quality forecasting model: Topics by Science.gov

Sample records for quality forecasting model

Winter wheat quality monitoring and forecasting system based on remote sensing and environmental factors

NASA Astrophysics Data System (ADS)

Haiyang, Yu; Yanmei, Liu; Guijun, Yang; Xiaodong, Yang; Dong, Ren; Chenwei, Nie

2014-03-01

To achieve dynamic winter wheat quality monitoring and forecasting in larger scale regions, the objective of this study was to design and develop a winter wheat quality monitoring and forecasting system by using a remote sensing index and environmental factors. The winter wheat quality trend was forecasted before the harvest and quality was monitored after the harvest, respectively. The traditional quality-vegetation index from remote sensing monitoring and forecasting models were improved. Combining with latitude information, the vegetation index was used to estimate agronomy parameters which were related with winter wheat quality in the early stages for forecasting the quality trend. A combination of rainfall in May, temperature in May, illumination at later May, the soil available nitrogen content and other environmental factors established the quality monitoring model. Compared with a simple quality-vegetation index, the remote sensing monitoring and forecasting model used in this system get greatly improved accuracy. Winter wheat quality was monitored and forecasted based on the above models, and this system was completed based on WebGIS technology. Finally, in 2010 the operation process of winter wheat quality monitoring system was presented in Beijing, the monitoring and forecasting results was outputted as thematic maps.
An enhanced PM 2.5 air quality forecast model based on nonlinear regression and back-trajectory concentrations

NASA Astrophysics Data System (ADS)

Cobourn, W. Geoffrey

2010-08-01

An enhanced PM 2.5 air quality forecast model based on nonlinear regression (NLR) and back-trajectory concentrations has been developed for use in the Louisville, Kentucky metropolitan area. The PM 2.5 air quality forecast model is designed for use in the warm season, from May through September, when PM 2.5 air quality is more likely to be critical for human health. The enhanced PM 2.5 model consists of a basic NLR model, developed for use with an automated air quality forecast system, and an additional parameter based on upwind PM 2.5 concentration, called PM24. The PM24 parameter is designed to be determined manually, by synthesizing backward air trajectory and regional air quality information to compute 24-h back-trajectory concentrations. The PM24 parameter may be used by air quality forecasters to adjust the forecast provided by the automated forecast system. In this study of the 2007 and 2008 forecast seasons, the enhanced model performed well using forecasted meteorological data and PM24 as input. The enhanced PM 2.5 model was compared with three alternative models, including the basic NLR model, the basic NLR model with a persistence parameter added, and the NLR model with persistence and PM24. The two models that included PM24 were of comparable accuracy. The two models incorporating back-trajectory concentrations had lower mean absolute errors and higher rates of detecting unhealthy PM2.5 concentrations compared to the other models.
Development of visibility forecasting modeling framework for the Lower Fraser Valley of British Columbia using Canada's Regional Air Quality Deterministic Prediction System.

PubMed

So, Rita; Teakles, Andrew; Baik, Jonathan; Vingarzan, Roxanne; Jones, Keith

2018-05-01

Visibility degradation, one of the most noticeable indicators of poor air quality, can occur despite relatively low levels of particulate matter when the risk to human health is low. The availability of timely and reliable visibility forecasts can provide a more comprehensive understanding of the anticipated air quality conditions to better inform local jurisdictions and the public. This paper describes the development of a visibility forecasting modeling framework, which leverages the existing air quality and meteorological forecasts from Canada's operational Regional Air Quality Deterministic Prediction System (RAQDPS) for the Lower Fraser Valley of British Columbia. A baseline model (GM-IMPROVE) was constructed using the revised IMPROVE algorithm based on unprocessed forecasts from the RAQDPS. Three additional prototypes (UMOS-HYB, GM-MLR, GM-RF) were also developed and assessed for forecast performance of up to 48 hr lead time during various air quality and meteorological conditions. Forecast performance was assessed by examining their ability to provide both numerical and categorical forecasts in the form of 1-hr total extinction and Visual Air Quality Ratings (VAQR), respectively. While GM-IMPROVE generally overestimated extinction more than twofold, it had skill in forecasting the relative species contribution to visibility impairment, including ammonium sulfate and ammonium nitrate. Both statistical prototypes, GM-MLR and GM-RF, performed well in forecasting 1-hr extinction during daylight hours, with correlation coefficients (R) ranging from 0.59 to 0.77. UMOS-HYB, a prototype based on postprocessed air quality forecasts without additional statistical modeling, provided reasonable forecasts during most daylight hours. In terms of categorical forecasts, the best prototype was approximately 75 to 87% correct, when forecasting for a condensed three-category VAQR. A case study, focusing on a poor visual air quality yet low Air Quality Health Index episode, illustrated that the statistical prototypes were able to provide timely and skillful visibility forecasts with lead time up to 48 hr. This study describes the development of a visibility forecasting modeling framework, which leverages the existing air quality and meteorological forecasts from Canada's operational Regional Air Quality Deterministic Prediction System. The main applications include tourism and recreation planning, input into air quality management programs, and educational outreach. Visibility forecasts, when supplemented with the existing air quality and health based forecasts, can assist jurisdictions to anticipate the visual air quality impacts as perceived by the public, which can potentially assist in formulating the appropriate air quality bulletins and recommendations.
THE EMERGENCE OF NUMERICAL AIR QUALITY FORECASTING MODELS AND THEIR APPLICATION

EPA Science Inventory

In recent years the U.S. and other nations have begun programs for short-term local through regional air quality forecasting based upon numerical three-dimensional air quality grid models. These numerical air quality forecast (NAQF) models and systems have been developed and test...
Performance of stochastic approaches for forecasting river water quality.

PubMed

Ahmad, S; Khan, I H; Parida, B P

2001-12-01

This study analysed water quality data collected from the river Ganges in India from 1981 to 1990 for forecasting using stochastic models. Initially the box and whisker plots and Kendall's tau test were used to identify the trends during the study period. For detecting the possible intervention in the data the time series plots and cusum charts were used. The three approaches of stochastic modelling which account for the effect of seasonality in different ways. i.e. multiplicative autoregressive integrated moving average (ARIMA) model. deseasonalised model and Thomas-Fiering model were used to model the observed pattern in water quality. The multiplicative ARIMA model having both nonseasonal and seasonal components were, in general, identified as appropriate models. In the deseasonalised modelling approach, the lower order ARIMA models were found appropriate for the stochastic component. The set of Thomas-Fiering models were formed for each month for all water quality parameters. These models were then used to forecast the future values. The error estimates of forecasts from the three approaches were compared to identify the most suitable approach for the reliable forecast. The deseasonalised modelling approach was recommended for forecasting of water quality parameters of a river.
Updating of states in operational hydrological models

NASA Astrophysics Data System (ADS)

Bruland, O.; Kolberg, S.; Engeland, K.; Gragne, A. S.; Liston, G.; Sand, K.; Tøfte, L.; Alfredsen, K.

2012-04-01

Operationally the main purpose of hydrological models is to provide runoff forecasts. The quality of the model state and the accuracy of the weather forecast together with the model quality define the runoff forecast quality. Input and model errors accumulate over time and may leave the model in a poor state. Usually model states can be related to observable conditions in the catchment. Updating of these states, knowing their relation to observable catchment conditions, influence directly the forecast quality. Norway is internationally in the forefront in hydropower scheduling both on short and long terms. The inflow forecasts are fundamental to this scheduling. Their quality directly influence the producers profit as they optimize hydropower production to market demand and at the same time minimize spill of water and maximize available hydraulic head. The quality of the inflow forecasts strongly depends on the quality of the models applied and the quality of the information they use. In this project the focus has been to improve the quality of the model states which the forecast is based upon. Runoff and snow storage are two observable quantities that reflect the model state and are used in this project for updating. Generally the methods used can be divided in three groups: The first re-estimates the forcing data in the updating period; the second alters the weights in the forecast ensemble; and the third directly changes the model states. The uncertainty related to the forcing data through the updating period is due to both uncertainty in the actual observation and to how well the gauging stations represent the catchment both in respect to temperatures and precipitation. The project looks at methodologies that automatically re-estimates the forcing data and tests the result against observed response. Model uncertainty is reflected in a joint distribution of model parameters estimated using the Dream algorithm.
Is the economic value of hydrological forecasts related to their quality? Case study of the hydropower sector.

