sewer networks. In this respect, the processes of the sulfur cycle are of fundamental importance in ultimately determining the extent of such problems. This study focused...... calibrated and validated against field data. In the extension to the WATS model, sulfur transformations were described by six processes: 1. Sulfide production taking place in the biofilm and sediments covering the permanently wetted sewer walls; 2. Biological sulfide oxidation in the permanently wetted...... to the sewer atmosphere, potentially resulting in concrete corrosion. The extended WATS model represents a major improvement over previously developed models for prediction of sulfide buildup in sewer networks. Compared to such models, the major processes governing sulfide buildup in sewer networks...

MODELING OF ISOTHERMAL PRECIPITATION KINETICS IN HSLA STEELS AND ITS APPLICATION

Institute of Scientific and Technical Information of China (English)

X.M. Zhao; D. Wu; L.Z. Zhang; Z.Y. Liu

2004-01-01

Microalloying elements in high-strength low-alloy steels, such as Nb, Ti and V, precipitate during hot-rolling processes. On the basis of classical theory of nucleation and growth, quantitative modeling of isothermal precipitation was developed, which was tested by the stress relaxation method, the calculated precipitation-time-temperature curve is in good agreements with the measured results, then the model was applied to predict the precipitation behavior during continuous cooling.

A precipitation-induced landslide susceptibility model for natural gas transmission pipelines

Energy Technology Data Exchange (ETDEWEB)

Finley, Jason P. [Fugro William Lettis and Associates, Inc., Valencia, California (United States); Slayter, David L.; Hitchcock, Chris S. [Fugro William Lettis and Associates, Inc., Walnut Creek, California (United States); Lee, Chih-Hung [Pacific Gas and Electric Company, Gas Systems Integrity Management, Walnut Creek, California (United States)

2010-07-01

Landslides related to heavy rainfall can cause extensive damage to natural gas transmission pipelines. Fugro William Lettis and Associates Inc. have developed and implemented a geographic information system (GIS) model that evaluates near real-time precipitation-induced landslide susceptibility. The model incorporates state-wide precipitation data and geologically-based landslide classifications to produce rapid landslide risk evaluation for Pacific Gas and Electric Company's (PGandE) gas transmission system during winter rain storms in California. The precipitation data include pre-storm event quantitative precipitation forecasts (QPF) and post-storm event quantitative precipitation estimate (QPE) from the United States National Oceanic and Atmospheric Administration (NOAA). The geologic classifications are based on slope, susceptible geologic formations, and the locations of historic or known landslide occurrences. Currently the model is calibrated using qualitative measures. This paper describes the development of the model algorithm and input data, model results, calibration efforts, and the on-going research and landslide collection warranted for continued refinement of the model.

Modeling of Jovian Auroral Polar Ion and Proton Precipitation

Science.gov (United States)

Houston, S. J.; Ozak, N. O.; Cravens, T.; Schultz, D. R.; Mauk, B.; Haggerty, D. K.; Young, J. T.

2017-12-01

Auroral particle precipitation dominates the chemical and physical environment of the upper atmospheres and ionospheres of the outer planets. Precipitation of energetic electrons from the middle magnetosphere is responsible for the main auroral oval at Jupiter, but energetic electron, proton, and ion precipitation take place in the polar caps. At least some of the ion precipitation is associated with soft X-ray emission with about 1 GW of power. Theoretical modeling has demonstrated that the incident sulfur and oxygen ion energies must exceed about 0.5 MeV/nucleon (u) in order to produce the measured X-ray emission. In this work we present a model of the transport of magnetospheric oxygen ions as they precipitate into Jupiter's polar atmosphere. We have revised and updated the hybrid Monte Carlo model originally developed by Ozak et al., 2010 to model the Jovian X-ray aurora. We now simulate a wider range of incident oxygen ion energies (10 keV/u - 5 MeV/u) and update the collision cross-sections to model the ionization of the atmospheric neutrals. The polar cap location of the emission and magnetosphere-ionosphere coupling both indicate the associated field-aligned currents must originate near the magnetopause or perhaps the distant tail. Secondary electrons produced in the upper atmosphere by ion precipitation could be accelerated upward to relativistic energies due to the same field-aligned potentials responsible for the downward ion acceleration. To further explore this, we simulate the effect of the secondary electrons generated from the heavy ion precipitation. We use a two-stream transport model that computes the secondary electron fluxes, their escape from the atmosphere, and characterization of the H2 Lyman-Werner band emission, including a predicted observable spectrum with the associated color ratio. Our model predicts that escaping electrons have an energy range from 1 eV to 6 keV, H2 band emission rates produced are on the order of 75 kR for an input

A comparison of monthly precipitation point estimates at 6 locations in Iran using integration of soft computing methods and GARCH time series model

Science.gov (United States)

Mehdizadeh, Saeid; Behmanesh, Javad; Khalili, Keivan

2017-11-01

Precipitation plays an important role in determining the climate of a region. Precise estimation of precipitation is required to manage and plan water resources, as well as other related applications such as hydrology, climatology, meteorology and agriculture. Time series of hydrologic variables such as precipitation are composed of deterministic and stochastic parts. Despite this fact, the stochastic part of the precipitation data is not usually considered in modeling of precipitation process. As an innovation, the present study introduces three new hybrid models by integrating soft computing methods including multivariate adaptive regression splines (MARS), Bayesian networks (BN) and gene expression programming (GEP) with a time series model, namely generalized autoregressive conditional heteroscedasticity (GARCH) for modeling of the monthly precipitation. For this purpose, the deterministic (obtained by soft computing methods) and stochastic (obtained by GARCH time series model) parts are combined with each other. To carry out this research, monthly precipitation data of Babolsar, Bandar Anzali, Gorgan, Ramsar, Tehran and Urmia stations with different climates in Iran were used during the period of 1965-2014. Root mean square error (RMSE), relative root mean square error (RRMSE), mean absolute error (MAE) and determination coefficient (R2) were employed to evaluate the performance of conventional/single MARS, BN and GEP, as well as the proposed MARS-GARCH, BN-GARCH and GEP-GARCH hybrid models. It was found that the proposed novel models are more precise than single MARS, BN and GEP models. Overall, MARS-GARCH and BN-GARCH models yielded better accuracy than GEP-GARCH. The results of the present study confirmed the suitability of proposed methodology for precise modeling of precipitation.

Spatial interpolation schemes of daily precipitation for hydrologic modeling

Science.gov (United States)

Hwang, Y.; Clark, M.R.; Rajagopalan, B.; Leavesley, G.

2012-01-01

Distributed hydrologic models typically require spatial estimates of precipitation interpolated from sparsely located observational points to the specific grid points. We compare and contrast the performance of regression-based statistical methods for the spatial estimation of precipitation in two hydrologically different basins and confirmed that widely used regression-based estimation schemes fail to describe the realistic spatial variability of daily precipitation field. The methods assessed are: (1) inverse distance weighted average; (2) multiple linear regression (MLR); (3) climatological MLR; and (4) locally weighted polynomial regression (LWP). In order to improve the performance of the interpolations, the authors propose a two-step regression technique for effective daily precipitation estimation. In this simple two-step estimation process, precipitation occurrence is first generated via a logistic regression model before estimate the amount of precipitation separately on wet days. This process generated the precipitation occurrence, amount, and spatial correlation effectively. A distributed hydrologic model (PRMS) was used for the impact analysis in daily time step simulation. Multiple simulations suggested noticeable differences between the input alternatives generated by three different interpolation schemes. Differences are shown in overall simulation error against the observations, degree of explained variability, and seasonal volumes. Simulated streamflows also showed different characteristics in mean, maximum, minimum, and peak flows. Given the same parameter optimization technique, LWP input showed least streamflow error in Alapaha basin and CMLR input showed least error (still very close to LWP) in Animas basin. All of the two-step interpolation inputs resulted in lower streamflow error compared to the directly interpolated inputs. ?? 2011 Springer-Verlag.

Development and evaluation of neural network models to estimate daily solar radiation at Córdoba, Argentina

International Nuclear Information System (INIS)

Bocco, M.

2006-01-01

The objective of this work was to develop neural network models of backpropagation type to estimate solar radiation based on extraterrestrial radiation data, daily temperature range, precipitation, cloudiness and relative sunshine duration. Data from Córdoba, Argentina, were used for development and validation. The behaviour and adjustment between values observed and estimates obtained by neural networks for different combinations of input were assessed. These estimations showed root mean square error between 3.15 and 3.88 MJ m -2 d -1 . The latter corresponds to the model that calculates radiation using only precipitation and daily temperature range. In all models, results show good adjustment to seasonal solar radiation. These results allow inferring the adequate performance and pertinence of this methodology to estimate complex phenomena, such as solar radiation [pt

Evaluation of precipitation extremes over the Asian domain: observation and modelling studies

Science.gov (United States)

Kim, In-Won; Oh, Jaiho; Woo, Sumin; Kripalani, R. H.

2018-04-01

In this study, a comparison in the precipitation extremes as exhibited by the seven reference datasets is made to ascertain whether the inferences based on these datasets agree or they differ. These seven datasets, roughly grouped in three categories i.e. rain-gauge based (APHRODITE, CPC-UNI), satellite-based (TRMM, GPCP1DD) and reanalysis based (ERA-Interim, MERRA, and JRA55), having a common data period 1998-2007 are considered. Focus is to examine precipitation extremes in the summer monsoon rainfall over South Asia, East Asia and Southeast Asia. Measures of extreme precipitation include the percentile thresholds, frequency of extreme precipitation events and other quantities. Results reveal that the differences in displaying extremes among the datasets are small over South Asia and East Asia but large differences among the datasets are displayed over the Southeast Asian region including the maritime continent. Furthermore, precipitation data appear to be more consistent over East Asia among the seven datasets. Decadal trends in extreme precipitation are consistent with known results over South and East Asia. No trends in extreme precipitation events are exhibited over Southeast Asia. Outputs of the Coupled Model Intercomparison Project Phase 5 (CMIP5) simulation data are categorized as high, medium and low-resolution models. The regions displaying maximum intensity of extreme precipitation appear to be dependent on model resolution. High-resolution models simulate maximum intensity of extreme precipitation over the Indian sub-continent, medium-resolution models over northeast India and South China and the low-resolution models over Bangladesh, Myanmar and Thailand. In summary, there are differences in displaying extreme precipitation statistics among the seven datasets considered here and among the 29 CMIP5 model data outputs.

Daily precipitation statistics in regional climate models

DEFF Research Database (Denmark)

Frei, Christoph; Christensen, Jens Hesselbjerg; Déqué, Michel

2003-01-01

An evaluation is undertaken of the statistics of daily precipitation as simulated by five regional climate models using comprehensive observations in the region of the European Alps. Four limited area models and one variable-resolution global model are considered, all with a grid spacing of 50 km...

Precipitation collector bias and its effects on temporal trends and spatial variability in National Atmospheric Deposition Program/National Trends Network data

Science.gov (United States)

Wetherbee, Gregory A.

2017-01-01

Precipitation samples have been collected by the National Atmospheric Deposition Program's (NADP) National Trends Network (NTN) using the Aerochem Metrics Model 301 (ACM) collector since 1978. Approximately one-third of the NTN ACM collectors have been replaced with N-CON Systems, Inc. Model ADS 00-120 (NCON) collectors. Concurrent data were collected over 6 years at 12 NTN sites using colocated ACM and NCON collectors in various precipitation regimes. Linear regression models of the colocated data were used to adjust for relative bias between the collectors. Replacement of ACM collectors with NCON collectors resulted in shifts in 10-year seasonal precipitation-weighted mean concentration (PWMC) trend slopes for: cations (−0.001 to −0.007 mgL−1yr−1), anions (−0.009 to −0.028 mgL−1yr−1), and hydrogen ion (+0.689 meqL-1yr−1). Larger shifts in NO3− and SO4−2 seasonal PWMC trend slopes were observed in the Midwest and Northeast US, where concentrations are generally higher than in other regions. Geospatial analysis of interpolated concentration rasters indicated regions of accentuated variability introduced by incorporation of NCON collectors into the NTN.

Mathematical modeling and simulation of nanopore blocking by precipitation

KAUST Repository

Wolfram, M-T

2010-10-29

High surface charges of polymer pore walls and applied electric fields can lead to the formation and subsequent dissolution of precipitates in nanopores. These precipitates block the pore, leading to current fluctuations. We present an extended Poisson-Nernst-Planck system which includes chemical reactions of precipitation and dissolution. We discuss the mathematical modeling and present 2D numerical simulations. © 2010 IOP Publishing Ltd.

Slovenian Network of Isotopes in Precipitation (SLONIP) - a review of activities in the period 1981-2015

Science.gov (United States)

Vreča, Polona; Kanduč, Tjaša; Kocman, David; Lojen, Sonja; Štrok, Marko; Robinson, Johanna Amalia

2017-04-01

The importance of collecting data on the water isotope composition of precipitation in the frame of the Global Network of Isotopes in Precipitation (GNIP) has been steadily increasing since it was initiated by the IAEA and the WMO in 1958, particularly in the last decade (Terzer et al., 2013). GNIP provides an important database for water resources management, verifying and improving atmospheric circulation models, studying climates and the interactions between water in the atmosphere and the biosphere, providing baseline information for the authentication of commodities, etc. Geographical diversity of Slovenia influences the climate and also the water cycle considerably, therefore monitoring of isotopes in precipitation is of particular interest. A review on monitoring of isotopes in precipitation was performed and information about sampling, analytical methods, available data and their evaluation was collected for the period 1981-2015. The first regular and systematic monitoring began in 1981 in Ljubljana (Pezdič, 1999). Later, a programme of collecting new data at a higher spatial density and temporal frequency in different parts of the country by different research groups has been initiated and was extended several times. Consequently, the number of sampling locations has grown within Slovenian Network of Isotopes in Precipitation (SLONIP) and altogether isotopes were monitored at more than 30 different locations countrywide (Vreča and Malenšek, 2016). However, the network is still not a part of a national monitoring programme, such as that operating in some European countries, for example, in Switzerland (Schürch et al., 2003). Only part of Slovenian data is available in GNIP database. Based on the collected data, we identified gaps in the research and made recommendations for future monitoring in the frame of the SLONIP. The list of main gaps includes limited information about sampling (e.g. missing coordinates, type of collector, period, frequency

Modelling and on-line estimation of zinc sulphide precipitation in

NARCIS (Netherlands)

Grootscholten, T.I.M.; Keesman, K.J.; Lens, P.N.L.

2008-01-01

In this paper the ZnS precipitation in a continuously stirred tank reactor (CSTR) is modelled using mass balances. The dynamics analysis of the model reveals that the ZnS precipitation shows a two time-scales behaviour with inherent numerical stability problems, which therefore needs special

Dynamic Modelling and Identification of Precipitation Reactions in Full-Scale WWTP

DEFF Research Database (Denmark)

Mbamba, Christian Kazadi; Tait, Stephan; Flores-Alsina, Xavier

, this paper evaluates plant-wide modelling of precipitation reactions using a generic approach integrated within activated sludge and anaerobic models. Preliminary results of anaerobic digester sludge in batch system suggest that the model is able to simulate the dynamics of precipitation reactions. Kinetic...

Precipitation model in microalloyed steels both isothermal and continuous cooling conditions

International Nuclear Information System (INIS)

Medina, S. F.; Quispe, A.; Gomez, M.

2015-01-01

Niobium and vanadium precipitates (nitrides and carbides) can inhibit the static recrystallization of austenite but this does not happen for Ti, which form nitrides at high temperatures. RPTT diagrams show the interaction between recrystallization and precipitation allowing study the strain induced precipitation kinetics and precipitate coarsening. Based on Dutta and Sellars expression for the start of strain-induced precipitation in microalloyed steels, a new model has been constructed which takes into account the influence of variables such as microalloying element percentages, strain, temperature, strain rate and grain size. Recrystallization- Precipitation-Time-Temperature (RPTT) diagrams have been plotted thanks to a new experimental study carried out by means of hot torsion tests on approximately twenty microalloyed steels with different Nb, V and Ti contents. Mathematical analysis of the results recommends the modification of some parameters such as the supersaturation ratio (ks) and constant B, which is no longer a constant but a function of ks. The expressions are now more consistent and predict the Precipitation-Time-Temperature (PTT) curves with remarkable accuracy. The model for strain-induced precipitation kinetics is completed by means of Avramis equation. Finally, the model constructed in isothermal testing conditions, it has been converted to continuous cooling conditions in order to apply it in hot rolling. (Author)

Modeling of hydride precipitation and re-orientation

Energy Technology Data Exchange (ETDEWEB)

Tikare, Veena [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Weck, Philippe F. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Mitchell, John Anthony [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2015-09-18

In this report, we present a thermodynamic-­based model of hydride precipitation in Zr-based claddings. The model considers the state of the cladding immediately following drying, after removal from cooling-pools, and presents the evolution of precipitate formation upon cooling as follows: The pilgering process used to form Zr-based cladding imparts strong crystallographic and grain shape texture, with the basal plane of the hexagonal α-Zr grains being strongly aligned in the rolling-­direction and the grains are elongated with grain size being approximately twice as long parallel to the rolling direction, which is also the long axis of the tubular cladding, as it is in the orthogonal directions.

Evaluation of high resolution spatio-temporal precipitation extremes from a stochastic weather generator

DEFF Research Database (Denmark)

Sørup, Hjalte Jomo Danielsen; Christensen, O. B.; Arnbjerg-Nielsen, Karsten

2017-01-01

Spatio-temporal rainfall is modelled for the North-Eastern part of Zealand (Denmark) using the Spatio-Temporal Neyman-Scott Rectangular Pulses model as implemented in the RainSim software. Hourly precipitation series for fitting the model are obtained from a dense network of tipping bucket rain...... gauges in the model area. The spatiotemporal performance of the model with respect to precipitation extremes is evaluated in the points of a 2x2 km regular grid covering the full model area. The model satisfactorily reproduces the extreme behaviour of the observed precipitation with respect to event...... intensity levels and unconditional spatial correlation when evaluated using an event based ranking approach at point scale and an advanced spatiotemporal coupling of extreme events. Prospectively the model can be used as a tool to evaluate the impact of climate change without relying on precipitation output...

Uncertainty of global summer precipitation in the CMIP5 models: a comparison between high-resolution and low-resolution models

Science.gov (United States)

Huang, Danqing; Yan, Peiwen; Zhu, Jian; Zhang, Yaocun; Kuang, Xueyuan; Cheng, Jing

2018-04-01

The uncertainty of global summer precipitation simulated by the 23 CMIP5 CGCMs and the possible impacts of model resolutions are investigated in this study. Large uncertainties exist over the tropical and subtropical regions, which can be mainly attributed to convective precipitation simulation. High-resolution models (HRMs) and low-resolution models (LRMs) are further investigated to demonstrate their different contributions to the uncertainties of the ensemble mean. It shows that the high-resolution model ensemble means (HMME) and low-resolution model ensemble mean (LMME) mitigate the biases between the MME and observation over most continents and oceans, respectively. The HMME simulates more precipitation than the LMME over most oceans, but less precipitation over some continents. The dominant precipitation category in the HRMs (LRMs) is the heavy precipitation (moderate precipitation) over the tropic regions. The combinations of convective and stratiform precipitation are also quite different: the HMME has much higher ratio of stratiform precipitation while the LMME has more convective precipitation. Finally, differences in precipitation between the HMME and LMME can be traced to their differences in the SST simulations via the local and remote air-sea interaction.

Future Simulated Intensification of Precipitation Extremes, CMIP5 Model Uncertainties and Dependencies

Science.gov (United States)

Bador, M.; Donat, M.; Geoffroy, O.; Alexander, L. V.

2017-12-01

Precipitation intensity during extreme events is expected to increase with climate change. Throughout the 21st century, CMIP5 climate models project a general increase in annual extreme precipitation in most regions. We investigate how robust this future increase is across different models, regions and seasons. We find that there is strong similarity in extreme precipitation changes between models that share atmospheric physics, reducing the ensemble of 27 models to 14 independent projections. We find that future simulated extreme precipitation increases in most models in the majority of land grid cells located in the dry, intermediate and wet regions according to each model's precipitation climatology. These increases significantly exceed the range of natural variability estimated from long equilibrium control runs. The intensification of extreme precipitation across the entire spectrum of dry to wet regions is particularly robust in the extra-tropics in both wet and dry season, whereas uncertainties are larger in the tropics. The CMIP5 ensemble therefore indicates robust future intensification of annual extreme rainfall in particular in extra-tropical regions. Generally, the CMIP5 robustness is higher during the dry season compared to the wet season and the annual scale, but inter-model uncertainties in the tropics remain important.

Model Fitting for Predicted Precipitation in Darwin: Some Issues with Model Choice

Science.gov (United States)

Farmer, Jim

2010-01-01

In Volume 23(2) of the "Australian Senior Mathematics Journal," Boncek and Harden present an exercise in fitting a Markov chain model to rainfall data for Darwin Airport (Boncek & Harden, 2009). Days are subdivided into those with precipitation and precipitation-free days. The author abbreviates these labels to wet days and dry days.…

Modelled Precipitation Over Greenland

Data.gov (United States)

National Aeronautics and Space Administration — This data set includes the annual total precipitation from 1985 to 1999 and monthly total precipitation from January 1985 to December 1999. The data is derived from...

Changes in precipitation extremes projected by a 20-km mesh global atmospheric model

Directory of Open Access Journals (Sweden)

Akio Kitoh

2016-03-01

Full Text Available High-resolution modeling is necessary to project weather and climate extremes and their future changes under global warming. A global high-resolution atmospheric general circulation model with grid size about 20 km is able to reproduce climate fields as well as regional-scale phenomena such as monsoonal rainfall, tropical and extratropical cyclones, and heavy precipitation. This 20-km mesh model is applied to project future changes in weather and climate extremes at the end of the 21st century with four different spatial patterns in sea surface temperature (SST changes: one with the mean SST changes by the 28 models of the Coupled Model Intercomparison Project Phase 5 (CMIP5 under the Representative Concentration Pathways (RCP-8.5 scenario, and the other three obtained from a cluster analysis, in which tropical SST anomalies derived from the 28 CMIP5 models were grouped. Here we focus on future changes in regional precipitation and its extremes. Various precipitation indices averaged over the Twenty-two regional land domains are calculated. Heavy precipitation indices (maximum 5-day precipitation total and maximum 1-day precipitation total increase in all regional domains, even where mean precipitation decrease (Southern Africa, South Europe/Mediterranean, Central America. South Asia is the domain of the largest extreme precipitation increase. In some domains, different SST patterns result in large precipitation changes, possibly related to changes in large-scale circulations in the tropical Pacific.

Test particle modeling of wave-induced energetic electron precipitation

International Nuclear Information System (INIS)

Chang, H.C.; Inan, U.S.

1985-01-01

A test particle computer model of the precipitation of radiation belt electrons is extended to compute the dynamic energy spectrum of transient electron fluxes induced by short-duration VLF wave packets traveling along the geomagnetic field lines. The model is adapted to estimate the count rate and associated spectrum of precipitated electrons that would be observed by satellite-based particle detectors with given geometric factor and orientation with respect to the magnetic field. A constant-frequency wave pulse and a lightning-induced whistler wave packet are used as examples of the stimulating wave signals. The effects of asymmetry of particle mirror heights in the two hemispheres and the atmospheric backscatter of loss cone particles on the computed precipitated fluxes are discussed

Precipitation interpolation in mountainous areas

Science.gov (United States)

Kolberg, Sjur

2015-04-01

Different precipitation interpolation techniques as well as external drift covariates are tested and compared in a 26000 km2 mountainous area in Norway, using daily data from 60 stations. The main method of assessment is cross-validation. Annual precipitation in the area varies from below 500 mm to more than 2000 mm. The data were corrected for wind-driven undercatch according to operational standards. While temporal evaluation produce seemingly acceptable at-station correlation values (on average around 0.6), the average daily spatial correlation is less than 0.1. Penalising also bias, Nash-Sutcliffe R2 values are negative for spatial correspondence, and around 0.15 for temporal. Despite largely violated assumptions, plain Kriging produces better results than simple inverse distance weighting. More surprisingly, the presumably 'worst-case' benchmark of no interpolation at all, simply averaging all 60 stations for each day, actually outperformed the standard interpolation techniques. For logistic reasons, high altitudes are under-represented in the gauge network. The possible effect of this was investigated by a) fitting a precipitation lapse rate as an external drift, and b) applying a linear model of orographic enhancement (Smith and Barstad, 2004). These techniques improved the results only marginally. The gauge density in the region is one for each 433 km2; higher than the overall density of the Norwegian national network. Admittedly the cross-validation technique reduces the gauge density, still the results suggest that we are far from able to provide hydrological models with adequate data for the main driving force.

Local difference measures between complex networks for dynamical system model evaluation.

Science.gov (United States)

Lange, Stefan; Donges, Jonathan F; Volkholz, Jan; Kurths, Jürgen

2015-01-01

A faithful modeling of real-world dynamical systems necessitates model evaluation. A recent promising methodological approach to this problem has been based on complex networks, which in turn have proven useful for the characterization of dynamical systems. In this context, we introduce three local network difference measures and demonstrate their capabilities in the field of climate modeling, where these measures facilitate a spatially explicit model evaluation.Building on a recent study by Feldhoff et al. [8] we comparatively analyze statistical and dynamical regional climate simulations of the South American monsoon system [corrected]. types of climate networks representing different aspects of rainfall dynamics are constructed from the modeled precipitation space-time series. Specifically, we define simple graphs based on positive as well as negative rank correlations between rainfall anomaly time series at different locations, and such based on spatial synchronizations of extreme rain events. An evaluation against respective networks built from daily satellite data provided by the Tropical Rainfall Measuring Mission 3B42 V7 reveals far greater differences in model performance between network types for a fixed but arbitrary climate model than between climate models for a fixed but arbitrary network type. We identify two sources of uncertainty in this respect. Firstly, climate variability limits fidelity, particularly in the case of the extreme event network; and secondly, larger geographical link lengths render link misplacements more likely, most notably in the case of the anticorrelation network; both contributions are quantified using suitable ensembles of surrogate networks. Our model evaluation approach is applicable to any multidimensional dynamical system and especially our simple graph difference measures are highly versatile as the graphs to be compared may be constructed in whatever way required. Generalizations to directed as well as edge- and node

Refinement of the daily precipitation simulated by the CMIP5 models over the north of the Northeast of Brazil

Directory of Open Access Journals (Sweden)

Gyrlene Aparecida Mendes da Silva

2015-04-01

Full Text Available The ability of the Artificial Neural Network (ANN and the Multiple Linear Regression (MLR in reproducing the area-average observed daily precipitation during the rainy season (Feb-Mar-Apr over the north of the Northeast of Brazil (NEB is examined. For the present climate of Dec-Jan-Feb from 1963 to 2003 period these statistical models are developed and validated using the observed daily precipitation and simulated from the historical outputs of 4 models of the fifth phase of the Coupled Model Intercomparison Project (CMIP5. The simulations from all the models during DJF and FMA seasons show an anomalous intensification of the ITCZ and southward displacement in comparison with the climatology. Correlations of 0.54, 0.66 and 0.66 are found between the simulated daily precipitation of the CCSM4, GFDL_ESM2M and MIROC_ESM models during DJF season and the observed values during FMA season. Only the CCSM4 model displays a slightly reasonable agreement with the observations. A comparison between the statistical downscaling using the nonlinear (ANN and linear model (MLR to identify the one most suitable for the analysis of daily precipitation was made. The ANN technique provides more ability to predict the present climate when compared to MLR technique. Based on this result, we examined the accuracy of the ANN model in project the changes for the future climate period from 2055 to 2095 over the same study region. For instance, a comparison between the daily precipitation changes projected indirectly from the ANN during Feb-Mar-Apr with those projected directly from the CMIP5 models forced by RCP 8.5 scenario is made. The results suggest that ANN model weights the CMIP5 projections according to the each model ability in simulating the present climate (and its variability. In others, the ANN model is a potentially promising approach to use as a complementary tool to improvement of the seasonal numerical simulations.

Modeling of present and Eemian stable water isotopes in precipitation

DEFF Research Database (Denmark)

Sjolte, Jesper

The subject of this thesis is the modeling of the isotopic temperature proxies d18O, dD and deuterium excess in precipitation. Two modeling studies were carried out, one using the regional climate model, and one using a global climate model. In the regional study the model was run for the period ...... the modeled isotopes do not agree with ice core data. The discrepancy between the model output and the ice core data is attributed to the boundary conditions, where changes in ice sheets and vegetation have not been accounted for.......The subject of this thesis is the modeling of the isotopic temperature proxies d18O, dD and deuterium excess in precipitation. Two modeling studies were carried out, one using the regional climate model, and one using a global climate model. In the regional study the model was run for the period...... 1959 to 2001 using meteorological data and a domain including Greenland and the surrounding North Atlantic. The model was found to reproduce the observed seasonal variability of temperature and precipitation well. In comparison with ice core data from Greenland and observations from coastal stations...

Development of Bread Board Model of TRMM precipitation radar

Science.gov (United States)

Okamoto, Ken'ichi; Ihara, Toshio; Kumagai, Hiroshi

The active array radar was selected as a reliable candidate for the TRMM (Tropical Rainfall Measuring Mission) precipitation radar after the trade off studies performed by Communications Research Laboratory (CRL) in the US-Japan joint feasibility study of TRMM in 1987-1988. Main system parameters and block diagram for TRMM precipitation radar are shown as the result of feasibility study. CRL developed key devices for the active array precipitation radar such as 8-element slotted waveguide array antenna, the 5 bit PIN diode phase shifters, solid state power amplifiers and low noise amplifiers in 1988-1990. Integration of these key devices was made to compose 8-element Bread Board Model of TRMM precipitation radar.

assessment of neural networks performance in modeling rainfall ...

African Journals Online (AJOL)

Sholagberu

neural network architecture for precipitation prediction of Myanmar, World Academy of. Science, Engineering and Technology, 48, pp. 130 – 134. Kumarasiri, A.D. and Sonnadara, D.U.J. (2006). Rainfall forecasting: an artificial neural network approach, Proceedings of the Technical Sessions,. 22, pp. 1-13 Institute of Physics ...

Two modelling approaches to water-quality simulation in a flooded iron-ore mine (Saizerais, Lorraine, France): a semi-distributed chemical reactor model and a physically based distributed reactive transport pipe network model.

Science.gov (United States)

Hamm, V; Collon-Drouaillet, P; Fabriol, R

2008-02-19

The flooding of abandoned mines in the Lorraine Iron Basin (LIB) over the past 25 years has degraded the quality of the groundwater tapped for drinking water. High concentrations of dissolved sulphate have made the water unsuitable for human consumption. This problematic issue has led to the development of numerical tools to support water-resource management in mining contexts. Here we examine two modelling approaches using different numerical tools that we tested on the Saizerais flooded iron-ore mine (Lorraine, France). A first approach considers the Saizerais Mine as a network of two chemical reactors (NCR). The second approach is based on a physically distributed pipe network model (PNM) built with EPANET 2 software. This approach considers the mine as a network of pipes defined by their geometric and chemical parameters. Each reactor in the NCR model includes a detailed chemical model built to simulate quality evolution in the flooded mine water. However, in order to obtain a robust PNM, we simplified the detailed chemical model into a specific sulphate dissolution-precipitation model that is included as sulphate source/sink in both a NCR model and a pipe network model. Both the NCR model and the PNM, based on different numerical techniques, give good post-calibration agreement between the simulated and measured sulphate concentrations in the drinking-water well and overflow drift. The NCR model incorporating the detailed chemical model is useful when a detailed chemical behaviour at the overflow is needed. The PNM incorporating the simplified sulphate dissolution-precipitation model provides better information of the physics controlling the effect of flow and low flow zones, and the time of solid sulphate removal whereas the NCR model will underestimate clean-up time due to the complete mixing assumption. In conclusion, the detailed NCR model will give a first assessment of chemical processes at overflow, and in a second time, the PNM model will provide more

Fronts and precipitation in CMIP5 models for the austral winter of the Southern Hemisphere

Science.gov (United States)

Blázquez, Josefina; Solman, Silvina A.

2018-04-01

Wintertime fronts climatology and the relationship between fronts and precipitation as depicted by a group of CMIP5 models are evaluated over the Southern Hemisphere (SH). The frontal activity is represented by an index that takes into account the vorticity, the gradient of temperature and the specific humidity at the 850 hPa level. ERA-Interim reanalysis and GPCP datasets are used to assess the performance of the models in the present climate. Overall, it is found that the models can reproduce adequately the main features of frontal activity and front frequency over the SH. The total precipitation is overestimated in most of the models, especially the maximum values over the mid latitudes. This overestimation could be related to the high values of precipitation frequency that are identified in some of the models evaluated. The relationship between fronts and precipitation has also been evaluated in terms of both frequency of frontal precipitation and percentage of precipitation due to fronts. In general terms, the models overestimate the proportion between frontal and total precipitation. In contrast with frequency of total precipitation, the frequency of frontal precipitation is well reproduced by the models, with the higher values located at the mid latitudes. The results suggest that models represent very well the dynamic forcing (fronts) and the frequency of frontal precipitation, though the amount of precipitation due to fronts is overestimated.

Improved Regional Climate Model Simulation of Precipitation by a Dynamical Coupling to a Hydrology Model

DEFF Research Database (Denmark)

Larsen, Morten Andreas Dahl; Drews, Martin; Hesselbjerg Christensen, Jens

convective precipitation systems. As a result climate model simulations let alone future projections of precipitation often exhibit substantial biases. Here we show that the dynamical coupling of a regional climate model to a detailed fully distributed hydrological model - including groundwater-, overland...... of local precipitation dynamics are seen for time scales of app. Seasonal duration and longer. We show that these results can be attributed to a more complete treatment of land surface feedbacks. The local scale effect on the atmosphere suggests that coupled high-resolution climate-hydrology models...... including a detailed 3D redistribution of sub- and land surface water have a significant potential for improving climate projections even diminishing the need for bias correction in climate-hydrology studies....

MODELING OF STRAIN-INDUCED PRECIPITATION KINETICS IN Nb MICROALLOYED STEELS

Institute of Scientific and Technical Information of China (English)

X.G. Zhou; Z.Y. Liu; D. Wu; Z.Li; C.M. Li

2006-01-01

On the basis of the thermodynamic calculation of precipitation and considering the effect of strain on the precipitation behavior and chemical composition (Si and Mn), the kinetics of precipitation from austenite has been investigated for different temperatures and strains. Nucleation theory and the solubility product of niobium, carbon, and nitrogen in austenite have been used to derive equations for the start time of precipitation as a function of temperature and composition. The value of n in Avrami equation was determined using the available experimental data from the published reports, which indicated that n is a constant independent of temperature and the end time of precipitation is a function of n and the start time of precipitation. The values of the start time and end time of precipitation predicted by the new model are compared with the experimental values and a good agreement was obtained between both.

Summary of Mercury and Trace Element Results in Precipitation from the Culpeper, Virginia, Mercury Deposition Network Site (VA-08), 2002-2006

Science.gov (United States)

Engle, Mark A.; Kolker, Allan; Mose, Douglas E.; East, Joseph A.; McCord, Jamey D.

2008-01-01

The VA-08 Mercury Deposition Network (MDN) site, southwest of Culpeper, Virginia, was established in autumn of 2002. This site, along with nearby VA-28 (~31 km west) at Big Meadows in Shenandoah National Park, fills a spatial gap in the Mid-Atlantic region of the MDN network and provides Hg deposition data immediately west of the Washington, D.C., metropolitan area. Results for the Culpeper site from autumn of 2002 to the end of 2006 suggest that the highest mercury (Hg) deposition (up to 5.0 ug/m2 per quarter of the 6.5-12.6 ug/m2 annual Hg deposition) is measured during the second and third quarters of the year (April-September). This is a result of both elevated Hg precipitation concentrations (up to 27 ng/L) and greater precipitation during these months. The data also exhibit a general statistically significant (peffect during larger precipitation events, especially during winter and spring. Comparison of results between the Culpeper and Big Meadows sites indicates that although quarterly Hg deposition was not significantly different (panalysis of the Hg and trace metal data identified 3 primary source categories, each with large loadings of characteristic elements: 1) Ca, Al, Mg, Sr, La, and Ce (crustal sources); 2) V, Na, and Ni (local wintertime heating oil); and 3) Zn, Cd, Mn, and Hg (regional anthropogenic emission sources). HYSPLIT air mass trajectory modeling and enrichment factor calculations are consistent with this interpretation. A preliminary source attribution model suggests that ~51% of the Hg in wet deposition is due to regional anthropogenic sources, while crustal sources and local oil combustion account for 9.5% and <1%, respectively. This calculation implies that the global Hg burden accounts for ~40% of the Hg in wet deposition.

Validation of precipitation over Japan during 1985-2004 simulated by three regional climate models and two multi-model ensemble means

Energy Technology Data Exchange (ETDEWEB)

Ishizaki, Yasuhiro [Meteorological Research Institute, Tsukuba (Japan); National Institute for Environmental Studies, Tsukuba (Japan); Nakaegawa, Toshiyuki; Takayabu, Izuru [Meteorological Research Institute, Tsukuba (Japan)

2012-07-15

We dynamically downscaled Japanese reanalysis data (JRA-25) for 60 regions of Japan using three regional climate models (RCMs): the Non-Hydrostatic Regional Climate Model (NHRCM), modified RAMS version 4.3 (NRAMS), and modified Weather Research and Forecasting model (TWRF). We validated their simulations of the precipitation climatology and interannual variations of summer and winter precipitation. We also validated precipitation for two multi-model ensemble means: the arithmetic ensemble mean (AEM) and an ensemble mean weighted according to model reliability. In the 60 regions NRAMS simulated both the winter and summer climatological precipitation better than JRA-25, and NHRCM simulated the wintertime precipitation better than JRA-25. TWRF, however, overestimated precipitation in the 60 regions in both the winter and summer, and NHRCM overestimated precipitation in the summer. The three RCMs simulated interannual variations, particularly summer precipitation, better than JRA-25. AEM simulated both climatological precipitation and interannual variations during the two seasons more realistically than JRA-25 and the three RCMs overall, but the best RCM was often superior to the AEM result. In contrast, the weighted ensemble mean skills were usually superior to those of the best RCM. Thus, both RCMs and multi-model ensemble means, especially multi-model ensemble means weighted according to model reliability, are powerful tools for simulating seasonal and interannual variability of precipitation in Japan under the current climate. (orig.)