NASA Astrophysics Data System (ADS)

Cassagnole, Manon; Ramos, Maria-Helena; Thirel, Guillaume; Gailhard, Joël; Garçon, Rémy

2017-04-01

The improvement of a forecasting system and the evaluation of the quality of its forecasts are recurrent steps in operational practice. However, the evaluation of forecast value or forecast usefulness for better decision-making is, to our knowledge, less frequent, even if it might be essential in many sectors such as hydropower and flood warning. In the hydropower sector, forecast value can be quantified by the economic gain obtained with the optimization of operations or reservoir management rules. Several hydropower operational systems use medium-range forecasts (up to 7-10 days ahead) and energy price predictions to optimize hydropower production. Hence, the operation of hydropower systems, including the management of water in reservoirs, is impacted by weather, climate and hydrologic variability as well as extreme events. In order to assess how the quality of hydrometeorological forecasts impact operations, it is essential to first understand if and how operations and management rules are sensitive to input predictions of different quality. This study investigates how 7-day ahead deterministic and ensemble streamflow forecasts of different quality might impact the economic gains of energy production. It is based on a research model developed by Irstea and EDF to investigate issues relevant to the links between quality and value of forecasts in the optimisation of energy production at the short range. Based on streamflow forecasts and pre-defined management constraints, the model defines the best hours (i.e., the hours with high energy prices) to produce electricity. To highlight the link between forecasts quality and their economic value, we built several synthetic ensemble forecasts based on observed streamflow time series. These inputs are generated in a controlled environment in order to obtain forecasts of different quality in terms of accuracy and reliability. These forecasts are used to assess the sensitivity of the decision model to forecast quality. Relationships between forecast quality and economic value are discussed. This work is part of the IMPREX project, a research project supported by the European Commission under the Horizon 2020 Framework programme, with grant No. 641811 (http://www.imprex.eu)
The Economic Value of Air Quality Forecasting

NASA Astrophysics Data System (ADS)

Anderson-Sumo, Tasha

Both long-term and daily air quality forecasts provide an essential component to human health and impact costs. According the American Lung Association, the estimated current annual cost of air pollution related illness in the United States, adjusted for inflation (3% per year), is approximately $152 billion. Many of the risks such as hospital visits and morality are associated with poor air quality days (where the Air Quality Index is greater than 100). Groups such as sensitive groups become more susceptible to the resulting conditions and more accurate forecasts would help to take more appropriate precautions. This research focuses on evaluating the utility of air quality forecasting in terms of its potential impacts by building on air quality forecasting and economical metrics. Our analysis includes data collected during the summertime ozone seasons between 2010 and 2012 from air quality models for the Washington, DC/Baltimore, MD region. The metrics that are relevant to our analysis include: (1) The number of times that a high ozone or particulate matter (PM) episode is correctly forecasted, (2) the number of times that high ozone or PM episode is forecasted when it does not occur and (3) the number of times when the air quality forecast predicts a cleaner air episode when the air was observed to have high ozone or PM. Our collection of data included available air quality model forecasts of ozone and particulate matter data from the U.S. Environmental Protection Agency (EPA)'s AIRNOW as well as observational data of ozone and particulate matter from Clean Air Partners. We evaluated the performance of the air quality forecasts with that of the observational data and found that the forecast models perform well for the Baltimore/Washington region and the time interval observed. We estimate the potential amount for the Baltimore/Washington region accrues to a savings of up to 5,905 lives and 5.9 billion dollars per year. This total assumes perfect compliance with bad air quality warning and forecast air quality forecasts. There is a difficulty presented with evaluating the economic utility of the forecasts. All may not comply and even with a low compliance rate of 5% and 72% as the average probability of detection of poor air quality days by the air quality models, we estimate that the forecasting program saves 412 lives or 412 million dollars per year for the region. The totals we found are great or greater than other typical yearly meteorological hazard programs such as tornado or hurricane forecasting and it is clear that the economic value of air quality forecasting in the Baltimore/Washington region is vital.
PERFORMANCE AND DIAGNOSTIC EVALUATION OF OZONE PREDICTIONS BY THE ETA-COMMUNITY MULTISCALE AIR QUALITY FORECAST SYSTEM DURING THE 2002 NEW ENGLAND AIR QUALITY STUDY

EPA Science Inventory

A real-time air quality forecasting system (Eta-CMAQ model suite) has been developed by linking the NCEP Eta model to the U.S. EPA CMAQ model. This work presents results from the application of the Eta-CMAQ modeling system for forecasting O₃ over the northeastern U.S d...
Evaluation of CMAQ and CAMx Ensemble Air Quality Forecasts during the 2015 MAPS-Seoul Field Campaign

NASA Astrophysics Data System (ADS)

Kim, E.; Kim, S.; Bae, C.; Kim, H. C.; Kim, B. U.

2015-12-01

The performance of Air quality forecasts during the 2015 MAPS-Seoul Field Campaign was evaluated. An forecast system has been operated to support the campaign's daily aircraft route decisions for airborne measurements to observe long-range transporting plume. We utilized two real-time ensemble systems based on the Weather Research and Forecasting (WRF)-Sparse Matrix Operator Kernel Emissions (SMOKE)-Comprehensive Air quality Model with extensions (CAMx) modeling framework and WRF-SMOKE- Community Multi_scale Air Quality (CMAQ) framework over northeastern Asia to simulate PM10 concentrations. Global Forecast System (GFS) from National Centers for Environmental Prediction (NCEP) was used to provide meteorological inputs for the forecasts. For an additional set of retrospective simulations, ERA Interim Reanalysis from European Centre for Medium-Range Weather Forecasts (ECMWF) was also utilized to access forecast uncertainties from the meteorological data used. Model Inter-Comparison Study for Asia (MICS-Asia) and National Institute of Environment Research (NIER) Clean Air Policy Support System (CAPSS) emission inventories are used for foreign and domestic emissions, respectively. In the study, we evaluate the CMAQ and CAMx model performance during the campaign by comparing the results to the airborne and surface measurements. Contributions of foreign and domestic emissions are estimated using a brute force method. Analyses on model performance and emissions will be utilized to improve air quality forecasts for the upcoming KORUS-AQ field campaign planned in 2016.
Improving of local ozone forecasting by integrated models.

PubMed

Gradišar, Dejan; Grašič, Boštjan; Božnar, Marija Zlata; Mlakar, Primož; Kocijan, Juš

2016-09-01

This paper discuss the problem of forecasting the maximum ozone concentrations in urban microlocations, where reliable alerting of the local population when thresholds have been surpassed is necessary. To improve the forecast, the methodology of integrated models is proposed. The model is based on multilayer perceptron neural networks that use as inputs all available information from QualeAria air-quality model, WRF numerical weather prediction model and onsite measurements of meteorology and air pollution. While air-quality and meteorological models cover large geographical 3-dimensional space, their local resolution is often not satisfactory. On the other hand, empirical methods have the advantage of good local forecasts. In this paper, integrated models are used for improved 1-day-ahead forecasting of the maximum hourly value of ozone within each day for representative locations in Slovenia. The WRF meteorological model is used for forecasting meteorological variables and the QualeAria air-quality model for gas concentrations. Their predictions, together with measurements from ground stations, are used as inputs to a neural network. The model validation results show that integrated models noticeably improve ozone forecasts and provide better alert systems.
THE EMERGENCE OF NUMERICAL AIR QUALITY FORCASTING MODELS AND THEIR APPLICATIONS

EPA Science Inventory

In recent years the U.S. and other nations have begun programs for short-term local through regional air quality forecasting based upon numerical three-dimensional air quality grid models. These numerical air quality forecast (NAQF) models and systems have been developed and test...
Daily air quality index forecasting with hybrid models: A case in China.

PubMed

Zhu, Suling; Lian, Xiuyuan; Liu, Haixia; Hu, Jianming; Wang, Yuanyuan; Che, Jinxing

2017-12-01

Air quality is closely related to quality of life. Air pollution forecasting plays a vital role in air pollution warnings and controlling. However, it is difficult to attain accurate forecasts for air pollution indexes because the original data are non-stationary and chaotic. The existing forecasting methods, such as multiple linear models, autoregressive integrated moving average (ARIMA) and support vector regression (SVR), cannot fully capture the information from series of pollution indexes. Therefore, new effective techniques need to be proposed to forecast air pollution indexes. The main purpose of this research is to develop effective forecasting models for regional air quality indexes (AQI) to address the problems above and enhance forecasting accuracy. Therefore, two hybrid models (EMD-SVR-Hybrid and EMD-IMFs-Hybrid) are proposed to forecast AQI data. The main steps of the EMD-SVR-Hybrid model are as follows: the data preprocessing technique EMD (empirical mode decomposition) is utilized to sift the original AQI data to obtain one group of smoother IMFs (intrinsic mode functions) and a noise series, where the IMFs contain the important information (level, fluctuations and others) from the original AQI series. LS-SVR is applied to forecast the sum of the IMFs, and then, S-ARIMA (seasonal ARIMA) is employed to forecast the residual sequence of LS-SVR. In addition, EMD-IMFs-Hybrid first separately forecasts the IMFs via statistical models and sums the forecasting results of the IMFs as EMD-IMFs. Then, S-ARIMA is employed to forecast the residuals of EMD-IMFs. To certify the proposed hybrid model, AQI data from June 2014 to August 2015 collected from Xingtai in China are utilized as a test case to investigate the empirical research. In terms of some of the forecasting assessment measures, the AQI forecasting results of Xingtai show that the two proposed hybrid models are superior to ARIMA, SVR, GRNN, EMD-GRNN, Wavelet-GRNN and Wavelet-SVR. Therefore, the proposed hybrid models can be used as effective and simple tools for air pollution forecasting and warning as well as for management. Copyright © 2017 Elsevier Ltd. All rights reserved.
VERIFICATION OF SURFACE LAYER OZONE FORECASTS IN THE NOAA/EPA AIR QUALITY FORECAST SYSTEM IN DIFFERENT REGIONS UNDER DIFFERENT SYNOPTIC SCENARIOS

EPA Science Inventory

An air quality forecast (AQF) system has been established at NOAA/NCEP since 2003 as a collaborative effort of NOAA and EPA. The system is based on NCEP's Eta mesoscale meteorological model and EPA's CMAQ air quality model (Davidson et al, 2004). The vision behind this system is ...
AIR QUALITY FORECAST DATABASE AND ANALYSIS

EPA Science Inventory

In 2003, NOAA and EPA signed a Memorandum of Agreement to collaborate on the design and implementation of a capability to produce daily air quality modeling forecast information for the U.S. NOAA's ETA meteorological model and EPA's Community Multiscale Air Quality (CMAQ) model ...
AN OPERATIONAL EVALUATION OF THE ETA-CMAQ AIR QUALITY FORECAST MODEL

EPA Science Inventory

The National Oceanic and Atmospheric Administration (NOAA), in collaboration with the Environmental Protection Agency (EPA), are developing an Air Quality Forecasting Program that will eventually result in an operational Nationwide Air Quality Forecasting System. The initial pha...
Development of Water Quality Forecasting Models Based on the SOM-ANN on TMDL Unit Watershed in Nakdong River

NASA Astrophysics Data System (ADS)

KIM, M.; Kim, J.; Baek, J.; Kim, C.; Shin, H.