Mixed precipitation occurrences over southern Québec, Canada, under warmer climate conditions using a regional climate model

Science.gov (United States)

Matte, Dominic; Thériault, Julie M.; Laprise, René

2018-05-01

Winter weather events with temperatures near 0°C are often associated with freezing rain. They can have major impacts on the society by causing power outages and disruptions to the transportation networks. Despite the catastrophic consequences of freezing rain, very few studies have investigated how their occurrences could evolve under climate change. This study aims to investigate the change of freezing rain and ice pellets over southern Québec using regional climate modeling at high resolution. The fifth-generation Canadian Regional Climate Model with climate scenario RCP 8.5 at 0.11° grid mesh was used. The precipitation types such as freezing rain, ice pellets or their combination are diagnosed using five methods (Cantin and Bachand, Bourgouin, Ramer, Czys and, Baldwin). The occurrences of the diagnosed precipitation types for the recent past (1980-2009) are found to be comparable to observations. The projections for the future scenario (2070-2099) suggested a general decrease in the occurrences of mixed precipitation over southern Québec from October to April. This is mainly due to a decrease in long-duration events (≥6 h ). Overall, this study contributes to better understand how the distribution of freezing rain and ice pellets might change in the future using high-resolution regional climate model.

Orographic precipitation at global and regional scales: Observational uncertainty and evaluation of 25-km global model simulations

Science.gov (United States)

Schiemann, Reinhard; Roberts, Charles J.; Bush, Stephanie; Demory, Marie-Estelle; Strachan, Jane; Vidale, Pier Luigi; Mizielinski, Matthew S.; Roberts, Malcolm J.

2015-04-01

Precipitation over land exhibits a high degree of variability due to the complex interaction of the precipitation generating atmospheric processes with coastlines, the heterogeneous land surface, and orography. Global general circulation models (GCMs) have traditionally had very limited ability to capture this variability on the mesoscale (here ~50-500 km) due to their low resolution. This has changed with recent investments in resolution and ensembles of multidecadal climate simulations of atmospheric GCMs (AGCMs) with ~25 km grid spacing are becoming increasingly available. Here, we evaluate the mesoscale precipitation distribution in one such set of simulations obtained in the UPSCALE (UK on PrACE - weather-resolving Simulations of Climate for globAL Environmental risk) modelling campaign with the HadGEM-GA3 AGCM. Increased model resolution also poses new challenges to the observational datasets used to evaluate models. Global gridded data products such as those provided by the Global Precipitation Climatology Project (GPCP) are invaluable for assessing large-scale features of the precipitation distribution but may not sufficiently resolve mesoscale structures. In the absence of independent estimates, the intercomparison of different observational datasets may be the only way to get some insight into the uncertainties associated with these observations. Here, we focus on mid-latitude continental regions where observations based on higher-density gauge networks are available in addition to the global data sets: Europe/the Alps, South and East Asia, and the continental US. The ability of GCMs to represent mesoscale variability is of interest in its own right, as climate information on this scale is required by impact studies. An additional motivation for the research proposed here arises from continuing efforts to quantify the components of the global radiation budget and water cycle. Recent estimates based on radiation measurements suggest that the global mean

A statistical intercomparison between 'urban' and 'rural' precipitation chemistry data from Greater Manchester and the two nearby secondary national network sites in the United Kingdom

Energy Technology Data Exchange (ETDEWEB)

Lee, D.S.; Longhurst, J.W.S. (Manchester Polytechnic, Manchester (United Kingdom). Acid Rain Information Centre, Dept. of Environmental and Graphical Studies)

1992-11-01

Precipitation chemistry data from a dense urban monitoring network in Greater Manchester, northwest England, were compared with interpolated values from the U.K. secondary national acid deposition monitoring network for the year 1988. Differences were found to be small. However, when data from individual sites from the Greater Manchester network were compared with data from the two nearest secondary national network sites, significant differences were found using simple and complex statistical analyses. Precipitation chemistry at rural sites could be similar to that at urban sites, but the sources of some ions were thought to be different. The synoptic-scale gradients of precipitation chemistry, as shown by the secondary national network, also accounted for some of the differences. 34 refs., 7 figs., 8 tabs.

Environmental isotope data no. 10: World survey of isotope concentration in precipitation (1988-1991). Report from a network

International Nuclear Information System (INIS)

1994-01-01

This is the tenth volume of the publication Environmental Isotope Data: World Survey of Isotope Concentration in Precipitation. This volume is primarily concerned with the concentration of the environmental isotopes (tritium, deuterium and oxygen-18) in monthly samples of precipitation taken by a global network of 169 stations in the period 1988 to 1991. Selected meteorological data, such as the amount of precipitation, mean water vapour pressure and surface air temperature, are also presented. Data before 1988 which were unavailable at the time of the earlier issues have also been included in the latter part of this volume as late reports. The data are being widely used in hydrological, hydrometeorological and climatological studies. 9 refs, 2 figs

Kinetics modeling of precipitation with characteristic shape during post-implantation annealing

Directory of Open Access Journals (Sweden)

Kun-Dar Li

2015-11-01

Full Text Available In this study, we investigated the precipitation with characteristic shape in the microstructure during post-implantation annealing via a theoretical modeling approach. The processes of precipitates formation and evolution during phase separation were based on a nucleation and growth mechanism of atomic diffusion. Different stages of the precipitation, including the nucleation, growth and coalescence, were distinctly revealed in the numerical simulations. In addition, the influences of ion dose, temperature and crystallographic symmetry on the processes of faceted precipitation were also demonstrated. To comprehend the kinetic mechanism, the simulation results were further analyzed quantitatively by the Kolmogorov-Johnson-Mehl-Avrami (KJMA equation. The Avrami exponents obtained from the regression curves varied from 1.47 to 0.52 for different conditions. With the increase of ion dose and temperature, the nucleation and growth of precipitations were expedited in accordance with the shortened incubation time and the raised coefficient of growth rate. A miscellaneous shape of precipitates in various crystallographic symmetry systems could be simulated through this anisotropic model. From the analyses of the kinetics, more fundamental information about the nucleation and growth mechanism of faceted precipitation during post-implantation annealing was acquired for future application.

Numerical simulation of Cr2N age-precipitation in high nitrogen stainless steels

International Nuclear Information System (INIS)

Dai, Q.X.; Yuan, Z.Z.; Luo, X.M.; Cheng, X.N.

2004-01-01

At the temperature raging from 700 to 950 deg. C, the Cr 2 N age-precipitation in high nitrogen austenitic stainless steels Fe24Mn18Cr3Ni0.62N was investigated in this paper. A qualitative mathematical model of Cr 2 N age-precipitation, ln t S = f (Me,1/T), was established based on the thermodynamics and kinetics and phase transformation theories. Satisfactory results were obtained by means of the test of artificial neural network. This mathematical model can be applied to the calculation design and predication of Cr 2 N age-precipitation in high nitrogen stainless steels

Effect of tropospheric models on derived precipitable water vapor over Southeast Asia

Science.gov (United States)

Rahimi, Zhoobin; Mohd Shafri, Helmi Zulhaidi; Othman, Faridah; Norman, Masayu

2017-05-01

An interesting subject in the field of GPS technology is estimating variation of precipitable water vapor (PWV). This estimation can be used as a data source to assess and monitor rapid changes in meteorological conditions. So far, numerous GPS stations are distributed across the world and the number of GPS networks is increasing. Despite these developments, a challenging aspect of estimating PWV through GPS networks is the need of tropospheric parameters such as temperature, pressure, and relative humidity (Liu et al., 2015). To estimate the tropospheric parameters, global pressure temperature (GPT) model developed by Boehm et al. (2007) is widely used in geodetic analysis for GPS observations. To improve the accuracy, Lagler et al. (2013) introduced GPT2 model by adding annual and semi-annual variation effects to GPT model. Furthermore, Boehm et al. (2015) proposed the GPT2 wet (GPT2w) model which uses water vapor pressure to improve the calculations. The global accuracy of GPT2 and GPT2w models has been evaluated by previous researches (Fund et al., 2011; Munekane and Boehm, 2010); however, investigations to assess the accuracy of global tropospheric models in tropical regions such as Southeast Asia is not sufficient. This study tests and examines the accuracy of GPT2w as one of the most recent versions of tropospheric models (Boehm et al., 2015). We developed a new regional model called Malaysian Pressure Temperature (MPT) model, and compared this model with GPT2w model. The compared results at one international GNSS service (IGS) station located in the south of Peninsula Malaysia shows that MPT model has a better performance than GPT2w model to produce PWV during monsoon season. According to the results, MPT has improved the accuracy of estimated pressure and temperature by 30% and 10%, respectively, in comparison with GPT2w model. These results indicate that MPT model can be a good alternative tool in the absence of meteorological sensors at GPS stations in

Small angle neutron scattering modeling of copper-rich precipitates in steel

International Nuclear Information System (INIS)

Spooner, S.

1997-11-01

The magnetic to nuclear scattering intensity ratio observed in the scattering from copper rich precipitates in irradiated pressure vessel steels is much smaller than the value of 11.4 expected for a pure copper precipitate in iron. A model for precipitates in pressure vessel steels which matches the observed scattering typically incorporates manganese, nickel, silicon and other elements and it is assumed that the precipitate is non-magnetic. In the present work consideration is given to the effect of composition gradients and ferromagnetic penetration into the precipitate on the small angle scattering cross section for copper rich clusters as distinguished from conventional precipitates. The calculation is an extension of a scattering model for micelles which consist of shells of varying scattering density. A discrepancy between recent SANS scattering experiments on pressure vessel steels was found to be related to applied magnetic field strength. The assumption of cluster structure and its relation to atom probe FIM findings as well as the effects of insufficient field for magnetic saturation is discussed

Mesoscale modeling of solute precipitation and radiation damage

Energy Technology Data Exchange (ETDEWEB)

Zhang, Yongfeng [Idaho National Lab. (INL), Idaho Falls, ID (United States); Schwen, Daniel [Idaho National Lab. (INL), Idaho Falls, ID (United States); Ke, Huibin [Idaho National Lab. (INL), Idaho Falls, ID (United States); Univ. of Wisconsin, Madison, WI (United States); Bai, Xianming [Idaho National Lab. (INL), Idaho Falls, ID (United States); Hales, Jason [Idaho National Lab. (INL), Idaho Falls, ID (United States)

2015-09-01

This report summarizes the low length scale effort during FY 2014 in developing mesoscale capabilities for microstructure evolution in reactor pressure vessels. During operation, reactor pressure vessels are subject to hardening and embrittlement caused by irradiation-induced defect accumulation and irradiation-enhanced solute precipitation. Both defect production and solute precipitation start from the atomic scale, and manifest their eventual effects as degradation in engineering-scale properties. To predict the property degradation, multiscale modeling and simulation are needed to deal with the microstructure evolution, and to link the microstructure feature to material properties. In this report, the development of mesoscale capabilities for defect accumulation and solute precipitation are summarized. Atomic-scale efforts that supply information for the mesoscale capabilities are also included.

CMIP5 model simulations of Ethiopian Kiremt-season precipitation: current climate and future changes

Science.gov (United States)

Li, Laifang; Li, Wenhong; Ballard, Tristan; Sun, Ge; Jeuland, Marc

2016-05-01

Kiremt-season (June-September) precipitation provides a significant water supply for Ethiopia, particularly in the central and northern regions. The response of Kiremt-season precipitation to climate change is thus of great concern to water resource managers. However, the complex processes that control Kiremt-season precipitation challenge the capability of general circulation models (GCMs) to accurately simulate precipitation amount and variability. This in turn raises questions about their utility for predicting future changes. This study assesses the impact of climate change on Kiremt-season precipitation using state-of-the-art GCMs participating in the Coupled Model Intercomparison Project Phase 5. Compared to models with a coarse resolution, high-resolution models (horizontal resolution <2°) can more accurately simulate precipitation, most likely due to their ability to capture precipitation induced by topography. Under the Representative Concentration Pathway (RCP) 4.5 scenario, these high-resolution models project an increase in precipitation over central Highlands and northern Great Rift Valley in Ethiopia, but a decrease in precipitation over the southern part of the country. Such a dipole pattern is attributable to the intensification of the North Atlantic subtropical high (NASH) in a warmer climate, which influences Ethiopian Kiremt-season precipitation mainly by modulating atmospheric vertical motion. Diagnosis of the omega equation demonstrates that an intensified NASH increases (decreases) the advection of warm air and positive vorticity into the central Highlands and northern Great Rift Valley (southern part of the country), enhancing upward motion over the northern Rift Valley but decreasing elsewhere. Under the RCP 4.5 scenario, the high-resolution models project an intensification of the NASH by 15 (3 × 105 m2 s-2) geopotential meters (stream function) at the 850-hPa level, contributing to the projected precipitation change over Ethiopia. The

Consultants' meeting on operational aspects of the global network ''isotopes in precipitation''

International Nuclear Information System (INIS)

1995-01-01

The Consultant's meeting on ''Operational Aspects of the Global Network - Isotopes in Precipitation'' was organized by the International Atomic Energy Agency (IAEA) in co-operation with the World Meteorological Organization (WMO), the Past Global Changes Project (PAGES) of the International Geosphere-Biosphere Programme (IGBP), the World Health Organization (WHO) and the International Association of Hydrological Sciences (IAHS). It was agreed to transfer the responsibility of running the GNIP and the collection of isotope data in precipitation to a Steering Committee, which will consist of representatives of the following organizations: IAEA, WMO, IGBP-PAGES, WHO, UNESCO and IAHS. The responsibilities of the International Atomic Energy Agency (IAEA) in the Steering Committee are as follows: Co-ordination of the sample analysis: Arrangements for and participation in the measuring programme. Monitoring analytical aspects of sample collection, storage, etc. Arrangements for interlaboratory comparison exercises; collection of isotope and meteorological data, maintenance of the GNIP database and data distribution to interested users; promotion of full use and wider application of GNIP data in practical hydrological applications. Figs, tabs

Local control on precipitation in a fully coupled climate-hydrology model.

Science.gov (United States)

Larsen, Morten A D; Christensen, Jens H; Drews, Martin; Butts, Michael B; Refsgaard, Jens C

2016-03-10

The ability to simulate regional precipitation realistically by climate models is essential to understand and adapt to climate change. Due to the complexity of associated processes, particularly at unresolved temporal and spatial scales this continues to be a major challenge. As a result, climate simulations of precipitation often exhibit substantial biases that affect the reliability of future projections. Here we demonstrate how a regional climate model (RCM) coupled to a distributed hydrological catchment model that fully integrates water and energy fluxes between the subsurface, land surface, plant cover and the atmosphere, enables a realistic representation of local precipitation. Substantial improvements in simulated precipitation dynamics on seasonal and longer time scales is seen for a simulation period of six years and can be attributed to a more complete treatment of hydrological sub-surface processes including groundwater and moisture feedback. A high degree of local influence on the atmosphere suggests that coupled climate-hydrology models have a potential for improving climate projections and the results further indicate a diminished need for bias correction in climate-hydrology impact studies.

The global precipitation response to volcanic eruptions in the CMIP5 models

International Nuclear Information System (INIS)

Iles, Carley E; Hegerl, Gabriele C

2014-01-01

We examine the precipitation response to volcanic eruptions in the Coupled Model Intercomparison Project Phase 5 (CMIP5) historical simulations compared to three observational datasets, including one with ocean coverage. Global precipitation decreases significantly following eruptions in CMIP5 models, with the largest decrease in wet tropical regions. This also occurs in observational land data, and ocean data in the boreal cold season. Monsoon rainfall decreases following eruptions in both models and observations. In response to individual eruptions, the ITCZ shifts away from the hemisphere with the greater concentration of aerosols in CMIP5. Models undergo a longer-lasting ocean precipitation response than over land, but the response in the short satellite record is too noisy to confirm this. We detect the influence of volcanism on precipitation in all three datasets in the cold season, although the models underestimate the size of the response. In the warm season the volcanic influence is only marginally detectable. (letter)

Evaluation of high resolution spatio-temporal precipitation extremes from a stochastic weather generator

DEFF Research Database (Denmark)

Sørup, Hjalte Jomo Danielsen; Christensen, O. B.; Arnbjerg-Nielsen, Karsten

gauges in the model area. The spatio-temporal performance of the model with respect to precipitation extremes is evaluated in the points of a 2x2 km regular grid covering the full model area. The model satisfactorily reproduces the extreme behaviour of the observed precipitation with respect to event...... intensity levels and unconditional spatial correlation when evaluated using an event based ranking approach at point scale and an advanced spatio-temporal coupling of extreme events. Prospectively the model can be used as a tool to evaluate the impact of climate change without relying onprecipitation output......Spatio-temporal rainfall is modelled for the North-Eastern part of Zealand (Denmark) using the Spatio-Temporal Neyman-Scott Rectangular Pulses model as implemented in the RainSim software. Hourly precipitation series for fitting the model are obtained from a dense network of tipping bucket rain...

The impact of ambient dose rate measuring network and precipitation radar system for detection of environmental radioactivity released by accident

International Nuclear Information System (INIS)

Bleher, M; Stoehlker, U.

2003-01-01

For the surveillance of environmental radioactivity, the German measuring network of BfS consists of more than 2000 stations where the ambient gamma dose rate is continuously measured. This network is a helpful tool to detect and localise enhanced environmental contamination from artificial radionuclides. The threshold for early warning is so low, that already an additional dose rate contribution of 0,07 μGy/h is detectable. However, this threshold is frequently exceeded due to precipitation events caused by washout of natural activity in air. Therefore, the precipitation radar system of the German Weather Service provides valuable information on the problem, whether the increase of the ambient dose rate is due to natural or man-made events. In case of an accidental release, the data of this radar system show small area precipitation events and potential local hot spots not detected by the measuring network. For the phase of cloud passage, the ambient dose rate measuring network provides a reliable database for the evaluation of the current situation and its further development. It is possible to compare measured data for dose rate with derived intervention levels for countermeasures like ''sheltering''. Thus, critical regions can be identified and it is possible to verify implemented countermeasures. During and after this phase of cloud passage the measured data of the monitoring network help to adapt the results of the national decision support systems PARK and RODOS. Therefore, it is necessary to derive the actual additional contribution to the ambient dose rate. Map representations of measured dose rate are rapidly available and helpful to optimise measurement strategies of mobile systems and collection strategies for samples of agricultural products. (orig.)

Modeling the citation network by network cosmology.

Science.gov (United States)

Xie, Zheng; Ouyang, Zhenzheng; Zhang, Pengyuan; Yi, Dongyun; Kong, Dexing

2015-01-01

Citation between papers can be treated as a causal relationship. In addition, some citation networks have a number of similarities to the causal networks in network cosmology, e.g., the similar in-and out-degree distributions. Hence, it is possible to model the citation network using network cosmology. The casual network models built on homogenous spacetimes have some restrictions when describing some phenomena in citation networks, e.g., the hot papers receive more citations than other simultaneously published papers. We propose an inhomogenous causal network model to model the citation network, the connection mechanism of which well expresses some features of citation. The node growth trend and degree distributions of the generated networks also fit those of some citation networks well.

Merging bottom-up and top-down precipitation products using a stochastic error model

Science.gov (United States)

Maggioni, Viviana; Massari, Christian; Brocca, Luca; Ciabatta, Luca

2017-04-01

operational hydrology. The study, conducted in Italy for a 5-yr period (2010-2014) using a dense network of raingauges (about 3000) as a benchmark, demonstrates that the integration is able to enhance the correlation and the root mean squared error of SM2RAIN+3B42RT with respect to the parent products. This suggests a potential benefit of merging SM2RAIN derived rainfall with state-of-the-art satellite precipitation estimates for creating a product characterized by higher accuracy and better performance when used in the contest of operational hydrology. REFERENCES 1. Brocca, L.; Ciabatta, L.; Massari, C.; Moramarco, T.; Hahn, S.; Hasenauer, S.; Kidd, R.; Dorigo, W.; Wagner, W.; Levizzani, V. Soil as a natural rain gauge: Estimating global rainfall from satellite soil moisture data. J. Geophys. Res. Atmos. 2014, 119, 5128-5141. 2. Hossain, F.; Anagnostou, E. N. A two-dimensional satellite rainfall error model. IEEE Trans. Geosci. Remote Sens. 2006, 44, 1511-1522.

IDF-curves for precipitation In Belgium

International Nuclear Information System (INIS)

Mohymont, Bernard; Demarde, Gaston R.

2004-01-01

The Intensity-Duration-Frequency (IDF) curves for precipitation constitute a relationship between the intensity, the duration and the frequency of rainfall amounts. The intensity of precipitation is expressed in mm/h, the duration or aggregation time is the length of the interval considered while the frequency stands for the probability of occurrence of the event. IDF-curves constitute a classical and useful tool that is primarily used to dimension hydraulic structures in general, as e.g., sewer systems and which are consequently used to assess the risk of inundation. In this presentation, the IDF relation for precipitation is studied for different locations in Belgium. These locations correspond to two long-term, high-quality precipitation networks of the RMIB: (a) the daily precipitation depths of the climatological network (more than 200 stations, 1951-2001 baseline period); (b) the high-frequency 10-minutes precipitation depths of the hydro meteorological network (more than 30 stations, 15 to 33 years baseline period). For the station of Uccle, an uninterrupted time-series of more than one hundred years of 10-minutes rainfall data is available. The proposed technique for assessing the curves is based on maximum annual values of precipitation. A new analytical formula for the IDF-curves was developed such that these curves stay valid for aggregation times ranging from 10 minutes to 30 days (when fitted with appropriate data). Moreover, all parameters of this formula have physical dimensions. Finally, adequate spatial interpolation techniques are used to provide nationwide extreme values precipitation depths for short- to long-term durations With a given return period. These values are estimated on the grid points of the Belgian ALADIN-domain used in the operational weather forecasts at the RMIB.(Author)

Modelling computer networks

International Nuclear Information System (INIS)

Max, G

2011-01-01

Traffic models in computer networks can be described as a complicated system. These systems show non-linear features and to simulate behaviours of these systems are also difficult. Before implementing network equipments users wants to know capability of their computer network. They do not want the servers to be overloaded during temporary traffic peaks when more requests arrive than the server is designed for. As a starting point for our study a non-linear system model of network traffic is established to exam behaviour of the network planned. The paper presents setting up a non-linear simulation model that helps us to observe dataflow problems of the networks. This simple model captures the relationship between the competing traffic and the input and output dataflow. In this paper, we also focus on measuring the bottleneck of the network, which was defined as the difference between the link capacity and the competing traffic volume on the link that limits end-to-end throughput. We validate the model using measurements on a working network. The results show that the initial model estimates well main behaviours and critical parameters of the network. Based on this study, we propose to develop a new algorithm, which experimentally determines and predict the available parameters of the network modelled.

Predisposing, precipitating and perpetuating factors and the common sense model of illness

DEFF Research Database (Denmark)

Carstensen, Tina; Kasch, Helge; Frostholm, Lisbeth

2017-01-01

Background: Various predisposing, precipitating and perpetuating factors are found to be associated with development of persistent symptoms and disability after whiplash trauma. According to the commonsense model of illness, people use commonsense knowledge to develop individual illness models when...... facing health threat. Question: Can we use the common-sense model as a unifying model to encompass the impact of predisposing, precipitating, and perpetuating factors in the development of chronic whiplash? Looking into specific factors and their interaction: Do illness perceptions mediate the effect...... of precollision sick leave on chronic whiplash? Methods: This presentation will integrate findings from research on predisposing, precipitating, perpetuating factors that are associated with poor outcome after whiplash trauma and propose the common-sense model as a unifying model. Data from a study including 740...

A global satellite assisted precipitation climatology

Science.gov (United States)

Funk, Christopher C.; Verdin, Andrew P.; Michaelsen, Joel C.; Pedreros, Diego; Husak, Gregory J.; Peterson, P.

2015-01-01

Accurate representations of mean climate conditions, especially in areas of complex terrain, are an important part of environmental monitoring systems. As high-resolution satellite monitoring information accumulates with the passage of time, it can be increasingly useful in efforts to better characterize the earth's mean climatology. Current state-of-the-science products rely on complex and sometimes unreliable relationships between elevation and station-based precipitation records, which can result in poor performance in food and water insecure regions with sparse observation networks. These vulnerable areas (like Ethiopia, Afghanistan, or Haiti) are often the critical regions for humanitarian drought monitoring. Here, we show that long period of record geo-synchronous and polar-orbiting satellite observations provide a unique new resource for producing high resolution (0.05°) global precipitation climatologies that perform reasonably well in data sparse regions. Traditionally, global climatologies have been produced by combining station observations and physiographic predictors like latitude, longitude, elevation, and slope. While such approaches can work well, especially in areas with reasonably dense observation networks, the fundamental relationship between physiographic variables and the target climate variables can often be indirect and spatially complex. Infrared and microwave satellite observations, on the other hand, directly monitor the earth's energy emissions. These emissions often correspond physically with the location and intensity of precipitation. We show that these relationships provide a good basis for building global climatologies. We also introduce a new geospatial modeling approach based on moving window regressions and inverse distance weighting interpolation. This approach combines satellite fields, gridded physiographic indicators, and in situ climate normals. The resulting global 0.05° monthly precipitation climatology, the Climate

A preliminary characterization of the spatial variability of precipitation at Yucca Mountain, Nevada

International Nuclear Information System (INIS)

Hevesi, J.A.; Flint, A.L.; Ambos, D.S.

1994-01-01

Isohyetal maps of precipitation and numerical models for simulating precipitation are needed to help characterize natural infiltration at Yucca Mountain, Nevada. A geostatistical analysis of measured precipitation accumulated from storm periods. Precipitation was measured during a 3.8 year period from January 1990 to October, 1993 using a network of precipitation gages. A total of 34 winter-type storms and 12 summer-type storm, categorized using synoptic weather records, were analyzed using the 1st and 2nd statistical moments and sample variograms. Average standardized variograms indicated good spatial correlation for both storm types with only slight differences in the general spatial structure. Coefficients of variation and average relative variograms indicated that summer storms are characterized by greater variability as compared to winter storms. Models were fitted to the average summer and winter standarized variograms for each storm using the mean storm depth and the coefficient of variation as scaling parameters. Isohyetal maps of 4 representative storms were created using the standarized models. Results indicate that standarized models can be used to simulate the spatial distribution of precipitation depth, provided that the 1st and 2nd moments are known or can be estimated, and that identifiable deterministic trends can be included in the models. A single, fixed model representing the spatial variability of precipitation at Yucca Mountain is not recommended

Integrated modeling of second phase precipitation in cold-worked 316 stainless steels under irradiation

International Nuclear Information System (INIS)

Mamivand, Mahmood; Yang, Ying; Busby, Jeremy T.; Morgan, Dane

2017-01-01

The current work combines the Cluster Dynamics (CD) technique and CALPHAD-based precipitation modeling to address the second phase precipitation in cold-worked (CW) 316 stainless steels (SS) under irradiation at 300–400 °C. CD provides the radiation enhanced diffusion and dislocation evolution as inputs for the precipitation model. The CALPHAD-based precipitation model treats the nucleation, growth and coarsening of precipitation processes based on classical nucleation theory and evolution equations, and simulates the composition, size and size distribution of precipitate phases. We benchmark the model against available experimental data at fast reactor conditions (9.4 × 10"–"7 dpa/s and 390 °C) and then use the model to predict the phase instability of CW 316 SS under light water reactor (LWR) extended life conditions (7 × 10"–"8 dpa/s and 275 °C). The model accurately predicts the γ' (Ni_3Si) precipitation evolution under fast reactor conditions and that the formation of this phase is dominated by radiation enhanced segregation. The model also predicts a carbide volume fraction that agrees well with available experimental data from a PWR reactor but is much higher than the volume fraction observed in fast reactors. We propose that radiation enhanced dissolution and/or carbon depletion at sinks that occurs at high flux could be the main sources of this inconsistency. The integrated model predicts ~1.2% volume fraction for carbide and ~3.0% volume fraction for γ' for typical CW 316 SS (with 0.054 wt% carbon) under LWR extended life conditions. Finally, this work provides valuable insights into the magnitudes and mechanisms of precipitation in irradiated CW 316 SS for nuclear applications.

Precipitates/Salts Model Calculations for Various Drift Temperature Environments

International Nuclear Information System (INIS)

Marnier, P.

2001-01-01

The objective and scope of this calculation is to assist Performance Assessment Operations and the Engineered Barrier System (EBS) Department in modeling the geochemical effects of evaporation within a repository drift. This work is developed and documented using procedure AP-3.12Q, Calculations, in support of ''Technical Work Plan For Engineered Barrier System Department Modeling and Testing FY 02 Work Activities'' (BSC 2001a). The primary objective of this calculation is to predict the effects of evaporation on the abstracted water compositions established in ''EBS Incoming Water and Gas Composition Abstraction Calculations for Different Drift Temperature Environments'' (BSC 2001c). A secondary objective is to predict evaporation effects on observed Yucca Mountain waters for subsequent cement interaction calculations (BSC 2001d). The Precipitates/Salts model is documented in an Analysis/Model Report (AMR), ''In-Drift Precipitates/Salts Analysis'' (BSC 2001b)

Impact of convective activity on precipitation δ18O in isotope-enabled models

Science.gov (United States)

Hu, J.; Emile-Geay, J.; Dee, S.

2017-12-01

The ^18O signal preserved in paleo-archives (e.g. speleothem, tree ring cellulose, ice cores) is widely used to reconstruct precipitation or temperature. In the tropics, the inverse relationship between precipitation ^18O and rainfall amount, namely "amount effect" [Dansgaard, Tellus, 1964], is often used to interpret precipitation ^18O. However, recent studies have shown that precipitation ^18O is also influenced by precipitation type [Kurita et al, JGR, 2009; Moerman et al, EPSL, 2013], and recent observations indicate that it is negatively correlated with the fraction of precipitation associated with stratiform clouds [Aggarwal et al, Nature Geosci, 2016]. It is thus important to determine to what extent isotope-enabled climate models can reproduce these relationships. Here we do so using output from LMDZ, CAM2, and isoGSM from the Stable Water Isotope Intercomparison Group, Phase 2 (SWING2) project and results of SPEEDY-IER [Dee et al, JGR, 2015] from an AMIP-style experiment. The results show that these models simulate the "amount effect" well in the tropics, and the relationship between precipitation ^18O and precipitation is reversed in many places in mid-latitudes, in accordance with observations [Bowen, JGR, 2008]. Also, these models can all reproduce the negative correlation between monthly precipitation ^18O and stratiform precipitation proportion in mid-latitude (30°N-50°N; 50°S-30°S), but in the tropics (30°S-30°N), models show a positive correlation instead. The reason for this bias will be investigated within idealized experiments with SPEEDY-IER. The correct simulations of the impact of convective activity on precipitation ^18O in isotope-enabled models will improve our interpretation of paleoclimate proxies with respect to hydroclimate variability. P. K. Aggarwal et al. (2016), Nature Geosci., 9, 624-629, doi:10.1038/ngeo2739. G. J. Bowen. (2008), J. Geophys. Res., 113, D05113, doi:10.1029/2007JD009295. W. Dansgaard (1964), Tellus, 16(4), 436

Performance of Optimally Merged Multisatellite Precipitation Products Using the Dynamic Bayesian Model Averaging Scheme Over the Tibetan Plateau

Science.gov (United States)

Ma, Yingzhao; Hong, Yang; Chen, Yang; Yang, Yuan; Tang, Guoqiang; Yao, Yunjun; Long, Di; Li, Changmin; Han, Zhongying; Liu, Ronghua

2018-01-01

Accurate estimation of precipitation from satellites at high spatiotemporal scales over the Tibetan Plateau (TP) remains a challenge. In this study, we proposed a general framework for blending multiple satellite precipitation data using the dynamic Bayesian model averaging (BMA) algorithm. The blended experiment was performed at a daily 0.25° grid scale for 2007-2012 among Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42RT and 3B42V7, Climate Prediction Center MORPHing technique (CMORPH), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR). First, the BMA weights were optimized using the expectation-maximization (EM) method for each member on each day at 200 calibrated sites and then interpolated to the entire plateau using the ordinary kriging (OK) approach. Thus, the merging data were produced by weighted sums of the individuals over the plateau. The dynamic BMA approach showed better performance with a smaller root-mean-square error (RMSE) of 6.77 mm/day, higher correlation coefficient of 0.592, and closer Euclid value of 0.833, compared to the individuals at 15 validated sites. Moreover, BMA has proven to be more robust in terms of seasonality, topography, and other parameters than traditional ensemble methods including simple model averaging (SMA) and one-outlier removed (OOR). Error analysis between BMA and the state-of-the-art IMERG in the summer of 2014 further proved that the performance of BMA was superior with respect to multisatellite precipitation data merging. This study demonstrates that BMA provides a new solution for blending multiple satellite data in regions with limited gauges.

Coupling Poisson rectangular pulse and multiplicative microcanonical random cascade models to generate sub-daily precipitation timeseries

Science.gov (United States)

Pohle, Ina; Niebisch, Michael; Müller, Hannes; Schümberg, Sabine; Zha, Tingting; Maurer, Thomas; Hinz, Christoph

2018-07-01

To simulate the impacts of within-storm rainfall variabilities on fast hydrological processes, long precipitation time series with high temporal resolution are required. Due to limited availability of observed data such time series are typically obtained from stochastic models. However, most existing rainfall models are limited in their ability to conserve rainfall event statistics which are relevant for hydrological processes. Poisson rectangular pulse models are widely applied to generate long time series of alternating precipitation events durations and mean intensities as well as interstorm period durations. Multiplicative microcanonical random cascade (MRC) models are used to disaggregate precipitation time series from coarse to fine temporal resolution. To overcome the inconsistencies between the temporal structure of the Poisson rectangular pulse model and the MRC model, we developed a new coupling approach by introducing two modifications to the MRC model. These modifications comprise (a) a modified cascade model ("constrained cascade") which preserves the event durations generated by the Poisson rectangular model by constraining the first and last interval of a precipitation event to contain precipitation and (b) continuous sigmoid functions of the multiplicative weights to consider the scale-dependency in the disaggregation of precipitation events of different durations. The constrained cascade model was evaluated in its ability to disaggregate observed precipitation events in comparison to existing MRC models. For that, we used a 20-year record of hourly precipitation at six stations across Germany. The constrained cascade model showed a pronounced better agreement with the observed data in terms of both the temporal pattern of the precipitation time series (e.g. the dry and wet spell durations and autocorrelations) and event characteristics (e.g. intra-event intermittency and intensity fluctuation within events). The constrained cascade model also

Diagnosis of inconsistencies in multi-year gridded precipitation data over mountainous areas and related impacts on hydrologic simulations

Science.gov (United States)

Mizukami, N.; Smith, M. B.

2010-12-01

It is common for the error characteristics of long-term precipitation data to change over time due to various factors such as gauge relocation and changes in data processing methods. The temporal consistency of precipitation data error characteristics is as important as data accuracy itself for hydrologic model calibration and subsequent use of the calibrated model for streamflow prediction. In mountainous areas, the generation of precipitation grids relies on sparse gage networks, the makeup of which often varies over time. This causes a change in error characteristics of the long-term precipitation data record. We will discuss the diagnostic analysis of the consistency of gridded precipitation time series and illustrate the adverse effect of inconsistent precipitation data on a hydrologic model simulation. We used hourly 4 km gridded precipitation time series over a mountainous basin in the Sierra Nevada Mountains of California from October 1988 through September 2006. The basin is part of the broader study area that served as the focus of the second phase of the Distributed Model Intercomparison Project (DMIP-2), organized by the U.S. National Weather Service (NWS) of the National Oceanographic and Atmospheric Administration (NOAA). To check the consistency of the gridded precipitation time series, double mass analysis was performed using single pixel and basin mean areal precipitation (MAP) values derived from gridded DMIP-2 and Parameter-Elevation Regressions on Independent Slopes Model (PRISM) precipitation data. The analysis leads to the conclusion that over the entire study time period, a clear change in error characteristics in the DMIP-2 data occurred in the beginning of 2003. This matches the timing of one of the major gage network changes. The inconsistency of two MAP time series computed from the gridded precipitation fields over two elevation zones was corrected by adjusting hourly values based on the double mass analysis. We show that model

Improving precipitation estimates over the western United States using GOES-R precipitation data

Science.gov (United States)

Karbalaee, N.; Kirstetter, P. E.; Gourley, J. J.

2017-12-01

Satellite remote sensing data with fine spatial and temporal resolution are widely used for precipitation estimation for different applications such as hydrological modeling, storm prediction, and flash flood monitoring. The Geostationary Operational Environmental Satellites-R series (GOES-R) is the next generation of environmental satellites that provides hydrologic, atmospheric, and climatic information every 30 seconds over the western hemisphere. The high-resolution and low-latency of GOES-R observations is essential for the monitoring and prediction of floods, specifically in the Western United States where the vantage point of space can complement the degraded weather radar coverage of the NEXRAD network. The GOES-R rainfall rate algorithm will yield deterministic quantitative precipitation estimates (QPE). Accounting for inherent uncertainties will further advance the GOES-R QPEs since with quantifiable error bars, the rainfall estimates can be more readily fused with ground radar products. On the ground, the high-resolution NEXRAD-based precipitation estimation from the Multi-Radar/Multi-Sensor (MRMS) system, which is now operational in the National Weather Service (NWS), is challenged due to a lack of suitable coverage of operational weather radars over complex terrain. Distribution of QPE uncertainties associated with the GOES-R deterministic retrievals are derived and analyzed using MRMS over regions with good radar coverage. They will be merged with MRMS-based probabilistic QPEs developed to advance multisensor QPE integration. This research aims at improving precipitation estimation over the CONUS by combining the observations from GOES-R and MRMS to provide consistent, accurate and fine resolution precipitation rates with uncertainties over the CONUS.

Multiscale modeling of θ' precipitation in Al-Cu binary alloys

International Nuclear Information System (INIS)

Vaithyanathan, V.; Wolverton, C.; Chen, L.Q.

2004-01-01

We present a multiscale model for studying the growth and coarsening of θ' precipitates in Al-Cu alloys. Our approach utilizes a novel combination of the mesoscale phase-field method with atomistic approaches such as first-principles total energy and linear response calculations, as well as a mixed-space cluster expansion coupled with Monte Carlo simulations. We give quantitative first-principles predictions of: (i) bulk energetics of the Al-Cu solid solution and θ ' precipitate phases, (ii) interfacial energies of the coherent and semi-coherent θ ' /Al interfaces, and (iii) stress-free misfit strains and coherency strain energies of the θ ' /Al system. These first-principles data comprise all the necessary energetic information to construct our phase-field model of microstructural evolution. Using our multiscale approach, we elucidate the effects of various energetic contributions on the equilibrium shape of θ ' precipitates, finding that both the elastic energy and interfacial energy anisotropy contributions play critical roles in determining the aspect ratio of θ ' precipitates. Additionally, we have performed a quantitative study of the morphology of two-dimensional multi-precipitate microstructures during growth and coarsening, and compared the calculated results with experimentally observed morphologies. Our multiscale first-principles/phase-field method is completely general and should therefore be applicable to a wide variety of problems in microstructural evolution

Improved hydrological modeling for remote regions using a combination of observed and simulated precipitation data

DEFF Research Database (Denmark)

van der Linden, Sandra; Christensen, Jens Hesselbjerg

2003-01-01

-resolution regional climate model (HIRHAM4) with a mean-field bias correction using observed precipitation. A hydrological model (USAFLOW) was applied to simulate runoff using observed precipitation and a combination of observed and simulated precipitation as input. The method was illustrated for the remote Usa basin......, situated in the European part of Arctic Russia, close to the Ural Mountains. It was shown that runoff simulations agree better with observations when the combined precipitation data set was used than when only observed precipitation was used. This appeared to be because the HIRHAM4 model data compensated...... for the absence of observed data from mountainous areas where precipitation is orographically enhanced. In both cases, the runoff simulated by USAFLOW was superior to the runoff simulated within the HIRHAM4 model itself. This was attributed to the rather simplistic description of the water balance in the HIRHAM4...