2013-12-01

It has being happened as flush flood or red/green tide in various natural phenomena due to climate change and indiscreet development of river or land. Especially, water being very important to man should be protected and managed from water quality pollution, and in water resources management, real-time watershed monitoring system is being operated with the purpose of keeping watch and managing on rivers. It is especially important to monitor and forecast water quality in watershed. A study area selected Nak_K as one site among TMDL unit watershed in Nakdong River. This study is to develop a water quality forecasting model connected with making full use of observed data of 8 day interval from Nakdong River Environment Research Center. When forecasting models for each of the BOD, DO, COD, and chlorophyll-a are established considering correlation of various water quality factors, it is needed to select water quality factors showing highly considerable correlation with each water quality factor which is BOD, DO, COD, and chlorophyll-a. For analyzing the correlation of the factors (reservoir discharge, precipitation, air temperature, DO, BOD, COD, Tw, TN, TP, chlorophyll-a), in this study, self-organizing map was used and cross correlation analysis method was also used for comparing results drawn. Based on the results, each forecasting model for BOD, DO, COD, and chlorophyll-a was developed during the short period as 8, 16, 24, 32 days at 8 day interval. The each forecasting model is based on neural network with back propagation algorithm. That is, the study is connected with self-organizing map for analyzing correlation among various factors and neural network model for forecasting of water quality. It is considerably effective to manage the water quality in plenty of rivers, then, it specially is possible to monitor a variety of accidents in water quality. It will work well to protect water quality and to prevent destruction of the environment becoming more and more serious before occurring.
Metrics for the Evaluation the Utility of Air Quality Forecasting

NASA Astrophysics Data System (ADS)

Sumo, T. M.; Stockwell, W. R.

2013-12-01

Global warming is expected to lead to higher levels of air pollution and therefore the forecasting of both long-term and daily air quality is an important component for the assessment of the costs of climate change and its impact on human health. Some of the risks associated with poor air quality days (where the Air Pollution Index is greater than 100), include hospital visits and mortality. Accurate air quality forecasting has the potential to allow sensitive groups to take appropriate precautions. This research builds metrics for evaluating the utility of air quality forecasting in terms of its potential impacts. Our analysis of air quality models focuses on the Washington, DC/Baltimore, MD region over the summertime ozone seasons between 2010 and 2012. The metrics that are relevant to our analysis include: (1) The number of times that a high ozone or particulate matter (PM) episode is correctly forecasted, (2) the number of times that high ozone or PM episode is forecasted when it does not occur and (3) the number of times when the air quality forecast predicts a cleaner air episode when the air was observed to have high ozone or PM. Our evaluation of the performance of air quality forecasts include those forecasts of ozone and particulate matter and data available from the U.S. Environmental Protection Agency (EPA)'s AIRNOW. We also examined observational ozone and particulate matter data available from Clean Air Partners. Overall the forecast models perform well for our region and time interval.
Ensemble and Bias-Correction Techniques for Air-Quality Model Forecasts of Surface O3 and PM2.5 during the TEXAQS-II Experiment of 2006

EPA Science Inventory

Several air quality forecasting ensembles were created from seven models, running in real-time during the 2006 Texas Air Quality (TEXAQS-II) experiment. These multi-model ensembles incorporated a diverse set of meteorological models, chemical mechanisms, and emission inventories...
National Centers for Environmental Prediction

Science.gov Websites

Products Operational Forecast Graphics Experimental Forecast Graphics Verification and Diagnostics Model PARALLEL/EXPERIMENTAL MODEL FORECAST GRAPHICS OPERATIONAL VERIFICATION / DIAGNOSTICS PARALLEL VERIFICATION Developmental Air Quality Forecasts and Verification Back to Table of Contents 2. PARALLEL/EXPERIMENTAL GRAPHICS

National Centers for Environmental Prediction

Science.gov Websites

Operational Forecast Graphics Experimental Forecast Graphics Verification and Diagnostics Model Configuration /EXPERIMENTAL MODEL FORECAST GRAPHICS OPERATIONAL VERIFICATION / DIAGNOSTICS PARALLEL VERIFICATION / DIAGNOSTICS Developmental Air Quality Forecasts and Verification Back to Table of Contents 2. PARALLEL/EXPERIMENTAL GRAPHICS
Development of a method for comprehensive water quality forecasting and its application in Miyun reservoir of Beijing, China.

PubMed

Zhang, Lei; Zou, Zhihong; Shan, Wei

2017-06-01

Water quality forecasting is an essential part of water resource management. Spatiotemporal variations of water quality and their inherent constraints make it very complex. This study explored a data-based method for short-term water quality forecasting. Prediction of water quality indicators including dissolved oxygen, chemical oxygen demand by KMnO 4 and ammonia nitrogen using support vector machine was taken as inputs of the particle swarm algorithm based optimal wavelet neural network to forecast the whole status index of water quality. Gubeikou monitoring section of Miyun reservoir in Beijing, China was taken as the study case to examine effectiveness of this approach. The experiment results also revealed that the proposed model has advantages of stability and time reduction in comparison with other data-driven models including traditional BP neural network model, wavelet neural network model and Gradient Boosting Decision Tree model. It can be used as an effective approach to perform short-term comprehensive water quality prediction. Copyright © 2016. Published by Elsevier B.V.
The Copernicus Atmosphere Monitoring Service: facilitating the prediction of air quality from global to local scales

NASA Astrophysics Data System (ADS)

Engelen, R. J.; Peuch, V. H.

2017-12-01

The European Copernicus Atmosphere Monitoring Service (CAMS) operationally provides daily forecasts of global atmospheric composition and regional air quality. The global forecasting system is using ECMWF's Integrated Forecasting System (IFS), which is used for numerical weather prediction and which has been extended with modules for atmospheric chemistry, aerosols and greenhouse gases. The regional forecasts are produced by an ensemble of seven operational European air quality models that take their boundary conditions from the global system and provide an ensemble median with ensemble spread as their main output. Both the global and regional forecasting systems are feeding their output into air quality models on a variety of scales in various parts of the world. We will introduce the CAMS service chain and provide illustrations of its use in downstream applications. Both the usage of the daily forecasts and the usage of global and regional reanalyses will be addressed.
APPLICATION OF BIAS AND ADJUSTMENT TECHNIQUES TO THE ETA-CMAQ AIR QUALITY FORECAST

EPA Science Inventory

The current air quality forecast system, based on linking NOAA's Eta meteorological model with EPA's Community Multiscale Air Quality (CMAQ) model, consistently overpredicts surface ozone concentrations, but simulates its day-to-day variability quite well. The ability of bias cor...
EMISSIONS PROCESSING FOR THE ETA/ CMAQ AIR QUALITY FORECAST SYSTEM

EPA Science Inventory

NOAA and EPA have created an Air Quality Forecast (AQF) system. This AQF system links an adaptation of the EPA's Community Multiscale Air Quality Model with the 12 kilometer ETA model running operationally at NOAA's National Center for Environmental Predication (NCEP). One of the...
COMPUTATIONAL ASPECTS OF THE AIR QUALITY FORECASTING VERSION OF CMAQ (CMAQ-F)

EPA Science Inventory

The air quality forecast version of the Community Modeling Air Quality (CMAQ) model (CMAQ-F) was developed from the public release version of CMAQ (available from http://www.cmascenter.org), and is running operationally at the National Weather Service's National Centers for Envir...
A Load-Based Temperature Prediction Model for Anomaly Detection

NASA Astrophysics Data System (ADS)

Sobhani, Masoud

Electric load forecasting, as a basic requirement for the decision-making in power utilities, has been improved in various aspects in the past decades. Many factors may affect the accuracy of the load forecasts, such as data quality, goodness of the underlying model and load composition. Due to the strong correlation between the input variables (e.g., weather and calendar variables) and the load, the quality of input data plays a vital role in forecasting practices. Even if the forecasting model were able to capture most of the salient features of the load, a low quality input data may result in inaccurate forecasts. Most of the data cleansing efforts in the load forecasting literature have been devoted to the load data. Few studies focused on weather data cleansing for load forecasting. This research proposes an anomaly detection method for the temperature data. The method consists of two components: a load-based temperature prediction model and a detection technique. The effectiveness of the proposed method is demonstrated through two case studies: one based on the data from the Global Energy Forecasting Competition 2014, and the other based on the data published by ISO New England. The results show that by removing the detected observations from the original input data, the final load forecast accuracy is enhanced.
THE EMISSION PROCESSING SYSTEM FOR THE ETA/CMAQ AIR QUALITY FORECAST SYSTEM

EPA Science Inventory

NOAA and EPA have created an Air Quality Forecast (AQF) system. This AQF system links an adaptation of the EPA's Community Multiscale Air Quality Model with the 12 kilometer ETA model running operationally at NOAA's National Center for Environmental Predication (NCEP). One of th...
APPLICATION AND EVALUATION OF CMAQ IN THE UNITED STATES: AIR QUALITY FORECASTING AND RETROSPECTIVE MODELING

EPA Science Inventory

Presentation slides provide background on model evaluation techniques. Also included in the presentation is an operational evaluation of 2001 Community Multiscale Air Quality (CMAQ) annual simulation, and an evaluation of PM_2.5 for the CMAQ air quality forecast (AQF) ...
Studies regarding the quality of numerical weather forecasts of the WRF model integrated at high-resolutions for the Romanian territory

DOE PAGES

Iriza, Amalia; Dumitrache, Rodica C.; Lupascu, Aurelia; ...

2016-01-01

Our paper aims to evaluate the quality of high-resolution weather forecasts from the Weather Research and Forecasting (WRF) numerical weather prediction model. The lateral and boundary conditions were obtained from the numerical output of the Consortium for Small-scale Modeling (COSMO) model at 7 km horizontal resolution. Furthermore, the WRF model was run for January and July 2013 at two horizontal resolutions (3 and 1 km). The numerical forecasts of the WRF model were evaluated using different statistical scores for 2 m temperature and 10 m wind speed. Our results showed a tendency of the WRF model to overestimate the valuesmore » of the analyzed parameters in comparison to observations.« less
Studies regarding the quality of numerical weather forecasts of the WRF model integrated at high-resolutions for the Romanian territory

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

Iriza, Amalia; Dumitrache, Rodica C.; Lupascu, Aurelia

Our paper aims to evaluate the quality of high-resolution weather forecasts from the Weather Research and Forecasting (WRF) numerical weather prediction model. The lateral and boundary conditions were obtained from the numerical output of the Consortium for Small-scale Modeling (COSMO) model at 7 km horizontal resolution. Furthermore, the WRF model was run for January and July 2013 at two horizontal resolutions (3 and 1 km). The numerical forecasts of the WRF model were evaluated using different statistical scores for 2 m temperature and 10 m wind speed. Our results showed a tendency of the WRF model to overestimate the valuesmore » of the analyzed parameters in comparison to observations.« less
Real-Time Bias-Adjusted O3 and PM2.5 Air Quality Index Forecasts and their Performance Evaluations over the Continental United States

EPA Science Inventory

The National Air Quality Forecast Capacity (NAQFC) system, which links NOAA's North American Mesoscale (NAM) meteorological model with EPA's Community Multiscale Air Quality (CMAQ) model, provided operational ozone (O₃) and experimental fine particular matter (PM_2...
Air Quality Forecasts Using the NASA GEOS Model

NASA Technical Reports Server (NTRS)

Keller, Christoph A.; Knowland, K. Emma; Nielsen, Jon E.; Orbe, Clara; Ott, Lesley; Pawson, Steven; Saunders, Emily; Duncan, Bryan; Follette-Cook, Melanie; Liu, Junhua;

2018-01-01

We provide an introduction to a new high-resolution (0.25 degree) global composition forecast produced by NASA's Global Modeling and Assimilation office. The NASA Goddard Earth Observing System version 5 (GEOS-5) model has been expanded to provide global near-real-time forecasts of atmospheric composition at a horizontal resolution of 0.25 degrees (25 km). Previously, this combination of detailed chemistry and resolution was only provided by regional models. This system combines the operational GEOS-5 weather forecasting model with the state-of-the-science GEOS-Chem chemistry module (version 11) to provide detailed chemical analysis of a wide range of air pollutants such as ozone, carbon monoxide, nitrogen oxides, and fine particulate matter (PM2.5). The resolution of the forecasts is the highest resolution compared to current, publically-available global composition forecasts. Evaluation and validation of modeled trace gases and aerosols compared to surface and satellite observations will be presented for constituents relative to health air quality standards. Comparisons of modeled trace gases and aerosols against satellite observations show that the model produces realistic concentrations of atmospheric constituents in the free troposphere. Model comparisons against surface observations highlight the model's capability to capture the diurnal variability of air pollutants under a variety of meteorological conditions. The GEOS-5 composition forecasting system offers a new tool for scientists and the public health community, and is being developed jointly with several government and non-profit partners. Potential applications include air quality warnings, flight campaign planning and exposure studies using the archived analysis fields.

CALIPSO Satellite Lidar Identification Of Elevated Dust Over Australia Compared With Air Quality Model PM60 Forecasts

NASA Technical Reports Server (NTRS)

Young, Stuart A.; Vaughan, Mark; Omar, Ali; Liu, Zhaoyan; Lee, Sunhee; Hu, Youngxiang; Cope, Martin

2008-01-01

Global measurements of the vertical distribution of clouds and aerosols have been recorded by the lidar on board the CALIPSO (Cloud Aerosol Lidar Infrared Pathfinder Satellite Observations) satellite since June 2006. Such extensive, height-resolved measurements provide a rare and valuable opportunity for developing, testing and validating various atmospheric models, including global climate, numerical weather prediction, chemical transport and air quality models. Here we report on the initial results of an investigation into the performance of the Australian Air Quality Forecast System (AAQFS) model in forecasting the distribution of elevated dust over the Australian region. The model forecasts of PM60 dust distribution are compared with the CALIPSO lidar Vertical Feature Mask (VFM) data product. The VFM classifies contiguous atmospheric regions of enhanced backscatter as either cloud or aerosols. Aerosols are further classified into six subtypes. By comparing forecast PM60 concentration profiles to the spatial distribution of dust reported in the CALIPSO VFM, we can assess the model s ability to predict the occurrence and the vertical and horizontal extents of dust events within the study area.
Evaluating the impact of AMDAR data quality control in China on the short-range convection forecasts using the WRF model

NASA Astrophysics Data System (ADS)

Wang, Xiaofeng; Jiang, Qin; Zhang, Lei

2016-04-01

A quality control system for the Aircraft Meteorological Data Relay (AMDAR) data has been implemented in China. This system is an extension to the AMDAR quality control system used at the US National Centers for Environmental Prediction. We present a study in which the characteristics of each AMDAR data quality type were examined and the impact of the AMDAR data quality system on short-range convective weather forecasts using the WRF model was investigated. The main results obtained from this study are as follows. (1) The hourly rejection rate of AMDAR data during 2014 was 5.79%, and most of the rejections happened in Near Duplicate Check. (2) There was a significant diurnal variation for both quantity and quality of AMDAR data. Duplicated reports increased with the increase of data quantity, while suspicious and disorderly reports decreased with the increase of data quantity. (3) The characteristics of the data quality were different in each model layer, with the quality problems occurring mainly at the surface as well as at the height where the power or the flight mode of the aircraft underwent adjustment. (4) Assimilating the AMDAR data improved the forecast accuracy, particularly over the region where strong convection occurred. (5) Significant improvements made by assimilating AMDAR data were found after six hours into the model forecast. The conclusion from this study is that the newly implemented AMDAR data quality system can help improve the accuracy of short-range convection forecasts using the WRF model.
Selection and Classification Using a Forecast Applicant Pool.

ERIC Educational Resources Information Center

Hendrix, William H.

The document presents a forecast model of the future Air Force applicant pool. By forecasting applicants' quality (means and standard deviations of aptitude scores) and quantity (total number of applicants), a potential enlistee could be compared to the forecasted pool. The data used to develop the model consisted of means, standard deviation, and…
An Evaluation of Real-time Air Quality Forecasts and their Urban Emissions over Eastern Texas During the Summer of 2006 Second Texas Air Quality Study Field Study

EPA Science Inventory

Forecasts of ozone (O₃) and particulate matter (diameter less than 2.5 µm, PM_2.5) from seven air quality forecast models (AQFMs) are statistically evaluated against observations collected during August and September of 2006 (49 days) through the AIRNow netwo...
Forecasting asthma-related hospital admissions in London using negative binomial models.

PubMed

Soyiri, Ireneous N; Reidpath, Daniel D; Sarran, Christophe

2013-05-01

Health forecasting can improve health service provision and individual patient outcomes. Environmental factors are known to impact chronic respiratory conditions such as asthma, but little is known about the extent to which these factors can be used for forecasting. Using weather, air quality and hospital asthma admissions, in London (2005-2006), two related negative binomial models were developed and compared with a naive seasonal model. In the first approach, predictive forecasting models were fitted with 7-day averages of each potential predictor, and then a subsequent multivariable model is constructed. In the second strategy, an exhaustive search of the best fitting models between possible combinations of lags (0-14 days) of all the environmental effects on asthma admission was conducted. Three models were considered: a base model (seasonal effects), contrasted with a 7-day average model and a selected lags model (weather and air quality effects). Season is the best predictor of asthma admissions. The 7-day average and seasonal models were trivial to implement. The selected lags model was computationally intensive, but of no real value over much more easily implemented models. Seasonal factors can predict daily hospital asthma admissions in London, and there is a little evidence that additional weather and air quality information would add to forecast accuracy.
Utilizing Operational and Improved Remote Sensing Measurements to Assess Air Quality Monitoring Model Forecasts

NASA Astrophysics Data System (ADS)

Gan, Chuen-Meei

Air quality model forecasts from Weather Research and Forecast (WRF) and Community Multiscale Air Quality (CMAQ) are often used to support air quality applications such as regulatory issues and scientific inquiries on atmospheric science processes. In urban environments, these models become more complex due to the inherent complexity of the land surface coupling and the enhanced pollutants emissions. This makes it very difficult to diagnose the model, if the surface parameter forecasts such as PM2.5 (particulate matter with aerodynamic diameter less than 2.5 microm) are not accurate. For this reason, getting accurate boundary layer dynamic forecasts is as essential as quantifying realistic pollutants emissions. In this thesis, we explore the usefulness of vertical sounding measurements on assessing meteorological and air quality forecast models. In particular, we focus on assessing the WRF model (12km x 12km) coupled with the CMAQ model for the urban New York City (NYC) area using multiple vertical profiling and column integrated remote sensing measurements. This assessment is helpful in probing the root causes for WRF-CMAQ overestimates of surface PM2.5 occurring both predawn and post-sunset in the NYC area during the summer. In particular, we find that the significant underestimates in the WRF PBL height forecast is a key factor in explaining this anomaly. On the other hand, the model predictions of the PBL height during daytime when convective heating dominates were found to be highly correlated to lidar derived PBL height with minimal bias. Additional topics covered in this thesis include mathematical method using direct Mie scattering approach to convert aerosol microphysical properties from CMAQ into optical parameters making direct comparisons with lidar and multispectral radiometers feasible. Finally, we explore some tentative ideas on combining visible (VIS) and mid-infrared (MIR) sensors to better separate aerosols into fine and coarse modes.
Intercomparison of air quality data using principal component analysis, and forecasting of PM₁₀ and PM₂.₅ concentrations using artificial neural networks, in Thessaloniki and Helsinki.