Phase Behavior Modeling of Asphaltene Precipitation for Heavy Crudes: A Promising Tool Along with Experimental Data

Science.gov (United States)

Tavakkoli, M.; Kharrat, R.; Masihi, M.; Ghazanfari, M. H.; Fadaei, S.

2012-12-01

Thermodynamic modeling is known as a promising tool for phase behavior modeling of asphaltene precipitation under different conditions such as pressure depletion and CO2 injection. In this work, a thermodynamic approach is used for modeling the phase behavior of asphaltene precipitation. The precipitated asphaltene phase is represented by an improved solid model, while the oil and gas phases are modeled with an equation of state. The PR-EOS was used to perform flash calculations. Then, the onset point and the amount of precipitated asphaltene were predicted. A computer code based on an improved solid model has been developed and used for predicting asphaltene precipitation data for one of Iranian heavy crudes, under pressure depletion and CO2 injection conditions. A significant improvement has been observed in predicting the asphaltene precipitation data under gas injection conditions. Especially for the maximum value of asphaltene precipitation and for the trend of the curve after the peak point, good agreement was observed. For gas injection conditions, comparison of the thermodynamic micellization model and the improved solid model showed that the thermodynamic micellization model cannot predict the maximum of precipitation as well as the improved solid model. The non-isothermal improved solid model has been used for predicting asphaltene precipitation data under pressure depletion conditions. The pressure depletion tests were done at different levels of temperature and pressure, and the parameters of a non-isothermal model were tuned using three onset pressures at three different temperatures for the considered crude. The results showed that the model is highly sensitive to the amount of solid molar volume along with the interaction coefficient parameter between the asphaltene component and light hydrocarbon components. Using a non-isothermal improved solid model, the asphaltene phase envelope was developed. It has been revealed that at high temperatures, an

Projected Changes in the Annual Cycle of Precipitation over Central Asia by CMIP5 Models

Science.gov (United States)

Yu, X.; Zhao, Y.

2017-12-01

Future changes in the annual cycle of the precipitation in central Asia (CA) were estimated based on the historical and Representative Concentration Pathway 8.5 (RCP8.5) experiments from 25 models of the Coupled Model Intercomparison Project phase 5 (CMIP5). Compared with the Global Precipitation Climatology Project (GPCP) observations, the historical (1979-1999) experiments showed that most models can capture the migration of rainfall centers, but remarkable discrepancies exist in the location and intensity of rainfall centers between simulations and observations. Considering the skill scores of precipitation and pattern correlations of circulations, which are closely related to the precipitation for each month, for the 25 models, the four best models (e.g., CanESM2, CMCC-CMS, MIROC5 and MPI-ESM-LR) with relatively good performance were selected. The four models' ensemble mean indicated that the migration and location of the precipitation centers were better reproduced, except the intensity of the centers was overestimated, compared with the result that only considered precipitation. Based on the four best models' ensemble mean under RCP8.5 scenarios, precipitation was projected to increase dramatically over most of the CA region in the boreal cold seasons (November, December, January, February, March, April and May) with the maximum in December in the end of twenty-first century (2079-2099), and several positive centers were located in the Pamirs Plateau and the Tianshan Mountains. By contrast, the precipitation changes were weak in the boreal warm seasons (June, July, August, September and October), with a wet center located in the northern Himalayas. Furthermore, there remain some uncertainties in the projected precipitation regions and periods obtained by comparing models' ensemble results of this paper and the results of previous studies. These uncertainties should be investigated in future work.

Impact of time displaced precipitation estimates for on-line updated models

DEFF Research Database (Denmark)

Borup, Morten; Grum, Morten; Mikkelsen, Peter Steen

2012-01-01

When an online runoff model is updated from system measurements the requirements to the precipitation estimates change. Using rain gauge data as precipitation input there will be a displacement between the time where the rain intensity hits the gauge and the time where the rain hits the actual...

Image-based Modeling of Biofilm-induced Calcium Carbonate Precipitation

Science.gov (United States)

Connolly, J. M.; Rothman, A.; Jackson, B.; Klapper, I.; Cunningham, A. B.; Gerlach, R.

2013-12-01

Pore scale biological processes in the subsurface environment are important to understand in relation to many engineering applications including environmental contaminant remediation, geologic carbon sequestration, and petroleum production. Specifically, biofilm induced calcium carbonate precipitation has been identified as an attractive option to reduce permeability in a lasting way in the subsurface. This technology may be able to replace typical cement-based grouting in some circumstances; however, pore-scale processes must be better understood for it to be applied in a controlled manor. The work presented will focus on efforts to observe biofilm growth and ureolysis-induced mineral precipitation in micro-fabricated flow cells combined with finite element modelling as a tool to predict local chemical gradients of interest (see figure). We have been able to observe this phenomenon over time using a novel model organism that is able to hydrolyse urea and express a fluorescent protein allowing for non-invasive observation over time with confocal microscopy. The results of this study show the likely existence of a wide range of local saturation indices even in a small (1 cm length scale) experimental system. Interestingly, the locations of high predicted index do not correspond to the locations of higher precipitation density, highlighting the need for further understanding. Figure 1 - A micro-fabricated flow cell containing biofilm-induced calcium carbonate precipitation. (A) Experimental results: Active biofilm is in green and dark circles are calcium carbonate crystals. Note the channeling behavior in the top of the image, leaving a large hydraulically inactive area in the biofilm mass. (B) Finite element model: The prediction of relative saturation of calcium carbonate (as calcite). Fluid enters the system at a low saturation state (blue) but areas of high supersaturation (red) are predicted within the hydraulically inactive area in the biofilm. If only effluent

Forecasting gastrointestinal precipitation and oral pharmacokinetics of dantrolene in dogs using an in vitro precipitation testing coupled with in silico modeling and simulation.

Science.gov (United States)

Kambayashi, Atsushi; Dressman, Jennifer B

2017-10-01

The aim of the current research was to determine the precipitation kinetics of dantrolene sodium using canine biorelevant in vitro testing and to model the precipitation kinetics by appropriately coupling the data with an in silico tool adapted for dogs. The precipitation profiles of dantrolene sodium solutions were obtained with the in vitro paddle apparatus at a revolution rate of 50rpm. The in silico prediction tool was designed using STELLA software and the predicted plasma concentration profiles of dantrolene using the in vitro precipitation data were compared with the observed in vivo pharmacokinetics in beagle dogs. The plasma profiles of dantrolene, which served as a model weakly acidic drug which precipitates in the upper gastrointestinal tract, was successfully predicted using the in vitro precipitation testing coupled with the in silico modeling and simulation approach. The approach was subsequently used to forecast the effect of pharmaceutical excipients (HPMC/PG) on the ability of the drug to supersaturate in the gut and the resulting pharmacokinetics. The agreement of the simulated pharmacokinetics with the observed values confirms the ability of canine biorelevant media to predict oral performance of enhanced dosage forms in dogs. Copyright © 2017 Elsevier B.V. All rights reserved.

Improved Hourly and Sub-Hourly Gauge Data for Assessing Precipitation Extremes in the U.S.

Science.gov (United States)

Lawrimore, J. H.; Wuertz, D.; Palecki, M. A.; Kim, D.; Stevens, S. E.; Leeper, R.; Korzeniewski, B.

2017-12-01

The NOAA/National Weather Service (NWS) Fischer-Porter (F&P) weighing bucket precipitation gauge network consists of approximately 2000 stations that comprise a subset of the NWS Cooperative Observers Program network. This network has operated since the mid-20th century, providing one of the longest records of hourly and 15-minute precipitation observations in the U.S. The lengthy record of this dataset combined with its relatively high spatial density, provides an important source of data for many hydrological applications including understanding trends and variability in the frequency and intensity of extreme precipitation events. In recent years NOAA's National Centers for Environmental Information initiated an upgrade of its end-to-end processing and quality control system for these data. This involved a change from a largely manual review and edit process to a fully automated system that removes the subjectivity that was previously a necessary part of dataset quality control and processing. An overview of improvements to this dataset is provided along with the results of an analysis of observed variability and trends in U.S. precipitation extremes since the mid-20th century. Multi-decadal trends in many parts of the nation are consistent with model projections of an increase in the frequency and intensity of heavy precipitation in a warming world.

Brain Network Modelling

DEFF Research Database (Denmark)

Andersen, Kasper Winther

Three main topics are presented in this thesis. The first and largest topic concerns network modelling of functional Magnetic Resonance Imaging (fMRI) and Diffusion Weighted Imaging (DWI). In particular nonparametric Bayesian methods are used to model brain networks derived from resting state f...... for their ability to reproduce node clustering and predict unseen data. Comparing the models on whole brain networks, BCD and IRM showed better reproducibility and predictability than IDM, suggesting that resting state networks exhibit community structure. This also points to the importance of using models, which...... allow for complex interactions between all pairs of clusters. In addition, it is demonstrated how the IRM can be used for segmenting brain structures into functionally coherent clusters. A new nonparametric Bayesian network model is presented. The model builds upon the IRM and can be used to infer...

Geophysical monitoring and reactive transport modeling of ureolytically-driven calcium carbonate precipitation

Energy Technology Data Exchange (ETDEWEB)

Wu, Y.; Ajo-Franklin, J.B.; Spycher, N.; Hubbard, S.S.; Zhang, G.; Williams, K.H.; Taylor, J.; Fujita, Y.; Smith, R.

2011-07-15

Ureolytically-driven calcium carbonate precipitation is the basis for a promising in-situ remediation method for sequestration of divalent radionuclide and trace metal ions. It has also been proposed for use in geotechnical engineering for soil strengthening applications. Monitoring the occurrence, spatial distribution, and temporal evolution of calcium carbonate precipitation in the subsurface is critical for evaluating the performance of this technology and for developing the predictive models needed for engineering application. In this study, we conducted laboratory column experiments using natural sediment and groundwater to evaluate the utility of geophysical (complex resistivity and seismic) sensing methods, dynamic synchrotron x-ray computed tomography (micro-CT), and reactive transport modeling for tracking ureolytically-driven calcium carbonate precipitation processes under site relevant conditions. Reactive transport modeling with TOUGHREACT successfully simulated the changes of the major chemical components during urea hydrolysis. Even at the relatively low level of urea hydrolysis observed in the experiments, the simulations predicted an enhanced calcium carbonate precipitation rate that was 3-4 times greater than the baseline level. Reactive transport modeling results, geophysical monitoring data and micro-CT imaging correlated well with reaction processes validated by geochemical data. In particular, increases in ionic strength of the pore fluid during urea hydrolysis predicted by geochemical modeling were successfully captured by electrical conductivity measurements and confirmed by geochemical data. The low level of urea hydrolysis and calcium carbonate precipitation suggested by the model and geochemical data was corroborated by minor changes in seismic P-wave velocity measurements and micro-CT imaging; the latter provided direct evidence of sparsely distributed calcium carbonate precipitation. Ion exchange processes promoted through NH{sub 4}{sup

A new approach for assimilation of two-dimensional radar precipitation in a high resolution NWP model

Science.gov (United States)

Korsholm, Ulrik; Petersen, Claus; Hansen Sass, Bent; Woetman, Niels; Getreuer Jensen, David; Olsen, Bjarke Tobias; GIll, Rasphal; Vedel, Henrik

2014-05-01

The DMI nowcasting system has been running in a pre-operational state for the past year. The system consists of hourly simulations with the High Resolution Limited Area weather model combined with surface and three-dimensional variational assimilation at each restart and nudging of satellite cloud products and radar precipitation. Nudging of a two-dimensional radar reflectivity CAPPI product is achieved using a new method where low level horizontal divergence is nudged towards pseudo observations. Pseudo observations are calculated based on an assumed relation between divergence and precipitation rate and the strength of the nudging is proportional to the offset between observed and modelled precipitation leading to increased moisture convergence below cloud base if there is an under-production of precipitation relative to the CAPPI product. If the model over-predicts precipitation, the low level moisture source is reduced, and in-cloud moisture is nudged towards environmental values. In this talk results will be discussed based on calculation of the fractions skill score in cases with heavy precipitation over Denmark. Furthermore, results from simulations combining reflectivity nudging and extrapolation of reflectivity will be shown. Results indicate that the new method leads to fast adjustment of the dynamical state of the model to facilitate precipitation release when the model precipitation intensity is too low. Removal of precipitation is also shown to be of importance and strong improvements were found in the position of the precipitation systems. Bias is reduced for low and extreme precipitation rates.

Spanish Network for Isotopes in Precipitation: Isotope Spatial distribution and contribution to the knowledge of the hydrological cycle; La Red Espanola de Vigilancia de Isotopos en la Precipitacion (REVIP): distribucion isotopica espacial y aportacion al conocimiento del ciclo hidrologico

Energy Technology Data Exchange (ETDEWEB)

Diaz-Teijeiro, M. F.; Rodriguez-Arevalo, J.; Castano, S.

2009-07-01

The results of seven years of operation of the Spanish Network for Isotopes ({sup 2}H, {sup 1}8O y {sup 3}H) in Precipitation (REVIP) are shown. this Network is managed since 2000 by the Centro de Estudios de Tecnicas Aplicadas of the Centro de Estudios y Experimentacion de Obras Publicas (CEDEX) in collaboration with the Agencia Estatal de Meteorologia (AEMET). The results of REVIP are sent to the International Atomic Energy Agency (IAEA) in order to be integrated in the Global Network for Isotopes in Precipitation (GNIP). The spatial distribution of stable isotopes ({sup 1}8O h {sup 2}H) in precipitation in Spain follows a multiple regression model, based on two geographic factors: latitude and elevation, which is strongly correlated with temperature, an important factor controlling isotope fractionation. This information on {sup 1}8O and {sup 2}H is useful to trace surface and ground waters and, combined with the information, about the spatial and temporal distribution of the Tritium ({sup 3}H) concentration in precipitation, allows to date these waters in order to estimate flow directions and velocities, and to evaluate the residence time of water resources and aquifer vulnerability. (Author)

Schottky effect model of electrical activity of metallic precipitates in silicon

International Nuclear Information System (INIS)

Plekhanov, P. S.; Tan, T. Y.

2000-01-01

A quantitative model of the electrical activity of metallic precipitates in Si is formulated with an emphasis on the Schottky junction effects of the precipitate-Si system. Carrier diffusion and carrier drift in the Si space charge region are accounted for. Carrier recombination is attributed to the thermionic emission mechanism of charge transport across the Schottky junction rather than the surface recombination. It is shown that the precipitates can have a very large minority carrier capture cross-section. Under weak carrier generation conditions, the supply of minority carriers is found to be the limiting factor of the recombination process. The plausibility of the model is demonstrated by a comparison of calculated and available experimental results. (c) 2000 American Institute of Physics

Precipitates/Salts Model Calculations for Various Drift Temperature Environments

Energy Technology Data Exchange (ETDEWEB)

P. Marnier

2001-12-20

The objective and scope of this calculation is to assist Performance Assessment Operations and the Engineered Barrier System (EBS) Department in modeling the geochemical effects of evaporation within a repository drift. This work is developed and documented using procedure AP-3.12Q, Calculations, in support of ''Technical Work Plan For Engineered Barrier System Department Modeling and Testing FY 02 Work Activities'' (BSC 2001a). The primary objective of this calculation is to predict the effects of evaporation on the abstracted water compositions established in ''EBS Incoming Water and Gas Composition Abstraction Calculations for Different Drift Temperature Environments'' (BSC 2001c). A secondary objective is to predict evaporation effects on observed Yucca Mountain waters for subsequent cement interaction calculations (BSC 2001d). The Precipitates/Salts model is documented in an Analysis/Model Report (AMR), ''In-Drift Precipitates/Salts Analysis'' (BSC 2001b).

Dynamic process model of a plutonium oxalate precipitator. Final report

Energy Technology Data Exchange (ETDEWEB)

Miller, C.L.; Hammelman, J.E.; Borgonovi, G.M.

1977-11-01

In support of LLL material safeguards program, a dynamic process model was developed which simulates the performance of a plutonium (IV) oxalate precipitator. The plutonium oxalate precipitator is a component in the plutonium oxalate process for making plutonium oxide powder from plutonium nitrate. The model is based on state-of-the-art crystallization descriptive equations, the parameters of which are quantified through the use of batch experimental data. The dynamic model predicts performance very similar to general Hanford oxalate process experience. The utilization of such a process model in an actual plant operation could promote both process control and material safeguards control by serving as a baseline predictor which could give early warning of process upsets or material diversion. The model has been incorporated into a FORTRAN computer program and is also compatible with the DYNSYS 2 computer code which is being used at LLL for process modeling efforts.

Dynamic process model of a plutonium oxalate precipitator. Final report

International Nuclear Information System (INIS)

Miller, C.L.; Hammelman, J.E.; Borgonovi, G.M.

1977-11-01

In support of LLL material safeguards program, a dynamic process model was developed which simulates the performance of a plutonium (IV) oxalate precipitator. The plutonium oxalate precipitator is a component in the plutonium oxalate process for making plutonium oxide powder from plutonium nitrate. The model is based on state-of-the-art crystallization descriptive equations, the parameters of which are quantified through the use of batch experimental data. The dynamic model predicts performance very similar to general Hanford oxalate process experience. The utilization of such a process model in an actual plant operation could promote both process control and material safeguards control by serving as a baseline predictor which could give early warning of process upsets or material diversion. The model has been incorporated into a FORTRAN computer program and is also compatible with the DYNSYS 2 computer code which is being used at LLL for process modeling efforts

Modelling the potential impacts of afforestation on extreme precipitation over West Africa

Science.gov (United States)

Odoulami, Romaric C.; Abiodun, Babatunde J.; Ajayi, Ayodele E.

2018-05-01

This study examines how afforestation in West Africa could influence extreme precipitation over the region, with a focus on widespread extreme rainfall events (WEREs) over the afforestation area. Two regional climate models (RegCM and WRF) were applied to simulate the present-day climate (1971-2000) and future climate (2031-2060, under IPCC RCP 4.5 emission scenario) with and without afforestation of the Savannah zone in West Africa. The models give a realistic simulation of precipitation indices and WEREs over the subcontinent. On average, the regional models projected future decreases in total annual wet day precipitation (PRCPTOT) and total annual daily precipitation greater than or equal to the 95th percentile of daily precipitation threshold (R95pTOT) and increases in maximum number of consecutive dry days (CDD) over Sahel. Over Savannah, the models projected decreases in PRCPTOT but increases in R95pTOT and CDD. Also, an increase in WEREs frequency is projected over west, central and east Savannah, except that RegCM simulated a decrease in WEREs over east Savannah. In general, afforestation increases PRCPTOT and R95pTOT but decreases CDD over the afforestation area. The forest-induced increases in PRCPTOT and decreases in CDD affect all ecological zones in West Africa. However, the simulations show that afforestation of Savannah also decreases R95pTOT over the Guinea Coast. It further increases WEREs over west and central Savannah and decreases them over east Savannah because of the local decrease in R95pTOT. Results of this study suggest that the future changes in characteristics of extreme precipitation events over West Africa are sensitive to the ongoing land modification.

A Multi-scale Modeling System with Unified Physics to Study Precipitation Processes

Science.gov (United States)

Tao, W. K.

2017-12-01

In recent years, exponentially increasing computer power has extended Cloud Resolving Model (CRM) integrations from hours to months, the number of computational grid points from less than a thousand to close to ten million. Three-dimensional models are now more prevalent. Much attention is devoted to precipitating cloud systems where the crucial 1-km scales are resolved in horizontal domains as large as 10,000 km in two-dimensions, and 1,000 x 1,000 km2 in three-dimensions. Cloud resolving models now provide statistical information useful for developing more realistic physically based parameterizations for climate models and numerical weather prediction models. It is also expected that NWP and mesoscale model can be run in grid size similar to cloud resolving model through nesting technique. Recently, a multi-scale modeling system with unified physics was developed at NASA Goddard. It consists of (1) a cloud-resolving model (Goddard Cumulus Ensemble model, GCE model), (2) a regional scale model (a NASA unified weather research and forecast, WRF), and (3) a coupled CRM and global model (Goddard Multi-scale Modeling Framework, MMF). The same microphysical processes, long and short wave radiative transfer and land processes and the explicit cloud-radiation, and cloud-land surface interactive processes are applied in this multi-scale modeling system. This modeling system has been coupled with a multi-satellite simulator to use NASA high-resolution satellite data to identify the strengths and weaknesses of cloud and precipitation processes simulated by the model. In this talk, a review of developments and applications of the multi-scale modeling system will be presented. In particular, the results from using multi-scale modeling system to study the precipitation, processes and their sensitivity on model resolution and microphysics schemes will be presented. Also how to use of the multi-satellite simulator to improve precipitation processes will be discussed.

SET UP OF THE NEW AUTOMATIC HYDROMETEOROLOGICAL NETWORK IN HUNGARY

Directory of Open Access Journals (Sweden)

J. NAGy

2013-03-01

Full Text Available The Hungarian Meteorological Service (OMSZ and General Directorate of Water Management (OVF in Hungary run conventional precipitation measurement networks consisting of at least 1000 stations. OMSZ automated its synoptic and climatological network in 90’s and now more than 100 automatic stations give data every 1-10 minutes via GPRS channel. In 2007 the experts from both institutions determined the requirements of a common network. The predecessor in title of OVF is general Directorate for Water and Environment gave a project proposal in 2008 for establishment of a new hydrometeorological network based on common aims for meteorology and hydrology. The new hydrometeorological network was set up in 2012 financed by KEOP project. This network has got 141 weighing precipitation gauges, 118 temperature - humidity sensors and 25 soil moisture and soil temperature instruments. Near by Tisza-Lake two wind sensors have been installed. The network is operated by OMSZ and OVF together. OVF and its institutions maintain the stations itself and support the electricity. OMSZ operates data collection and transmission, maintaines and calibrates the sensors. Using precipitation data of enhanced network the radar precipitation field quality may be more precise, which are input of run-off model. Thereby the time allowance may be increased in flood-control events. Based on soil moisture and temperature water balance in soil may be modelled and forecast can be produced in different conditions. It is very important task in drought and inland water conditions. Considering OMSZ investment project in which new Doppler dual polarisation radar and 14 disdrometers will be installed, the precipitation estimation may be improved since 2015.

Phase-field modeling of Mn-Ni-Si precipitate behavior on the bcc-Fe matrix

International Nuclear Information System (INIS)

Chang, Kun Ok; Kwon, Jun Hyun

2016-01-01

The formation of Mn-Ni-Si precipitate (hereafter MNS precipitate) is widely accepted by one of the main reasons of late stage hardening and embrittlement of Reactor Pressure Vessel (RPV) during nuclear power plant (NPP) operation. Since MNS precipitate is not considered in current regulatory model, this late stage hardening can be a limiting factor for life extension of nuclear power plants up to 80 or more years. The stability of the MNS precipitate was investigated from the thermodynamic view point and they concluded that MNS precipitate is a stable phase even with very little Cu contents, and they assessed UW1 thermodynamic database which can predict the thermodynamic stability of MNS precipitate at operating temperature of NPP ( ∼ 290 .deg. C). Based on the non-classical nucleation theory, we performed the phase-field modeling of nucleation and growth of MNS precipitate. The microstructure evolution of Mn-Ni-Cu precipitate has been simulated using the phase-field method and their approaches are focused on a role of the Cu contents. Also, a role of the interstitial loop on the nucleation and growth kinetics of MNS precipitate was analyzed.

A model for the biological precipitation of Precambrian iron-formation

Science.gov (United States)

Laberge, G. L.

1986-01-01

A biological model for the precipitation of Precambrian iron formations is presented. Assuming an oxygen deficient atmosphere and water column to allow sufficient Fe solubility, it is proposed that local oxidizing environments, produced biologically, led to precipitation of iron formations. It is further suggested that spheroidal structures about 30 mm in diameter, which are widespread in low grade cherty rion formations, are relict forms of the organic walled microfossil Eosphaera tylerii. The presence of these structures suggests that the organism may have had a siliceous test, which allowed sufficient rigidity for accumulation and preservation. The model involves precipitation of ferric hydrates by oxidation of iron in the photic zone by a variety of photosynthetic organisms. Silica may have formed in the frustules of silica secreting organisms, including Eosphaera tylerii. Iron formates formed, therefore, by a sediment rain of biologically produced ferric hydrates and silica and other organic material. Siderite and hematite formed diagenetically on basin floors, and subsequent metamorphism produced magnetite and iron silicates.

The Austrian Network of Isotopes in Precipitation and Surface water: more than 50 years applications and interpretations of basic isotope-hydrological data for Central Europe

Science.gov (United States)

Wyhlidal, S.; Rank, D.; Kralik, M.

2017-12-01

Austria runs one of the longest-standing and most dense isotope precipitation collection networks worldwide, resulting in a unique isotope time series. Stable isotope variations in precipitation are a consequence of isotope effects accompanying each step of the water cycle. Therefore, stable isotope ratios of oxygen (18O/16O) and hydrogen (2H/1H) in precipitation provide important information about the origin and atmospheric transport of water vapour. The separation of a remote moisture source signals from local influences is thereby challenging. The amount of precipitation in Austria is highly influenced by the Alpine mountain range (400-3.000 mm/a). The amount of annual precipitation increases towards the mountain ranges. However, strong regional differences exist between the north and south of the Austrian Alps because the Alpine range functions as weather divide. The isotope time series of the stations of the Austrian precipitation network show significant but not uniform long-term trends. While the 10-year running mean of some mountain stations exhibit a highly significant increase in δ18O of about 1 ‰ since 1975, the change of δ18O at the valley stations is less pronounced. The increasing δ18O values can be correlated to an increase mean air temperature in the Alpine area and can be used as an additional indicator of climate change in this region. The differences in δ18O-values of sampling stations at similar altitudes can be explained by the origin of the air moisture. An Atlantic influence causes lower δ18O-values than sources from the Mediterranean. This can be explained by the different distances to the sea. Deuterium excess is a second-order isotopic parameter which is often interpreted as a tracer of the evaporation conditions of water vapor at the moisture source in terms of relative humidity, wind speed, and sea surface temperature, but can also be modified by local influences, such as below-cloud evaporation and equilibrium fractionation under

Links between meteorological conditions and spatial/temporal variations in long-term isotope records from the Austrian precipitation network

International Nuclear Information System (INIS)

Kaiser, A.; Scheifinger, H.; Kralik, M.; Papesch, W.; Rank, D.; Stichler, W.

2002-01-01

The isotope records from the Austrian Network for Isotopes in Precipitation (ANIP) show significant but not uniform long-term trends. While the 10-year running means of some mountain stations exhibit a pronounced increase in δ 18 O of about 1 per mille since 1975, the change of δ 18 O at the valley stations is much lower. There are also differences in the time behaviour. The differences in the δ 18 O-values of sampling stations at similar altitudes can be explained by different origins of the air moisture (Atlantic or Mediterranean influence). Furthermore, a significant difference in the behaviour of the deuterium excess at neighbouring mountain and valley stations has been observed. There is a slight increase of the yearly mean of the deuterium excess with increasing altitude of the sampling station. But moreover, the seasonal pattern of the deuterium excess is quite different. While the valley stations exhibit the expected minimum in summer, the mountain stations show a distinct maximum between June and October. As a first step into a comprehensive analysis of the meteorological effects on the isotope patterns, the role of advection of different air masses is studied by trajectory statistics. Back trajectories, based on the three dimensional wind fields of the ECMWF model, are calculated for each hour within each precipitation event. Thus, the frequency of the origin of air masses and their contribution to the isotope patterns of the monthly precipitation samples are studied for two selected mountain stations north and south of the main ridge of the Alps. (author)

Mathematical modelling of brittle phase precipitation in complex ruthenium containing nickel-based superalloys

International Nuclear Information System (INIS)

Rettig, Ralf

2010-01-01

A new model has been developed in this work which is capable of simulating the precipitation kinetics of brittle phases, especially TCP-phases (topologically close packed phases) in ruthenium containing superalloys. The model simultaneously simulates the nucleation and the growth stage of precipitation for any number of precipitating phases. The CALPHAD method (Calculation of Phase Diagrams) is employed to calculate thermodynamic properties, such as the driving force or phase compositions in equilibrium. For calculation of diffusion coefficients, kinetic mobility databases which are also based on the CALPHAD-method are used. The model is fully capable of handling multicomponent effects, which are common in complex superalloys. Metastable phases can be treated and will automatically be dissolved if they get unstable. As the model is based on the general CALPHAD method, it can be applied to a broad range of precipitation processes in different alloys as long as the relevant thermodynamic and kinetic databases are available. The developed model proves that the TCP-phases precipitate in a sequence of phases. The first phase that is often formed is the metastable σ-phase because it has the lowest interface energy due to low-energy planes at the interface between matrix and precipitate. After several hundred hours the stable μ- and P-phases start to precipitate by nucleating at the σ-phase which is energetically favourable. During the growth of these stable phases the sigma-phase is continuously dissolved. It can be shown by thermodynamic CALPHAD calculations that the sigma-phase has a lower Gibbs free enthalpy than the μ- and P-phase. All required parameters of the model, such as interface energy and nucleate densities, have been estimated. The mechanisms of suppression of TCP-phase precipitation in the presence of ruthenium in superalloys were investigated with the newly developed model. It is shown by the simulations that ruthenium mostly affects the nucleation

Using Multi-Scale Modeling Systems and Satellite Data to Study the Precipitation Processes

Science.gov (United States)

Tao, Wei-Kuo; Chern, J.; Lamg, S.; Matsui, T.; Shen, B.; Zeng, X.; Shi, R.

2011-01-01

In recent years, exponentially increasing computer power has extended Cloud Resolving Model (CRM) integrations from hours to months, the number of computational grid points from less than a thousand to close to ten million. Three-dimensional models are now more prevalent. Much attention is devoted to precipitating cloud systems where the crucial 1-km scales are resolved in horizontal domains as large as 10,000 km in two-dimensions, and 1,000 x 1,000 km2 in three-dimensions. Cloud resolving models now provide statistical information useful for developing more realistic physically based parameterizations for climate models and numerical weather prediction models. It is also expected that NWP and mesoscale model can be run in grid size similar to cloud resolving model through nesting technique. Recently, a multi-scale modeling system with unified physics was developed at NASA Goddard. It consists of (l) a cloud-resolving model (Goddard Cumulus Ensemble model, GCE model), (2) a regional scale model (a NASA unified weather research and forecast, WRF), (3) a coupled CRM and global model (Goddard Multi-scale Modeling Framework, MMF), and (4) a land modeling system. The same microphysical processes, long and short wave radiative transfer and land processes and the explicit cloud-radiation, and cloud-land surface interactive processes are applied in this multi-scale modeling system. This modeling system has been coupled with a multi-satellite simulator to use NASA high-resolution satellite data to identify the strengths and weaknesses of cloud and precipitation processes simulated by the model. In this talk, the recent developments and applications of the multi-scale modeling system will be presented. In particular, the results from using multi-scale modeling system to study the precipitating systems and hurricanes/typhoons will be presented. The high-resolution spatial and temporal visualization will be utilized to show the evolution of precipitation processes. Also how to

Quantification of Linkages between Large-Scale Climate Patterns and Annual Precipitation for the Colorado River Basin

Science.gov (United States)

Kalra, A.; Ahmad, S.

2010-12-01

Precipitation is regarded as one of the key variables driving various hydrologic processes and the future precipitation information can be useful to better understand the long-term climate dynamics. In this paper, a simple, robust, and parsimonious precipitation forecast model, Support Vector Machine (SVM) is proposed which uses large-scale climate information and predict annual precipitation 1-year in advance. SVM’s are a novel class of neural networks (NNs) which are based on the statistical learning theory. The SVM’s has three main advantages over the traditional NNs: 1) better generalization ability, 2) the architecture and weights of SVM’s are guaranteed to be unique and globally optimum, and 3) SVM’s are trained more rapidly than the corresponding NN. With these advantages, an application of SVM incorporating large-scale climate information is developed and applied to seventeen climate divisions encompassing the Colorado River Basin in the western United States. Annual oceanic-atmospheric indices, comprising of Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), Atlantic Multidecadal Oscillation (AMO), and El Nino-Southern Oscillations (ENSO) for a period of 1900-2007 are used to generate annual precipitation estimates with 1-year lead time. The results from the present study indicate that long-term precipitation predictions for the Upper Colorado River Basin can be successfully obtained using a combination of NAO and ENSO indices whereas coupling PDO and AMO results in improved precipitation predictions for the Lower Colorado River Basin. Precipitation predictions from the SVM model are found to be better when compared with the predictions obtained from feed-forward back propagation Artificial Neural Network and Multivariate Linear Regression models. The overall results of this study revealed that the annual precipitation of the Colorado River Basin was significantly influenced by oceanic-atmospheric oscillations and the proposed SVM

Precipitation in Powder Metallurgy, Nickel Base Superalloys: Review of Modeling Approach and Formulation of Engineering (Postprint)

Science.gov (United States)

2016-12-01

AFRL-RX-WP-JA-2016-0333 PRECIPITATION IN POWDER- METALLURGY , NICKEL-BASE SUPERALLOYS: REVIEW OF MODELING APPROACH AND FORMULATION OF...PRECIPITATION IN POWDER- METALLURGY , NICKEL- BASE SUPERALLOYS: REVIEW OF MODELING APPROACH AND FORMULATION OF ENGINEERING (POSTPRINT) 5a...and kinetic parameters required for the modeling of γ′ precipitation in powder- metallurgy (PM), nickel-base superalloys are summarized. These

Quantitative precipitation climatology over the Himalayas by using Precipitation Radar on Tropical Rainfall Measuring Mission (TRMM) and a dense network of rain-gauges

Science.gov (United States)

Yatagai, A.

2010-09-01

Quantified grid observation data at a reasonable resolution are indispensable for environmental monitoring as well as for predicting future change of mountain environment. However quantified datasets have not been available for the Himalayan region. Hence we evaluate climatological precipitation data around the Himalayas by using Precipitation Radar (PR) data acquired by the Tropical Rainfall Measuring Mission (TRMM) over 10 years of observation. To validate and adjust these patterns, we used a dense network of rain gauges collected by the Asian Precipitation—Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE Water Resources) project (http://www.chikyu.ac.jp/precip/). We used more than 2600 stations which have more than 10-year monthly precipitation over the Himalayan region (75E-105E, 20-36N) including country data of Nepal, Bangladesh, Bhutan, Pakistan, India, Myanmar, and China. The region we studied is so topographically complicated that horizontal patterns are not uniform. Therefore, every path data of PR2A25 (near-surface rain) was averaged in a 0.05-degree grid and a 10-year monthly average was computed (hereafter we call PR). On the other hand, for rain-gauge, we first computed cell averages if each 0.05-degree grid cell has 10 years observation or more. Here we refer to the 0.05-degree rain-gauge climatology data as RG data. On the basis of comparisons between the RG and PR composite values, we defined the parameters of the regressions to correct the monthly climatology value based on the rain gauge observations. Compared with the RG, the PR systematically underestimated precipitation by 28-38% in summer (July-September). Significant correlation between TRMM/PR and rain-gauge data was found for all months, but the correlation is relatively low in winter. The relationship is investigated for different elevation zones, and the PR was found to underestimate RG data in most zones, except for certain zones in

Assessment of the Effects of Various Precipitation Forcings on Flood Forecasting Potential Using WRF-Hydro Modeling

Science.gov (United States)

Zhang, J.; Fang, N. Z.

2017-12-01

A potential flood forecast system is under development for the Upper Trinity River Basin (UTRB) in North Central of Texas using the WRF-Hydro model. The Routing Application for the Parallel Computation of Discharge (RAPID) is utilized as channel routing module to simulate streamflow. Model performance analysis was conducted based on three quantitative precipitation estimates (QPE): the North Land Data Assimilation System (NLDAS) rainfall, the Multi-Radar Multi-Sensor (MRMS) QPE and the National Centers for Environmental Prediction (NCEP) quality-controlled stage IV estimates. Prior to hydrologic simulation, QPE performance is assessed on two time scales (daily and hourly) using the Community Collaborative Rain, Hail and Snow Network (CoCoRaHS) and Hydrometeorological Automated Data System (HADS) hourly products. The calibrated WRF-Hydro model was then evaluated by comparing the simulated against the USGS observed using various QPE products. The results imply that the NCEP stage IV estimates have the best accuracy among the three QPEs on both time scales, while the NLDAS rainfall performs poorly because of its coarse spatial resolution. Furthermore, precipitation bias demonstrates pronounced impact on flood forecasting skills, as the root mean squared errors are significantly reduced by replacing NLDAS rainfall with NCEP stage IV estimates. This study also demonstrates that accurate simulated results can be achieved when initial soil moisture values are well understood in the WRF-Hydro model. Future research effort will therefore be invested on incorporating data assimilation with focus on initial states of the soil properties for UTRB.

MAP3S Precipitation Chemistry Network: second periodic summary report, July 1977--June 1978

Energy Technology Data Exchange (ETDEWEB)

1979-01-01

The MAP3S Precipitation Chemistry Network consists of eight sites located in the northeastern United States. Precipitation event samples are collected by cooperating site operators, using specially developed sampling equipment. The concentration data collected over the period July 1, 1977 to July 1, 1978, are listed as a summary of the data reported monthly throughout the year. Samples were chemically analyzed at a central laboratory for 13 pollutant species - pH, conductivity, SO/sub 2/, SO/sub 4//sup =/, NH/sub 4//sup +/, NO/sub 2//sup -/, NO/sub 3//sup -/, Cl/sup -/, PO/sub 4//sup 3 -/, Na/sup +/, K/sup +/, Ca/sup + +/, and Mg/sup + +/ - using ion chromatography, automated wet chemistry, atomic absorption spectrophotometry, and electrode techniques. Second-year developments included: the installation of refrigeration equipment in all Battelle collectors; the initiation of an externally administered quality control program; and use of ion chromatography for cation as well as anion species. Supplementary research efforts included a special collector comparison study at the Pennsylvania State site and further analysis of sulfite versus sulfate deposition.

A new approach for assimilation of 2D radar precipitation in a high-resolution NWP model

DEFF Research Database (Denmark)

Korsholm, Ulrik Smith; Petersen, Claus; Sass, Bent Hansen

2015-01-01

of precipitation, the strength of the nudging is proportional to the offset between observed and modelled precipitation, leading to increased moisture convergence. If the model over-predicts precipitation, the low level moisture source is reduced, and in-cloud moisture is nudged towards environmental values......A new approach for assimilation of 2D precipitation in numerical weather prediction models is presented and tested in a case with convective, heavy precipitation. In the scheme a nudging term is added to the horizontal velocity divergence tendency equation. In case of underproduction....... The method was implemented in the Danish Meteorological Institute numerical weather prediction (DMI NWP) nowcasting system, running with hourly cycles, performing a surface analysis and 3D variational analysis for upper air assimilation at each cycle restart, followed by nudging assimilation of precipitation...