PubMed

Voukantsis, Dimitris; Karatzas, Kostas; Kukkonen, Jaakko; Räsänen, Teemu; Karppinen, Ari; Kolehmainen, Mikko

2011-03-01

In this paper we propose a methodology consisting of specific computational intelligence methods, i.e. principal component analysis and artificial neural networks, in order to inter-compare air quality and meteorological data, and to forecast the concentration levels for environmental parameters of interest (air pollutants). We demonstrate these methods to data monitored in the urban areas of Thessaloniki and Helsinki in Greece and Finland, respectively. For this purpose, we applied the principal component analysis method in order to inter-compare the patterns of air pollution in the two selected cities. Then, we proceeded with the development of air quality forecasting models for both studied areas. On this basis, we formulated and employed a novel hybrid scheme in the selection process of input variables for the forecasting models, involving a combination of linear regression and artificial neural networks (multi-layer perceptron) models. The latter ones were used for the forecasting of the daily mean concentrations of PM₁₀ and PM₂.₅ for the next day. Results demonstrated an index of agreement between measured and modelled daily averaged PM₁₀ concentrations, between 0.80 and 0.85, while the kappa index for the forecasting of the daily averaged PM₁₀ concentrations reached 60% for both cities. Compared with previous corresponding studies, these statistical parameters indicate an improved performance of air quality parameters forecasting. It was also found that the performance of the models for the forecasting of the daily mean concentrations of PM₁₀ was not substantially different for both cities, despite the major differences of the two urban environments under consideration. Copyright © 2011 Elsevier B.V. All rights reserved.

New Developments in Wildfire Pollution Forecasting at the Canadian Meteorological Centre

NASA Astrophysics Data System (ADS)

Pavlovic, Radenko; Chen, Jack; Munoz-Alpizar, Rodrigo; Davignon, Didier; Beaulieu, Paul-Andre; Landry, Hugo; Menard, Sylvain; Gravel, Sylvie; Moran, Michael

2017-04-01

Environment and Climate Change Canada's air quality forecast system with near-real-time wildfire emissions, named FireWork, was developed in 2012 and has been run by the Canadian Meteorological Centre Operations division (CMCO) since 2013. In June 2016 this system was upgraded to operational status and wildfire smoke forecasts for North America are now available to the general public. FireWork's ability to model the transport and diffusion of wildfire smoke plumes has proved to be valuable to regional air quality forecasters and emergency first responders. Some of the most challenging issues with wildfire pollution modelling concern the production of wildfire emission estimates and near-source dispersion within the air quality model. As a consequence, FireWork is undergoing constant development. During the massive Fort McMurray wildfire event in western Canada in May 2016, for example, different wildfire emissions processing approaches and wildfire emissions injection and dispersion schemes were tested within the air quality model. Work on various FireWork components will continue in order to deliver a new operational version of the forecasting system for the 2017 wildfire season. Some of the proposed improvements will be shown in this presentation along with current and planned FireWork post-processing products.
Air Quality Forecasts Using the NASA GEOS Model: A Unified Tool from Local to Global Scales

NASA Technical Reports Server (NTRS)

Knowland, E. Emma; Keller, Christoph; Nielsen, J. Eric; Orbe, Clara; Ott, Lesley; Pawson, Steven; Saunders, Emily; Duncan, Bryan; Cook, Melanie; Liu, Junhua;

2017-01-01

We provide an introduction to a new high-resolution (0.25 degree) global composition forecast produced by NASA's Global Modeling and Assimilation office. The NASA Goddard Earth Observing System version 5 (GEOS-5) model has been expanded to provide global near-real-time forecasts of atmospheric composition at a horizontal resolution of 0.25 degrees (approximately 25 km). Previously, this combination of detailed chemistry and resolution was only provided by regional models. This system combines the operational GEOS-5 weather forecasting model with the state-of-the-science GEOS-Chem chemistry module (version 11) to provide detailed chemical analysis of a wide range of air pollutants such as ozone, carbon monoxide, nitrogen oxides, and fine particulate matter (PM2.5). The resolution of the forecasts is the highest resolution compared to current, publically-available global composition forecasts. Evaluation and validation of modeled trace gases and aerosols compared to surface and satellite observations will be presented for constituents relative to health air quality standards. Comparisons of modeled trace gases and aerosols against satellite observations show that the model produces realistic concentrations of atmospheric constituents in the free troposphere. Model comparisons against surface observations highlight the model's capability to capture the diurnal variability of air pollutants under a variety of meteorological conditions. The GEOS-5 composition forecasting system offers a new tool for scientists and the public health community, and is being developed jointly with several government and non-profit partners. Potential applications include air quality warnings, flight campaign planning and exposure studies using the archived analysis fields.

Human-model hybrid Korean air quality forecasting system.

PubMed

Chang, Lim-Seok; Cho, Ara; Park, Hyunju; Nam, Kipyo; Kim, Deokrae; Hong, Ji-Hyoung; Song, Chang-Keun

2016-09-01

The Korean national air quality forecasting system, consisting of the Weather Research and Forecasting, the Sparse Matrix Operator Kernel Emissions, and the Community Modeling and Analysis (CMAQ), commenced from August 31, 2013 with target pollutants of particulate matters (PM) and ozone. Factors contributing to PM forecasting accuracy include CMAQ inputs of meteorological field and emissions, forecasters' capacity, and inherent CMAQ limit. Four numerical experiments were conducted including two global meteorological inputs from the Global Forecast System (GFS) and the Unified Model (UM), two emissions from the Model Intercomparison Study Asia (MICS-Asia) and the Intercontinental Chemical Transport Experiment (INTEX-B) for the Northeast Asia with Clear Air Policy Support System (CAPSS) for South Korea, and data assimilation of the Monitoring Atmospheric Composition and Climate (MACC). Significant PM underpredictions by using both emissions were found for PM mass and major components (sulfate and organic carbon). CMAQ predicts PM2.5 much better than PM10 (NMB of PM2.5: -20~-25%, PM10: -43~-47%). Forecasters' error usually occurred at the next day of high PM event. Once CMAQ fails to predict high PM event the day before, forecasters are likely to dismiss the model predictions on the next day which turns out to be true. The best combination of CMAQ inputs is the set of UM global meteorological field, MICS-Asia and CAPSS 2010 emissions with the NMB of -12.3%, the RMSE of 16.6μ/m(3) and the R(2) of 0.68. By using MACC data as an initial and boundary condition, the performance skill of CMAQ would be improved, especially in the case of undefined coarse emission. A variety of methods such as ensemble and data assimilation are considered to improve further the accuracy of air quality forecasting, especially for high PM events to be comparable to for all cases. The growing utilization of the air quality forecast induced the public strongly to demand that the accuracy of the national forecasting be improved. In this study, we investigated the problems in the current forecasting as well as various alternatives to solve the problems. Such efforts to improve the accuracy of the forecast are expected to contribute to the protection of public health by increasing the availability of the forecast system.
ASSESSMENT OF ETA-CMAQ FORECASTS OF PARTICULATE MATTER DISTRIBUTIONS THROUGH COMPARISONS WITH SURFACE NETWORK AND SPECIALIZED MEASUREMENTS

EPA Science Inventory

An air-quality forecasting (AQF) system based on the National Weather Service (NWS) National Centers for Environmental Prediction's (NCEP's) Eta model and the U.S. EPA's Community Multiscale Air Quality (CMAQ) Modeling System is used to simulate the distributions of tropospheric ...
Development and implementation of a remote-sensing and in situ data-assimilating version of CMAQ for operational PM2.5 forecasting. Part 1: MODIS aerosol optical depth (AOD) data-assimilation design and testing.