High resolution reconstruction of monthly precipitation of Iberian Peninsula using circulation weather types

Science.gov (United States)

Cortesi, N.; Trigo, R.; Gonzalez-Hidalgo, J. C.; Ramos, A. M.

2012-06-01

Precipitation over the Iberian Peninsula (IP) is highly variable and shows large spatial contrasts between wet mountainous regions, to the north, and dry regions in the inland plains and southern areas. In this work, a high-density monthly precipitation dataset for the IP was coupled with a set of 26 atmospheric circulation weather types (Trigo and DaCamara, 2000) to reconstruct Iberian monthly precipitation from October to May with a very high resolution of 3030 precipitation series (overall mean density one station each 200 km2). A stepwise linear regression model with forward selection was used to develop monthly reconstructed precipitation series calibrated and validated over 1948-2003 period. Validation was conducted by means of a leave-one-out cross-validation over the calibration period. The results show a good model performance for selected months, with a mean coefficient of variation (CV) around 0.6 for validation period, being particularly robust over the western and central sectors of IP, while the predicted values in the Mediterranean and northern coastal areas are less acute. We show for three long stations (Lisbon, Madrid and Valencia) the comparison between model and original data as an example to how these models can be used in order to obtain monthly precipitation fields since the 1850s over most of IP for this very high density network.

Evaluation of global fine-resolution precipitation products and their uncertainty quantification in ensemble discharge simulations

Science.gov (United States)

Qi, W.; Zhang, C.; Fu, G.; Sweetapple, C.; Zhou, H.

2016-02-01

The applicability of six fine-resolution precipitation products, including precipitation radar, infrared, microwave and gauge-based products, using different precipitation computation recipes, is evaluated using statistical and hydrological methods in northeastern China. In addition, a framework quantifying uncertainty contributions of precipitation products, hydrological models, and their interactions to uncertainties in ensemble discharges is proposed. The investigated precipitation products are Tropical Rainfall Measuring Mission (TRMM) products (TRMM3B42 and TRMM3B42RT), Global Land Data Assimilation System (GLDAS)/Noah, Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and a Global Satellite Mapping of Precipitation (GSMAP-MVK+) product. Two hydrological models of different complexities, i.e. a water and energy budget-based distributed hydrological model and a physically based semi-distributed hydrological model, are employed to investigate the influence of hydrological models on simulated discharges. Results show APHRODITE has high accuracy at a monthly scale compared with other products, and GSMAP-MVK+ shows huge advantage and is better than TRMM3B42 in relative bias (RB), Nash-Sutcliffe coefficient of efficiency (NSE), root mean square error (RMSE), correlation coefficient (CC), false alarm ratio, and critical success index. These findings could be very useful for validation, refinement, and future development of satellite-based products (e.g. NASA Global Precipitation Measurement). Although large uncertainty exists in heavy precipitation, hydrological models contribute most of the uncertainty in extreme discharges. Interactions between precipitation products and hydrological models can have the similar magnitude of contribution to discharge uncertainty as the hydrological models. A

Forecasting SPEI and SPI Drought Indices Using the Integrated Artificial Neural Networks

Directory of Open Access Journals (Sweden)

Petr Maca

2016-01-01

Full Text Available The presented paper compares forecast of drought indices based on two different models of artificial neural networks. The first model is based on feedforward multilayer perceptron, sANN, and the second one is the integrated neural network model, hANN. The analyzed drought indices are the standardized precipitation index (SPI and the standardized precipitation evaporation index (SPEI and were derived for the period of 1948–2002 on two US catchments. The meteorological and hydrological data were obtained from MOPEX experiment. The training of both neural network models was made by the adaptive version of differential evolution, JADE. The comparison of models was based on six model performance measures. The results of drought indices forecast, explained by the values of four model performance indices, show that the integrated neural network model was superior to the feedforward multilayer perceptron with one hidden layer of neurons.

Effect of precipitation spatial distribution uncertainty on the uncertainty bounds of a snowmelt runoff model output

Science.gov (United States)

Jacquin, A. P.

2012-04-01

This study analyses the effect of precipitation spatial distribution uncertainty on the uncertainty bounds of a snowmelt runoff model's discharge estimates. Prediction uncertainty bounds are derived using the Generalized Likelihood Uncertainty Estimation (GLUE) methodology. The model analysed is a conceptual watershed model operating at a monthly time step. The model divides the catchment into five elevation zones, where the fifth zone corresponds to the catchment glaciers. Precipitation amounts at each elevation zone i are estimated as the product between observed precipitation (at a single station within the catchment) and a precipitation factor FPi. Thus, these factors provide a simplified representation of the spatial variation of precipitation, specifically the shape of the functional relationship between precipitation and height. In the absence of information about appropriate values of the precipitation factors FPi, these are estimated through standard calibration procedures. The catchment case study is Aconcagua River at Chacabuquito, located in the Andean region of Central Chile. Monte Carlo samples of the model output are obtained by randomly varying the model parameters within their feasible ranges. In the first experiment, the precipitation factors FPi are considered unknown and thus included in the sampling process. The total number of unknown parameters in this case is 16. In the second experiment, precipitation factors FPi are estimated a priori, by means of a long term water balance between observed discharge at the catchment outlet, evapotranspiration estimates and observed precipitation. In this case, the number of unknown parameters reduces to 11. The feasible ranges assigned to the precipitation factors in the first experiment are slightly wider than the range of fixed precipitation factors used in the second experiment. The mean squared error of the Box-Cox transformed discharge during the calibration period is used for the evaluation of the

Collaborative networks: Reference modeling

NARCIS (Netherlands)

Camarinha-Matos, L.M.; Afsarmanesh, H.

2008-01-01

Collaborative Networks: Reference Modeling works to establish a theoretical foundation for Collaborative Networks. Particular emphasis is put on modeling multiple facets of collaborative networks and establishing a comprehensive modeling framework that captures and structures diverse perspectives of

Mineral Precipitation in Fractures: Multiscale Imaging and Geochemical Modeling

Science.gov (United States)

Hajirezaie, S.; Peters, C. A.; Swift, A.; Sheets, J. M.; Cole, D. R.; Crandall, D.; Cheshire, M.; Stack, A. G.; Anovitz, L. M.

2017-12-01

For subsurface energy technologies such as geologic carbon sequestration, fractures are potential pathways for fluid migration from target formations. Highly permeable fractures may become sealed by mineral precipitation. In this study, we examined shale specimens with existing cemented fractures as natural analogues, using an array of imaging methods to characterize mineralogy and porosity at several spatial scales. In addition, we used reactive transport modeling to investigate geochemical conditions that can lead to extensive mineral precipitation and to simulate the impacts on fracture hydraulic properties. The naturally-cemented fractured rock specimens were from the Upper Wolfcamp formation in Texas, at 10,000 ft depth. The specimens were scanned using x-ray computed tomography (xCT) at resolution of 13 microns. The xCT images revealed an original fracture aperture of 1.9 mm filled with several distinct mineral phases and vuggy void regions, and the mineral phase volumes and surface areas were quantified and mapped in 3D. Specimens were thin-sectioned and examined at micron- and submicron-scales using petrographic microscopy (PM), scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDX), and small angle X-ray scattering (SAXS). Collectively these methods revealed crystals of dolomite as large as 900 microns in length overlain with a heterogeneous mixture of carbonate minerals including calcite, dolomite, and Fe-rich dolomite, interspersed at spatial scales as small as 5 microns. In addition, secondary precipitation of SiO2 was found to fill some of the void space. This multiscale imaging was used to inform the reactive transport modeling employed to examine the conditions that can cause the observed mineral precipitation in fractures at a larger scale. Two brines containing solutions that when mixed would lead to precipitation of various carbonate minerals were simulated as injectants into a fracture domain. In particular, the competing

Complex Environmental Data Modelling Using Adaptive General Regression Neural Networks

Science.gov (United States)

Kanevski, Mikhail

2015-04-01

The research deals with an adaptation and application of Adaptive General Regression Neural Networks (GRNN) to high dimensional environmental data. GRNN [1,2,3] are efficient modelling tools both for spatial and temporal data and are based on nonparametric kernel methods closely related to classical Nadaraya-Watson estimator. Adaptive GRNN, using anisotropic kernels, can be also applied for features selection tasks when working with high dimensional data [1,3]. In the present research Adaptive GRNN are used to study geospatial data predictability and relevant feature selection using both simulated and real data case studies. The original raw data were either three dimensional monthly precipitation data or monthly wind speeds embedded into 13 dimensional space constructed by geographical coordinates and geo-features calculated from digital elevation model. GRNN were applied in two different ways: 1) adaptive GRNN with the resulting list of features ordered according to their relevancy; and 2) adaptive GRNN applied to evaluate all possible models N [in case of wind fields N=(2^13 -1)=8191] and rank them according to the cross-validation error. In both cases training were carried out applying leave-one-out procedure. An important result of the study is that the set of the most relevant features depends on the month (strong seasonal effect) and year. The predictabilities of precipitation and wind field patterns, estimated using the cross-validation and testing errors of raw and shuffled data, were studied in detail. The results of both approaches were qualitatively and quantitatively compared. In conclusion, Adaptive GRNN with their ability to select features and efficient modelling of complex high dimensional data can be widely used in automatic/on-line mapping and as an integrated part of environmental decision support systems. 1. Kanevski M., Pozdnoukhov A., Timonin V. Machine Learning for Spatial Environmental Data. Theory, applications and software. EPFL Press

In-Drift Precipitates/Salts Model

International Nuclear Information System (INIS)

P. Mariner

2004-01-01

This report documents the development and validation of the in-drift precipitates/salts (IDPS) model. The IDPS model is a geochemical model designed to predict the postclosure effects of evaporation and deliquescence on the chemical composition of water within the Engineered Barrier System (EBS) in support of the Total System Performance Assessment for the License Application (TSPA-LA). Application of the model in support of TSPA-LA is documented in ''Engineered Barrier System: Physical and Chemical Environment Model'' (BSC 2004 [DIRS 169860]). Technical Work Plan for: Near-Field Environment and Transport In-Drift Geochemistry Model Report Integration (BSC 2004 [DIRS 171156]) is the technical work plan (TWP) for this report. It called for a revision of the previous version of the report (BSC 2004 [DIRS 167734]) to achieve greater transparency, readability, data traceability, and report integration. The intended use of the IDPS model is to estimate and tabulate, within an appropriate level of confidence, the effects of evaporation, deliquescence, and potential environmental conditions on the pH, ionic strength, and chemical compositions of water and minerals on the drip shield or other location within the drift during the postclosure period. Specifically, the intended use is as follows: (1) To estimate, within an appropriate level of confidence, the effects of evaporation and deliquescence on the presence and composition of water occurring within the repository during the postclosure period (i.e., effects on pH, ionic strength, deliquescence relative humidity, total concentrations of dissolved components in the system Na-K-H-Mg-Ca-Al-Cl-F-NO 3 -SO 4 -Br-CO 3 -SiO 2 -CO 2 -O 2 -H 2 O, and concentrations of the following aqueous species that potentially affect acid neutralizing capacity: HCO 3 - , CO 3 2- , OH - , H + , HSO 4 - , Ca 2+ , Mg 2+ , CaHCO 3 + , MgHCO 3 + , HSiO 3 - , and MgOH + ); (2) To estimate, within an appropriate level of confidence, mineral

A new model for prediction of dispersoid precipitation in aluminium alloys containing zirconium and scandium

International Nuclear Information System (INIS)

Robson, J.D.

2004-01-01

A model has been developed to predict precipitation of ternary Al 3 (Sc, Zr) dispersoids in aluminium alloys containing zirconium and scandium. The model is based on the classical numerical method of Kampmann and Wagner, extended to predict precipitation of a ternary phase. The model has been applied to the precipitation of dispersoids in scandium containing AA7050. The dispersoid precipitation kinetics and number density are predicted to be sensitive to the scandium concentration, whilst the dispersoid radius is not. The dispersoids are predicted to enrich in zirconium during precipitation. Coarsening has been investigated in detail and it has been predicted that a steady-state size distribution is only reached once coarsening is well advanced. The addition of scandium is predicted to eliminate the dispersoid free zones observed in scandium free 7050, greatly increasing recrystallization resistance

Impact of Asian Aerosols on Precipitation Over California: An Observational and Model Based Approach

Science.gov (United States)

Naeger, Aaron R.; Molthan, Andrew L.; Zavodsky, Bradley T.; Creamean, Jessie M.

2015-01-01

Dust and pollution emissions from Asia are often transported across the Pacific Ocean to over the western United States. Therefore, it is essential to fully understand the impact of these aerosols on clouds and precipitation forming over the eastern Pacific and western United States, especially during atmospheric river events that account for up to half of California's annual precipitation and can lead to widespread flooding. In order for numerical modeling simulations to accurately represent the present and future regional climate of the western United States, we must account for the aerosol-cloud-precipitation interactions associated with Asian dust and pollution aerosols. Therefore, we have constructed a detailed study utilizing multi-sensor satellite observations, NOAA-led field campaign measurements, and targeted numerical modeling studies where Asian aerosols interacted with cloud and precipitation processes over the western United States. In particular, we utilize aerosol optical depth retrievals from the NASA Moderate Resolution Imaging Spectroradiometer (MODIS), NOAA Geostationary Operational Environmental Satellite (GOES-11), and Japan Meteorological Agency (JMA) Multi-functional Transport Satellite (MTSAT) to effectively detect and monitor the trans-Pacific transport of Asian dust and pollution. The aerosol optical depth (AOD) retrievals are used in assimilating the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) in order to provide the model with an accurate representation of the aerosol spatial distribution across the Pacific. We conduct WRF-Chem model simulations of several cold-season atmospheric river events that interacted with Asian aerosols and brought significant precipitation over California during February-March 2011 when the NOAA CalWater field campaign was ongoing. The CalWater field campaign consisted of aircraft and surface measurements of aerosol and precipitation processes that help extensively validate our WRF

Simulation of daily streamflows at gaged and ungaged locations within the Cedar River Basin, Iowa, using a Precipitation-Runoff Modeling System model

Science.gov (United States)

Christiansen, Daniel E.

2012-01-01

The U.S. Geological Survey, in cooperation with the Iowa Department of Natural Resources, conducted a study to examine techniques for estimation of daily streamflows using hydrological models and statistical methods. This report focuses on the use of a hydrologic model, the U.S. Geological Survey's Precipitation-Runoff Modeling System, to estimate daily streamflows at gaged and ungaged locations. The Precipitation-Runoff Modeling System is a modular, physically based, distributed-parameter modeling system developed to evaluate the impacts of various combinations of precipitation, climate, and land use on surface-water runoff and general basin hydrology. The Cedar River Basin was selected to construct a Precipitation-Runoff Modeling System model that simulates the period from January 1, 2000, to December 31, 2010. The calibration period was from January 1, 2000, to December 31, 2004, and the validation periods were from January 1, 2005, to December 31, 2010 and January 1, 2000 to December 31, 2010. A Geographic Information System tool was used to delineate the Cedar River Basin and subbasins for the Precipitation-Runoff Modeling System model and to derive parameters based on the physical geographical features. Calibration of the Precipitation-Runoff Modeling System model was completed using a U.S. Geological Survey calibration software tool. The main objective of the calibration was to match the daily streamflow simulated by the Precipitation-Runoff Modeling System model with streamflow measured at U.S. Geological Survey streamflow gages. The Cedar River Basin daily streamflow model performed with a Nash-Sutcliffe efficiency ranged from 0.82 to 0.33 during the calibration period, and a Nash-Sutcliffe efficiency ranged from 0.77 to -0.04 during the validation period. The Cedar River Basin model is meeting the criteria of greater than 0.50 Nash-Sutcliffe and is a good fit for streamflow conditions for the calibration period at all but one location, Austin, Minnesota

Descriptive and predictive evaluation of high resolution Markov chain precipitation models

DEFF Research Database (Denmark)

Sørup, Hjalte Jomo Danielsen; Madsen, Henrik; Arnbjerg-Nielsen, Karsten

2012-01-01

A time series of tipping bucket recordings of very high temporal and volumetric resolution precipitation is modelled using Markov chain models. Both first and second‐order Markov models as well as seasonal and diurnal models are investigated and evaluated using likelihood based techniques. The fi...

Projected changes in precipitation intensity and frequency over complex topography: a multi-model perspective

Science.gov (United States)

Fischer, Andreas; Keller, Denise; Liniger, Mark; Rajczak, Jan; Schär, Christoph; Appenzeller, Christof

2014-05-01

Fundamental changes in the hydrological cycle are expected in a future warmer climate. This is of particular relevance for the Alpine region, as a source and reservoir of several major rivers in Europe and being prone to extreme events such as floodings. For this region, climate change assessments based on the ENSEMBLES regional climate models (RCMs) project a significant decrease in summer mean precipitation under the A1B emission scenario by the mid-to-end of this century, while winter mean precipitation is expected to slightly rise. From an impact perspective, projected changes in seasonal means, however, are often insufficient to adequately address the multifaceted challenges of climate change adaptation. In this study, we revisit the full matrix of the ENSEMBLES RCM projections regarding changes in frequency and intensity, precipitation-type (convective versus stratiform) and temporal structure (wet/dry spells and transition probabilities) over Switzerland and surroundings. As proxies for raintype changes, we rely on the model parameterized convective and large-scale precipitation components. Part of the analysis involves a Bayesian multi-model combination algorithm to infer changes from the multi-model ensemble. The analysis suggests a summer drying that evolves altitude-specific: over low-land regions it is associated with wet-day frequency decreases of convective and large-scale precipitation, while over elevated regions it is primarily associated with a decline in large-scale precipitation only. As a consequence, almost all the models project an increase in the convective fraction at elevated Alpine altitudes. The decrease in the number of wet days during summer is accompanied by decreases (increases) in multi-day wet (dry) spells. This shift in multi-day episodes also lowers the likelihood of short dry spell occurrence in all of the models. For spring and autumn the combined multi-model projections indicate higher mean precipitation intensity north of the

Hourly and Daily Precipitation Data

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Precipitation reports submitted on many form types, including tabular and autographic charts. Reports are almost exclusively from the US Cooperative Observer Network.

Structural changes in precipitates and cell model for the conversion of amorphous calcium phosphate to hydroxyapatite during the initial stage of precipitation

Science.gov (United States)

Zyman, Z.; Rokhmistrov, D.; Glushko, V.

2012-08-01

A new insight on the conversion of an amorphous calcium phosphate, ACP, to hydroxyapatite, HA, has been proposed. The ACP has been precipitated under appropriate conditions of the nitrous method (low concentrations of reactants, pH>10, 25 °С, fast mixing). The ACP to HA conversion has been found to commence immediately after the ACP precipitation. The conversion reveals itself in the first detected shift of the diffuse maximum from 29.5° 2θ (ACP) to about 32° 2θ (the position of principal peaks of HA) in the XRD patterns for the precipitates of 2 min-6 h lifetimes. The precipitates are biphasic mixtures of ACP and nanocrystalline HA, nHA, with increasing nHA/ACP ratio for longer lifetimes. Characteristics of the simulated XRD profiles calculated proceeding on such a picture are excellently confirmed by experimental results. At the end of the conversion, HA nanocrystals start growing. This follows from the appearance of broadened diffraction maxima, which gradually sharpen, along with the appearance and gradual increase of splitting of the initially featureless υ3 and υ4PO43- bands in the IR spectra of precipitates with their aging (after 6 h of the precipitation). Based on the detected structural and compositional peculiarities of ACP in the early stage of precipitation, a cell model for the HA crystallization has been proposed. Proceeding on the model, the principal data in this and earlier studies, considering the ACP to HA conversion as an internal rearrangement process in the ACP particles, has been reasonably explained.

The new Passive microwave Neural network Precipitation Retrieval (PNPR algorithm for the cross-track scanning ATMS radiometer: description and verification study over Europe and Africa using GPM and TRMM spaceborne radars

Directory of Open Access Journals (Sweden)

P. Sanò

2016-11-01

Full Text Available The objective of this paper is to describe the development and evaluate the performance of a completely new version of the Passive microwave Neural network Precipitation Retrieval (PNPR v2, an algorithm based on a neural network approach, designed to retrieve the instantaneous surface precipitation rate using the cross-track Advanced Technology Microwave Sounder (ATMS radiometer measurements. This algorithm, developed within the EUMETSAT H-SAF program, represents an evolution of the previous version (PNPR v1, developed for AMSU/MHS radiometers (and used and distributed operationally within H-SAF, with improvements aimed at exploiting the new precipitation-sensing capabilities of ATMS with respect to AMSU/MHS. In the design of the neural network the new ATMS channels compared to AMSU/MHS, and their combinations, including the brightness temperature differences in the water vapor absorption band, around 183 GHz, are considered. The algorithm is based on a single neural network, for all types of surface background, trained using a large database based on 94 cloud-resolving model simulations over the European and the African areas. The performance of PNPR v2 has been evaluated through an intercomparison of the instantaneous precipitation estimates with co-located estimates from the TRMM Precipitation Radar (TRMM-PR and from the GPM Core Observatory Ku-band Precipitation Radar (GPM-KuPR. In the comparison with TRMM-PR, over the African area the statistical analysis was carried out for a 2-year (2013–2014 dataset of coincident observations over a regular grid at 0.5°  ×  0.5° resolution. The results have shown a good agreement between PNPR v2 and TRMM-PR for the different surface types. The correlation coefficient (CC was equal to 0.69 over ocean and 0.71 over vegetated land (lower values were obtained over arid land and coast, and the root mean squared error (RMSE was equal to 1.30 mm h−1 over ocean and 1.11 mm h−1 over

Changes and Attribution of Extreme Precipitation in Climate Models: Subdaily and Daily Scales

Science.gov (United States)

Zhang, W.; Villarini, G.; Scoccimarro, E.; Vecchi, G. A.

2017-12-01

Extreme precipitation events are responsible for numerous hazards, including flooding, soil erosion, and landslides. Because of their significant socio-economic impacts, the attribution and projection of these events is of crucial importance to improve our response, mitigation and adaptation strategies. Here we present results from our ongoing work.In terms of attribution, we use idealized experiments [pre-industrial control experiment (PI) and 1% per year increase (1%CO2) in atmospheric CO2] from ten general circulation models produced under the Coupled Model Intercomparison Project Phase 5 (CMIP5) and the fraction of attributable risk to examine the CO2 effects on extreme precipitation at the sub-daily and daily scales. We find that the increased CO2 concentration substantially increases the odds of the occurrence of sub-daily precipitation extremes compared to the daily scale in most areas of the world, with the exception of some regions in the sub-tropics, likely in relation to the subsidence of the Hadley Cell. These results point to the large role that atmospheric CO2 plays in extreme precipitation under an idealized framework. Furthermore, we investigate the changes in extreme precipitation events with the Community Earth System Model (CESM) climate experiments using the scenarios consistent with the 1.5°C and 2°C temperature targets. We find that the frequency of annual extreme precipitation at a global scale increases in both 1.5°C and 2°C scenarios until around 2070, after which the magnitudes of the trend become much weaker or even negative. Overall, the frequency of global annual extreme precipitation is similar between 1.5°C and 2°C for the period 2006-2035, and the changes in extreme precipitation in individual seasons are consistent with those for the entire year. The frequency of extreme precipitation in the 2°C experiments is higher than for the 1.5°C experiment after the late 2030s, particularly for the period 2071-2100.

Impact of precipitation spatial resolution on the hydrological response of an integrated distributed water resources model

DEFF Research Database (Denmark)

Fu, Suhua; Sonnenborg, Torben; Jensen, Karsten Høgh

2011-01-01

Precipitation is a key input variable to hydrological models, and the spatial variability of the input is expected to impact the hydrological response predicted by a distributed model. In this study, the effect of spatial resolution of precipitation on runoff , recharge and groundwater head...... of the total catchment and runoff discharge hydrograph at watershed outlet. On the other hand, groundwater recharge and groundwater head were both aff ected. The impact of the spatial resolution of precipitation input is reduced with increasing catchment size. The effect on stream discharge is relatively low...... was analyzed in the Alergaarde catchment in Denmark. Six different precipitation spatial resolutions were used as inputs to a physically based, distributed hydrological model, the MIKE SHE model. The results showed that the resolution of precipitation input had no apparent effect on annual water balance...

Modelling Particulate Removal in Tubular Wet Electrostatic Precipitators Using a Modified Drift Flux Model

Directory of Open Access Journals (Sweden)

S Ramechecandane

2016-09-01

Full Text Available Tubular electrostatic precipitators (ESP have been used in a number of chemical processing industries. The tubular ESPs have many advantages over conventional plate-plate and wire-plate ESPs. The present study is concerned with the numerical modeling of particulate removal in a tubular wet single-stage electrostatic precipitator (wESP. The geometric parameters of a model wESP and the corresponding inlet gas velocities for the wESP are chosen from available experimental data. In addition to the RNG k - ε model for the mean turbulent flow field inside the wESP, the Poisson equation for the electric field, the charge continuity equation and the concentration equation are solved sequentially to obtain a full-fledged solution to the problem under investigation. The proposed drift flux model is implemented in the opensource CFD code OpenFOAM®. The paper discusses the influence of the number of charges acquired by the particles and the corresponding inlet gas velocities on particle concentration distribution within the wESP. Two representative cases with monodispersed particles of 1 μm and 10 μm diameter are considered for the numerical analysis. It is seen from the present analysis that the number of units of charge on particles, the particle size and the inlet gas velocities play a vital role in determining the efficiency of electrostatic precipitation.

Multi-model Projection of July-August Climate Extreme Changes over China under CO2 Doubling. Part Ⅰ:Precipitation

Institute of Scientific and Technical Information of China (English)

LI Hongmei; FENG Lei; ZHOU Tianjun

2011-01-01

Potential changes in precipitation extremes in July-August over China in response to CO2 doubling are analyzed based on the output of 24 coupled climate models from the Twentieth-Century Climate in Coupled Models (20C3M) experiment and the 1％ per year CO2 increase experiment (to doubling) (lpctto2x) of phase 3 of the Coupled Model Inter-comparison Project (CMIP3). Evaluation of the models' performance in simulating the mean state shows that the majority of models fairly reproduce the broad spatial pattern of observed precipitation. However, all the models underestimate extreme precipitation by ～50％. The spread among the models over the Tibetan Plateau is ～2-3 times larger than that over the other areas.Models with higher resolution generally perform better than those with lower resolutions in terms of spatial pattern and precipitation amount. Under the lpctto2x scenario, the ratio between the absolute value of MME extreme precipitation change and model spread is larger than that of total precipitation, indicating a relatively robust change of extremes. The change of extreme precipitation is more homogeneous than the total precipitation. Analysis on the output of Geophysical Fluid Dynamics Laboratory coupled climate model version 2.1 (GFDL-CM2.1) indicates that the spatially consistent increase of surface temperature and water vapor content contribute to the large increase of extreme precipitation over contiguous China,which follows the Clausius-Clapeyron relationship. Whereas, the meridionally tri-polar pattern of mean precipitation change over eastern China is dominated by the change of water vapor convergence, which is determined by the response of monsoon circulation to global warming.

Characterizing 3-D flow velocity in evolving pore networks driven by CaCO3 precipitation and dissolution

Science.gov (United States)

Chojnicki, K. N.; Yoon, H.; Martinez, M. J.

2015-12-01

Understanding reactive flow in geomaterials is important for optimizing geologic carbon storage practices, such as using pore space efficiently. Flow paths can be complex in large degrees of geologic heterogeneities across scales. In addition, local heterogeneity can evolve as reactive transport processes alter the pore-scale morphology. For example, dissolved carbon dioxide may react with minerals in fractured rocks, confined aquifers, or faults, resulting in heterogeneous cementation (and/or dissolution) and evolving flow conditions. Both path and flow complexities are important and poorly characterized, making it difficult to determine their evolution with traditional 2-D transport models. Here we characterize the development of 3-D pore-scale flow with an evolving pore configuration due to calcium carbonate (CaCO3) precipitation and dissolution. A simple pattern of a microfluidic pore network is used initially and pore structures will become more complex due to precipitation and dissolution processes. At several stages of precipitation and dissolution, we directly visualize 3-D velocity vectors using micro particle image velocimetry and a laser scanning confocal microscope. Measured 3-D velocity vectors are then compared to 3-D simulated flow fields which will be used to simulate reactive transport. Our findings will highlight the importance of the 3-D flow dynamics and its impact on estimating reactive surface area over time. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000. This material is based upon work supported as part of the Center for Frontiers of Subsurface Energy Security, an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences under Award Number DE-SC0001114.

Wax Precipitation Modeled with Many Mixed Solid Phases

DEFF Research Database (Denmark)

Heidemann, Robert A.; Madsen, Jesper; Stenby, Erling Halfdan

2005-01-01

The behavior of the Coutinho UNIQUAC model for solid wax phases has been examined. The model can produce as many mixed solid phases as the number of waxy components. In binary mixtures, the solid rich in the lighter component contains little of the heavier component but the second phase shows sub......-temperature and low-temperature forms, are pure. Model calculations compare well with the data of Pauly et al. for C18 to C30 waxes precipitating from n-decane solutions. (C) 2004 American Institute of Chemical Engineers....

Geochemical models of the precipitation of halite (NaCl) in gas fields

International Nuclear Information System (INIS)

Lombana O, Jose L; Jaramillo, Elizabeth A; Alzate E, Guillermo A

2005-01-01

The reservoir modeling is a tool that every day takes more importance in the petroleum industry due to multiple problems presented an also that can be afforded by it during the production of reservoir fluids from the porous media in petroleum reservoir. One of these is the formation damage which is reflected as a petrophysic properties change, caused among other factors by the scale precipitation of halite (NaCl) as a consequence of the original state alteration and thermodynamic balance disruption between the porous media and the fluids inside by the gas flow. By the gas flow over the connate water, the porous media reduce its water saturation due to water transferring from liquid to gas state. In this study, a numeric model is developed to model the formation damage for halite precipitation. The model covers one-dimensional monophasic and iso thermic gas flow and evaluates the porosity and permeability changes of porous media due to halite precipitation. The model application for different conditions of temperature, connate water salinity, water saturation,and porosity indicates the following: the biggest damage is caused to the beginning of the porous media,temperature influences considerably the water vaporization rate and therefore the amount of halite precipitation, the lower the porosity of the porous media the bigger the formation damage degree, and finally, higher salinity and water saturation for the connate water in the porous media higher the formation damage degree is reached by the gas flow.

Conditional Stochastic Models in Reduced Space: Towards Efficient Simulation of Tropical Cyclone Precipitation Patterns

Science.gov (United States)

Dodov, B.

2017-12-01

Stochastic simulation of realistic and statistically robust patterns of Tropical Cyclone (TC) induced precipitation is a challenging task. It is even more challenging in a catastrophe modeling context, where tens of thousands of typhoon seasons need to be simulated in order to provide a complete view of flood risk. Ultimately, one could run a coupled global climate model and regional Numerical Weather Prediction (NWP) model, but this approach is not feasible in the catastrophe modeling context and, most importantly, may not provide TC track patterns consistent with observations. Rather, we propose to leverage NWP output for the observed TC precipitation patterns (in terms of downscaled reanalysis 1979-2015) collected on a Lagrangian frame along the historical TC tracks and reduced to the leading spatial principal components of the data. The reduced data from all TCs is then grouped according to timing, storm evolution stage (developing, mature, dissipating, ETC transitioning) and central pressure and used to build a dictionary of stationary (within a group) and non-stationary (for transitions between groups) covariance models. Provided that the stochastic storm tracks with all the parameters describing the TC evolution are already simulated, a sequence of conditional samples from the covariance models chosen according to the TC characteristics at a given moment in time are concatenated, producing a continuous non-stationary precipitation pattern in a Lagrangian framework. The simulated precipitation for each event is finally distributed along the stochastic TC track and blended with a non-TC background precipitation using a data assimilation technique. The proposed framework provides means of efficient simulation (10000 seasons simulated in a couple of days) and robust typhoon precipitation patterns consistent with observed regional climate and visually undistinguishable from high resolution NWP output. The framework is used to simulate a catalog of 10000 typhoon

Understanding SMAP-L4 soil moisture estimation skill and their dependence with topography, precipitation and vegetation type using Mesonet and Micronet networks.

Science.gov (United States)

Moreno, H. A.; Basara, J. B.; Thompson, E.; Bertrand, D.; Johnston, C. S.

2017-12-01

Soil moisture measurements using satellite information can benefit from a land data assimilation model Goddard Earth Observing System (GEOS-5) and land data assimilation system (LDAS) to improve the representation of fine-scale dynamics and variability. This work presents some advances to understand the predictive skill of L4-SM product across different land-cover types, topography and precipitation totals, by using a dense network of multi-level soil moisture sensors (i.e. Mesonet and Micronet) in Oklahoma. 130 soil moisture stations are used across different precipitation gradients (i.e. arid vs wet), land cover (e.g. forest, shrubland, grasses, crops), elevation (low, mid and high) and slope to assess the improvements by the L4_SM product relative to the raw SMAP L-band brightness temperatures. The comparisons are conducted between July 2015 and July 2016 at the daily time scale. Results show the highest L4-SM overestimations occur in pastures and cultivated crops, during the rainy season and at higher elevation lands (over 800 meters asl). The smallest errors occur in low elevation lands, low rainfall and developed lands. Forested area's soil moisture biases lie in between pastures (max biases) and low intensity/developed lands (min biases). Fine scale assessment of L4-SM should help GEOS-5 and LDAS teams refine model parameters in light of observed differences and improve assimilation techniques in light of land-cover, topography and precipitation regime. Additionally, regional decision makers could have a framework to weight the utility of this product for water resources applications.

On the importance of appropriate precipitation gauge catch correction for hydrological modelling at mid to high latitudes

Science.gov (United States)

Stisen, S.; Højberg, A. L.; Troldborg, L.; Refsgaard, J. C.; Christensen, B. S. B.; Olsen, M.; Henriksen, H. J.

2012-11-01

Precipitation gauge catch correction is often given very little attention in hydrological modelling compared to model parameter calibration. This is critical because significant precipitation biases often make the calibration exercise pointless, especially when supposedly physically-based models are in play. This study addresses the general importance of appropriate precipitation catch correction through a detailed modelling exercise. An existing precipitation gauge catch correction method addressing solid and liquid precipitation is applied, both as national mean monthly correction factors based on a historic 30 yr record and as gridded daily correction factors based on local daily observations of wind speed and temperature. The two methods, named the historic mean monthly (HMM) and the time-space variable (TSV) correction, resulted in different winter precipitation rates for the period 1990-2010. The resulting precipitation datasets were evaluated through the comprehensive Danish National Water Resources model (DK-Model), revealing major differences in both model performance and optimised model parameter sets. Simulated stream discharge is improved significantly when introducing the TSV correction, whereas the simulated hydraulic heads and multi-annual water balances performed similarly due to recalibration adjusting model parameters to compensate for input biases. The resulting optimised model parameters are much more physically plausible for the model based on the TSV correction of precipitation. A proxy-basin test where calibrated DK-Model parameters were transferred to another region without site specific calibration showed better performance for parameter values based on the TSV correction. Similarly, the performances of the TSV correction method were superior when considering two single years with a much dryer and a much wetter winter, respectively, as compared to the winters in the calibration period (differential split-sample tests). We conclude that TSV

On the importance of appropriate precipitation gauge catch correction for hydrological modelling at mid to high latitudes

Directory of Open Access Journals (Sweden)

S. Stisen

2012-11-01

Full Text Available Precipitation gauge catch correction is often given very little attention in hydrological modelling compared to model parameter calibration. This is critical because significant precipitation biases often make the calibration exercise pointless, especially when supposedly physically-based models are in play. This study addresses the general importance of appropriate precipitation catch correction through a detailed modelling exercise. An existing precipitation gauge catch correction method addressing solid and liquid precipitation is applied, both as national mean monthly correction factors based on a historic 30 yr record and as gridded daily correction factors based on local daily observations of wind speed and temperature. The two methods, named the historic mean monthly (HMM and the time–space variable (TSV correction, resulted in different winter precipitation rates for the period 1990–2010. The resulting precipitation datasets were evaluated through the comprehensive Danish National Water Resources model (DK-Model, revealing major differences in both model performance and optimised model parameter sets. Simulated stream discharge is improved significantly when introducing the TSV correction, whereas the simulated hydraulic heads and multi-annual water balances performed similarly due to recalibration adjusting model parameters to compensate for input biases. The resulting optimised model parameters are much more physically plausible for the model based on the TSV correction of precipitation. A proxy-basin test where calibrated DK-Model parameters were transferred to another region without site specific calibration showed better performance for parameter values based on the TSV correction. Similarly, the performances of the TSV correction method were superior when considering two single years with a much dryer and a much wetter winter, respectively, as compared to the winters in the calibration period (differential split-sample tests

A model for the formation of lattice defects at silicon oxide precipitates in silicon

International Nuclear Information System (INIS)

Vanhellemont, J.; Gryse, O. de; Clauws, P.

2003-01-01

The critical size of silicon oxide precipitates and the formation of lattice defects by the precipitates are discussed. An expression is derived allowing estimation of self-interstitial emission by spherical precipitates as well as strain build-up during precipitate growth. The predictions are compared with published experimental data. A model for stacking fault nucleation at oxide precipitates is developed based on strain and self-interstitial accumulation during the thermal history of the wafer. During a low-temperature treatment high levels of strain develop. During subsequent high-temperature treatment, excess strain energy in the precipitate is released by self-interstitial emission leading to favourable conditions for stacking fault nucleation

Future Precipitation Extremes in China Under Climate Change and Their Possible Mechanisms by Regional Climate Model and Earth System Model Simulations

Science.gov (United States)

Qin, P.; Xie, Z.

2017-12-01

Future precipitation extremes in China for the mid and end of 21st century were detected with six simulations using the regional climate model RegCM4 (RCM) and 17 global climate models (GCM) participated in the coupled Model Intercomparison Project Phase 5 (CMIP5). Prior to understanding the future changes in precipitation extremes, we overviewed the performance of precipitation extremes simulated by the CMIP5s and RCMs, and found both CMIP5s and RCMs could capture the temporal and spatial pattern of the historical precipitation extremes in China. In the mid-future period 2039-2058 (MF) and far-future 2079-2098 (FF), more wet precipitation extremes will occur in most area of China relative to the present period 1982-2001 (RF). We quantified the rates of the changes in precipitation extremes in China with the changes in air surface temperature (T2M) for the MF and FF period. Changes in precipitation extremes R95p were found around 5% K-1 for the MF period and 10% K-1 for the FF period, and changes in maximum 5 day precipitation (Rx5day) were detected around 4% K-1 for the MF period and 7% K-1 for the FF period, respectively. Finally, the possible physical mechanisms behind the changes in precipitation extremes in China were also discussed through the changes in specific humidity and vertical wind.