PubMed

McHenry, John N; Vukovich, Jeffery M; Hsu, N Christina

2015-12-01

This two-part paper reports on the development, implementation, and improvement of a version of the Community Multi-Scale Air Quality (CMAQ) model that assimilates real-time remotely-sensed aerosol optical depth (AOD) information and ground-based PM2.5 monitor data in routine prognostic application. The model is being used by operational air quality forecasters to help guide their daily issuance of state or local-agency-based air quality alerts (e.g. action days, health advisories). Part 1 describes the development and testing of the initial assimilation capability, which was implemented offline in partnership with NASA and the Visibility Improvement State and Tribal Association of the Southeast (VISTAS) Regional Planning Organization (RPO). In the initial effort, MODIS-derived aerosol optical depth (AOD) data are input into a variational data-assimilation scheme using both the traditional Dark Target and relatively new "Deep Blue" retrieval methods. Evaluation of the developmental offline version, reported in Part 1 here, showed sufficient promise to implement the capability within the online, prognostic operational model described in Part 2. In Part 2, the addition of real-time surface PM2.5 monitoring data to improve the assimilation and an initial evaluation of the prognostic modeling system across the continental United States (CONUS) is presented. Air quality forecasts are now routinely used to understand when air pollution may reach unhealthy levels. For the first time, an operational air quality forecast model that includes the assimilation of remotely-sensed aerosol optical depth and ground based PM2.5 observations is being used. The assimilation enables quantifiable improvements in model forecast skill, which improves confidence in the accuracy of the officially-issued forecasts. This helps air quality stakeholders be more effective in taking mitigating actions (reducing power consumption, ride-sharing, etc.) and avoiding exposures that could otherwise result in more serious air quality episodes or more deleterious health effects.
A novel hybrid model for air quality index forecasting based on two-phase decomposition technique and modified extreme learning machine.

PubMed

Wang, Deyun; Wei, Shuai; Luo, Hongyuan; Yue, Chenqiang; Grunder, Olivier

2017-02-15

The randomness, non-stationarity and irregularity of air quality index (AQI) series bring the difficulty of AQI forecasting. To enhance forecast accuracy, a novel hybrid forecasting model combining two-phase decomposition technique and extreme learning machine (ELM) optimized by differential evolution (DE) algorithm is developed for AQI forecasting in this paper. In phase I, the complementary ensemble empirical mode decomposition (CEEMD) is utilized to decompose the AQI series into a set of intrinsic mode functions (IMFs) with different frequencies; in phase II, in order to further handle the high frequency IMFs which will increase the forecast difficulty, variational mode decomposition (VMD) is employed to decompose the high frequency IMFs into a number of variational modes (VMs). Then, the ELM model optimized by DE algorithm is applied to forecast all the IMFs and VMs. Finally, the forecast value of each high frequency IMF is obtained through adding up the forecast results of all corresponding VMs, and the forecast series of AQI is obtained by aggregating the forecast results of all IMFs. To verify and validate the proposed model, two daily AQI series from July 1, 2014 to June 30, 2016 collected from Beijing and Shanghai located in China are taken as the test cases to conduct the empirical study. The experimental results show that the proposed hybrid model based on two-phase decomposition technique is remarkably superior to all other considered models for its higher forecast accuracy. Copyright © 2016 Elsevier B.V. All rights reserved.
The Automation of Nowcast Model Assessment Processes

DTIC Science & Technology

2016-09-01

that will automate real-time WRE-N model simulations, collect and quality control check weather observations for assimilation and verification, and...domains centered near White Sands Missile Range, New Mexico, where the Meteorological Sensor Array (MSA) will be located. The MSA will provide...observations and performing quality -control checks for the pre-forecast data assimilation period. 2. Run the WRE-N model to generate model forecast data
Ensemble Statistical Post-Processing of the National Air Quality Forecast Capability: Enhancing Ozone Forecasts in Baltimore, Maryland

NASA Technical Reports Server (NTRS)

Garner, Gregory G.; Thompson, Anne M.

2013-01-01

An ensemble statistical post-processor (ESP) is developed for the National Air Quality Forecast Capability (NAQFC) to address the unique challenges of forecasting surface ozone in Baltimore, MD. Air quality and meteorological data were collected from the eight monitors that constitute the Baltimore forecast region. These data were used to build the ESP using a moving-block bootstrap, regression tree models, and extreme-value theory. The ESP was evaluated using a 10-fold cross-validation to avoid evaluation with the same data used in the development process. Results indicate that the ESP is conditionally biased, likely due to slight overfitting while training the regression tree models. When viewed from the perspective of a decision-maker, the ESP provides a wealth of additional information previously not available through the NAQFC alone. The user is provided the freedom to tailor the forecast to the decision at hand by using decision-specific probability thresholds that define a forecast for an ozone exceedance. Taking advantage of the ESP, the user not only receives an increase in value over the NAQFC, but also receives value for An ensemble statistical post-processor (ESP) is developed for the National Air Quality Forecast Capability (NAQFC) to address the unique challenges of forecasting surface ozone in Baltimore, MD. Air quality and meteorological data were collected from the eight monitors that constitute the Baltimore forecast region. These data were used to build the ESP using a moving-block bootstrap, regression tree models, and extreme-value theory. The ESP was evaluated using a 10-fold cross-validation to avoid evaluation with the same data used in the development process. Results indicate that the ESP is conditionally biased, likely due to slight overfitting while training the regression tree models. When viewed from the perspective of a decision-maker, the ESP provides a wealth of additional information previously not available through the NAQFC alone. The user is provided the freedom to tailor the forecast to the decision at hand by using decision-specific probability thresholds that define a forecast for an ozone exceedance. Taking advantage of the ESP, the user not only receives an increase in value over the NAQFC, but also receives value for
Improvement of PM concentration predictability using WRF-CMAQ-DLM coupled system and its applications

NASA Astrophysics Data System (ADS)

Lee, Soon Hwan; Kim, Ji Sun; Lee, Kang Yeol; Shon, Keon Tae

2017-04-01

Air quality due to increasing Particulate Matter(PM) in Korea in Asia is getting worse. At present, the PM forecast is announced based on the PM concentration predicted from the air quality prediction numerical model. However, forecast accuracy is not as high as expected due to various uncertainties for PM physical and chemical characteristics. The purpose of this study was to develop a numerical-statistically ensemble models to improve the accuracy of prediction of PM10 concentration. Numerical models used in this study are the three dimensional atmospheric model Weather Research and Forecasting(WRF) and the community multiscale air quality model (CMAQ). The target areas for the PM forecast are Seoul, Busan, Daegu, and Daejeon metropolitan areas in Korea. The data used in the model development are PM concentration and CMAQ predictions and the data period is 3 months (March 1 - May 31, 2014). The dynamic-statistical technics for reducing the systematic error of the CMAQ predictions was applied to the dynamic linear model(DLM) based on the Baysian Kalman filter technic. As a result of applying the metrics generated from the dynamic linear model to the forecasting of PM concentrations accuracy was improved. Especially, at the high PM concentration where the damage is relatively large, excellent improvement results are shown.
Comparison of CMAQ Modeling Study with Discover-AQ 2014 Aircraft Measurements over Colorado

NASA Astrophysics Data System (ADS)

Tang, Y.; Pan, L.; Lee, P.; Tong, D.; Kim, H. C.; Artz, R. S.

2014-12-01

NASA and NCAR jointly led a recent multiple platform-based (space, air and ground) measurement intensive to study air quality and to validate satellite data. The Discover-AQ/FRAPPE field experiment took place along the Colorado Front Range in July and August, 2014. The air quality modeling team of the NOAA Air Resources Laboratory was one of the three teams that provided real-time air quality forecasting for the campaign. The U.S. EPA Community Multi-scale Air Quality (CMAQ) Model was used with emission inventories based on the data set used by the NOAA National Air Quality Forecasting Capacity (NAQFC). By analyzing the forecast results calculated using aircraft measurements, it was found that CO emissions tended to be overestimated, while ethane emissions were underestimated. Biogenic VOCs were also underpredicted. Due to their relatively high altitude, ozone concentrations in Denver and the surrounding areas are affected by both local emissions and transported ozone. The modeled ozone was highly dependent on the meteorological predictions over this region. The complex terrain over the Rocky Mountains also contributed to the model uncertainty. This study discussed the causes of model biases, the forecast performance under different meteorology, and results from using different model grid resolutions. Several data assimilation techniques were further tested to improve the "post-analysis" performance of the modeling system for the period.
Impact of chemical lateral boundary conditions in a regional air quality forecast model on surface ozone predictions during stratospheric intrusions

NASA Astrophysics Data System (ADS)

Pendlebury, Diane; Gravel, Sylvie; Moran, Michael D.; Lupu, Alexandru

2018-02-01

A regional air quality forecast model, GEM-MACH, is used to examine the conditions under which a limited-area air quality model can accurately forecast near-surface ozone concentrations during stratospheric intrusions. Periods in 2010 and 2014 with known stratospheric intrusions over North America were modelled using four different ozone lateral boundary conditions obtained from a seasonal climatology, a dynamically-interpolated monthly climatology, global air quality forecasts, and global air quality reanalyses. It is shown that the mean bias and correlation in surface ozone over the course of a season can be improved by using time-varying ozone lateral boundary conditions, particularly through the correct assignment of stratospheric vs. tropospheric ozone along the western lateral boundary (for North America). Part of the improvement in surface ozone forecasts results from improvements in the characterization of near-surface ozone along the lateral boundaries that then directly impact surface locations near the boundaries. However, there is an additional benefit from the correct characterization of the location of the tropopause along the western lateral boundary such that the model can correctly simulate stratospheric intrusions and their associated exchange of ozone from stratosphere to troposphere. Over a three-month period in spring 2010, the mean bias was seen to improve by as much as 5 ppbv and the correlation by 0.1 depending on location, and on the form of the chemical lateral boundary condition.
Improved Forecasting of Next Day Ozone Concentrations in the Eastern U.S.

EPA Science Inventory

There is an urgent need to provide accurate air quality information and forecasts to the general public. A hierarchical space-time model is used to forecast next day spatial patterns of daily maximum 8-hr ozone concentrations. The model combines ozone monitoring data and gridded...
Diagnostic Evaluation of Nmme Precipitation and Temperature Forecasts for the Continental United States

NASA Astrophysics Data System (ADS)

Karlovits, G. S.; Villarini, G.; Bradley, A.; Vecchi, G. A.

2014-12-01

Forecasts of seasonal precipitation and temperature can provide information in advance of potentially costly disruptions caused by flood and drought conditions. The consequences of these adverse hydrometeorological conditions may be mitigated through informed planning and response, given useful and skillful forecasts of these conditions. However, the potential value and applicability of these forecasts is unavoidably linked to their forecast quality. In this work we evaluate the skill of four global circulation models (GCMs) part of the North American Multi-Model Ensemble (NMME) project in forecasting seasonal precipitation and temperature over the continental United States. The GCMs we consider are the Geophysical Fluid Dynamics Laboratory (GFDL)-CM2.1, NASA Global Modeling and Assimilation Office (NASA-GMAO)-GEOS-5, The Center for Ocean-Land-Atmosphere Studies - Rosenstiel School of Marine & Atmospheric Science (COLA-RSMAS)-CCSM3, Canadian Centre for Climate Modeling and Analysis (CCCma) - CanCM4. These models are available at a resolution of 1-degree and monthly, with a minimum forecast lead time of nine months, up to one year. These model ensembles are compared against gridded monthly temperature and precipitation data created by the PRISM Climate Group, which represent the reference observation dataset in this work. Aspects of forecast quality are quantified using a diagnostic skill score decomposition that allows the evaluation of the potential skill and conditional and unconditional biases associated with these forecasts. The evaluation of the decomposed GCM forecast skill over the continental United States, by season and by lead time allows for a better understanding of the utility of these models for flood and drought predictions. Moreover, it also represents a diagnostic tool that could provide model developers feedback about strengths and weaknesses of their models.
Water quality in the Schuylkill River, Pennsylvania: the potential for long-lead forecasts

NASA Astrophysics Data System (ADS)

Block, P. J.; Peralez, J.

2012-12-01

Prior analysis of pathogen levels in the Schuylkill River has led to a categorical daily forecast of water quality (denoted as red, yellow, or green flag days.) The forecast, available to the public online through the Philadelphia Water Department, is predominantly based on the local precipitation forecast. In this study, we explore the feasibility of extending the forecast to the seasonal scale by associating large-scale climate drivers with local precipitation and water quality parameter levels. This advance information is relevant for recreational activities, ecosystem health, and water treatment (energy, chemicals), as the Schuylkill provides 40% of Philadelphia's water supply. Preliminary results indicate skillful prediction of average summertime water quality parameters and characteristics, including chloride, coliform, turbidity, alkalinity, and others, using season-ahead oceanic and atmospheric variables, predominantly from the North Atlantic. Water quality parameter trends, including historic land use changes along the river, association with climatic variables, and prediction models will be presented.
Development of the GEM-MACH-FireWork System: An Air Quality Model with On-line Wildfire Emissions within the Canadian Operational Air Quality Forecast System

NASA Astrophysics Data System (ADS)

Pavlovic, Radenko; Chen, Jack; Beaulieu, Paul-Andre; Anselmp, David; Gravel, Sylvie; Moran, Mike; Menard, Sylvain; Davignon, Didier

2014-05-01

A wildfire emissions processing system has been developed to incorporate near-real-time emissions from wildfires and large prescribed burns into Environment Canada's real-time GEM-MACH air quality (AQ) forecast system. Since the GEM-MACH forecast domain covers Canada and most of the U.S.A., including Alaska, fire location information is needed for both of these large countries. During AQ model runs, emissions from individual fire sources are injected into elevated model layers based on plume-rise calculations and then transport and chemistry calculations are performed. This "on the fly" approach to the insertion of the fire emissions provides flexibility and efficiency since on-line meteorology is used and computational overhead in emissions pre-processing is reduced. GEM-MACH-FireWork, an experimental wildfire version of GEM-MACH, was run in real-time mode for the summers of 2012 and 2013 in parallel with the normal operational version. 48-hour forecasts were generated every 12 hours (at 00 and 12 UTC). Noticeable improvements in the AQ forecasts for PM2.5 were seen in numerous regions where fire activity was high. Case studies evaluating model performance for specific regions and computed objective scores will be included in this presentation. Using the lessons learned from the last two summers, Environment Canada will continue to work towards the goal of incorporating near-real-time intermittent wildfire emissions into the operational air quality forecast system.
ADAPTATION AND APPLICATION OF THE COMMUNITY MULTISCALE AIR QUALITY (CMAQ) MODELING SYSTEM FOR REAL-TIME AIR QUALITY FORECASTING DURING THE SUMMER OF 2004

EPA Science Inventory

The ability to forecast local and regional air pollution events is challenging since the processes governing the production and sustenance of atmospheric pollutants are complex and often non-linear. Comprehensive atmospheric models, by representing in as much detail as possible t...
Methodology for Air Quality Forecast Downscaling from Regional- to Street-Scale

NASA Astrophysics Data System (ADS)

Baklanov, Alexander; Nuterman, Roman; Mahura, Alexander; Amstrup, Bjarne; Hansen Saas, Bent; Havskov Sørensen, Jens; Lorenzen, Thomas; Weismann, Jakob

2010-05-01

The most serious air pollution events occur in cities where there is a combination of high population density and air pollution, e.g. from vehicles. The pollutants can lead to serious human health problems, including asthma, irritation of the lungs, bronchitis, pneumonia, decreased resistance to respiratory infections, and premature death. In particular air pollution is associated with increase in cardiovascular disease and lung cancer. In 2000 WHO estimated that between 2.5 % and 11 % of total annual deaths are caused by exposure to air pollution. However, European-scale air quality models are not suited for local forecasts, as their grid-cell is typically of the order of 5 to 10km and they generally lack detailed representation of urban effects. Two suites are used in the framework of the EC FP7 project MACC (Monitoring of Atmosphere Composition and Climate) to demonstrate how downscaling from the European MACC ensemble to local-scale air quality forecast will be carried out: one will illustrate capabilities for the city of Copenhagen (Denmark); the second will focus on the city of Bucharest (Romania). This work is devoted to the first suite, where methodological aspects of downscaling from regional (European/ Denmark) to urban scale (Copenhagen), and from the urban down to street scale. The first results of downscaling according to the proposed methodology are presented. The potential for downscaling of European air quality forecasts by operating urban and street-level forecast models is evaluated. This will bring a strong support for continuous improvement of the regional forecast modelling systems for air quality in Europe, and underline clear perspectives for the future regional air quality core and downstream services for end-users. At the end of the MACC project, requirements on "how-to-do" downscaling of European air-quality forecasts to the city and street levels with different approaches will be formulated.
Comparative assessment of several post-processing methods for correcting evapotranspiration forecasts derived from TIGGE datasets.

NASA Astrophysics Data System (ADS)

Tian, D.; Medina, H.

2017-12-01

Post-processing of medium range reference evapotranspiration (ETo) forecasts based on numerical weather prediction (NWP) models has the potential of improving the quality and utility of these forecasts. This work compares the performance of several post-processing methods for correcting ETo forecasts over the continental U.S. generated from The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) database using data from Europe (EC), the United Kingdom (MO), and the United States (NCEP). The pondered post-processing techniques are: simple bias correction, the use of multimodels, the Ensemble Model Output Statistics (EMOS, Gneitting et al., 2005) and the Bayesian Model Averaging (BMA, Raftery et al., 2005). ETo estimates based on quality-controlled U.S. Regional Climate Reference Network measurements, and computed with the FAO 56 Penman Monteith equation, are adopted as baseline. EMOS and BMA are generally the most efficient post-processing techniques of the ETo forecasts. Nevertheless, the simple bias correction of the best model is commonly much more rewarding than using multimodel raw forecasts. Our results demonstrate the potential of different forecasting and post-processing frameworks in operational evapotranspiration and irrigation advisory systems at national scale.
Assessment of reservoir system variable forecasts

NASA Astrophysics Data System (ADS)

Kistenmacher, Martin; Georgakakos, Aris P.

2015-05-01

Forecast ensembles are a convenient means to model water resources uncertainties and to inform planning and management processes. For multipurpose reservoir systems, forecast types include (i) forecasts of upcoming inflows and (ii) forecasts of system variables and outputs such as reservoir levels, releases, flood damage risks, hydropower production, water supply withdrawals, water quality conditions, navigation opportunities, and environmental flows, among others. Forecasts of system variables and outputs are conditional on forecasted inflows as well as on specific management policies and can provide useful information for decision-making processes. Unlike inflow forecasts (in ensemble or other forms), which have been the subject of many previous studies, reservoir system variable and output forecasts are not formally assessed in water resources management theory or practice. This article addresses this gap and develops methods to rectify potential reservoir system forecast inconsistencies and improve the quality of management-relevant information provided to stakeholders and managers. The overarching conclusion is that system variable and output forecast consistency is critical for robust reservoir management and needs to be routinely assessed for any management model used to inform planning and management processes. The above are demonstrated through an application from the Sacramento-American-San Joaquin reservoir system in northern California.
EVALUATION OF SEVERAL PM 2.5 FORECAST MODELS USING DATA COLLECTED DURING THE ICARTT/NEAQS 2004 FIELD STUDY

EPA Science Inventory

Real-time forecasts of PM_2.5 aerosol mass from seven air-quality forecast models (AQFMs) are statistically evaluated against observations collected in the northeastern U.S. and southeastern Canada from two surface networks and aircraft data during the summer of 2004 IC...