Precipitation observations for operational flood forecasting in Scotland: Data availability, limitations and the impact of observational uncertainty

Science.gov (United States)

Parry, Louise; Neely, Ryan, III; Bennett, Lindsay; Collier, Chris; Dufton, David

2017-04-01

The Scottish Environment Protection Agency (SEPA) has a statutory responsibility to provide flood warning across Scotland. It achieves this through an operational partnership with the UK Met Office wherein meteorological forecasts are applied to a national distributed hydrological model, Grid- to- Grid (G2G), and catchment specific lumped PDM models. Both of these model types rely on observed precipitation input for model development and calibration, and operationally for historical runs to generate initial conditions. Scotland has an average annual precipitation of 1430mm per annum (1971-2000), but the spatial variability in totals is high, predominantly in relation to the topography and prevailing winds, which poses different challenges to both radar and point measurement methods of observation. In addition, the high elevations mean that in winter a significant proportion of precipitation falls as snow. For the operational forecasting models, observed rainfall data is provided in Near Real Time (NRT) from SEPA's network of approximately 260 telemetered TBR gauges and 4 UK Met Office C-band radars. Both data sources have their strengths and weaknesses, particularly in relation to the orography and spatial representativeness, but estimates of rainfall from the two methods can vary greatly. Northern Scotland, particularly near Inverness, is a comparatively sparse part of the radar network. Rainfall totals and distribution in this area are determined by the Northern Western Highlands and Cairngorms mountain ranges, which also have a negative impact on radar observations. In recognition of this issue, the NCAS mobile X-band weather radar (MXWR) was deployed in this area between February and August 2016. This study presents a comparison of rainfall estimates for the Inverness and Moray Firth region generated from the operational radar network, the TBR network, and the MXWR. Quantitative precipitation estimates (QPEs) from both sources of radar data were compared to

Getting water right: A case study in water yield modelling based on precipitation data.

Science.gov (United States)

Pessacg, Natalia; Flaherty, Silvia; Brandizi, Laura; Solman, Silvina; Pascual, Miguel

2015-12-15

Water yield is a key ecosystem service in river basins and especially in dry regions around the World. In this study we carry out a modelling analysis of water yields in the Chubut River basin, located in one of the driest districts of Patagonia, Argentina. We focus on the uncertainty around precipitation data, a driver of paramount importance for water yield. The objectives of this study are to: i) explore the spatial and numeric differences among six widely used global precipitation datasets for this region, ii) test them against data from independent ground stations, and iii) explore the effects of precipitation data uncertainty on simulations of water yield. The simulations were performed using the ecosystem services model InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) with each of the six different precipitation datasets as input. Our results show marked differences among datasets for the Chubut watershed region, both in the magnitude of precipitations and their spatial arrangement. Five of the precipitation databases overestimate the precipitation over the basin by 50% or more, particularly over the more humid western range. Meanwhile, the remaining dataset (Tropical Rainfall Measuring Mission - TRMM), based on satellite measurements, adjusts well to the observed rainfall in different stations throughout the watershed and provides a better representation of the precipitation gradient characteristic of the rain shadow of the Andes. The observed differences among datasets in the representation of the rainfall gradient translate into large differences in water yield simulations. Errors in precipitation of +30% (-30%) amplify to water yield errors ranging from 50 to 150% (-45 to -60%) in some sub-basins. These results highlight the importance of assessing uncertainties in main input data when quantifying and mapping ecosystem services with biophysical models and cautions about the undisputed use of global environmental datasets. Copyright �

The effect of a giant wind farm on precipitation in a regional climate model

International Nuclear Information System (INIS)

Fiedler, B H; Bukovsky, M S

2011-01-01

The Weather Research and Forecasting (WRF) model is employed as a nested regional climate model to study the effect of a giant wind farm on warm-season precipitation in the eastern two-thirds of the USA. The boundary conditions for WRF are supplied by 62 years of NCEP/NCAR (National Center for Environmental Prediction/National Center for Atmospheric Research) global reanalysis. In the model, the presence of a mid-west wind farm, either giant or small, can have an enormous impact on the weather and the amount of precipitation for one season, which is consistent with the known sensitivity of long-term weather forecasts to initial conditions. The effect on climate is less strong. In the average precipitation of 62 warm seasons, there is a statistically significant 1.0% enhancement of precipitation in a multi-state area surrounding and to the south-east of the wind farm.

Precipitation of metal sulphides using gaseous hydrogen sulphide: mathematical modelling

NARCIS (Netherlands)

Al Tarazi, M.Y.M.; Heesink, Albertus B.M.; Versteeg, Geert

2004-01-01

A mathematical model has been developed that describes the precipitation of metal sulffides in an aqueous solution containing two different heavy metal ions. The solution is assumed to consist of a well-mixed bulk and a boundary layer that is contacted with hydrogen sulphide gas. The model makes use

Precipitation of metal sulphides using gaseous hydrogen sulphide : mathematical modelling

NARCIS (Netherlands)

Tarazi, Mousa Al-; Heesink, A. Bert M.; Versteeg, Geert F.

2004-01-01

A mathematical model has been developed that describes the precipitation of metal sulphides in an aqueous solution containing two different heavy metal ions. The solution is assumed to consist of a well-mixed bulk and a boundary layer that is contacted with hydrogen sulphide gas. The model makes use

Modelling the Spatial Isotope Variability of Precipitation in Syria

Energy Technology Data Exchange (ETDEWEB)

Kattan, Z.; Kattaa, B. [Department of Geology, Atomic Energy Commission of Syria (AECS), Damascus (Syrian Arab Republic)

2013-07-15

Attempts were made to model the spatial variability of environmental isotope ({sup 18}O, {sup 2}H and {sup 3}H) compositions of precipitation in syria. Rainfall samples periodically collected on a monthly basis from 16 different stations were used for processing and demonstrating the spatial distributions of these isotopes, together with those of deuterium excess (d) values. Mathematically, the modelling process was based on applying simple polynomial models that take into consideration the effects of major geographic factors (Lon.E., Lat.N., and altitude). The modelling results of spatial distribution of stable isotopes ({sup 18}O and {sup 2}H) were generally good, as shown from the high correlation coefficients (R{sup 2} = 0.7-0.8), calculated between the observed and predicted values. In the case of deuterium excess and tritium distributions, the results were most likely approximates (R{sup 2} = 0.5-0.6). Improving the simulation of spatial isotope variability probably requires the incorporation of other local meteorological factors, such as relative air humidity, precipitation amount and vapour pressure, which are supposed to play an important role in such an arid country. (author)

Daily precipitation grids for Austria since 1961—development and evaluation of a spatial dataset for hydroclimatic monitoring and modelling

Science.gov (United States)

Hiebl, Johann; Frei, Christoph

2018-04-01

Spatial precipitation datasets that are long-term consistent, highly resolved and extend over several decades are an increasingly popular basis for modelling and monitoring environmental processes and planning tasks in hydrology, agriculture, energy resources management, etc. Here, we present a grid dataset of daily precipitation for Austria meant to promote such applications. It has a grid spacing of 1 km, extends back till 1961 and is continuously updated. It is constructed with the classical two-tier analysis, involving separate interpolations for mean monthly precipitation and daily relative anomalies. The former was accomplished by kriging with topographic predictors as external drift utilising 1249 stations. The latter is based on angular distance weighting and uses 523 stations. The input station network was kept largely stationary over time to avoid artefacts on long-term consistency. Example cases suggest that the new analysis is at least as plausible as previously existing datasets. Cross-validation and comparison against experimental high-resolution observations (WegenerNet) suggest that the accuracy of the dataset depends on interpretation. Users interpreting grid point values as point estimates must expect systematic overestimates for light and underestimates for heavy precipitation as well as substantial random errors. Grid point estimates are typically within a factor of 1.5 from in situ observations. Interpreting grid point values as area mean values, conditional biases are reduced and the magnitude of random errors is considerably smaller. Together with a similar dataset of temperature, the new dataset (SPARTACUS) is an interesting basis for modelling environmental processes, studying climate change impacts and monitoring the climate of Austria.

Improving the Statistical Modeling of the TRMM Extreme Precipitation Monitoring System

Science.gov (United States)

Demirdjian, L.; Zhou, Y.; Huffman, G. J.

2016-12-01

This project improves upon an existing extreme precipitation monitoring system based on the Tropical Rainfall Measuring Mission (TRMM) daily product (3B42) using new statistical models. The proposed system utilizes a regional modeling approach, where data from similar grid locations are pooled to increase the quality and stability of the resulting model parameter estimates to compensate for the short data record. The regional frequency analysis is divided into two stages. In the first stage, the region defined by the TRMM measurements is partitioned into approximately 27,000 non-overlapping clusters using a recursive k-means clustering scheme. In the second stage, a statistical model is used to characterize the extreme precipitation events occurring in each cluster. Instead of utilizing the block-maxima approach used in the existing system, where annual maxima are fit to the Generalized Extreme Value (GEV) probability distribution at each cluster separately, the present work adopts the peak-over-threshold (POT) method of classifying points as extreme if they exceed a pre-specified threshold. Theoretical considerations motivate the use of the Generalized-Pareto (GP) distribution for fitting threshold exceedances. The fitted parameters can be used to construct simple and intuitive average recurrence interval (ARI) maps which reveal how rare a particular precipitation event is given its spatial location. The new methodology eliminates much of the random noise that was produced by the existing models due to a short data record, producing more reasonable ARI maps when compared with NOAA's long-term Climate Prediction Center (CPC) ground based observations. The resulting ARI maps can be useful for disaster preparation, warning, and management, as well as increased public awareness of the severity of precipitation events. Furthermore, the proposed methodology can be applied to various other extreme climate records.

Isotope composition of winter precipitation and snow cover in the foothills of the Altai

Directory of Open Access Journals (Sweden)

N. S. Malygina

2017-01-01

Full Text Available Over the past three decades, several general circulation models of the atmosphere and ocean (atmospheric and oceanic general circulation models  – GCMs have been improved by modeling the hydrological cycle with the use of isotopologues (isotopes of water HDO and H2 18O. Input parameters for the GCM models taking into account changes in the isotope composition of atmospheric precipitation were, above all, the results obtained by the network GNIP – Global Network of Isotopes in Precipitation. At different times, on the vast territory of Russia there were only about 40 simultaneously functioning stations where the sampling of atmospheric precipitation was performed. In this study we present the results of the isotope composition of samples taken on the foothills of the Altai during two winter seasons of 2014/15 and 2015/16. Values of the isotope composition of precipitation changed in a wide range and their maximum fluctuations were 25, 202 and 18‰ for δ18О, dexc and δD, respectively. The weighted-mean values of δ18О and δD of the precipitation analyzed for the above two seasons were close to each other (−21.1 and −158.1‰ for the first season and −21.1 and −161.9‰ for the second one, while dexc values differed significantly. The comparison of the results of isotope analysis of the snow cover integral samples with the corresponding in the time interval the weighted-mean values of precipitation showed high consistency. However, despite the similarity of values of δ18О and δD, calculated for precipitation and snow cover, and the results, interpolated in IsoMAP (from data of the GNIP stations for 1960–2010, the dexc values were close to mean annual values of IsoMAP for only the second winter season. According to the trajectory analysis (the HYSPLIT model, the revealed differences between both, the seasons, and the long-term average values of IsoMAP, were associated with a change of main regions where the air masses

Effective Assimilation of Global Precipitation

Science.gov (United States)

Lien, G.; Kalnay, E.; Miyoshi, T.; Huffman, G. J.

2012-12-01

Assimilating precipitation observations by modifying the moisture and sometimes temperature profiles has been shown successful in forcing the model precipitation to be close to the observed precipitation, but only while the assimilation is taking place. After the forecast start, the model tends to "forget" the assimilation changes and lose their extra skill after few forecast hours. This suggests that this approach is not an efficient way to modify the potential vorticity field, since this is the variable that the model would remember. In this study, the ensemble Kalman filter (EnKF) method is used to effectively change the potential vorticity field by allowing ensemble members with better precipitation to receive higher weights. In addition to using an EnKF, two other changes in the precipitation assimilation process are proposed to solve the problems related to the highly non-Gaussian nature of the precipitation variable: a) transform precipitation into a Gaussian distribution based on its climatological distribution, and b) only assimilate precipitation at the location where some ensemble members have positive precipitation. The idea is first tested by the observing system simulation experiments (OSSEs) using SPEEDY, a simplified but realistic general circulation model. When the global precipitation is assimilated in addition to conventional rawinsonde observations, both the analyses and the medium range forecasts are significantly improved as compared to only having rawinsonde observations. The improvement is much reduced when only modifying the moisture field with the same approach, which shows the importance of the error covariance between precipitation and all other model variables. The effect of precipitation assimilation is larger in the Southern Hemisphere than that in the Northern Hemisphere because the Northern Hemisphere analyses are already accurate as a result of denser rawinsonde stations. Assimilation of precipitation using a more comprehensive

In-Drift Precipitates/Salts Model

Energy Technology Data Exchange (ETDEWEB)

P. Mariner

2004-11-09

This report documents the development and validation of the in-drift precipitates/salts (IDPS) model. The IDPS model is a geochemical model designed to predict the postclosure effects of evaporation and deliquescence on the chemical composition of water within the Engineered Barrier System (EBS) in support of the Total System Performance Assessment for the License Application (TSPA-LA). Application of the model in support of TSPA-LA is documented in ''Engineered Barrier System: Physical and Chemical Environment Model'' (BSC 2004 [DIRS 169860]). Technical Work Plan for: Near-Field Environment and Transport In-Drift Geochemistry Model Report Integration (BSC 2004 [DIRS 171156]) is the technical work plan (TWP) for this report. It called for a revision of the previous version of the report (BSC 2004 [DIRS 167734]) to achieve greater transparency, readability, data traceability, and report integration. The intended use of the IDPS model is to estimate and tabulate, within an appropriate level of confidence, the effects of evaporation, deliquescence, and potential environmental conditions on the pH, ionic strength, and chemical compositions of water and minerals on the drip shield or other location within the drift during the postclosure period. Specifically, the intended use is as follows: (1) To estimate, within an appropriate level of confidence, the effects of evaporation and deliquescence on the presence and composition of water occurring within the repository during the postclosure period (i.e., effects on pH, ionic strength, deliquescence relative humidity, total concentrations of dissolved components in the system Na-K-H-Mg-Ca-Al-Cl-F-NO{sub 3}-SO{sub 4}-Br-CO{sub 3}-SiO{sub 2}-CO{sub 2}-O{sub 2}-H{sub 2}O, and concentrations of the following aqueous species that potentially affect acid neutralizing capacity: HCO{sub 3}{sup -}, CO{sub 3}{sup 2-}, OH{sup -}, H{sup +}, HSO{sub 4}{sup -}, Ca{sup 2+}, Mg{sup 2+}, CaHCO{sub 3}{sup +}, MgHCO{sub 3

A neighbourhood evolving network model

International Nuclear Information System (INIS)

Cao, Y.J.; Wang, G.Z.; Jiang, Q.Y.; Han, Z.X.

2006-01-01

Many social, technological, biological and economical systems are best described by evolved network models. In this short Letter, we propose and study a new evolving network model. The model is based on the new concept of neighbourhood connectivity, which exists in many physical complex networks. The statistical properties and dynamics of the proposed model is analytically studied and compared with those of Barabasi-Albert scale-free model. Numerical simulations indicate that this network model yields a transition between power-law and exponential scaling, while the Barabasi-Albert scale-free model is only one of its special (limiting) cases. Particularly, this model can be used to enhance the evolving mechanism of complex networks in the real world, such as some social networks development

Gamma prime precipitation modeling and strength responses in powder metallurgy superalloys

Science.gov (United States)

Mao, Jian

Precipitation-hardened nickel-based superalloys have been widely used as high temperature structural materials in gas turbine engine applications for more than 50 years. Powder metallurgy (P/M) technology was introduced as an innovative manufacturing process to overcome severe segregation and poor workability of alloys with high alloying contents. The excellent mechanical properties of P/M superalloys also depend upon the characteristic microstructures, including grain size and size distribution of gamma' precipitates. Heat treatment is the most critical processing step that has ultimate influences on the microstructure, and hence, on the mechanical properties of the materials. The main objective of this research was to study the gamma ' precipitation kinetics in various cooling circumstances and also study the strength response to the cooling history in two model alloys, Rne88DT and U720LI. The research is summarized below: (1) An experimental method was developed to allow accurate simulation and control of any desired cooling profile. Two novel cooling methods were introduced: continuous cooling and interrupt cooling. Isothermal aging was also carried out. (2) The growth and coarsening kinetics of the cooling gamma' precipitates were experimentally studied under different cooling and aging conditions, and the empirical equations were established. It was found that the cooling gamma' precipitate versus the cooling rate follows a power law. The gamma' precipitate size versus aging time obeys the LSW cube law for coarsening. (3) The strengthening of the material responses to the cooling rate and the decreasing temperature during cooling was investigated in both alloys. The tensile strength increases with the cooling rate. In addition, the non-monotonic response of strength versus interrupt temperature is of great interest. (4) An energy-driven model integrated with the classic growth and coarsen theories was successfully embedded in a computer program developed to

Modeling Precipitation Extremes using Log-Histospline

Science.gov (United States)

Huang, W. K.; Nychka, D. W.; Zhang, H.

2017-12-01

One of the commonly used approaches to modeling univariate extremes is the peaks-overthreshold (POT) method. The POT method models exceedances over a (sufficiently high/low) threshold as a generalized Pareto distribution (GPD). To apply this method, a threshold has to be chosen and the estimates might be sensitive to the chosen threshold. Here we propose an alternative, the "Log-Histospline", to explore modeling the tail behavior and the remainder of the density in one step using the full range of the data. Log-Histospline applies a smoothing spline model on a finely binned histogram of the log transformed data to estimate its log density. By construction, we are able to preserve the polynomial upper tail behavior, a feature commonly observed in geophysical observations. The Log-Histospline can be extended to the spatial setting by treating the marginal (log) density at each location as spatially indexed functional data, and perform a dimension reduction and spatial smoothing. We illustrate the proposed method by analyzing precipitation data from regional climate model output (North American Regional Climate Change and Assessment Program (NARCCAP)).

Two-stage precipitation of plutonium trifluoride

International Nuclear Information System (INIS)

Luerkens, D.W.

1984-04-01

Plutonium trifluoride was precipitated using a two-stage precipitation system. A series of precipitation experiments identified the significant process variables affecting precipitate characteristics. A mathematical precipitation model was developed which was based on the formation of plutonium fluoride complexes. The precipitation model relates all process variables, in a single equation, to a single parameter that can be used to control particle characteristics

Statistical evaluation of the performance of gridded monthly precipitation products from reanalysis data, satellite estimates, and merged analyses over China

Science.gov (United States)

Deng, Xueliang; Nie, Suping; Deng, Weitao; Cao, Weihua

2018-04-01

In this study, we compared the following four different gridded monthly precipitation products: the National Centers for Environmental Prediction version 2 (NCEP-2) reanalysis data, the satellite-based Climate Prediction Center Morphing technique (CMORPH) data, the merged satellite-gauge Global Precipitation Climatology Project (GPCP) data, and the merged satellite-gauge-model data from the Beijing Climate Center Merged Estimation of Precipitation (BMEP). We evaluated the performances of these products using monthly precipitation observations spanning the period of January 2003 to December 2013 from a dense, national, rain gauge network in China. Our assessment involved several statistical techniques, including spatial pattern, temporal variation, bias, root-mean-square error (RMSE), and correlation coefficient (CC) analysis. The results show that NCEP-2, GPCP, and BMEP generally overestimate monthly precipitation at the national scale and CMORPH underestimates it. However, all of the datasets successfully characterized the northwest to southeast increase in the monthly precipitation over China. Because they include precipitation gauge information from the Global Telecommunication System (GTS) network, GPCP and BMEP have much smaller biases, lower RMSEs, and higher CCs than NCEP-2 and CMORPH. When the seasonal and regional variations are considered, NCEP-2 has a larger error over southern China during the summer. CMORPH poorly reproduces the magnitude of the precipitation over southeastern China and the temporal correlation over western and northwestern China during all seasons. BMEP has a lower RMSE and higher CC than GPCP over eastern and southern China, where the station network is dense. In contrast, BMEP has a lower CC than GPCP over western and northwestern China, where the gauge network is relatively sparse.

Stochastic model for simulating Souris River Basin precipitation, evapotranspiration, and natural streamflow

Science.gov (United States)

Kolars, Kelsey A.; Vecchia, Aldo V.; Ryberg, Karen R.

2016-02-24

The Souris River Basin is a 61,000-square-kilometer basin in the Provinces of Saskatchewan and Manitoba and the State of North Dakota. In May and June of 2011, record-setting rains were seen in the headwater areas of the basin. Emergency spillways of major reservoirs were discharging at full or nearly full capacity, and extensive flooding was seen in numerous downstream communities. To determine the probability of future extreme floods and droughts, the U.S. Geological Survey, in cooperation with the North Dakota State Water Commission, developed a stochastic model for simulating Souris River Basin precipitation, evapotranspiration, and natural (unregulated) streamflow. Simulations from the model can be used in future studies to simulate regulated streamflow, design levees, and other structures; and to complete economic cost/benefit analyses.Long-term climatic variability was analyzed using tree-ring chronologies to hindcast precipitation to the early 1700s and compare recent wet and dry conditions to earlier extreme conditions. The extended precipitation record was consistent with findings from the Devils Lake and Red River of the North Basins (southeast of the Souris River Basin), supporting the idea that regional climatic patterns for many centuries have consisted of alternating wet and dry climate states.A stochastic climate simulation model for precipitation, temperature, and potential evapotranspiration for the Souris River Basin was developed using recorded meteorological data and extended precipitation records provided through tree-ring analysis. A significant climate transition was seen around1970, with 1912–69 representing a dry climate state and 1970–2011 representing a wet climate state. Although there were some distinct subpatterns within the basin, the predominant differences between the two states were higher spring through early fall precipitation and higher spring potential evapotranspiration for the wet compared to the dry state.A water

Climate variability from isotope records in precipitation

International Nuclear Information System (INIS)

Grassl, H.; Latif, M.; Schotterer, U.; Gourcy, L.

2002-01-01

Selected time series from the Global Network for Isotopes in Precipitation (GNIP) revealed a close relationship to climate variability phenomena like El Nino - Southern Oscillation (ENSO) or the North Atlantic Oscillation (NAO) although the precipitation anomaly in the case studies of Manaus (Brazil) and Groningen (The Netherlands) is rather weak. For a sound understanding of this relationship especially in the case of Manaus, the data should include major events like the 1997/98 El Nino, however, the time series are interrupted frequently or important stations are even closed. Improvements are only possible if existing key stations and new ones (placed at 'hot spots' derived from model experiments) are supported continuously. A close link of GNIP to important scientific programmes like CLIVAR, the Climate Variability and Predictability Programme seems to be indispensable for a successful continuation. (author)

Cool-season precipitation in the southwestern USA since AD 1000: comparison of linear and nonlinear techniques for reconstruction

Science.gov (United States)

Ni, Fenbiao; Cavazos, Tereza; Hughes, Malcolm K.; Comrie, Andrew C.; Funkhouser, Gary

2002-11-01

A 1000 year reconstruction of cool-season (November-April) precipitation was developed for each climate division in Arizona and New Mexico from a network of 19 tree-ring chronologies in the southwestern USA. Linear regression (LR) and artificial neural network (NN) models were used to identify the cool-season precipitation signal in tree rings. Using 1931-88 records, the stepwise LR model was cross-validated with a leave-one-out procedure and the NN was validated with a bootstrap technique. The final models were also independently validated using the 1896-1930 precipitation data. In most of the climate divisions, both techniques can successfully reconstruct dry and normal years, and the NN seems to capture large precipitation events and more variability better than the LR. In the 1000 year reconstructions the NN also produces more distinctive wet events and more variability, whereas the LR produces more distinctive dry events. The 1000 year reconstructed precipitation from the two models shows several sustained dry and wet periods comparable to the 1950s drought (e.g. 16th century mega drought) and to the post-1976 wet period (e.g. 1330s, 1610s). The impact of extreme periods on the environment may be stronger during sudden reversals from dry to wet, which were not uncommon throughout the millennium, such as the 1610s wet interval that followed the 16th century mega drought. The instrumental records suggest that strong dry to wet precipitation reversals in the past 1000 years might be linked to strong shifts from cold to warm El Niño-southern oscillation events and from a negative to positive Pacific decadal oscillation.

A Semiempirical Model for Sigma-Phase Precipitation in Duplex and Superduplex Stainless Steels

Science.gov (United States)

Ferro, P.; Bonollo, F.

2012-04-01

Sigma phase is known to reduce the mechanical properties and corrosion resistance of duplex and superduplex stainless steels. Therefore, heat treatments and welding must be carefully performed so as to avoid the appearance of such a detrimental phase, and clearly, models suitable to faithfully predict σ-phase precipitation are very useful tools. Most fully analytical models are based on thermodynamic calculations whose agreement with experimental results is not always good, so that such models should be used for qualitative purposes only. Alternatively, it is possible to exploit semiempirical models, where time-temperature-transformation (TTT) diagrams are empirically determined for a given alloy and the continuous-cooling-transformation (CCT) diagram is calculated from the TTT diagram. In this work, a semiempirical model for σ-phase precipitation in duplex and superduplex stainless steels, under both isothermal and unisothermal conditions, is proposed. Model parameters are calculated from empirical data and CCT diagrams are obtained by means of the additivity rule, whereas experimental measurements for model validation are taken from the literature. This model gives a satisfactory estimation of σ-phase precipitates during both isothermal aging and the continuous cooling process.

Scaling precipitation input to spatially distributed hydrological models by measured snow distribution

Directory of Open Access Journals (Sweden)

Christian Vögeli

2016-12-01

Full Text Available Accurate knowledge on snow distribution in alpine terrain is crucial for various applicationssuch as flood risk assessment, avalanche warning or managing water supply and hydro-power.To simulate the seasonal snow cover development in alpine terrain, the spatially distributed,physics-based model Alpine3D is suitable. The model is typically driven by spatial interpolationsof observations from automatic weather stations (AWS, leading to errors in the spatial distributionof atmospheric forcing. With recent advances in remote sensing techniques, maps of snowdepth can be acquired with high spatial resolution and accuracy. In this work, maps of the snowdepth distribution, calculated from summer and winter digital surface models based on AirborneDigital Sensors (ADS, are used to scale precipitation input data, with the aim to improve theaccuracy of simulation of the spatial distribution of snow with Alpine3D. A simple method toscale and redistribute precipitation is presented and the performance is analysed. The scalingmethod is only applied if it is snowing. For rainfall the precipitation is distributed by interpolation,with a simple air temperature threshold used for the determination of the precipitation phase.It was found that the accuracy of spatial snow distribution could be improved significantly forthe simulated domain. The standard deviation of absolute snow depth error is reduced up toa factor 3.4 to less than 20 cm. The mean absolute error in snow distribution was reducedwhen using representative input sources for the simulation domain. For inter-annual scaling, themodel performance could also be improved, even when using a remote sensing dataset from adifferent winter. In conclusion, using remote sensing data to process precipitation input, complexprocesses such as preferential snow deposition and snow relocation due to wind or avalanches,can be substituted and modelling performance of spatial snow distribution is improved.

Climate change and precipitation: Detecting changes Climate change and precipitation: Detecting changes

International Nuclear Information System (INIS)

Van Boxel, John H

2001-01-01

Precipitation is one of the most, if not the most important climate parameter In most studies on climate change the emphasis is on temperature and sea level rise. Often too little attention is given to precipitation. For a large part this is due to the large spatial en temporal variability of precipitation, which makes the detection of changes difficult. This paper describes methods to detect changes in precipitation. In order to arrive at statistically significant changes one must use long time series and spatial averages containing the information from several stations. In the Netherlands the average yearly precipitation increased by 11% during the 20th century .In the temperate latitudes on the Northern Hemisphere (40-60QN) the average increase was about 7% over the 20th century and the globally averaged precipitation increased by about 3%. During the 20th century 38% of the land surface of the earth became wetter, 42% experienced little change (less than 5% change) and 20% became dryer. More important than the average precipitation is the occurrence of extremes. In the Netherlands there is a tendency to more extreme precipitations, whereas the occurrence of relatively dry months has not changed. Also in many other countries increases in heavy precipitation events are observed. All climate models predict a further increase of mean global precipitation if the carbon dioxide concentration doubles. Nevertheless some areas get dryer, others have little change and consequently there are also areas where the increase is much more than the global average. On a regional scale however there are large differences between the models. Climate models do not yet provide adequate information on changes in extreme precipitations

On the performance of satellite precipitation products in riverine flood modeling: A review

Science.gov (United States)

Maggioni, Viviana; Massari, Christian

2018-03-01

This work is meant to summarize lessons learned on using satellite precipitation products for riverine flood modeling and to propose future directions in this field of research. Firstly, the most common satellite precipitation products (SPPs) during the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Mission (GPM) eras are reviewed. Secondly, we discuss the main errors and uncertainty sources in these datasets that have the potential to affect streamflow and runoff model simulations. Thirdly, past studies that focused on using SPPs for predicting streamflow and runoff are analyzed. As the impact of floods depends not only on the characteristics of the flood itself, but also on the characteristics of the region (population density, land use, geophysical and climatic factors), a regional analysis is required to assess the performance of hydrologic models in monitoring and predicting floods. The performance of SPP-forced hydrological models was shown to largely depend on several factors, including precipitation type, seasonality, hydrological model formulation, topography. Across several basins around the world, the bias in SPPs was recognized as a major issue and bias correction methods of different complexity were shown to significantly reduce streamflow errors. Model re-calibration was also raised as a viable option to improve SPP-forced streamflow simulations, but caution is necessary when recalibrating models with SPP, which may result in unrealistic parameter values. From a general standpoint, there is significant potential for using satellite observations in flood forecasting, but the performance of SPP in hydrological modeling is still inadequate for operational purposes.

Precipitation from Space: Advancing Earth System Science

Science.gov (United States)

Kucera, Paul A.; Ebert, Elizabeth E.; Turk, F. Joseph; Levizzani, Vicenzo; Kirschbaum, Dalia; Tapiador, Francisco J.; Loew, Alexander; Borsche, M.

2012-01-01

Of the three primary sources of spatially contiguous precipitation observations (surface networks, ground-based radar, and satellite-based radar/radiometers), only the last is a viable source over ocean and much of the Earth's land. As recently as 15 years ago, users needing quantitative detail of precipitation on anything under a monthly time scale relied upon products derived from geostationary satellite thermal infrared (IR) indices. The Special Sensor Microwave Imager (SSMI) passive microwave (PMW) imagers originated in 1987 and continue today with the SSMI sounder (SSMIS) sensor. The fortunate longevity of the joint National Aeronautics and Space Administration (NASA) and Japan Aerospace Exploration Agency (JAXA) Tropical Rainfall Measuring Mission (TRMM) is providing the environmental science community a nearly unbroken data record (as of April 2012, over 14 years) of tropical and sub-tropical precipitation processes. TRMM was originally conceived in the mid-1980s as a climate mission with relatively modest goals, including monthly averaged precipitation. TRMM data were quickly exploited for model data assimilation and, beginning in 1999 with the availability of near real time data, for tropical cyclone warnings. To overcome the intermittently spaced revisit from these and other low Earth-orbiting satellites, many methods to merge PMW-based precipitation data and geostationary satellite observations have been developed, such as the TRMM Multisatellite Precipitation Product and the Climate Prediction Center (CPC) morphing method (CMORPH. The purpose of this article is not to provide a survey or assessment of these and other satellite-based precipitation datasets, which are well summarized in several recent articles. Rather, the intent is to demonstrate how the availability and continuity of satellite-based precipitation data records is transforming the ways that scientific and societal issues related to precipitation are addressed, in ways that would not be

Linear Friction Welding Process Model for Carpenter Custom 465 Precipitation-Hardened Martensitic Stainless Steel

Science.gov (United States)

2014-04-11

Carpenter Custom 465 precipitation-hardened martensitic stainless steel to develop a linear friction welding (LFW) process model for this material...Model for Carpenter Custom 465 Precipitation-Hardened Martensitic Stainless Steel The views, opinions and/or findings contained in this report are... Martensitic Stainless Steel Report Title An Arbitrary Lagrangian-Eulerian finite-element analysis is combined with thermo-mechanical material

Modeling precipitation-runoff relationships to determine water yield from a ponderosa pine forest watershed

Science.gov (United States)

Assefa S. Desta

2006-01-01

A stochastic precipitation-runoff modeling is used to estimate a cold and warm-seasons water yield from a ponderosa pine forested watershed in the north-central Arizona. The model consists of two parts namely, simulation of the temporal and spatial distribution of precipitation using a stochastic, event-based approach and estimation of water yield from the watershed...

Evaluation of CMIP5 models for projection of future precipitation change in Bornean tropical rainforests

Science.gov (United States)

Hussain, Mubasher; Yusof, Khamaruzaman Wan; Mustafa, Muhammad Raza Ul; Mahmood, Rashid; Jia, Shaofeng

2017-10-01

We present the climate change impact on the annual and seasonal precipitation over Rajang River Basin (RRB) in Sarawak by employing a set of models from Coupled Model Intercomparison Project Phase 5 (CMIP5). Based on the capability to simulate the historical precipitation, we selected the three most suitable GCMs (i.e. ACCESS1.0, ACCESS1.3, and GFDL-ESM2M) and their mean ensemble (B3MMM) was used to project the future precipitation over the RRB. Historical (1976-2005) and future (2011-2100) precipitation ensembles of B3MMM were used to perturb the stochastically generated future precipitation over 25 rainfall stations in the river basin. The B3MMM exhibited a significant increase in precipitation during 2080s, up to 12 and 8% increase in annual precipitation over upper and lower RRB, respectively, under RCP8.5, and up to 7% increase in annual precipitation under RCP4.5. On the seasonal scale, Mann-Kendal trend test estimated statistically significant positive trend in the future precipitation during all seasons; except September to November when we only noted significant positive trend for the lower RRB under RCP4.5. Overall, at the end of the twenty-first century, an increase in annual precipitation is noteworthy in the whole RRB, with 7 and 10% increase in annual precipitation under the RCP4.5 and the RCP8.5, respectively.

Do climate model predictions agree with long-term precipitation trends in the arid southwestern United States?

Science.gov (United States)

Elias, E.; Rango, A.; James, D.; Maxwell, C.; Anderson, J.; Abatzoglou, J. T.

2016-12-01

Researchers evaluating climate projections across southwestern North America observed a decreasing precipitation trend. Aridification was most pronounced in the cold (non-monsoonal) season, whereas downward trends in precipitation were smaller in the warm (monsoonal) season. In this region, based upon a multimodel mean of 20 Coupled Model Intercomparison Project 5 models using a business-as-usual (Representative Concentration Pathway 8.5) trajectory, midcentury precipitation is projected to increase slightly during the monsoonal time period (July-September; 6%) and decrease slightly during the remainder of the year (October-June; -4%). We use observed long-term (1915-2015) monthly precipitation records from 16 weather stations to investigate how well measured trends corroborate climate model predictions during the monsoonal and non-monsoonal timeframe. Running trend analysis using the Mann-Kendall test for 15 to 101 year moving windows reveals that half the stations showed significant (p≤0.1), albeit small, increasing trends based on the longest term record. Trends based on shorter-term records reveal a period of significant precipitation decline at all stations representing the 1950s drought. Trends from 1930 to 2015 reveal significant annual, monsoonal and non-monsoonal increases in precipitation (Fig 1). The 1960 to 2015 time window shows no significant precipitation trends. The more recent time window (1980 to 2015) shows a slight, but not significant, increase in monsoonal precipitation and a larger, significant decline in non-monsoonal precipitation. GCM precipitation projections are consistent with more recent trends for the region. Running trends from the most recent time window (mid-1990s to 2015) at all stations show increasing monsoonal precipitation and decreasing Oct-Jun precipitation, with significant trends at 6 of 16 stations. Running trend analysis revealed that the long-term trends were not persistent throughout the series length, but depended

Improving aerosol interaction with clouds and precipitation in a regional chemical weather modeling system

Science.gov (United States)

Zhou, C.; Zhang, X.; Gong, S.; Wang, Y.; Xue, M.

2016-01-01

A comprehensive aerosol-cloud-precipitation interaction (ACI) scheme has been developed under a China Meteorological Administration (CMA) chemical weather modeling system, GRAPES/CUACE (Global/Regional Assimilation and PrEdiction System, CMA Unified Atmospheric Chemistry Environment). Calculated by a sectional aerosol activation scheme based on the information of size and mass from CUACE and the thermal-dynamic and humid states from the weather model GRAPES at each time step, the cloud condensation nuclei (CCN) are interactively fed online into a two-moment cloud scheme (WRF Double-Moment 6-class scheme - WDM6) and a convective parameterization to drive cloud physics and precipitation formation processes. The modeling system has been applied to study the ACI for January 2013 when several persistent haze-fog events and eight precipitation events occurred.The results show that aerosols that interact with the WDM6 in GRAPES/CUACE obviously increase the total cloud water, liquid water content, and cloud droplet number concentrations, while decreasing the mean diameters of cloud droplets with varying magnitudes of the changes in each case and region. These interactive microphysical properties of clouds improve the calculation of their collection growth rates in some regions and hence the precipitation rate and distributions in the model, showing 24 to 48 % enhancements of threat score for 6 h precipitation in almost all regions. The aerosols that interact with the WDM6 also reduce the regional mean bias of temperature by 3 °C during certain precipitation events, but the monthly means bias is only reduced by about 0.3 °C.

Improving aerosol interaction with clouds and precipitation in a regional chemical weather modeling system

Directory of Open Access Journals (Sweden)

C. Zhou

2016-01-01

Full Text Available A comprehensive aerosol–cloud–precipitation interaction (ACI scheme has been developed under a China Meteorological Administration (CMA chemical weather modeling system, GRAPES/CUACE (Global/Regional Assimilation and PrEdiction System, CMA Unified Atmospheric Chemistry Environment. Calculated by a sectional aerosol activation scheme based on the information of size and mass from CUACE and the thermal-dynamic and humid states from the weather model GRAPES at each time step, the cloud condensation nuclei (CCN are interactively fed online into a two-moment cloud scheme (WRF Double-Moment 6-class scheme – WDM6 and a convective parameterization to drive cloud physics and precipitation formation processes. The modeling system has been applied to study the ACI for January 2013 when several persistent haze-fog events and eight precipitation events occurred.The results show that aerosols that interact with the WDM6 in GRAPES/CUACE obviously increase the total cloud water, liquid water content, and cloud droplet number concentrations, while decreasing the mean diameters of cloud droplets with varying magnitudes of the changes in each case and region. These interactive microphysical properties of clouds improve the calculation of their collection growth rates in some regions and hence the precipitation rate and distributions in the model, showing 24 to 48 % enhancements of threat score for 6 h precipitation in almost all regions. The aerosols that interact with the WDM6 also reduce the regional mean bias of temperature by 3 °C during certain precipitation events, but the monthly means bias is only reduced by about 0.3 °C.

Telecommunications network modelling, planning and design

CERN Document Server

Evans, Sharon

2003-01-01

Telecommunication Network Modelling, Planning and Design addresses sophisticated modelling techniques from the perspective of the communications industry and covers some of the major issues facing telecommunications network engineers and managers today. Topics covered include network planning for transmission systems, modelling of SDH transport network structures and telecommunications network design and performance modelling, as well as network costs and ROI modelling and QoS in 3G networks.

Evaluating 20th Century precipitation characteristics between multi-scale atmospheric models with different land-atmosphere coupling

Science.gov (United States)

Phillips, M.; Denning, A. S.; Randall, D. A.; Branson, M.

2016-12-01

Multi-scale models of the atmosphere provide an opportunity to investigate processes that are unresolved by traditional Global Climate Models while at the same time remaining viable in terms of computational resources for climate-length time scales. The MMF represents a shift away from large horizontal grid spacing in traditional GCMs that leads to overabundant light precipitation and lack of heavy events, toward a model where precipitation intensity is allowed to vary over a much wider range of values. Resolving atmospheric motions on the scale of 4 km makes it possible to recover features of precipitation, such as intense downpours, that were previously only obtained by computationally expensive regional simulations. These heavy precipitation events may have little impact on large-scale moisture and energy budgets, but are outstanding in terms of interaction with the land surface and potential impact on human life. Three versions of the Community Earth System Model were used in this study; the standard CESM, the multi-scale `Super-Parameterized' CESM where large-scale parameterizations have been replaced with a 2D cloud-permitting model, and a multi-instance land version of the SP-CESM where each column of the 2D CRM is allowed to interact with an individual land unit. These simulations were carried out using prescribed Sea Surface Temperatures for the period from 1979-2006 with daily precipitation saved for all 28 years. Comparisons of the statistical properties of precipitation between model architectures and against observations from rain gauges were made, with specific focus on detection and evaluation of extreme precipitation events.

Simulating the convective precipitation diurnal cycle in a North American scale convection-permitting model

Science.gov (United States)

Scaff, L.; Li, Y.; Prein, A. F.; Liu, C.; Rasmussen, R.; Ikeda, K.

2017-12-01

A better representation of the diurnal cycle of convective precipitation is essential for the analysis of the energy balance and the water budget components such as runoff, evaporation and infiltration. Convection-permitting regional climate modeling (CPM) has been shown to improve the models' performance of summer precipitation, allowing to: (1) simulate the mesoscale processes in more detail and (2) to provide more insights in future changes in convective precipitation under climate change. In this work we investigate the skill of the Weather Research and Forecast model (WRF) in simulating the summer precipitation diurnal cycle over most of North America. We use 4 km horizontal grid spacing in a 13-years long current and future period. The future scenario is assuming no significant changes in large-scale weather patterns and aims to answer how the weather of the current climate would change if it would reoccur at the end of the century under a high-end emission scenario (Pseudo Global Warming). We emphasize on a region centered on the lee side of the Canadian Rocky Mountains, where the summer precipitation amount shows a regional maximum. The historical simulations are capable to correctly represent the diurnal cycle. At the lee-side of the Canadian Rockies the increase in the convective available potential energy as well as pronounced low-level moisture flux from the southeast Prairies explains the local maximum in summer precipitation. The PGW scenario shows an increase in summer precipitation amount and intensity in this region, consistently with a stronger source of moisture and convective energy.

A generalised chemical precipitation modelling approach in wastewater treatment applied to calcite

DEFF Research Database (Denmark)

Mbamba, Christian Kazadi; Batstone, Damien J.; Flores Alsina, Xavier

2015-01-01

, the present study aims to identify a broadly applicable precipitation modelling approach. The study uses two experimental platforms applied to calcite precipitating from synthetic aqueous solutions to identify and validate the model approach. Firstly, dynamic pH titration tests are performed to define...... an Arrhenius-style correction of kcryst. The influence of magnesium (a common and representative added impurity) on kcryst was found to be significant but was considered an optional correction because of a lesser influence as compared to that of temperature. Other variables such as ionic strength and pH were...

Thermodynamic modeling and kinetics simulation of precipitate phases in AISI 316 stainless steels

International Nuclear Information System (INIS)

Yang, Y.; Busby, J.T.

2014-01-01

This work aims at utilizing modern computational microstructural modeling tools to accelerate the understanding of phase stability in austenitic steels under extended thermal aging. Using the CALPHAD approach, a thermodynamic database OCTANT (ORNL Computational Thermodynamics for Applied Nuclear Technology), including elements of Fe, C, Cr, Ni, Mn, Mo, Si, and Ti, has been developed with a focus on reliable thermodynamic modeling of precipitate phases in AISI 316 austenitic stainless steels. The thermodynamic database was validated by comparing the calculated results with experimental data from commercial 316 austenitic steels. The developed computational thermodynamics was then coupled with precipitation kinetics simulation to understand the temporal evolution of precipitates in austenitic steels under long-term thermal aging (up to 600,000 h) at a temperature regime from 300 to 900 °C. This study discusses the effect of dislocation density and difusion coefficients on the precipitation kinetics at low temperatures, which shed a light on investigating the phase stability and transformation in austenitic steels used in light water reactors

Development of a Unified Dissolution and Precipitation Model and Its Use for the Prediction of Oral Drug Absorption.

Science.gov (United States)

Jakubiak, Paulina; Wagner, Björn; Grimm, Hans Peter; Petrig-Schaffland, Jeannine; Schuler, Franz; Alvarez-Sánchez, Rubén

2016-02-01

Drug absorption is a complex process involving dissolution and precipitation, along with other kinetic processes. The purpose of this work was to (1) establish an in vitro methodology to study dissolution and precipitation in early stages of drug development where low compound consumption and high throughput are necessary, (2) develop a mathematical model for a mechanistic explanation of generated in vitro dissolution and precipitation data, and (3) extrapolate in vitro data to in vivo situations using physiologically based models to predict oral drug absorption. Small-scale pH-shift studies were performed in biorelevant media to monitor the precipitation of a set of poorly soluble weak bases. After developing a dissolution-precipitation model from this data, it was integrated into a simplified, physiologically based absorption model to predict clinical pharmacokinetic profiles. The model helped explain the consequences of supersaturation behavior of compounds. The predicted human pharmacokinetic profiles closely aligned with the observed clinical data. In summary, we describe a novel approach combining experimental dissolution/precipitation methodology with a mechanistic model for the prediction of human drug absorption kinetics. The approach unifies the dissolution and precipitation theories and enables accurate predictions of in vivo oral absorption by means of physiologically based modeling.

The global historical climatology network: Long-term monthly temperature, precipitation, and pressure data

International Nuclear Information System (INIS)

Vose, R.S.; Schmoyer, R.L.; Peterson, T.C.; Steurer, P.M.; Heim, R.R. Jr.; Karl, T.R.; Eischeid, J.K.

1992-01-01

Interest in global climate change has risen dramatically during the past several decades. In a similar fashion, the number of data sets available to study global change has also increased. Unfortunately, many different organizations and researchers have compiled these data sets, making it confusing and time consuming for individuals to acquire the most comprehensive data. In response to this rapid growth in the number of global data sets, DOE's Carbon Dioxide Information Analysis Center (CDIAC) and NOAA's National Climatic Data Center (NCDC) established the Global Historical Climatology Network (GHCN) project. The purpose of this project is to compile an improved data set of long-term monthly mean temperature, precipitation, sea level pressure, and station pressure for as dense a network of global stations as possible. Specifically, the GHCN project seeks to consolidate the numerous preexisting national-, regional-, and global-scale data sets into a single global data base; to subject the data to rigorous quality control; and to update, enhance, and distribute the data set at regular intervals. The purpose of this paper is to describe the compilation and contents of the GHCN data base (i.e., GHCN Version 1.0)

An Approach for Generating Precipitation Input for Worst-Case Flood Modelling

Science.gov (United States)

Felder, Guido; Weingartner, Rolf

2015-04-01

There is a lack of suitable methods for creating precipitation scenarios that can be used to realistically estimate peak discharges with very low probabilities. On the one hand, existing methods are methodically questionable when it comes to physical system boundaries. On the other hand, the spatio-temporal representativeness of precipitation patterns as system input is limited. In response, this study proposes a method of deriving representative spatio-temporal precipitation patterns and presents a step towards making methodically correct estimations of infrequent floods by using a worst-case approach. A Monte-Carlo rainfall-runoff model allows for the testing of a wide range of different spatio-temporal distributions of an extreme precipitation event and therefore for the generation of a hydrograph for each of these distributions. Out of these numerous hydrographs and their corresponding peak discharges, the worst-case catchment reactions on the system input can be derived. The spatio-temporal distributions leading to the highest peak discharges are identified and can eventually be used for further investigations.

Characteristics of sub-daily precipitation extremes in observed data and regional climate model simulations

Science.gov (United States)

Beranová, Romana; Kyselý, Jan; Hanel, Martin

2018-04-01

The study compares characteristics of observed sub-daily precipitation extremes in the Czech Republic with those simulated by Hadley Centre Regional Model version 3 (HadRM3) and Rossby Centre Regional Atmospheric Model version 4 (RCA4) regional climate models (RCMs) driven by reanalyses and examines diurnal cycles of hourly precipitation and their dependence on intensity and surface temperature. The observed warm-season (May-September) maxima of short-duration (1, 2 and 3 h) amounts show one diurnal peak in the afternoon, which is simulated reasonably well by RCA4, although the peak occurs too early in the model. HadRM3 provides an unrealistic diurnal cycle with a nighttime peak and an afternoon minimum coinciding with the observed maximum for all three ensemble members, which suggests that convection is not captured realistically. Distorted relationships of the diurnal cycles of hourly precipitation to daily maximum temperature in HadRM3 further evidence that underlying physical mechanisms are misrepresented in this RCM. Goodness-of-fit tests indicate that generalised extreme value distribution is an applicable model for both observed and RCM-simulated precipitation maxima. However, the RCMs are not able to capture the range of the shape parameter estimates of distributions of short-duration precipitation maxima realistically, leading to either too many (nearly all for HadRM3) or too few (RCA4) grid boxes in which the shape parameter corresponds to a heavy tail. This means that the distributions of maxima of sub-daily amounts are distorted in the RCM-simulated data and do not match reality well. Therefore, projected changes of sub-daily precipitation extremes in climate change scenarios based on RCMs not resolving convection need to be interpreted with caution.

Coevolutionary modeling in network formation

KAUST Repository

Al-Shyoukh, Ibrahim

2014-12-03

Network coevolution, the process of network topology evolution in feedback with dynamical processes over the network nodes, is a common feature of many engineered and natural networks. In such settings, the change in network topology occurs at a comparable time scale to nodal dynamics. Coevolutionary modeling offers the possibility to better understand how and why network structures emerge. For example, social networks can exhibit a variety of structures, ranging from almost uniform to scale-free degree distributions. While current models of network formation can reproduce these structures, coevolutionary modeling can offer a better understanding of the underlying dynamics. This paper presents an overview of recent work on coevolutionary models of network formation, with an emphasis on the following three settings: (i) dynamic flow of benefits and costs, (ii) transient link establishment costs, and (iii) latent preferential attachment.

Coevolutionary modeling in network formation

KAUST Repository

Al-Shyoukh, Ibrahim; Chasparis, Georgios; Shamma, Jeff S.

2014-01-01

Network coevolution, the process of network topology evolution in feedback with dynamical processes over the network nodes, is a common feature of many engineered and natural networks. In such settings, the change in network topology occurs at a comparable time scale to nodal dynamics. Coevolutionary modeling offers the possibility to better understand how and why network structures emerge. For example, social networks can exhibit a variety of structures, ranging from almost uniform to scale-free degree distributions. While current models of network formation can reproduce these structures, coevolutionary modeling can offer a better understanding of the underlying dynamics. This paper presents an overview of recent work on coevolutionary models of network formation, with an emphasis on the following three settings: (i) dynamic flow of benefits and costs, (ii) transient link establishment costs, and (iii) latent preferential attachment.

Local control on precipitation in a fully coupled climate-hydrology model

DEFF Research Database (Denmark)

Larsen, Morten A. D.; Christensen, Jens H.; Drews, Martin

2016-01-01

simulations of precipitation often exhibit substantial biases that affect the reliability of future projections. Here we demonstrate how a regional climate model (RCM) coupled to a distributed hydrological catchment model that fully integrates water and energy fluxes between the subsurface, land surface...

Radar adjusted data versus modelled precipitation: a case study over Cyprus

Directory of Open Access Journals (Sweden)

M. Casaioli

2006-01-01

Full Text Available In the framework of the European VOLTAIRE project (Fifth Framework Programme, simulations of relatively heavy precipitation events, which occurred over the island of Cyprus, by means of numerical atmospheric models were performed. One of the aims of the project was indeed the comparison of modelled rainfall fields with multi-sensor observations. Thus, for the 5 March 2003 event, the 24-h accumulated precipitation BOlogna Limited Area Model (BOLAM forecast was compared with the available observations reconstructed from ground-based radar data and estimated by rain gauge data. Since radar data may be affected by errors depending on the distance from the radar, these data could be range-adjusted by using other sensors. In this case, the Precipitation Radar aboard the Tropical Rainfall Measuring Mission (TRMM satellite was used to adjust the ground-based radar data with a two-parameter scheme. Thus, in this work, two observational fields were employed: the rain gauge gridded analysis and the observational analysis obtained by merging the range-adjusted radar and rain gauge fields. In order to verify the modelled precipitation, both non-parametric skill scores and the contiguous rain area (CRA analysis were applied. Skill score results show some differences when using the two observational fields. CRA results are instead quite in agreement, showing that in general a 0.27° eastward shift optimizes the forecast with respect to the two observational analyses. This result is also supported by a subjective inspection of the shifted forecast field, whose gross features agree with the analysis pattern more than the non-shifted forecast one. However, some open questions, especially regarding the effect of other range adjustment techniques, remain open and need to be addressed in future works.

Modeling online social signed networks

Science.gov (United States)

Li, Le; Gu, Ke; Zeng, An; Fan, Ying; Di, Zengru

2018-04-01

People's online rating behavior can be modeled by user-object bipartite networks directly. However, few works have been devoted to reveal the hidden relations between users, especially from the perspective of signed networks. We analyze the signed monopartite networks projected by the signed user-object bipartite networks, finding that the networks are highly clustered with obvious community structure. Interestingly, the positive clustering coefficient is remarkably higher than the negative clustering coefficient. Then, a Signed Growing Network model (SGN) based on local preferential attachment is proposed to generate a user's signed network that has community structure and high positive clustering coefficient. Other structural properties of the modeled networks are also found to be similar to the empirical networks.

V and V Efforts of Auroral Precipitation Models: Preliminary Results

Science.gov (United States)

Zheng, Yihua; Kuznetsova, Masha; Rastaetter, Lutz; Hesse, Michael

2011-01-01

Auroral precipitation models have been valuable both in terms of space weather applications and space science research. Yet very limited testing has been performed regarding model performance. A variety of auroral models are available, including empirical models that are parameterized by geomagnetic indices or upstream solar wind conditions, now casting models that are based on satellite observations, or those derived from physics-based, coupled global models. In this presentation, we will show our preliminary results regarding V&V efforts of some of the models.

A meteo-hydrological prediction system based on a multi-model approach for precipitation forecasting

Directory of Open Access Journals (Sweden)

S. Davolio

2008-02-01

Full Text Available The precipitation forecasted by a numerical weather prediction model, even at high resolution, suffers from errors which can be considerable at the scales of interest for hydrological purposes. In the present study, a fraction of the uncertainty related to meteorological prediction is taken into account by implementing a multi-model forecasting approach, aimed at providing multiple precipitation scenarios driving the same hydrological model. Therefore, the estimation of that uncertainty associated with the quantitative precipitation forecast (QPF, conveyed by the multi-model ensemble, can be exploited by the hydrological model, propagating the error into the hydrological forecast.

The proposed meteo-hydrological forecasting system is implemented and tested in a real-time configuration for several episodes of intense precipitation affecting the Reno river basin, a medium-sized basin located in northern Italy (Apennines. These episodes are associated with flood events of different intensity and are representative of different meteorological configurations responsible for severe weather affecting northern Apennines.

The simulation results show that the coupled system is promising in the prediction of discharge peaks (both in terms of amount and timing for warning purposes. The ensemble hydrological forecasts provide a range of possible flood scenarios that proved to be useful for the support of civil protection authorities in their decision.

Modeled Watershed Runoff Associated with Variations in Precipitation Data, with Implications for Contaminant Fluxes: Initial Results

Science.gov (United States)

Precipitation is one of the primary forcing functions of hydrologic and watershed fate and transport models; however, in light of advances in precipitation estimates across watersheds, data remain highly uncertain. A wide variety of simulated and observed precipitation data are a...

An Improved Plutonium Trifluoride Precipitation Flowsheet

Energy Technology Data Exchange (ETDEWEB)

Harmon, H.D.

2001-06-26

This report discusses results of the plutonium trifluoride two-stage precipitation study. A series of precipitation experiments was used to identify the significant process variables affecting precipitation performance. A mathematical model of the precipitation process was developed which is based on the formation of plutonium fluoride complexes. The precipitation model relates all process variables, in a single equation, to a single parameter which can be used to control the performance of the plutonium trifluoride precipitation process. Recommendations have been made which will optimize the FB-Line plutonium trifluoride precipitation process.

An Improved Plutonium Trifluoride Precipitation Flowsheet

International Nuclear Information System (INIS)

Harmon, H.D.

2001-01-01

This report discusses results of the plutonium trifluoride two-stage precipitation study. A series of precipitation experiments was used to identify the significant process variables affecting precipitation performance. A mathematical model of the precipitation process was developed which is based on the formation of plutonium fluoride complexes. The precipitation model relates all process variables, in a single equation, to a single parameter which can be used to control the performance of the plutonium trifluoride precipitation process. Recommendations have been made which will optimize the FB-Line plutonium trifluoride precipitation process

Reproducibility of precipitation distributions over extratropical continental regions in the CMIP5

Science.gov (United States)

Hirota, Nagio; Takayabu, Yukari

2013-04-01

Reproducibility of precipitation distributions over extratropical continental regions in the CMIP5 Nagio Hirota1,2 and Yukari N. Takayabu2 (1) National Institute of Polar Research (NIPR) (2) Atmosphere and Ocean Research Institute (AORI), the University of Tokyo Reproducibility of precipitation distributions over extratropical continental regions by CMIP5 climate models in their historical runs are evaluated, in comparison with GPCP(V2.2), CMAP(V0911), daily gridded gauge data APHRODITE. Surface temperature, cloud radiative forcing, and atmospheric circulations are also compared with observations of CRU-UEA, CERES, and ERA-interim/ERA40/JRA reanalysis data. It is shown that many CMIP5 models underestimate and overestimate summer precipitation over West and East Eurasia, respectively. These precipitation biases correspond to moisture transport associated with a cyclonic circulation bias over the whole continent of Eurasia. Meanwhile, many models underestimate cloud over the Eurasian continent, and associated shortwave cloud radiative forcing result in a significant warm bias. Evaporation feedback amplify the warm bias over West Eurasia. These processes consistently explain the precipitation biases over the Erasian continent in summer. We also examined reproducibility of winter precipitation, but robust results are not obtained yet due to the large uncertainty in observation associated with the adjustment of snow measurement in windy condition. Better observational data sets are necessary for further model validation. Acknowledgment: This study is supported by the PMM RA of JAXA, Green Network of Excellence (GRENE) Program by the Ministry of Education, Culture, Sports, Science and Technology, Japan, and Environment Research and Technology Development Fund (A-1201) of the Ministry of the Environment, Japan.

Bivariate spatial analysis of temperature and precipitation from general circulation models and observation proxies

KAUST Repository

Philbin, R.

2015-05-22

This study validates the near-surface temperature and precipitation output from decadal runs of eight atmospheric ocean general circulation models (AOGCMs) against observational proxy data from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis temperatures and Global Precipitation Climatology Project (GPCP) precipitation data. We model the joint distribution of these two fields with a parsimonious bivariate Matérn spatial covariance model, accounting for the two fields\\' spatial cross-correlation as well as their own smoothnesses. We fit output from each AOGCM (30-year seasonal averages from 1981 to 2010) to a statistical model on each of 21 land regions. Both variance and smoothness values agree for both fields over all latitude bands except southern mid-latitudes. Our results imply that temperature fields have smaller smoothness coefficients than precipitation fields, while both have decreasing smoothness coefficients with increasing latitude. Models predict fields with smaller smoothness coefficients than observational proxy data for the tropics. The estimated spatial cross-correlations of these two fields, however, are quite different for most GCMs in mid-latitudes. Model correlation estimates agree well with those for observational proxy data for Australia, at high northern latitudes across North America, Europe and Asia, as well as across the Sahara, India, and Southeast Asia, but elsewhere, little consistent agreement exists.

Bivariate spatial analysis of temperature and precipitation from general circulation models and observation proxies

KAUST Repository

Philbin, R.; Jun, M.

2015-01-01

This study validates the near-surface temperature and precipitation output from decadal runs of eight atmospheric ocean general circulation models (AOGCMs) against observational proxy data from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis temperatures and Global Precipitation Climatology Project (GPCP) precipitation data. We model the joint distribution of these two fields with a parsimonious bivariate Matérn spatial covariance model, accounting for the two fields' spatial cross-correlation as well as their own smoothnesses. We fit output from each AOGCM (30-year seasonal averages from 1981 to 2010) to a statistical model on each of 21 land regions. Both variance and smoothness values agree for both fields over all latitude bands except southern mid-latitudes. Our results imply that temperature fields have smaller smoothness coefficients than precipitation fields, while both have decreasing smoothness coefficients with increasing latitude. Models predict fields with smaller smoothness coefficients than observational proxy data for the tropics. The estimated spatial cross-correlations of these two fields, however, are quite different for most GCMs in mid-latitudes. Model correlation estimates agree well with those for observational proxy data for Australia, at high northern latitudes across North America, Europe and Asia, as well as across the Sahara, India, and Southeast Asia, but elsewhere, little consistent agreement exists.

Precipitation kinetics of a continuous precipitator, with application to the precipitation of ammonium polyuranate

International Nuclear Information System (INIS)

Hoyt, R.C.

1978-04-01

A mathematical model describing the kinetics of continuous precipitation was developed which accounts for crystal nucleation, crystal growth, primary coagulation, and secondary coagulation. Population density distributions, average particle sizes, dominant particle sizes, and suspension density fractions of the crystallites, primary agglomerates, and secondary agglomerates leaving the continuous precipitator can be determined. This kinetic model was applied to the continuous precipitation of ammonium polyuranate, which consists of: (1) elementary crystals, (2) clusters or primary coagulated particles, and (3) agglomerates or secondary coagulated particles. The crystallites are thin, submicron, hexagonal platelets. The clusters had an upper size limit of about 7 μ in diameter and contained numerous small voids (less than 0.3 μm) due to the packing of the crystallites. The agglomerates had an upper size limit of about 40 μm in diameter and contained large voids (approximately 1 μm). The particle size distribution and particle structure of the ammonium polyuranate precipitate can be controlled through proper regulation of the precipitation conditions. The ratio of clusters to agglomerates can be best controlled through the uranium concentration, and the cohesiveness or internal bonding strength of the particles can be controlled with the ammonium to uranium reacting feed mole ratio. These two conditions, in conjunction with the residence time, will determine the nucleation rates, growth rates, and size distributions of the particles leaving the continuous precipitator. With proper control of these physical particle characteristics, the use of pore formers, ball-milling, and powder blending can probably be eliminated from the nuclear fuel fabrication process, substantially reducing the cost

Precipitation kinetics of a continuous precipitator, with application to the precipitation of ammonium polyuranate

Energy Technology Data Exchange (ETDEWEB)

Hoyt, R.C.

1978-04-01

A mathematical model describing the kinetics of continuous precipitation was developed which accounts for crystal nucleation, crystal growth, primary coagulation, and secondary coagulation. Population density distributions, average particle sizes, dominant particle sizes, and suspension density fractions of the crystallites, primary agglomerates, and secondary agglomerates leaving the continuous precipitator can be determined. This kinetic model was applied to the continuous precipitation of ammonium polyuranate, which consists of: (1) elementary crystals, (2) clusters or primary coagulated particles, and (3) agglomerates or secondary coagulated particles. The crystallites are thin, submicron, hexagonal platelets. The clusters had an upper size limit of about 7 ..mu.. in diameter and contained numerous small voids (less than 0.3 ..mu..m) due to the packing of the crystallites. The agglomerates had an upper size limit of about 40 ..mu..m in diameter and contained large voids (approximately 1 ..mu..m). The particle size distribution and particle structure of the ammonium polyuranate precipitate can be controlled through proper regulation of the precipitation conditions. The ratio of clusters to agglomerates can be best controlled through the uranium concentration, and the cohesiveness or internal bonding strength of the particles can be controlled with the ammonium to uranium reacting feed mole ratio. These two conditions, in conjunction with the residence time, will determine the nucleation rates, growth rates, and size distributions of the particles leaving the continuous precipitator. With proper control of these physical particle characteristics, the use of pore formers, ball-milling, and powder blending can probably be eliminated from the nuclear fuel fabrication process, substantially reducing the cost.

Thermodynamics Prediction of Wax Precipitation in Black Oil Using Regular Solution Model and Plus Fraction Characterization

Directory of Open Access Journals (Sweden)

Wei Wang

2013-01-01

Full Text Available The precipitation of wax/solid paraffin during production, transportation, and processing of crude oil is a serious problem. It is essential to have a reliable model to predict the wax appearance temperature and the amount of solid precipitated at different conditions. This paper presents a work to predict the solid precipitation based on solid-liquid equilibrium with regular solution-molecular thermodynamic theory and characterization of the crude oil plus fraction. Due to the differences of solubility characteristics between solid and liquid phase, the solubility parameters of liquid and solid phase are calculated by a modified model. The heat capacity change between solid and liquid phase is considered and estimated in the thermodynamic model. An activity coefficient based thermodynamic method combined with two characteristic methods to calculate wax precipitation in crude oil, especially heavy oil, has been tested with experimental data. The results show that the wax appearance temperature and the amount of weight precipitated can be predicted well with the experimental data.

Impact of Precipitating Ice Hydrometeors on Longwave Radiative Effect Estimated by a Global Cloud-System Resolving Model

Science.gov (United States)

Chen, Ying-Wen; Seiki, Tatsuya; Kodama, Chihiro; Satoh, Masaki; Noda, Akira T.

2018-02-01

Satellite observation and general circulation model (GCM) studies suggest that precipitating ice makes nonnegligible contributions to the radiation balance of the Earth. However, in most GCMs, precipitating ice is diagnosed and its radiative effects are not taken into account. Here we examine the longwave radiative impact of precipitating ice using a global nonhydrostatic atmospheric model with a double-moment cloud microphysics scheme. An off-line radiation model is employed to determine cloud radiative effects according to the amount and altitude of each type of ice hydrometeor. Results show that the snow radiative effect reaches 2 W m-2 in the tropics, which is about half the value estimated by previous studies. This effect is strongly dependent on the vertical separation of ice categories and is partially generated by differences in terminal velocities, which are not represented in GCMs with diagnostic precipitating ice. Results from sensitivity experiments that artificially change the categories and altitudes of precipitating ice show that the simulated longwave heating profile and longwave radiation field are sensitive to the treatment of precipitating ice in models. This study emphasizes the importance of incorporating appropriate treatments for the radiative effects of precipitating ice in cloud and radiation schemes in GCMs in order to capture the cloud radiative effects of upper level clouds.

Simulated precipitation diurnal cycles over East Asia using different CAPE-based convective closure schemes in WRF model

Science.gov (United States)

Yang, Ben; Zhou, Yang; Zhang, Yaocun; Huang, Anning; Qian, Yun; Zhang, Lujun

2018-03-01

Closure assumption in convection parameterization is critical for reasonably modeling the precipitation diurnal variation in climate models. This study evaluates the precipitation diurnal cycles over East Asia during the summer of 2008 simulated with three convective available potential energy (CAPE) based closure assumptions, i.e. CAPE-relaxing (CR), quasi-equilibrium (QE), and free-troposphere QE (FTQE) and investigates the impacts of planetary boundary layer (PBL) mixing, advection, and radiation on the simulation by using the weather research and forecasting model. The sensitivity of precipitation diurnal cycle to PBL vertical resolution is also examined. Results show that the precipitation diurnal cycles simulated with different closures all exhibit large biases over land and the simulation with FTQE closure agrees best with observation. In the simulation with QE closure, the intensified PBL mixing after sunrise is responsible for the late-morning peak of convective precipitation, while in the simulation with FTQE closure, convective precipitation is mainly controlled by advection cooling. The relative contributions of different processes to precipitation formation are functions of rainfall intensity. In the simulation with CR closure, the dynamical equilibrium in the free troposphere still can be reached, implying the complex cause-effect relationship between atmospheric motion and convection. For simulations in which total CAPE is consumed for the closures, daytime precipitation decreases with increased PBL resolution because thinner model layer produces lower convection starting layer, leading to stronger downdraft cooling and CAPE consumption. The sensitivity of the diurnal peak time of precipitation to closure assumption can also be modulated by changes in PBL vertical resolution. The results of this study help us better understand the impacts of various processes on the precipitation diurnal cycle simulation.

Statistical Models for Social Networks

NARCIS (Netherlands)

Snijders, Tom A. B.; Cook, KS; Massey, DS

2011-01-01

Statistical models for social networks as dependent variables must represent the typical network dependencies between tie variables such as reciprocity, homophily, transitivity, etc. This review first treats models for single (cross-sectionally observed) networks and then for network dynamics. For

Geographic patterns of networks derived from extreme precipitation over the Indian subcontinent

Science.gov (United States)

Stolbova, Veronika; Bookhagen, Bodo; Marwan, Norbert; Kurths, Juergen

2014-05-01

Complex networks (CN) and event synchronization (ES) methods have been applied to study a number of climate phenomena such as Indian Summer Monsoon (ISM), South-American Monsoon, and African Monsoon. These methods proved to be powerful tools to infer interdependencies in climate dynamics between geographical sites, spatial structures, and key regions of the considered climate phenomenon. Here, we use these methods to study the spatial temporal variability of the extreme rainfall over the Indian subcontinent, in order to filter the data by coarse-graining the network, and to identify geographic patterns that are signature features (spatial signatures) of the ISM. We find four main geographic patterns of networks derived from extreme precipitation over the Indian subcontinent using up-to-date satellite-derived, and high temporal and spatial resolution rain-gauge interpolated daily rainfall datasets. In order to prove that our results are also relevant for other climatic variables like pressure and temperature, we use re-analysis data provided by the National Center for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR). We find that two of the patterns revealed from the CN extreme rainfall analysis coincide with those obtained for the pressure and temperature fields, and all four above mentioned patterns can be explained by topography, winds, and monsoon circulation. CN and ES enable to select the most informative regions for the ISM, providing realistic description of the ISM dynamics with fewer data, and also help to infer geographic pattern that are spatial signatures of the ISM. These patterns deserve a special attention for the meteorologists and can be used as markers of the ISM variability.

Agent-based modeling and network dynamics

CERN Document Server

Namatame, Akira

2016-01-01

The book integrates agent-based modeling and network science. It is divided into three parts, namely, foundations, primary dynamics on and of social networks, and applications. The book begins with the network origin of agent-based models, known as cellular automata, and introduce a number of classic models, such as Schelling’s segregation model and Axelrod’s spatial game. The essence of the foundation part is the network-based agent-based models in which agents follow network-based decision rules. Under the influence of the substantial progress in network science in late 1990s, these models have been extended from using lattices into using small-world networks, scale-free networks, etc. The book also shows that the modern network science mainly driven by game-theorists and sociophysicists has inspired agent-based social scientists to develop alternative formation algorithms, known as agent-based social networks. The book reviews a number of pioneering and representative models in this family. Upon the gi...

Modeling Epidemic Network Failures

DEFF Research Database (Denmark)

Ruepp, Sarah Renée; Fagertun, Anna Manolova

2013-01-01

This paper presents the implementation of a failure propagation model for transport networks when multiple failures occur resulting in an epidemic. We model the Susceptible Infected Disabled (SID) epidemic model and validate it by comparing it to analytical solutions. Furthermore, we evaluate...... the SID model’s behavior and impact on the network performance, as well as the severity of the infection spreading. The simulations are carried out in OPNET Modeler. The model provides an important input to epidemic connection recovery mechanisms, and can due to its flexibility and versatility be used...... to evaluate multiple epidemic scenarios in various network types....

Validation of mechanistic models for gas precipitation in solids during postirradiation annealing experiments

Science.gov (United States)

Rest, J.

1989-12-01

A number of different phenomenological models for gas precipitation in solids during postirradiation annealing experiments have been proposed. Validation of such mechanistic models for gas release and swelling is complicated by the use of data containing large systematic errors, and phenomena characterized by synergistic effects as well as uncertainties in materials properties. Statistical regression analysis is recommended for the selection of a reasonably well characterized data base for gas release from irradiated fuel under transient heating conditions. It is demonstrated that an appropriate data selection method is required in order to realistically examine the impact of differing descriptions of the phenomena, and uncertainties in selected materials properties, on the validation results. The results of the analysis show that the kinetics of gas precipitation in solids depend on bubble overpressurization effects and need to be accounted for during the heatup phase of isothermal heating experiments. It is shown that if only the total gas release values (as opposed to time-dependent data) were available, differentiation between different gas precipitation models would be ambiguous. The observed sustained increase in the fractional release curve at relatively high temperatures after the total precipitation of intragranular gas in fission gas bubbles is ascribed to the effects of a grain-growth/grain-boundary sweeping mechanism.

Validation of mechanistic models for gas precipitation in solids during postirradiation annealing experiments

International Nuclear Information System (INIS)

Rest, J.

1989-01-01

A number of different phenomenological models for gas precipitation in solids during postirradiation annealing experiments have been proposed. Validation of such mechanistic models for gas release and swelling is complicated by the use of data containing large systematic errors, and phenomena characterized by synergistic effects as well as uncertainties in materials properties. Statistical regression analysis is recommended for the selection of a reasonably well characterized data base for gas release from irradiated fuel under transient heating conditions. It is demonstrated that an appropriate data selection method is required in order to realistically examine the impact of differing descriptions of the phenomena, and uncertainties in selected materials properties, on the validation results. The results of the analysis show that the kinetics of gas precipitation in solid depend on bubble overpressurization effects and need to be accounted for during the heatup phase of isothermal heating experiments. It is shown that if only the total gas release values (as opposed to time-dependent data) were available, differentiation between different gas precipitation models would be ambiguous. The observed sustained increase in the fractional release curve at relatively high temperatures after the total precipitation of intragranular gas in fission gas bubbles is ascribed to the effects of a grain-growth/grain-boundary sweeping mechanism. (orig.)

Patterns of Precipitation and Streamflow Responses to Moisture Fluxes during Atmospheric Rivers

Science.gov (United States)

Henn, B. M.; Wilson, A. M.; Asgari Lamjiri, M.; Ralph, M.

2017-12-01

Precipitation from landfalling atmospheric rivers (ARs) have been shown to dominate the hydroclimate of many parts of the world. ARs are associated with saturated, neutrally-stable profiles in the lower atmosphere, in which forced ascent by topography induces precipitation. Understanding the spatial and temporal variability of precipitation over complex terrain during AR-driven precipitation is critical for accurate forcing of distributed hydrologic models and streamflow forecasts. Past studies using radar wind profilers and radiosondes have demonstrated predictability of precipitation rates based on upslope water vapor flux over coastal terrain, with certain levels of moisture flux exhibiting the greatest influence on precipitation. Additionally, these relationships have been extended to show that streamflow in turn responds predictably to upslope vapor flux. However, past studies have focused on individual pairs of profilers and precipitation gauges; the question of how orographic precipitation in ARs is distributed spatially over complex terrain, at different topographic scales, is less well known. Here, we examine profiles of atmospheric moisture transport from radiosondes and wind profilers, against a relatively dense network of precipitation gauges, as well as stream gauges, to assess relationships between upslope moisture flux and the spatial response of precipitation and streamflow. We focus on California's Russian River watershed in the 2016-2017 cool season, when regular radiosonde launches were made at two locations during an active sequence of landfalling ARs. We examine how atmospheric water vapor flux results in precipitation patterns across gauges with different topographic relationships to the prevailing moisture-bearing winds, and conduct a similar comparison of runoff volume response from several unimpaired watersheds in the upper Russian watershed, taking into account antecedent soil moisture conditions that influence runoff generation. Finally

Quantitative estimation of orographic precipitation over the Himalayas by using TRMM/PR and a dense network of rain gauges

Science.gov (United States)

Yatagai, A.

2009-04-01

Precipitation Radar (PR) data acquired by the Tropical Rainfall Measuring Mission (TRMM) over 10 years of observation were used to show the monthly rainfall patterns over the Himalayas. To validate and adjust these patterns, we used a dense network of rain gauges to measure daily precipitation over Nepal, Bangladesh, Bhutan, Pakistan, India, Myanmar, and China. We then compared TRMM/PR and rain gauge data in 0.05-degree grid cells (an approximately 5.5-km mesh). Compared with the rain gauge observations, the PR systematically underestimated precipitation by 28-38% in summer (July-September).Significant correlation between TRMM/PR and RG data was found for all months, but the correlation is relatively low in winter. The relationship is investigated for different elevation zones, and the PR was found to underestimate RG data in most zones, except for certain zones in February (250-1000m), March (0-1000m), and April (0-1500m). Monthly PR climatology was adjusted on the basis of monthly regressions between the two sets of data and depicted.

Evaluation of Uncertainty in Precipitation Datasets for New Mexico, USA

Science.gov (United States)

Besha, A. A.; Steele, C. M.; Fernald, A.

2014-12-01

Climate change, population growth and other factors are endangering water availability and sustainability in semiarid/arid areas particularly in the southwestern United States. Wide coverage of spatial and temporal measurements of precipitation are key for regional water budget analysis and hydrological operations which themselves are valuable tool for water resource planning and management. Rain gauge measurements are usually reliable and accurate at a point. They measure rainfall continuously, but spatial sampling is limited. Ground based radar and satellite remotely sensed precipitation have wide spatial and temporal coverage. However, these measurements are indirect and subject to errors because of equipment, meteorological variability, the heterogeneity of the land surface itself and lack of regular recording. This study seeks to understand precipitation uncertainty and in doing so, lessen uncertainty propagation into hydrological applications and operations. We reviewed, compared and evaluated the TRMM (Tropical Rainfall Measuring Mission) precipitation products, NOAA's (National Oceanic and Atmospheric Administration) Global Precipitation Climatology Centre (GPCC) monthly precipitation dataset, PRISM (Parameter elevation Regression on Independent Slopes Model) data and data from individual climate stations including Cooperative Observer Program (COOP), Remote Automated Weather Stations (RAWS), Soil Climate Analysis Network (SCAN) and Snowpack Telemetry (SNOTEL) stations. Though not yet finalized, this study finds that the uncertainty within precipitation estimates datasets is influenced by regional topography, season, climate and precipitation rate. Ongoing work aims to further evaluate precipitation datasets based on the relative influence of these phenomena so that we can identify the optimum datasets for input to statewide water budget analysis.

Predictability of Precipitation Over the Conterminous U.S. Based on the CMIP5 Multi-Model Ensemble

Science.gov (United States)

Jiang, Mingkai; Felzer, Benjamin S.; Sahagian, Dork

2016-01-01

Characterizing precipitation seasonality and variability in the face of future uncertainty is important for a well-informed climate change adaptation strategy. Using the Colwell index of predictability and monthly normalized precipitation data from the Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model ensembles, this study identifies spatial hotspots of changes in precipitation predictability in the United States under various climate scenarios. Over the historic period (1950–2005), the recurrent pattern of precipitation is highly predictable in the East and along the coastal Northwest, and is less so in the arid Southwest. Comparing the future (2040–2095) to the historic period, larger changes in precipitation predictability are observed under Representative Concentration Pathways (RCP) 8.5 than those under RCP 4.5. Finally, there are region-specific hotspots of future changes in precipitation predictability, and these hotspots often coincide with regions of little projected change in total precipitation, with exceptions along the wetter East and parts of the drier central West. Therefore, decision-makers are advised to not rely on future total precipitation as an indicator of water resources. Changes in precipitation predictability and the subsequent changes on seasonality and variability are equally, if not more, important factors to be included in future regional environmental assessment. PMID:27425819

United States Historical Climatology Network (US HCN) monthly temperature and precipitation data

Energy Technology Data Exchange (ETDEWEB)

Daniels, R.C. [ed.] [Univ. of Tennessee, Knoxville, TN (United States). Energy, Environment and Resources Center; Boden, T.A. [ed.] [Oak Ridge National Lab., TN (United States); Easterling, D.R.; Karl, T.R.; Mason, E.H.; Hughes, P.Y.; Bowman, D.P. [National Climatic Data Center, Asheville, NC (United States)

1996-01-11

This document describes a database containing monthly temperature and precipitation data for 1221 stations in the contiguous United States. This network of stations, known as the United States Historical Climatology Network (US HCN), and the resulting database were compiled by the National Climatic Data Center, Asheville, North Carolina. These data represent the best available data from the United States for analyzing long-term climate trends on a regional scale. The data for most stations extend through December 31, 1994, and a majority of the station records are serially complete for at least 80 years. Unlike many data sets that have been used in past climate studies, these data have been adjusted to remove biases introduced by station moves, instrument changes, time-of-observation differences, and urbanization effects. These monthly data are available free of charge as a numeric data package (NDP) from the Carbon Dioxide Information Analysis Center. The NDP includes this document and 27 machine-readable data files consisting of supporting data files, a descriptive file, and computer access codes. This document describes how the stations in the US HCN were selected and how the data were processed, defines limitations and restrictions of the data, describes the format and contents of the magnetic media, and provides reprints of literature that discuss the editing and adjustment techniques used in the US HCN.

Evaluation of precipitation input for SWAT modeling in Alpine catchment: A case study in the Adige river basin (Italy).

Science.gov (United States)

Tuo, Ye; Duan, Zheng; Disse, Markus; Chiogna, Gabriele

2016-12-15

Precipitation is often the most important input data in hydrological models when simulating streamflow. The Soil and Water Assessment Tool (SWAT), a widely used hydrological model, only makes use of data from one precipitation gauge station that is nearest to the centroid of each subbasin, which is eventually corrected using the elevation band method. This leads in general to inaccurate representation of subbasin precipitation input data, particularly in catchments with complex topography. To investigate the impact of different precipitation inputs on the SWAT model simulations in Alpine catchments, 13years (1998-2010) of daily precipitation data from four datasets including OP (Observed precipitation), IDW (Inverse Distance Weighting data), CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) and TRMM (Tropical Rainfall Measuring Mission) has been considered. Both model performances (comparing simulated and measured streamflow data at the catchment outlet) as well as parameter and prediction uncertainties have been quantified. For all three subbasins, the use of elevation bands is fundamental to match the water budget. Streamflow predictions obtained using IDW inputs are better than those obtained using the other datasets in terms of both model performance and prediction uncertainty. Models using the CHIRPS product as input provide satisfactory streamflow estimation, suggesting that this satellite product can be applied to this data-scarce Alpine region. Comparing the performance of SWAT models using different precipitation datasets is therefore important in data-scarce regions. This study has shown that, precipitation is the main source of uncertainty, and different precipitation datasets in SWAT models lead to different best estimate ranges for the calibrated parameters. This has important implications for the interpretation of the simulated hydrological processes. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

A Global Model for Circumgalactic and Cluster-core Precipitation

Science.gov (United States)

Voit, G. Mark; Meece, Greg; Li, Yuan; O'Shea, Brian W.; Bryan, Greg L.; Donahue, Megan

2017-08-01

We provide an analytic framework for interpreting observations of multiphase circumgalactic gas that is heavily informed by recent numerical simulations of thermal instability and precipitation in cool-core galaxy clusters. We start by considering the local conditions required for the formation of multiphase gas via two different modes: (1) uplift of ambient gas by galactic outflows, and (2) condensation in a stratified stationary medium in which thermal balance is explicitly maintained. Analytic exploration of these two modes provides insights into the relationships between the local ratio of the cooling and freefall timescales (I.e., {t}{cool}/{t}{ff}), the large-scale gradient of specific entropy, and the development of precipitation and multiphase media in circumgalactic gas. We then use these analytic findings to interpret recent simulations of circumgalactic gas in which global thermal balance is maintained. We show that long-lasting configurations of gas with 5≲ \\min ({t}{cool}/{t}{ff})≲ 20 and radial entropy profiles similar to observations of cool cores in galaxy clusters are a natural outcome of precipitation-regulated feedback. We conclude with some observational predictions that follow from these models. This work focuses primarily on precipitation and AGN feedback in galaxy-cluster cores, because that is where the observations of multiphase gas around galaxies are most complete. However, many of the physical principles that govern condensation in those environments apply to circumgalactic gas around galaxies of all masses.

An assessment of historical Antarctic precipitation and temperature trend using CMIP5 models and reanalysis datasets

Science.gov (United States)

Tang, Malcolm S. Y.; Chenoli, Sheeba Nettukandy; Samah, Azizan Abu; Hai, Ooi See

2018-03-01

The study of Antarctic precipitation has attracted a lot of attention recently. The reliability of climate models in simulating Antarctic precipitation, however, is still debatable. This work assess the precipitation and surface air temperature (SAT) of Antarctica (90 oS to 60 oS) using 49 Coupled Model Intercomparison Project phase 5 (CMIP5) global climate models and the European Centre for Medium-range Weather Forecasts "Interim" reanalysis (ERA-Interim); the National Centers for Environmental Prediction Climate Forecast System Reanalysis (CFSR); the Japan Meteorological Agency 55-year Reanalysis (JRA-55); and the Modern Era Retrospective-analysis for Research and Applications (MERRA) datasets for 1979-2005 (27 years). For precipitation, the time series show that the MERRA and JRA-55 have significantly increased from 1979 to 2005, while the ERA-Int and CFSR have insignificant changes. The reanalyses also have low correlation with one another (generally less than +0.69). 37 CMIP5 models show increasing trend, 18 of which are significant. The resulting CMIP5 MMM also has a significant increasing trend of 0.29 ± 0.06 mm year-1. For SAT, the reanalyses show insignificant changes and have high correlation with one another, while the CMIP5 MMM shows a significant increasing trend. Nonetheless, the variability of precipitation and SAT of MMM could affect the significance of its trend. One of the many reasons for the large differences of precipitation is the CMIP5 models' resolution.

Time-series Oxygen-18 Precipitation Isoscapes for Canada and the Northern United States

Science.gov (United States)

Delavau, Carly J.; Chun, Kwok P.; Stadnyk, Tricia A.; Birks, S. Jean; Welker, Jeffrey M.

2014-05-01

The present and past hydrological cycle from the watershed to regional scale can be greatly enhanced using water isotopes (δ18O and δ2H), displayed today as isoscapes. The development of water isoscapes has both hydrological and ecological applications, such as ground water recharge and food web ecology, and can provide critical information when observations are not available due to spatial and temporal gaps in sampling and data networks. This study focuses on the creation of δ18O precipitation (δ18Oppt) isoscapes at a monthly temporal frequency across Canada and the northern United States (US) utilizing CNIP (Canadian Network for Isotopes in Precipitation) and USNIP (United States Network for Isotopes in Precipitation) measurements. Multiple linear stepwise regressions of CNIP and USNIP observations alongside NARR (North American Regional Reanalysis) climatological variables, teleconnection indices, and geographic indicators are utilized to create empirical models that predict the δ18O of monthly precipitation across Canada and the northern US. Pooling information from nearby locations within a region can be useful due to the similarity of processes and mechanisms controlling the variability of δ18O. We expect similarity in the controls on isotopic composition to strengthen the correlation between δ18Oppt and predictor variables, resulting in model simulation improvements. For this reason, three different regionalization approaches are used to separate the study domain into 'isotope zones' to explore the effect of regionalization on model performance. This methodology results in 15 empirical models, five within each regionalization. A split sample calibration and validation approach is employed for model development, and parameter selection is based on demonstrated improvement of the Akaike Information Criteria (AIC). Simulation results indicate the empirical models are generally able to capture the overall monthly variability in δ18Oppt. For the three

Current approaches to gene regulatory network modelling

Directory of Open Access Journals (Sweden)

Brazma Alvis

2007-09-01

Full Text Available Abstract Many different approaches have been developed to model and simulate gene regulatory networks. We proposed the following categories for gene regulatory network models: network parts lists, network topology models, network control logic models, and dynamic models. Here we will describe some examples for each of these categories. We will study the topology of gene regulatory networks in yeast in more detail, comparing a direct network derived from transcription factor binding data and an indirect network derived from genome-wide expression data in mutants. Regarding the network dynamics we briefly describe discrete and continuous approaches to network modelling, then describe a hybrid model called Finite State Linear Model and demonstrate that some simple network dynamics can be simulated in this model.

Network Bandwidth Utilization Forecast Model on High Bandwidth Network

Energy Technology Data Exchange (ETDEWEB)

Yoo, Wucherl; Sim, Alex

2014-07-07

With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology, our forecast model reduces computation time by 83.2percent. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.

Network bandwidth utilization forecast model on high bandwidth networks

Energy Technology Data Exchange (ETDEWEB)

Yoo, Wuchert (William) [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Sim, Alex [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

2015-03-30

With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology, our forecast model reduces computation time by 83.2%. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.

RMBNToolbox: random models for biochemical networks

Directory of Open Access Journals (Sweden)

Niemi Jari

2007-05-01

Full Text Available Abstract Background There is an increasing interest to model biochemical and cell biological networks, as well as to the computational analysis of these models. The development of analysis methodologies and related software is rapid in the field. However, the number of available models is still relatively small and the model sizes remain limited. The lack of kinetic information is usually the limiting factor for the construction of detailed simulation models. Results We present a computational toolbox for generating random biochemical network models which mimic real biochemical networks. The toolbox is called Random Models for Biochemical Networks. The toolbox works in the Matlab environment, and it makes it possible to generate various network structures, stoichiometries, kinetic laws for reactions, and parameters therein. The generation can be based on statistical rules and distributions, and more detailed information of real biochemical networks can be used in situations where it is known. The toolbox can be easily extended. The resulting network models can be exported in the format of Systems Biology Markup Language. Conclusion While more information is accumulating on biochemical networks, random networks can be used as an intermediate step towards their better understanding. Random networks make it possible to study the effects of various network characteristics to the overall behavior of the network. Moreover, the construction of artificial network models provides the ground truth data needed in the validation of various computational methods in the fields of parameter estimation and data analysis.

Multisite stochastic simulation of daily precipitation from copula modeling with a gamma marginal distribution

Science.gov (United States)

Lee, Taesam

2018-05-01

Multisite stochastic simulations of daily precipitation have been widely employed in hydrologic analyses for climate change assessment and agricultural model inputs. Recently, a copula model with a gamma marginal distribution has become one of the common approaches for simulating precipitation at multiple sites. Here, we tested the correlation structure of the copula modeling. The results indicate that there is a significant underestimation of the correlation in the simulated data compared to the observed data. Therefore, we proposed an indirect method for estimating the cross-correlations when simulating precipitation at multiple stations. We used the full relationship between the correlation of the observed data and the normally transformed data. Although this indirect method offers certain improvements in preserving the cross-correlations between sites in the original domain, the method was not reliable in application. Therefore, we further improved a simulation-based method (SBM) that was developed to model the multisite precipitation occurrence. The SBM preserved well the cross-correlations of the original domain. The SBM method provides around 0.2 better cross-correlation than the direct method and around 0.1 degree better than the indirect method. The three models were applied to the stations in the Nakdong River basin, and the SBM was the best alternative for reproducing the historical cross-correlation. The direct method significantly underestimates the correlations among the observed data, and the indirect method appeared to be unreliable.

Multi-scale Quantitative Precipitation Forecasting Using Nonlinear and Nonstationary Teleconnection Signals and Artificial Neural Network Models

Science.gov (United States)

Global sea surface temperature (SST) anomalies can affect terrestrial precipitation via ocean-atmosphere interaction known as climate teleconnection. Non-stationary and non-linear characteristics of the ocean-atmosphere system make the identification of the teleconnection signals...

Evaluation of modeled changes in extreme precipitation in Europe and the Rhine basin

International Nuclear Information System (INIS)

Haren, Ronald van; Oldenborgh, Geert Jan van; Lenderink, Geert; Hazeleger, Wilco

2013-01-01

In this study, we investigate the change in multi-day precipitation extremes in late winter in Europe using observations and climate models. The objectives of the analysis are to determine whether climate models can accurately reproduce observed trends and, if not, to find the causes of the difference in trends. Similarly to an earlier finding for mean precipitation trends, and despite a lower signal to noise ratio, climate models fail to reproduce the increase in extremes in much of northern Europe: the model simulations do not cover the observed trend in large parts of this area. A dipole in the sea-level pressure trend over continental Europe causes positive trends in extremes in northern Europe and negative trends in the Iberian Peninsula. Climate models have a much weaker pressure trend dipole and as a result a much weaker (extreme) precipitation response. The inability of climate models to correctly simulate observed changes in atmospheric circulation is also primarily responsible for the underestimation of trends in the Rhine basin. When it has been adjusted for the circulation trend mismatch, the observed trend is well within the spread of the climate model simulations. Therefore, it is important that we improve our understanding of circulation changes, in particular related to the cause of the apparent mismatch between observed and modeled circulation trends over the past century. (letter)

Can nudging be used to quantify model sensitivities in precipitation and cloud forcing?: NUDGING AND MODEL SENSITIVITIES

Energy Technology Data Exchange (ETDEWEB)

Lin, Guangxing [Pacific Northwest National Laboratory, Atmospheric Science and Global Change Division, Richland Washington USA; Wan, Hui [Pacific Northwest National Laboratory, Atmospheric Science and Global Change Division, Richland Washington USA; Zhang, Kai [Pacific Northwest National Laboratory, Atmospheric Science and Global Change Division, Richland Washington USA; Qian, Yun [Pacific Northwest National Laboratory, Atmospheric Science and Global Change Division, Richland Washington USA; Ghan, Steven J. [Pacific Northwest National Laboratory, Atmospheric Science and Global Change Division, Richland Washington USA

2016-07-10

Efficient simulation strategies are crucial for the development and evaluation of high resolution climate models. This paper evaluates simulations with constrained meteorology for the quantification of parametric sensitivities in the Community Atmosphere Model version 5 (CAM5). Two parameters are perturbed as illustrating examples: the convection relaxation time scale (TAU), and the threshold relative humidity for the formation of low-level stratiform clouds (rhminl). Results suggest that the fidelity and computational efficiency of the constrained simulations depend strongly on 3 factors: the detailed implementation of nudging, the mechanism through which the perturbed parameter affects precipitation and cloud, and the magnitude of the parameter perturbation. In the case of a strong perturbation in convection, temperature and/or wind nudging with a 6-hour relaxation time scale leads to non-negligible side effects due to the distorted interactions between resolved dynamics and parameterized convection, while a 1-year free running simulation can satisfactorily capture the annual mean precipitation sensitivity in terms of both global average and geographical distribution. In the case of a relatively weak perturbation the large-scale condensation scheme, results from 1-year free-running simulations are strongly affected by noise associated with internal variability, while nudging winds effectively reduces the noise, and reasonably reproduces the response of precipitation and cloud forcing to parameter perturbation. These results indicate that caution is needed when using nudged simulations to assess precipitation and cloud forcing sensitivities to parameter changes in general circulation models. We also demonstrate that ensembles of short simulations are useful for understanding the evolution of model sensitivities.

Characterization and structure of precipitates in 6xxx Aluminium Alloys

International Nuclear Information System (INIS)

Holmestad, Randi; Bjørge, Ruben; Ehlers, Flemming J H; Torsæter, Malin; Marioara, Calin D; Andersen, Sigmund J

2012-01-01

Solute atom nanoscale precipitates are responsible for the favourable mechanical properties of heat treatable aluminium alloys such as Al-Mg-Si (6xxx). The shape, structure and strengthening properties of age-hardening precipitates depend on alloy composition and thermo-mechanical history. We seek an improved understanding of the physics related to nucleation and precipitation on the atomistic level in these alloys. Once these mechanisms are sufficiently well described and understood, the hope is that 'alloy design' simulations can assist tailoring of materials with desired properties. In pure Al-Mg-Si we have determined the structure of nearly all the known metastable precipitate phases, by combining advanced TEM techniques (such as high resolution TEM and nano-beam diffraction) with atom probe tomography and density functional theory. We are now studying effects of additions /substitutions of Cu, Ag and/or Ge that promote formation of more disordered precipitates, employing aberration corrected high angle annular dark field scanning TEM. We find that all metastable precipitates contain variations of a widely spaced 'Si/Ge network'. In spite of disorder or defects, this network is surprisingly well ordered, with hexagonal projected sub-cell dimensions a = b ≅ 0.4 nm and c (along the fully coherent precipitate main growth direction) equal to 0.405 nm or a multiple of it.

The Day-1 GPM Combined Precipitation Algorithm: IMERG

Science.gov (United States)

Huffman, G. J.; Bolvin, D. T.; Braithwaite, D.; Hsu, K.; Joyce, R.; Kidd, C.; Sorooshian, S.; Xie, P.

2012-12-01

The Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) mission (IMERG) algorithm will provide the at-launch combined-sensor precipitation dataset being produced by the U.S. GPM Science Team. IMERG is being developed as a unified U.S. algorithm that takes advantage of strengths in three current U.S. algorithms: - the TRMM Multi-satellite Precipitation Analysis (TMPA), which addresses inter-satellite calibration of precipitation estimates and monthly scale combination of satellite and gauge analyses; - the CPC Morphing algorithm with Kalman Filtering (KF-CMORPH), which provides quality-weighted time interpolation of precipitation patterns following storm motion; and - the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks using a Cloud Classification System (PERSIANN-CCS), which provides a neural-network-based scheme for generating microwave-calibrated precipitation estimates from geosynchronous infrared brightness temperatures, and filters out some non-raining cold clouds. The goal is to provide a long-term, fine-scale record of global precipitation from the entire constellation of precipitation-relevant satellite sensors, with input from surface precipitation gauges. The record will begin January 1998 at the start of the Tropical Rainfall Measuring Mission (TRMM) and extend as GPM records additional data. Although homogeneity is considered desirable, the use of diverse and evolving data sources works against the strict long-term homogeneity that characterizes a Climate Data Record (CDR). This talk will briefly review the design requirements for IMERG, including multiple runs at different latencies (most likely around 4 hours, 12 hours, and 2 months after observation time), various intermediate data fields as part of the IMERG data file, and the plans to bring up IMERG with calibration by TRMM initially, transitioning to GPM when its individual-sensor precipitation algorithms are fully functional

Modelling and characterization of chi-phase grain boundary precipitation during aging of Fe-Cr-Ni-Mo stainless steel

International Nuclear Information System (INIS)

Xu, W.; San Martin, D.; Rivera Diaz del Castillo, P.E.J.; Zwaag, S. van der

2007-01-01

High molybdenum stainless steels may contain the chi-phase precipitate (χ, Fe 36 Cr 12 Mo 10 ) which may lead to undesirable effects on strength, toughness and corrosion resistance. In the present work, specimens of a 12Cr-9Ni-4Mo wt% steel are heat treated at different temperatures and times, and the average particle size and particle size distribution of chi-phase precipitate are studied quantitatively. A computer model based on the KWN framework has been developed to describe the evolution of chi-phase precipitation. The kinetic model takes advantage of the KWN model to describe the precipitate particle size distribution, and is coupled with the thermodynamic software ThermoCalc for calculating the instantaneous local thermodynamic equilibrium condition at the interface and the driving force for nucleation. A modified version of Zener's theory accounting for capillarity effects at early growth stages is implemented in this model. The prediction of the model for chi-phase precipitation at a grain boundary is compared to experimental results and both the average particle size and the particle size distribution are found to be in good agreement with experimental observations at late precipitation stages

Precipitation projections under GCMs perspective and Turkish Water Foundation (TWF) statistical downscaling model procedures

Science.gov (United States)

Dabanlı, İsmail; Şen, Zekai

2018-04-01

The statistical climate downscaling model by the Turkish Water Foundation (TWF) is further developed and applied to a set of monthly precipitation records. The model is structured by two phases as spatial (regional) and temporal downscaling of global circulation model (GCM) scenarios. The TWF model takes into consideration the regional dependence function (RDF) for spatial structure and Markov whitening process (MWP) for temporal characteristics of the records to set projections. The impact of climate change on monthly precipitations is studied by downscaling Intergovernmental Panel on Climate Change-Special Report on Emission Scenarios (IPCC-SRES) A2 and B2 emission scenarios from Max Plank Institute (EH40PYC) and Hadley Center (HadCM3). The main purposes are to explain the TWF statistical climate downscaling model procedures and to expose the validation tests, which are rewarded in same specifications as "very good" for all stations except one (Suhut) station in the Akarcay basin that is in the west central part of Turkey. Eventhough, the validation score is just a bit lower at the Suhut station, the results are "satisfactory." It is, therefore, possible to say that the TWF model has reasonably acceptable skill for highly accurate estimation regarding standard deviation ratio (SDR), Nash-Sutcliffe efficiency (NSE), and percent bias (PBIAS) criteria. Based on the validated model, precipitation predictions are generated from 2011 to 2100 by using 30-year reference observation period (1981-2010). Precipitation arithmetic average and standard deviation have less than 5% error for EH40PYC and HadCM3 SRES (A2 and B2) scenarios.

Biological transportation networks: Modeling and simulation

KAUST Repository

Albi, Giacomo

2015-09-15

We present a model for biological network formation originally introduced by Cai and Hu [Adaptation and optimization of biological transport networks, Phys. Rev. Lett. 111 (2013) 138701]. The modeling of fluid transportation (e.g., leaf venation and angiogenesis) and ion transportation networks (e.g., neural networks) is explained in detail and basic analytical features like the gradient flow structure of the fluid transportation network model and the impact of the model parameters on the geometry and topology of network formation are analyzed. We also present a numerical finite-element based discretization scheme and discuss sample cases of network formation simulations.

In Silico Modeling Approach for the Evaluation of Gastrointestinal Dissolution, Supersaturation, and Precipitation of Posaconazole.

Science.gov (United States)

Hens, Bart; Pathak, Shriram M; Mitra, Amitava; Patel, Nikunjkumar; Liu, Bo; Patel, Sanjaykumar; Jamei, Masoud; Brouwers, Joachim; Augustijns, Patrick; Turner, David B

2017-12-04

The aim of this study was to evaluate gastrointestinal (GI) dissolution, supersaturation, and precipitation of posaconazole, formulated as an acidified (pH 1.6) and neutral (pH 7.1) suspension. A physiologically based pharmacokinetic (PBPK) modeling and simulation tool was applied to simulate GI and systemic concentration-time profiles of posaconazole, which were directly compared with intraluminal and systemic data measured in humans. The Advanced Dissolution Absorption and Metabolism (ADAM) model of the Simcyp Simulator correctly simulated incomplete gastric dissolution and saturated duodenal concentrations of posaconazole in the duodenal fluids following administration of the neutral suspension. In contrast, gastric dissolution was approximately 2-fold higher after administration of the acidified suspension, which resulted in supersaturated concentrations of posaconazole upon transfer to the upper small intestine. The precipitation kinetics of posaconazole were described by two precipitation rate constants, extracted by semimechanistic modeling of a two-stage medium change in vitro dissolution test. The 2-fold difference in exposure in the duodenal compartment for the two formulations corresponded with a 2-fold difference in systemic exposure. This study demonstrated for the first time predictive in silico simulations of GI dissolution, supersaturation, and precipitation for a weakly basic compound in part informed by modeling of in vitro dissolution experiments and validated via clinical measurements in both GI fluids and plasma. Sensitivity analysis with the PBPK model indicated that the critical supersaturation ratio (CSR) and second precipitation rate constant (sPRC) are important parameters of the model. Due to the limitations of the two-stage medium change experiment the CSR was extracted directly from the clinical data. However, in vitro experiments with the BioGIT transfer system performed after completion of the in silico modeling provided an almost

A phase field model for segregation and precipitation induced by irradiation in alloys

Science.gov (United States)

Badillo, A.; Bellon, P.; Averback, R. S.

2015-04-01

A phase field model is introduced to model the evolution of multicomponent alloys under irradiation, including radiation-induced segregation and precipitation. The thermodynamic and kinetic components of this model are derived using a mean-field model. The mobility coefficient and the contribution of chemical heterogeneity to free energy are rescaled by the cell size used in the phase field model, yielding microstructural evolutions that are independent of the cell size. A new treatment is proposed for point defect clusters, using a mixed discrete-continuous approach to capture the stochastic character of defect cluster production in displacement cascades, while retaining the efficient modeling of the fate of these clusters using diffusion equations. The model is tested on unary and binary alloy systems using two-dimensional simulations. In a unary system, the evolution of point defects under irradiation is studied in the presence of defect clusters, either pre-existing ones or those created by irradiation, and compared with rate theory calculations. Binary alloys with zero and positive heats of mixing are then studied to investigate the effect of point defect clustering on radiation-induced segregation and precipitation in undersaturated solid solutions. Lastly, irradiation conditions and alloy parameters leading to irradiation-induced homogeneous precipitation are investigated. The results are discussed in the context of experimental results reported for Ni-Si and Al-Zn undersaturated solid solutions subjected to irradiation.

A phase field model for segregation and precipitation induced by irradiation in alloys

International Nuclear Information System (INIS)

Badillo, A; Bellon, P; Averback, R S

2015-01-01

A phase field model is introduced to model the evolution of multicomponent alloys under irradiation, including radiation-induced segregation and precipitation. The thermodynamic and kinetic components of this model are derived using a mean-field model. The mobility coefficient and the contribution of chemical heterogeneity to free energy are rescaled by the cell size used in the phase field model, yielding microstructural evolutions that are independent of the cell size. A new treatment is proposed for point defect clusters, using a mixed discrete-continuous approach to capture the stochastic character of defect cluster production in displacement cascades, while retaining the efficient modeling of the fate of these clusters using diffusion equations. The model is tested on unary and binary alloy systems using two-dimensional simulations. In a unary system, the evolution of point defects under irradiation is studied in the presence of defect clusters, either pre-existing ones or those created by irradiation, and compared with rate theory calculations. Binary alloys with zero and positive heats of mixing are then studied to investigate the effect of point defect clustering on radiation-induced segregation and precipitation in undersaturated solid solutions. Lastly, irradiation conditions and alloy parameters leading to irradiation-induced homogeneous precipitation are investigated. The results are discussed in the context of experimental results reported for Ni–Si and Al–Zn undersaturated solid solutions subjected to irradiation. (paper)

An integrated modeling study on the effects of mineral dust and sea salt particles on clouds and precipitation

Directory of Open Access Journals (Sweden)

S. Solomos

2011-01-01

Full Text Available This report addresses the effects of pollution on the development of precipitation in clean ("pristine" and polluted ("hazy" environments in the Eastern Mediterranean by using the Integrated Community Limited Area Modeling System (ICLAMS (an extended version of the Regional Atmospheric Modeling System, RAMS. The use of this model allows one to investigate the interactions of the aerosols with cloud development. The simulations show that the onset of precipitation in hazy clouds is delayed compared to pristine conditions. Adding small concentrations of GCCN to polluted clouds promotes early-stage rain. The addition of GCCN to pristine clouds has no effect on precipitation amounts. Topography was found to be more important for the distribution of precipitation than aerosol properties. Increasing by 15% the concentration of hygroscopic dust particles for a case study over the Eastern Mediterranean resulted in more vigorous convection and more intense updrafts. The clouds that were formed extended about three kilometers higher, delaying the initiation of precipitation by one hour. Prognostic treatment of the aerosol concentrations in the explicit cloud droplet nucleation scheme of the model, improved the model performance for the twenty-four hour accumulated precipitation. The spatial distribution and the amounts of precipitation were found to vary greatly between the different aerosol scenarios. These results indicate the large uncertainty that remains and the need for more accurate description of aerosol feedbacks in atmospheric models and climate change predictions.

Multidecadal trends in the duration of wet spells and associated intensity of precipitation as revealed by a very dense observational German network

International Nuclear Information System (INIS)

Zolina, Olga

2014-01-01

Precipitation durations and intensities over the period 1950–2008 are analysed using daily rain gauge data from the Deutsche Wetterdienst raingauge network—one of the densest and most properly maintained precipitation observational networks in Europe. Truncated geometric distribution of the family of discrete distributions was applied for quantifying probability distribution of the durations of wet spells. Further intensities of wet spells of different durations were analysed along with wet spell lengths. During the cold season (October–March) wet periods over the whole of Germany demonstrate a robust pattern of lengthening by about 2–3% for the mean durations of wet spells and up to 6% for extremely long wet periods. This tendency is clearly associated with growing (up to 10% per decade in Eastern Germany) intensity of precipitation during long wet periods (more than 5 days) and the weakening of precipitation events associated with short and moderately long wet periods with both signals being statistically significant. Trends are superimposed with interdecadal variability, which is the strongest in Northern and Central Germany. In the warm season (April–September) there is no robust pan-German trend pattern in the wet spell durations and associated precipitation intensities. Strong structural changes in winter precipitation over Germany potentially imply growing rates of winter ground water recharge over Germany and increasing probability of winter flash and river flooding. (paper)

Atomistic modeling of zirconium hydride precipitation: methodology for deriving a tight-binding potential

International Nuclear Information System (INIS)

Dufresne, Alice

2014-01-01

The zirconium-hydrogen system is of nuclear safety interest, as the hydride precipitation leads to the cladding embrittlement, which is made of zirconium-based alloys. The cladding is the first safety barrier confining the radioactive products: its integrity shall be kept during the entire fuel-assemblies life, in reactor, including accidental situation, and post-operation (transport and storage). Many uncertainties remain regarding the hydrides precipitation kinetics and the local stress impact on their precipitation. The atomic scale modeling of this system would bring clarifications on the relevant mechanisms. The usual atomistic modeling methods are based on thermo-statistic approaches, whose precision and reliability depend on the interatomic potential used. However, there was no potential allowing a rigorous study of the Zr-H system. The present work has indeed addressed this issue: a new tight-binding potential for zirconium hydrides modeling is now available. Moreover, this thesis provides a detailed manual for deriving such potentials accounting for spd hybridization, and fitted here on DFT results. This guidebook has be written in light of modeling a pure transition metal followed by a metal-covalent coupling (metallic carbides, nitrides and silicides). (author)

SYSNET: A salt-site systems network model for scenario assessments

International Nuclear Information System (INIS)

Reeves, M.; Banda, R.S.

1986-12-01

This document contains a description of the initial version of the systems model SYSNET. This model is being developed to analyze potentially disruptive scenarios of salt repository systems. Currently the model features a general three-dimensional network topology and simulates the processes of flow, heat transport in rock, heat transport in fluid, brine transport, salt creep dissolution, and precipitation. Of necessity, the SYSNET Code uses relatively simple semi-analytic algorithms so that it may be implemented statistically. Uncertain parameters may be sampled with a compatible preprocessor and then analyzed statistically with a compatible postprocessor. When used in this fashion, SYSNET may be caused to calculate distributions of various performance measures and sensitivities of performance measures to uncertain parameters. The ultimate objective of the SYSNET development is to prioritize data needs by computing sensitivities relative to a particular performance measure, namely the 10,000-year cumulative release, and to evaluate repository systems for compliance with the US Environmental Protection Agency (EPA) Standard. 16 refs., 25 figs., 31 tabs

Sea surface salinity and temperature-based predictive modeling of southwestern US winter precipitation: improvements, errors, and potential mechanisms

Science.gov (United States)

Liu, T.; Schmitt, R. W.; Li, L.

2017-12-01

Using 69 years of historical data from 1948-2017, we developed a method to globally search for sea surface salinity (SSS) and temperature (SST) predictors of regional terrestrial precipitation. We then applied this method to build an autumn (SON) SSS and SST-based 3-month lead predictive model of winter (DJF) precipitation in southwestern United States. We also find that SSS-only models perform better than SST-only models. We previously used an arbitrary correlation coefficient (r) threshold, |r| > 0.25, to define SSS and SST predictor polygons for best subset regression of southwestern US winter precipitation; from preliminary sensitivity tests, we find that |r| > 0.18 yields the best models. The observed below-average precipitation (0.69 mm/day) in winter 2015-2016 falls within the 95% confidence interval of the prediction model. However, the model underestimates the anomalous high precipitation (1.78 mm/day) in winter 2016-2017 by more than three-fold. Moisture transport mainly attributed to "pineapple express" atmospheric rivers (ARs) in winter 2016-2017 suggests that the model falls short on a sub-seasonal scale, in which case storms from ARs contribute a significant portion of seasonal terrestrial precipitation. Further, we identify a potential mechanism for long-range SSS and precipitation teleconnections: standing Rossby waves. The heat applied to the atmosphere from anomalous tropical rainfall can generate standing Rossby waves that propagate to higher latitudes. SSS anomalies may be indicative of anomalous tropical rainfall, and by extension, standing Rossby waves that provide the long-range teleconnections.

Nonlinear regression and ARIMA models for precipitation chemistry in East Central Florida from 1978 to 1997

International Nuclear Information System (INIS)

Nickerson, David M.; Madsen, Brooks C.

2005-01-01

Continuous monitoring of precipitation in East Central Florida has occurred since 1978 at a sampling site located on the University of Central Florida (UCF) campus. Monthly volume-weighted average (VWA) concentration for several major analytes that are present in precipitation samples was calculated from samples collected daily. Monthly VWA concentration and wet deposition of H + , NH 4 + , Ca 2+ , Mg 2+ , NO 3 - , Cl - and SO 4 2- were evaluated by a nonlinear regression (NLR) model that considered 10-year data (from 1978 to 1987) and 20-year data (from 1978 to 1997). Little change in the NLR parameter estimates was indicated among the 10-year and 20-year evaluations except for general decreases in the predicted trends from the 10-year to the 20-year fits. Box-Jenkins autoregressive integrated moving average (ARIMA) models with linear trend were considered as an alternative to the NLR models for these data. The NLR and ARIMA model forecasts for 1998 were compared to the actual 1998 data. For monthly VWA concentration values, the two models gave similar results. For the wet deposition values, the ARIMA models performed considerably better. - Autoregressive integrated moving average models of precipitation data are an improvement over nonlinear models for the prediction of precipitation chemistry composition

Evaluation of globally available precipitation data products as input for water balance models

Science.gov (United States)

Lebrenz, H.; Bárdossy, A.

2009-04-01

Subject of this study is the evaluation of globally available precipitation data products, which are intended to be used as input variables for water balance models in ungauged basins. The selected data sources are a) the Global Precipitation Climatology Centre (GPCC), b) the Global Precipitation Climatology Project (GPCP) and c) the Climate Research Unit (CRU), resulting into twelve globally available data products. The data products imply different data bases, different derivation routines and varying resolutions in time and space. For validation purposes, the ground data from South Africa were screened on homogeneity and consistency by various tests and an outlier detection using multi-linear regression was performed. External Drift Kriging was subsequently applied on the ground data and the resulting precipitation arrays were compared to the different products with respect to quantity and variance.

Reduced Moment-Based Models for Oxygen Precipitates and Dislocation Loops in Silicon

Science.gov (United States)

Trzynadlowski, Bart

The demand for ever smaller, higher-performance integrated circuits and more efficient, cost-effective solar cells continues to push the frontiers of process technology. Fabrication of silicon devices requires extremely precise control of impurities and crystallographic defects. Failure to do so not only reduces performance, efficiency, and yield, it threatens the very survival of commercial enterprises in today's fiercely competitive and price-sensitive global market. The presence of oxygen in silicon is an unavoidable consequence of the Czochralski process, which remains the most popular method for large-scale production of single-crystal silicon. Oxygen precipitates that form during thermal processing cause distortion of the surrounding silicon lattice and can lead to the formation of dislocation loops. Localized deformation caused by both of these defects introduces potential wells that trap diffusing impurities such as metal atoms, which is highly desirable if done far away from sensitive device regions. Unfortunately, dislocations also reduce the mechanical strength of silicon, which can cause wafer warpage and breakage. Engineers must negotiate this and other complex tradeoffs when designing fabrication processes. Accomplishing this in a complex, modern process involving a large number of thermal steps is impossible without the aid of computational models. In this dissertation, new models for oxygen precipitation and dislocation loop evolution are described. An oxygen model using kinetic rate equations to evolve the complete precipitate size distribution was developed first. This was then used to create a reduced model tracking only the moments of the size distribution. The moment-based model was found to run significantly faster than its full counterpart while accurately capturing the evolution of oxygen precipitates. The reduced model was fitted to experimental data and a sensitivity analysis was performed to assess the robustness of the results. Source

New technique for ensemble dressing combining Multimodel SuperEnsemble and precipitation PDF

Science.gov (United States)

Cane, D.; Milelli, M.

2009-09-01

The Multimodel SuperEnsemble technique (Krishnamurti et al., Science 285, 1548-1550, 1999) is a postprocessing method for the estimation of weather forecast parameters reducing direct model output errors. It differs from other ensemble analysis techniques by the use of an adequate weighting of the input forecast models to obtain a combined estimation of meteorological parameters. Weights are calculated by least-square minimization of the difference between the model and the observed field during a so-called training period. Although it can be applied successfully on the continuous parameters like temperature, humidity, wind speed and mean sea level pressure (Cane and Milelli, Meteorologische Zeitschrift, 15, 2, 2006), the Multimodel SuperEnsemble gives good results also when applied on the precipitation, a parameter quite difficult to handle with standard post-processing methods. Here we present our methodology for the Multimodel precipitation forecasts applied on a wide spectrum of results over Piemonte very dense non-GTS weather station network. We will focus particularly on an accurate statistical method for bias correction and on the ensemble dressing in agreement with the observed precipitation forecast-conditioned PDF. Acknowledgement: this work is supported by the Italian Civil Defence Department.

Generalized Network Psychometrics : Combining Network and Latent Variable Models

NARCIS (Netherlands)

Epskamp, S.; Rhemtulla, M.; Borsboom, D.

2017-01-01

We introduce the network model as a formal psychometric model, conceptualizing the covariance between psychometric indicators as resulting from pairwise interactions between observable variables in a network structure. This contrasts with standard psychometric models, in which the covariance between

An adaptive two-stage analog/regression model for probabilistic prediction of small-scale precipitation in France

Science.gov (United States)

Chardon, Jérémy; Hingray, Benoit; Favre, Anne-Catherine

2018-01-01

Statistical downscaling models (SDMs) are often used to produce local weather scenarios from large-scale atmospheric information. SDMs include transfer functions which are based on a statistical link identified from observations between local weather and a set of large-scale predictors. As physical processes driving surface weather vary in time, the most relevant predictors and the regression link are likely to vary in time too. This is well known for precipitation for instance and the link is thus often estimated after some seasonal stratification of the data. In this study, we present a two-stage analog/regression model where the regression link is estimated from atmospheric analogs of the current prediction day. Atmospheric analogs are identified from fields of geopotential heights at 1000 and 500 hPa. For the regression stage, two generalized linear models are further used to model the probability of precipitation occurrence and the distribution of non-zero precipitation amounts, respectively. The two-stage model is evaluated for the probabilistic prediction of small-scale precipitation over France. It noticeably improves the skill of the prediction for both precipitation occurrence and amount. As the analog days vary from one prediction day to another, the atmospheric predictors selected in the regression stage and the value of the corresponding regression coefficients can vary from one prediction day to another. The model allows thus for a day-to-day adaptive and tailored downscaling. It can also reveal specific predictors for peculiar and non-frequent weather configurations.

Bayesian inference of uncertainties in precipitation-streamflow modeling in a snow affected catchment

Science.gov (United States)

Koskela, J. J.; Croke, B. W. F.; Koivusalo, H.; Jakeman, A. J.; Kokkonen, T.

2012-11-01

Bayesian inference is used to study the effect of precipitation and model structural uncertainty on estimates of model parameters and confidence limits of predictive variables in a conceptual rainfall-runoff model in the snow-fed Rudbäck catchment (142 ha) in southern Finland. The IHACRES model is coupled with a simple degree day model to account for snow accumulation and melt. The posterior probability distribution of the model parameters is sampled by using the Differential Evolution Adaptive Metropolis (DREAM(ZS)) algorithm and the generalized likelihood function. Precipitation uncertainty is taken into account by introducing additional latent variables that were used as multipliers for individual storm events. Results suggest that occasional snow water equivalent (SWE) observations together with daily streamflow observations do not contain enough information to simultaneously identify model parameters, precipitation uncertainty and model structural uncertainty in the Rudbäck catchment. The addition of an autoregressive component to account for model structure error and latent variables having uniform priors to account for input uncertainty lead to dubious posterior distributions of model parameters. Thus our hypothesis that informative priors for latent variables could be replaced by additional SWE data could not be confirmed. The model was found to work adequately in 1-day-ahead simulation mode, but the results were poor in the simulation batch mode. This was caused by the interaction of parameters that were used to describe different sources of uncertainty. The findings may have lessons for other cases where parameterizations are similarly high in relation to available prior information.

Bayesian Network Webserver: a comprehensive tool for biological network modeling.

Science.gov (United States)

Ziebarth, Jesse D; Bhattacharya, Anindya; Cui, Yan

2013-11-01

The Bayesian Network Webserver (BNW) is a platform for comprehensive network modeling of systems genetics and other biological datasets. It allows users to quickly and seamlessly upload a dataset, learn the structure of the network model that best explains the data and use the model to understand relationships between network variables. Many datasets, including those used to create genetic network models, contain both discrete (e.g. genotype) and continuous (e.g. gene expression traits) variables, and BNW allows for modeling hybrid datasets. Users of BNW can incorporate prior knowledge during structure learning through an easy-to-use structural constraint interface. After structure learning, users are immediately presented with an interactive network model, which can be used to make testable hypotheses about network relationships. BNW, including a downloadable structure learning package, is available at http://compbio.uthsc.edu/BNW. (The BNW interface for adding structural constraints uses HTML5 features that are not supported by current version of Internet Explorer. We suggest using other browsers (e.g. Google Chrome or Mozilla Firefox) when accessing BNW). ycui2@uthsc.edu. Supplementary data are available at Bioinformatics online.

Hydrological modelling of the Mabengnong catchment in the southeast Tibet with support of short term intensive precipitation observation

Science.gov (United States)

Wang, L.; Zhang, F.; Zhang, H.; Scott, C. A.; Zeng, C.; SHI, X.

2017-12-01

Precipitation is one of the crucial inputs for models used to better understand hydrological processes. In high mountain areas, it is a difficult task to obtain a reliable precipitation data set describing the spatial and temporal characteristic due to the limited meteorological observations and high variability of precipitation. This study carries out intensive observation of precipitation in a high mountain catchment in the southeast of the Tibet during July to August 2013. According to the rain gauges set up at different altitudes, it is found that precipitation is greatly influenced by altitude. The observed precipitation is used to depict the precipitation gradient (PG) and hourly distribution (HD), showing that the average duration is around 0.1, 0.8 and 6.0 hours and the average PG is 0.10, 0.28 and 0.26 mm/d/100m for trace, light and moderate rain, respectively. Based on the gridded precipitation derived from the PG and HD and the nearby Linzhi meteorological station at lower altitude, a distributed biosphere hydrological model based on water and energy budgets (WEB-DHM) is applied to simulate the hydrological processes. Beside the observed runoff, MODIS/Terra snow cover area (SCA) data, and MODIS/Terra land surface temperature (LST) data are also used for model calibration and validation. The resulting runoff, SCA and LST simulations are all reasonable. Sensitivity analyses indicate that runoff is greatly underestimated without considering PG, illustrating that short-term intensive precipitation observation contributes to improving hydrological modelling of poorly gauged high mountain catchments.

Evaluation of Extratropical Cyclone Precipitation in the North Atlantic Basin: An analysis of ERA-Interim, WRF, and two CMIP5 models.

Science.gov (United States)

Booth, James F; Naud, Catherine M; Willison, Jeff

2018-03-01

The representation of extratropical cyclones (ETCs) precipitation in general circulation models (GCMs) and a weather research and forecasting (WRF) model is analyzed. This work considers the link between ETC precipitation and dynamical strength and tests if parameterized convection affects this link for ETCs in the North Atlantic Basin. Lagrangian cyclone tracks of ETCs in ERA-Interim reanalysis (ERAI), the GISS and GFDL CMIP5 models, and WRF with two horizontal resolutions are utilized in a compositing analysis. The 20-km resolution WRF model generates stronger ETCs based on surface wind speed and cyclone precipitation. The GCMs and ERAI generate similar composite means and distributions for cyclone precipitation rates, but GCMs generate weaker cyclone surface winds than ERAI. The amount of cyclone precipitation generated by the convection scheme differs significantly across the datasets, with GISS generating the most, followed by ERAI and then GFDL. The models and reanalysis generate relatively more parameterized convective precipitation when the total cyclone-averaged precipitation is smaller. This is partially due to the contribution of parameterized convective precipitation occurring more often late in the ETC life cycle. For reanalysis and models, precipitation increases with both cyclone moisture and surface wind speed, and this is true if the contribution from the parameterized convection scheme is larger or not. This work shows that these different models generate similar total ETC precipitation despite large differences in the parameterized convection, and these differences do not cause unexpected behavior in ETC precipitation sensitivity to cyclone moisture or surface wind speed.

Eight challenges for network epidemic models

Directory of Open Access Journals (Sweden)

Lorenzo Pellis

2015-03-01

Full Text Available Networks offer a fertile framework for studying the spread of infection in human and animal populations. However, owing to the inherent high-dimensionality of networks themselves, modelling transmission through networks is mathematically and computationally challenging. Even the simplest network epidemic models present unanswered questions. Attempts to improve the practical usefulness of network models by including realistic features of contact networks and of host–pathogen biology (e.g. waning immunity have made some progress, but robust analytical results remain scarce. A more general theory is needed to understand the impact of network structure on the dynamics and control of infection. Here we identify a set of challenges that provide scope for active research in the field of network epidemic models.

Comparison of Extreme Precipitation Return Levels using Spatial Bayesian Hierarchical Modeling versus Regional Frequency Analysis

Science.gov (United States)

Love, C. A.; Skahill, B. E.; AghaKouchak, A.; Karlovits, G. S.; England, J. F.; Duren, A. M.

2017-12-01

We compare gridded extreme precipitation return levels obtained using spatial Bayesian hierarchical modeling (BHM) with their respective counterparts from a traditional regional frequency analysis (RFA) using the same set of extreme precipitation data. Our study area is the 11,478 square mile Willamette River basin (WRB) located in northwestern Oregon, a major tributary of the Columbia River whose 187 miles long main stem, the Willamette River, flows northward between the Coastal and Cascade Ranges. The WRB contains approximately two ­thirds of Oregon's population and 20 of the 25 most populous cities in the state. The U.S. Army Corps of Engineers (USACE) Portland District operates thirteen dams and extreme precipitation estimates are required to support risk­ informed hydrologic analyses as part of the USACE Dam Safety Program. Our intent is to profile for the USACE an alternate methodology to an RFA that was developed in 2008 due to the lack of an official NOAA Atlas 14 update for the state of Oregon. We analyze 24-hour annual precipitation maxima data for the WRB utilizing the spatial BHM R package "spatial.gev.bma", which has been shown to be efficient in developing coherent maps of extreme precipitation by return level. Our BHM modeling analysis involved application of leave-one-out cross validation (LOO-CV), which not only supported model selection but also a comprehensive assessment of location specific model performance. The LOO-CV results will provide a basis for the BHM RFA comparison.

Complex networks-based energy-efficient evolution model for wireless sensor networks

Energy Technology Data Exchange (ETDEWEB)

Zhu Hailin [Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, P.O. Box 106, Beijing 100876 (China)], E-mail: zhuhailin19@gmail.com; Luo Hong [Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, P.O. Box 106, Beijing 100876 (China); Peng Haipeng; Li Lixiang; Luo Qun [Information Secure Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, P.O. Box 145, Beijing 100876 (China)

2009-08-30

Based on complex networks theory, we present two self-organized energy-efficient models for wireless sensor networks in this paper. The first model constructs the wireless sensor networks according to the connectivity and remaining energy of each sensor node, thus it can produce scale-free networks which have a performance of random error tolerance. In the second model, we not only consider the remaining energy, but also introduce the constraint of links to each node. This model can make the energy consumption of the whole network more balanced. Finally, we present the numerical experiments of the two models.

Complex networks-based energy-efficient evolution model for wireless sensor networks

International Nuclear Information System (INIS)

Zhu Hailin; Luo Hong; Peng Haipeng; Li Lixiang; Luo Qun

2009-01-01

Based on complex networks theory, we present two self-organized energy-efficient models for wireless sensor networks in this paper. The first model constructs the wireless sensor networks according to the connectivity and remaining energy of each sensor node, thus it can produce scale-free networks which have a performance of random error tolerance. In the second model, we not only consider the remaining energy, but also introduce the constraint of links to each node. This model can make the energy consumption of the whole network more balanced. Finally, we present the numerical experiments of the two models.

21st Century Changes in Precipitation Extremes Over the United States: Can Climate Analogues Help or Hinder?

Science.gov (United States)

Gao, X.; Schlosser, C. A.

2013-12-01

Global warming is expected to alter the frequency and/or magnitude of extreme precipitation events. Such changes could have substantial ecological, economic, and sociological consequences. However, climate models in general do not correctly reproduce the frequency and intensity distribution of precipitation, especially at the regional scale. In this study, gridded data from a dense network of surface precipitation gauges and a global atmospheric analysis at a coarser scale are combined to develop a diagnostic framework for the large-scale meteorological conditions (i.e. flow features, moisture supply) that dominate during extreme precipitation. Such diagnostic framework is first evaluated with the late 20th century simulations from an ensemble of climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5), and is found to produce more consistent (and less uncertain) total and interannaul number of extreme days with the observations than the model-based precipitation over the south-central United States and the Western United States examined in this study. The framework is then applied to the CMIP5 multi-model projections for two radiative forcing scenarios (Representative Concentration Pathways 4.5 and 8.5) to assess the potential future changes in the probability of precipitation extremes over the same study regions. We further analyze the accompanying circulation features and their changes that may be responsible for shifts in extreme precipitation in response to changed climate. The results from this study may guide hazardous weather watches and help society develop adaptive strategies for preventing catastrophic losses.

Sensitivity of U.S. summer precipitation to model resolution and convective parameterizations across gray zone resolutions

Science.gov (United States)

Gao, Yang; Leung, L. Ruby; Zhao, Chun; Hagos, Samson

2017-03-01

Simulating summer precipitation is a significant challenge for climate models that rely on cumulus parameterizations to represent moist convection processes. Motivated by recent advances in computing that support very high-resolution modeling, this study aims to systematically evaluate the effects of model resolution and convective parameterizations across the gray zone resolutions. Simulations using the Weather Research and Forecasting model were conducted at grid spacings of 36 km, 12 km, and 4 km for two summers over the conterminous U.S. The convection-permitting simulations at 4 km grid spacing are most skillful in reproducing the observed precipitation spatial distributions and diurnal variability. Notable differences are found between simulations with the traditional Kain-Fritsch (KF) and the scale-aware Grell-Freitas (GF) convection schemes, with the latter more skillful in capturing the nocturnal timing in the Great Plains and North American monsoon regions. The GF scheme also simulates a smoother transition from convective to large-scale precipitation as resolution increases, resulting in reduced sensitivity to model resolution compared to the KF scheme. Nonhydrostatic dynamics has a positive impact on precipitation over complex terrain even at 12 km and 36 km grid spacings. With nudging of the winds toward observations, we show that the conspicuous warm biases in the Southern Great Plains are related to precipitation biases induced by large-scale circulation biases, which are insensitive to model resolution. Overall, notable improvements in simulating summer rainfall and its diurnal variability through convection-permitting modeling and scale-aware parameterizations suggest promising venues for improving climate simulations of water cycle processes.

Brand Marketing Model on Social Networks

Directory of Open Access Journals (Sweden)

Jolita Jezukevičiūtė

2014-04-01

Full Text Available The paper analyzes the brand and its marketing solutions onsocial networks. This analysis led to the creation of improvedbrand marketing model on social networks, which will contributeto the rapid and cheap organization brand recognition, increasecompetitive advantage and enhance consumer loyalty. Therefore,the brand and a variety of social networks are becoming a hotresearch area for brand marketing model on social networks.The world‘s most successful brand marketing models exploratoryanalysis of a single case study revealed a brand marketingsocial networking tools that affect consumers the most. Basedon information analysis and methodological studies, develop abrand marketing model on social networks.

Climate Drivers of Spatiotemporal Variability of Precipitation in the Source Region of Yangtze River

Science.gov (United States)

Du, Y.; Berndtsson, R.; An, D.; Yuan, F.

2017-12-01

Variability of precipitation regime has significant influence on the environment sustainability in the source region of Yangtze River, especially when the vegetation degradation and biodiversity reduction have already occurred. Understanding the linkage between variability of local precipitation and global teleconnection patterns is essential for water resources management. Based on physical reasoning, indices of the climate drivers can provide a practical way of predicting precipitation. Due to high seasonal variability of precipitation, climate drivers of the seasonal precipitation also varies. However, few reports have gone through the teleconnections between large scale patterns with seasonal precipitation in the source region of Yangtze River. The objectives of this study are therefore (1) assessment of temporal trend and spatial variability of precipitation in the source region of Yangtze River; (2) identification of climate indices with strong influence on seasonal precipitation anomalies; (3) prediction of seasonal precipitation based on revealed climate indices. Principal component analysis and Spearman rank correlation were used to detect significant relationships. A feed-forward artificial neural network(ANN) was developed to predict seasonal precipitation using significant correlated climate indices. Different influencing climate indices were revealed for precipitation in each season, with significant level and lag times. Significant influencing factors were selected to be the predictors for ANN model. With correlation coefficients between observed and simulated precipitation over 0.5, the results were eligible to predict the precipitation of spring, summer and winter using teleconnections, which can improve integrated water resources management in the source region of Yangtze River.

Introducing Synchronisation in Deterministic Network Models

DEFF Research Database (Denmark)

Schiøler, Henrik; Jessen, Jan Jakob; Nielsen, Jens Frederik D.

2006-01-01

The paper addresses performance analysis for distributed real time systems through deterministic network modelling. Its main contribution is the introduction and analysis of models for synchronisation between tasks and/or network elements. Typical patterns of synchronisation are presented leading...... to the suggestion of suitable network models. An existing model for flow control is presented and an inherent weakness is revealed and remedied. Examples are given and numerically analysed through deterministic network modelling. Results are presented to highlight the properties of the suggested models...

Assimilation of Spatially Sparse In Situ Soil Moisture Networks into a Continuous Model Domain

Science.gov (United States)

Gruber, A.; Crow, W. T.; Dorigo, W. A.

2018-02-01

Growth in the availability of near-real-time soil moisture observations from ground-based networks has spurred interest in the assimilation of these observations into land surface models via a two-dimensional data assimilation system. However, the design of such systems is currently hampered by our ignorance concerning the spatial structure of error afflicting ground and model-based soil moisture estimates. Here we apply newly developed triple collocation techniques to provide the spatial error information required to fully parameterize a two-dimensional (2-D) data assimilation system designed to assimilate spatially sparse observations acquired from existing ground-based soil moisture networks into a spatially continuous Antecedent Precipitation Index (API) model for operational agricultural drought monitoring. Over the contiguous United States (CONUS), the posterior uncertainty of surface soil moisture estimates associated with this 2-D system is compared to that obtained from the 1-D assimilation of remote sensing retrievals to assess the value of ground-based observations to constrain a surface soil moisture analysis. Results demonstrate that a fourfold increase in existing CONUS ground station density is needed for ground network observations to provide a level of skill comparable to that provided by existing satellite-based surface soil moisture retrievals.

Future changes in extreme precipitation in the Rhine basin based on global and regional climate model simulations

NARCIS (Netherlands)

Pelt, van S.C.; Beersma, J.J.; Buishand, T.A.; Hurk, van den B.J.J.M.; Kabat, P.

2012-01-01

Probability estimates of the future change of extreme precipitation events are usually based on a limited number of available global climate model (GCM) or regional climate model (RCM) simulations. Since floods are related to heavy precipitation events, this restricts the assessment of flood risks.

Potential of commercial microwave link network derived rainfall for river runoff simulations

Science.gov (United States)

Smiatek, Gerhard; Keis, Felix; Chwala, Christian; Fersch, Benjamin; Kunstmann, Harald

2017-03-01

Commercial microwave link networks allow for the quantification of path integrated precipitation because the attenuation by hydrometeors correlates with rainfall between transmitter and receiver stations. The networks, operated and maintained by cellphone companies, thereby provide completely new and country wide precipitation measurements. As the density of traditional precipitation station networks worldwide is significantly decreasing, microwave link derived precipitation estimates receive increasing attention not only by hydrologists but also by meteorological and hydrological services. We investigate the potential of microwave derived precipitation estimates for streamflow prediction and water balance analyses, exemplarily shown for an orographically complex region in the German Alps (River Ammer). We investigate the additional value of link derived rainfall estimations combined with station observations compared to station and weather radar derived values. Our river runoff simulation system employs a distributed hydrological model at 100 × 100 m grid resolution. We analyze the potential of microwave link derived precipitation estimates for two episodes of 30 days with typically moderate river flow and an episode of extreme flooding. The simulation results indicate the potential of this novel precipitation monitoring method: a significant improvement in hydrograph reproduction has been achieved in the extreme flooding period that was characterized by a large number of local strong precipitation events. The present rainfall monitoring gauges alone were not able to correctly capture these events.

A glacier runoff extension to the Precipitation Runoff Modeling System

Science.gov (United States)

A. E. Van Beusekom; R. J. Viger

2016-01-01

A module to simulate glacier runoff, PRMSglacier, was added to PRMS (Precipitation Runoff Modeling System), a distributed-parameter, physical-process hydrological simulation code. The extension does not require extensive on-glacier measurements or computational expense but still relies on physical principles over empirical relations as much as is feasible while...

Towards validation of the Canadian precipitation analysis (CaPA) for hydrologic modeling applications in the Canadian Prairies

Science.gov (United States)

Boluwade, Alaba; Zhao, K.-Y.; Stadnyk, T. A.; Rasmussen, P.

2018-01-01

This study presents a three-step validation technique to compare the performance of the Canadian Precipitation Analysis (CaPA) product relative to actual observation as a hydrologic forcing in regional watershed simulation. CaPA is an interpolated (6 h or 24 h accumulation) reanalysis precipitation product in near real time covering all of North America. The analysis procedure involves point-to-point (P2P) and map-to-map (M2M) comparisons, followed by proxy validation using an operational version of the WATFLOOD™ hydrologic model from 2002 to 2005 in the Lake Winnipeg Basin (LWB), Canada. The P2P technique using a Bayesian change point analysis shows that CaPA corresponds with actual observations (Canadian daily climate data, CDCD), on both an annual and seasonal basis. CaPA has the same spatial pattern, dependency and autocorrelation properties as CDCD pixel by pixel (M2M). When used as hydrologic forcing in WATFLOOD™, results indicate that CaPA is a reliable product for water resource modeling and predictions, but that the quality of CaPA data varies annually and seasonally, as does the quality of observations. CaPA proved most beneficial as a hydrologic forcing during winter seasons where observation quality is the lowest. Reanalysis products, such as CaPA, can be a reliable option in sparse network areas, and is beneficial for regional governments when the cost of new weather stations is prohibitive.

Nowcasting of precipitation – Advective statistical forecast model (SAM) for the Czech Republic

Czech Academy of Sciences Publication Activity Database

Sokol, Zbyněk; Pešice, Petr

2012-01-01

Roč. 103, - (2012), s. 70-79 ISSN 0169-8095 R&D Projects: GA MŠk ME09033; GA ČR GA205/07/0905 Institutional research plan: CEZ:AV0Z30420517 Keywords : Precipitation forecast * Statistical models * Regression * Quantitative precipitation forecast * Extrapolation forecast Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 2.200, year: 2012 http://dx.doi.org/10.1016/j. atm osres.2011.07.013

Object-Based Assessment of Satellite Precipitation Products

Directory of Open Access Journals (Sweden)

Jingjing Li

2016-06-01

Full Text Available An object-based verification approach is employed to assess the performance of the commonly used high-resolution satellite precipitation products: Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN, Climate Prediction center MORPHing technique (CMORPH, and Tropical Rainfall Measurement Mission (TRMM Multi-Satellite Precipitation Analysis (TMPA 3B42RT. The evaluation of the satellite precipitation products focuses on the skill of depicting the geometric features of the localized precipitation areas. Seasonal variability of the performances of these products against the ground observations is investigated through the examples of warm and cold seasons. It is found that PERSIANN is capable of depicting the orientation of the localized precipitation areas in both seasons. CMORPH has the ability to capture the sizes of the localized precipitation areas and performs the best in the overall assessment for both seasons. 3B42RT is capable of depicting the location of the precipitation areas for both seasons. In addition, all of the products perform better on capturing the sizes and centroids of precipitation areas in the warm season than in the cold season, while they perform better on depicting the intersection area and orientation in the cold season than in the warm season. These products are more skillful on correctly detecting the localized precipitation areas against the observations in the warm season than in the cold season.

Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting

OpenAIRE

Shi, Xingjian; Chen, Zhourong; Wang, Hao; Yeung, Dit-Yan; Wong, Wai-kin; Woo, Wang-chun

2015-01-01

The goal of precipitation nowcasting is to predict the future rainfall intensity in a local region over a relatively short period of time. Very few previous studies have examined this crucial and challenging weather forecasting problem from the machine learning perspective. In this paper, we formulate precipitation nowcasting as a spatiotemporal sequence forecasting problem in which both the input and the prediction target are spatiotemporal sequences. By extending the fully connected LSTM (F...

Modeling Network Interdiction Tasks

Science.gov (United States)

2015-09-17

118 xiii Table Page 36 Computation times for weighted, 100-node random networks for GAND Approach testing in Python ...in Python . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 38 Accuracy measures for weighted, 100-node random networks for GAND...networks [15:p. 1]. A common approach to modeling network interdiction is to formulate the problem in terms of a two-stage strategic game between two

Development of a methodology for probable maximum precipitation estimation over the American River watershed using the WRF model

Science.gov (United States)

Tan, Elcin

A new physically-based methodology for probable maximum precipitation (PMP) estimation is developed over the American River Watershed (ARW) using the Weather Research and Forecast (WRF-ARW) model. A persistent moisture flux convergence pattern, called Pineapple Express, is analyzed for 42 historical extreme precipitation events, and it is found that Pineapple Express causes extreme precipitation over the basin of interest. An average correlation between moisture flux convergence and maximum precipitation is estimated as 0.71 for 42 events. The performance of the WRF model is verified for precipitation by means of calibration and independent validation of the model. The calibration procedure is performed only for the first ranked flood event 1997 case, whereas the WRF model is validated for 42 historical cases. Three nested model domains are set up with horizontal resolutions of 27 km, 9 km, and 3 km over the basin of interest. As a result of Chi-square goodness-of-fit tests, the hypothesis that "the WRF model can be used in the determination of PMP over the ARW for both areal average and point estimates" is accepted at the 5% level of significance. The sensitivities of model physics options on precipitation are determined using 28 microphysics, atmospheric boundary layer, and cumulus parameterization schemes combinations. It is concluded that the best triplet option is Thompson microphysics, Grell 3D ensemble cumulus, and YSU boundary layer (TGY), based on 42 historical cases, and this TGY triplet is used for all analyses of this research. Four techniques are proposed to evaluate physically possible maximum precipitation using the WRF: 1. Perturbations of atmospheric conditions; 2. Shift in atmospheric conditions; 3. Replacement of atmospheric conditions among historical events; and 4. Thermodynamically possible worst-case scenario creation. Moreover, climate change effect on precipitation is discussed by emphasizing temperature increase in order to determine the

Numerical modelling of landscape and sediment flux response to precipitation rate change

Science.gov (United States)

Armitage, John J.; Whittaker, Alexander C.; Zakari, Mustapha; Campforts, Benjamin

2018-02-01

Laboratory-scale experiments of erosion have demonstrated that landscapes have a natural (or intrinsic) response time to a change in precipitation rate. In the last few decades there has been growth in the development of numerical models that attempt to capture landscape evolution over long timescales. However, there is still an uncertainty regarding the validity of the basic assumptions of mass transport that are made in deriving these models. In this contribution we therefore return to a principal assumption of sediment transport within the mass balance for surface processes; we explore the sensitivity of the classic end-member landscape evolution models and the sediment fluxes they produce to a change in precipitation rates. One end-member model takes the mathematical form of a kinetic wave equation and is known as the stream power model, in which sediment is assumed to be transported immediately out of the model domain. The second end-member model is the transport model and it takes the form of a diffusion equation, assuming that the sediment flux is a function of the water flux and slope. We find that both of these end-member models have a response time that has a proportionality to the precipitation rate that follows a negative power law. However, for the stream power model the exponent on the water flux term must be less than one, and for the transport model the exponent must be greater than one, in order to match the observed concavity of natural systems. This difference in exponent means that the transport model generally responds more rapidly to an increase in precipitation rates, on the order of 105 years for post-perturbation sediment fluxes to return to within 50 % of their initial values, for theoretical landscapes with a scale of 100×100 km. Additionally from the same starting conditions, the amplitude of the sediment flux perturbation in the transport model is greater, with much larger sensitivity to catchment size. An important finding is that

Numerical modelling of landscape and sediment flux response to precipitation rate change

Directory of Open Access Journals (Sweden)

J. J. Armitage

2018-02-01

Full Text Available Laboratory-scale experiments of erosion have demonstrated that landscapes have a natural (or intrinsic response time to a change in precipitation rate. In the last few decades there has been growth in the development of numerical models that attempt to capture landscape evolution over long timescales. However, there is still an uncertainty regarding the validity of the basic assumptions of mass transport that are made in deriving these models. In this contribution we therefore return to a principal assumption of sediment transport within the mass balance for surface processes; we explore the sensitivity of the classic end-member landscape evolution models and the sediment fluxes they produce to a change in precipitation rates. One end-member model takes the mathematical form of a kinetic wave equation and is known as the stream power model, in which sediment is assumed to be transported immediately out of the model domain. The second end-member model is the transport model and it takes the form of a diffusion equation, assuming that the sediment flux is a function of the water flux and slope. We find that both of these end-member models have a response time that has a proportionality to the precipitation rate that follows a negative power law. However, for the stream power model the exponent on the water flux term must be less than one, and for the transport model the exponent must be greater than one, in order to match the observed concavity of natural systems. This difference in exponent means that the transport model generally responds more rapidly to an increase in precipitation rates, on the order of 105 years for post-perturbation sediment fluxes to return to within 50 % of their initial values, for theoretical landscapes with a scale of 100×100 km. Additionally from the same starting conditions, the amplitude of the sediment flux perturbation in the transport model is greater, with much larger sensitivity to catchment size. An

Global precipitations and climate change. Proceedings

International Nuclear Information System (INIS)

Desbois, M.; Desalmand, F.

1994-01-01

The workshop reviewed the present status of knowledge concerning the past and present evolution of the distribution of precipitations at global scale, related to climate evolution at different time scales. This review was intended to assess the availability and quality of data which could help, through validation and initialization of model studies, to improve our understanding of the processes determining these precipitation changes. On another hand, the modelling specialists presented their actual use of precipitation data. Exchanges of views between the modelling and observing communities were thus made possible, leading to a set of recommendations for future studies. Sessions were then devoted to specific themes: 1) Paleoclimatology, 2) data collection, history and statistics, programmes, 3) methodologies and accuracy of large scale estimation of precipitation from conventional data, 4) estimation of precipitation from satellite data, 5) modelling studies. (orig.)

A test for Improvement of high resolution Quantitative Precipitation Estimation for localized heavy precipitation events

Science.gov (United States)

Lee, Jung-Hoon; Roh, Joon-Woo; Park, Jeong-Gyun

2017-04-01

Accurate estimation of precipitation is one of the most difficult and significant tasks in the area of weather diagnostic and forecasting. In the Korean Peninsula, heavy precipitations are caused by various physical mechanisms, which are affected by shortwave trough, quasi-stationary moisture convergence zone among varying air masses, and a direct/indirect effect of tropical cyclone. In addition to, various geographical and topographical elements make production of temporal and spatial distribution of precipitation is very complicated. Especially, localized heavy rainfall events in South Korea generally arise from mesoscale convective systems embedded in these synoptic scale disturbances. In weather radar data with high temporal and spatial resolution, accurate estimation of rain rate from radar reflectivity data is too difficult. Z-R relationship (Marshal and Palmer 1948) have adapted representatively. In addition to, several methods such as support vector machine (SVM), neural network, Fuzzy logic, Kriging were utilized in order to improve the accuracy of rain rate. These methods show the different quantitative precipitation estimation (QPE) and the performances of accuracy are different for heavy precipitation cases. In this study, in order to improve the accuracy of QPE for localized heavy precipitation, ensemble method for Z-R relationship and various techniques was tested. This QPE ensemble method was developed by a concept based on utilizing each advantage of precipitation calibration methods. The ensemble members were produced for a combination of different Z-R coefficient and calibration method.

An adaptive two-stage analog/regression model for probabilistic prediction of small-scale precipitation in France

Directory of Open Access Journals (Sweden)

J. Chardon

2018-01-01

Full Text Available Statistical downscaling models (SDMs are often used to produce local weather scenarios from large-scale atmospheric information. SDMs include transfer functions which are based on a statistical link identified from observations between local weather and a set of large-scale predictors. As physical processes driving surface weather vary in time, the most relevant predictors and the regression link are likely to vary in time too. This is well known for precipitation for instance and the link is thus often estimated after some seasonal stratification of the data. In this study, we present a two-stage analog/regression model where the regression link is estimated from atmospheric analogs of the current prediction day. Atmospheric analogs are identified from fields of geopotential heights at 1000 and 500 hPa. For the regression stage, two generalized linear models are further used to model the probability of precipitation occurrence and the distribution of non-zero precipitation amounts, respectively. The two-stage model is evaluated for the probabilistic prediction of small-scale precipitation over France. It noticeably improves the skill of the prediction for both precipitation occurrence and amount. As the analog days vary from one prediction day to another, the atmospheric predictors selected in the regression stage and the value of the corresponding regression coefficients can vary from one prediction day to another. The model allows thus for a day-to-day adaptive and tailored downscaling. It can also reveal specific predictors for peculiar and non-frequent weather configurations.

Mobility Models for Next Generation Wireless Networks Ad Hoc, Vehicular and Mesh Networks

CERN Document Server

Santi, Paolo

2012-01-01

Mobility Models for Next Generation Wireless Networks: Ad Hoc, Vehicular and Mesh Networks provides the reader with an overview of mobility modelling, encompassing both theoretical and practical aspects related to the challenging mobility modelling task. It also: Provides up-to-date coverage of mobility models for next generation wireless networksOffers an in-depth discussion of the most representative mobility models for major next generation wireless network application scenarios, including WLAN/mesh networks, vehicular networks, wireless sensor networks, and

Evaluating precipitation in a regional climate model using ground-based radar measurements in Dronning Maud Land, East Antarctica

Science.gov (United States)

Gorodetskaya, Irina; Maahn, Maximilan; Gallée, Hubert; Souverijns, Niels; Gossart, Alexandra; Kneifel, Stefan; Crewell, Susanne; Van Lipzig, Nicole

2017-04-01

Occasional very intense snowfall events over Dronning Maud Land (DML) region in East Antarctica, contributed significantly to the entire Antarctic ice sheet surface mass balance (SMB) during the last years. The meteorological-cloud-precipitation observatory running at the Princess Elisabeth station (PE) in the DML escarpment zone since 2009 (HYDRANT/AEROCLOUD projects), provides unique opportunity to estimate contribution of precipitation to the local snow accumulation and new data for evaluating precipitation in climate models. Our previous work using PE measurements showed that occasional intense precipitation events determine the total local yearly SMB and account for its large interannual variability. Here we use radar measurements to evaluate precipitation in a regional climate model with a special focus on intense precipitation events together with the large-scale atmospheric dynamics responsible for these events. The coupled snow-atmosphere regional climate model MAR (Modèle Atmosphérique Régional) is used to simulate climate and SMB in DML at 5-km horizontal resolution during 2012 using initial and boundary conditions from the European Centre for Medium-range Weather Forecasts (ECMWF) Interim re-analysis atmospheric and oceanic fields. Two evaluation approaches are used: observations-to-model and model-to-observations. In the first approach, snowfall rate (S) is derived from the MRR (vertically profiling 24-GHz precipitation radar) effective reflectivity factor (Ze) at 400 m agl using various Ze-S relationships for dry snow. The uncertainty in Ze-S relationships is constrained using snow particle size distribution from Snow Video Imager - Precipitation Imaging Package (SVI/PIP) and information about particle shapes. For the second approach we apply the Passive and Active Microwave radiative TRAnsfer model (PAMTRA), which allows direct comparison of the radar-measured and climate model-based vertical profiles of the radar Ze and Doppler velocity. In MAR

Complex Networks in Psychological Models

Science.gov (United States)

Wedemann, R. S.; Carvalho, L. S. A. V. D.; Donangelo, R.

We develop schematic, self-organizing, neural-network models to describe mechanisms associated with mental processes, by a neurocomputational substrate. These models are examples of real world complex networks with interesting general topological structures. Considering dopaminergic signal-to-noise neuronal modulation in the central nervous system, we propose neural network models to explain development of cortical map structure and dynamics of memory access, and unify different mental processes into a single neurocomputational substrate. Based on our neural network models, neurotic behavior may be understood as an associative memory process in the brain, and the linguistic, symbolic associative process involved in psychoanalytic working-through can be mapped onto a corresponding process of reconfiguration of the neural network. The models are illustrated through computer simulations, where we varied dopaminergic modulation and observed the self-organizing emergent patterns at the resulting semantic map, interpreting them as different manifestations of mental functioning, from psychotic through to normal and neurotic behavior, and creativity.

Surface Runoff Estimation Using SMOS Observations, Rain-gauge Measurements and Satellite Precipitation Estimations. Comparison with Model Predictions

Science.gov (United States)

Garcia Leal, Julio A.; Lopez-Baeza, Ernesto; Khodayar, Samiro; Estrela, Teodoro; Fidalgo, Arancha; Gabaldo, Onofre; Kuligowski, Robert; Herrera, Eddy

Surface runoff is defined as the amount of water that originates from precipitation, does not infiltrates due to soil saturation and therefore circulates over the surface. A good estimation of runoff is useful for the design of draining systems, structures for flood control and soil utilisation. For runoff estimation there exist different methods such as (i) rational method, (ii) isochrone method, (iii) triangular hydrograph, (iv) non-dimensional SCS hydrograph, (v) Temez hydrograph, (vi) kinematic wave model, represented by the dynamics and kinematics equations for a uniforme precipitation regime, and (vii) SCS-CN (Soil Conservation Service Curve Number) model. This work presents a way of estimating precipitation runoff through the SCS-CN model, using SMOS (Soil Moisture and Ocean Salinity) mission soil moisture observations and rain-gauge measurements, as well as satellite precipitation estimations. The area of application is the Jucar River Basin Authority area where one of the objectives is to develop the SCS-CN model in a spatial way. The results were compared to simulations performed with the 7-km COSMO-CLM (COnsortium for Small-scale MOdelling, COSMO model in CLimate Mode) model. The use of SMOS soil moisture as input to the COSMO-CLM model will certainly improve model simulations.

Mercury Wet Scavenging and Deposition Differences by Precipitation Type.

Science.gov (United States)

Kaulfus, Aaron S; Nair, Udaysankar; Holmes, Christopher D; Landing, William M

2017-03-07

We analyze the effect of precipitation type on mercury wet deposition using a new database of individual rain events spanning the contiguous United States. Measurements from the Mercury Deposition Network (MDN) containing single rainfall events were identified and classified into six precipitation types. Mercury concentrations in surface precipitation follow a power law of precipitation depth that is modulated by precipitation system morphology. After controlling for precipitation depth, the highest mercury deposition occurs in supercell thunderstorms, with decreasing deposition in disorganized thunderstorms, quasi-linear convective systems (QLCS), extratropical cyclones, light rain, and land-falling tropical cyclones. Convective morphologies (supercells, disorganized, and QLCS) enhance wet deposition by a factor of at least 1.6 relative to nonconvective morphologies. Mercury wet deposition also varies by geographic region and season. After controlling for other factors, we find that mercury wet deposition is greater over high-elevation sites, seasonally during summer, and in convective precipitation.

Doubly stochastic Poisson process models for precipitation at fine time-scales