Regional Air Quality forecAST (RAQAST) Over the U.S

NASA Astrophysics Data System (ADS)

Yoshida, Y.; Choi, Y.; Zeng, T.; Wang, Y.

2005-12-01

A regional chemistry and transport modeling system is used to provide 48-hour forecast of the concentrations of ozone and its precursors over the United States. Meteorological forecast is conducted using the NCAR/Penn State MM5 model. The regional chemistry and transport model simulates the sources, transport, chemistry, and deposition of 24 chemical tracers. The lateral and upper boundary conditions of trace gas concentrations are specified using the monthly mean output from the global GEOS-CHEM model. The initial and boundary conditions for meteorological fields are taken from the NOAA AVN forecast. The forecast has been operational since August, 2003. Model simulations are evaluated using surface, aircraft, and satellite measurements in the A'hindcast' mode. The next step is an automated forecast evaluation system.
AN OPERATIONAL EVALUATION OF THE ETA - CMAQ AIR QUALITY FORECAST MODEL

EPA Science Inventory

The National Oceanic and Atmospheric Administration (NOAA), in partnership with the United States Environmental Protection Agency (EPA), are developing an operational, nationwide Air Quality Forecasting (AQF) system. An experimental phase of this program, which couples NOAA's Et...
The Good, the Bad, and the Ugly: Numerical Prediction for Hurricane Juan (2003)

NASA Astrophysics Data System (ADS)

Gyakum, J.; McTaggart-Cowan, R.

2004-05-01

The range of accuracy of the numerical weather prediction (NWP) guidance for the landfall of Hurricane Juan (2003), from nearly perfect to nearly useless, motivates a study of the NWP forecast errors on 28-29 September 2003 in the eastern North Atlantic. Although the forecasts issued over the period were of very high quality, this is primarily because of the diligence of the forecasters, and not related to the reliability of the numerical predictions provided to them by the North American operational centers and the research community. A bifurcation in the forecast fields from various centers and institutes occurred beginning with the 0000 UTC run of 28 September, and continuing until landfall just after 0000 UTC on 29 September. The GFS (NCEP), Eta (NCEP), GEM (Canadian Meteorological Centre; CMC), and MC2 (McGill) forecast models all showed an extremely weak (minimum SLP above 1000 hPa) remnant vortex moving north-northwestward into the Gulf of Maine and merging with a diabatically-developed surface low offshore. The GFS uses a vortex-relocation scheme, the Eta a vortex bogus, and the GEM and MC2 are run on CMC analyses that contain no enhanced vortex. The UK Met Office operational, the GFDL, and the NOGAPS (US Navy) forecast models all ran a small-scale hurricane-like vortex directly into Nova Scotia and verified very well for this case. The UKMO model uses synthetic observations to enhance structures in poorly-forecasted areas during the analysis cycle and both the GFDL and NOGAPS model use advanced idealized vortex bogusing in their initial conditions. The quality of the McGill MC2 forecast is found to be significantly enhanced using a bogusing technique similar to that used in the initialization of the successful forecast models. A verification of the improved forecast is presented along with a discussion of the need for operational quality control of the background fields in the analysis cycle and for proper representation of strong, small-scale tropical vortices.
Evaluation and intercomparison of air quality forecasts over Korea during the KORUS-AQ campaign

NASA Astrophysics Data System (ADS)

Lee, Seungun; Park, Rokjin J.; Kim, Soontae; Song, Chul H.; Kim, Cheol-Hee; Woo, Jung-Hun

2017-04-01

We evaluate and intercompare ozone and aerosol simulations over Korea during the KORUS-AQ campaign, which was conducted in May-June 2016. Four global and regional air quality models participated in the campaign and provided daily air quality forecasts over Korea to guide aircraft flight paths for detecting air pollution events over Korean peninsula and its nearby oceans. We first evaluate the model performance by comparing simulated and observed hourly surface ozone and PM2.5 concentrations at ground sites in Korea and find that the models successfully capture intermittent air pollution events and reproduce the daily variation of ozone and PM2.5 concentrations. However, significant underestimates of peak ozone concentrations in the afternoon are also found in most models. Among chemical constituents of PM2.5, the models typically overestimate observed nitrate aerosol concentrations and underestimate organic aerosol concentrations, although the observed mass concentrations of PM2.5 are seemingly reproduced by the models. In particular, all models used the same anthropogenic emission inventory (KU-CREATE) for daily air quality forecast, but they show a considerable discrepancy for ozone and aerosols. Compared to individual model results, the ensemble mean of all models shows the best performance with correlation coefficients of 0.73 for ozone and 0.57 for PM2.5. We here investigate contributing factors to the discrepancy, which will serve as a guidance to improve the performance of the air quality forecast.
PM2.5 forecasting using SVR with PSOGSA algorithm based on CEEMD, GRNN and GCA considering meteorological factors

NASA Astrophysics Data System (ADS)

Zhu, Suling; Lian, Xiuyuan; Wei, Lin; Che, Jinxing; Shen, Xiping; Yang, Ling; Qiu, Xuanlin; Liu, Xiaoning; Gao, Wenlong; Ren, Xiaowei; Li, Juansheng

2018-06-01

The PM2.5 is the culprit of air pollution, and it leads to respiratory system disease when the fine particles are inhaled. Therefore, it is increasingly significant to develop an effective model for PM2.5 forecasting and warnings that informs people to foresee the air quality. People can reduce outdoor activities and take preventive measures if they know the air quality is bad ahead of time. In addition, reliable forecasting results can remind the relevant departments to control and reduce pollutants discharge. According to our knowledge, the current hybrid forecasting techniques of PM2.5 do not take the meteorological factors into consideration. Actually, meteorological factors affect the concentrations of air pollution, but it is unclear whether meteorological factors are helpful for improving the PM2.5 forecasting results or not. This paper proposes a hybrid model called CEEMD-PSOGSA-SVR-GRNN, based on complementary ensemble empirical mode decomposition (CEEMD), particle swarm optimization and gravitational search algorithm (PSOGSA), support vector regression (SVR), generalized regression neural network (GRNN) and grey correlation analysis (GCA), for the daily PM2.5 concentrations forecasting. The main steps of proposed model are described as follows: the original PM2.5 data decomposition with CEEMD, optimal SVR selection with PSOGCA, meteorological factors selection with GCA, residual revision by GRNN and forecasting results analysis. Three cities (Chongqing, Harbin and Jinan) in China with different characteristics of climate, terrain and pollution sources are selected to verify the effectiveness of proposed model, and CEEMD-PSOGSA-SVR*, EEMD-PSOGSA-SVR, PSOGSA-SVR, CEEMD-PSO-SVR, CEEMD-GSA-SVR, CEEMD-GWO-SVR are considered to be compared models. The experimental results show that the hybrid CEEMD-PSOGSA-SVR-GRNN model outperforms other six compared models. Therefore, the proposed CEEMD-PSOGSA-SVR-GRNN model can be used to develop air quality forecasting and warnings.
The Role of Model and Initial Condition Error in Numerical Weather Forecasting Investigated with an Observing System Simulation Experiment

NASA Technical Reports Server (NTRS)

Prive, Nikki C.; Errico, Ronald M.

2013-01-01

A series of experiments that explore the roles of model and initial condition error in numerical weather prediction are performed using an observing system simulation experiment (OSSE) framework developed at the National Aeronautics and Space Administration Global Modeling and Assimilation Office (NASA/GMAO). The use of an OSSE allows the analysis and forecast errors to be explicitly calculated, and different hypothetical observing networks can be tested with ease. In these experiments, both a full global OSSE framework and an 'identical twin' OSSE setup are utilized to compare the behavior of the data assimilation system and evolution of forecast skill with and without model error. The initial condition error is manipulated by varying the distribution and quality of the observing network and the magnitude of observation errors. The results show that model error has a strong impact on both the quality of the analysis field and the evolution of forecast skill, including both systematic and unsystematic model error components. With a realistic observing network, the analysis state retains a significant quantity of error due to systematic model error. If errors of the analysis state are minimized, model error acts to rapidly degrade forecast skill during the first 24-48 hours of forward integration. In the presence of model error, the impact of observation errors on forecast skill is small, but in the absence of model error, observation errors cause a substantial degradation of the skill of medium range forecasts.
Oregon Washington Coastal Ocean Forecast System: Real-time Modeling and Data Assimilation

NASA Astrophysics Data System (ADS)

Erofeeva, S.; Kurapov, A. L.; Pasmans, I.

2016-02-01

Three-day forecasts of ocean currents, temperature and salinity along the Oregon and Washington coasts are produced daily by a numerical ROMS-based ocean circulation model. NAM is used to derive atmospheric forcing for the model. Fresh water discharge from Columbia River, Fraser River, and small rivers in Puget Sound are included. The forecast is constrained by open boundary conditions derived from the global Navy HYCOM model and once in 3 days assimilation of recent data, including HF radar surface currents, sea surface temperature from the GOES satellite, and SSH from several satellite altimetry missions. 4-dimensional variational data assimilation is implemented in 3-day time windows using the tangent linear and adjoint codes developed at OSU. The system is semi-autonomous - all the data, including NAM and HYCOM fields are automatically updated, and daily operational forecast is automatically initiated. The pre-assimilation data quality control and post-assimilation forecast quality control require the operator's involvement. The daily forecast and 60 days of hindcast fields are available for public on opendap. As part of the system model validation plots to various satellites and SEAGLIDER are also automatically updated and available on the web (http://ingria.coas.oregonstate.edu/rtdavow/). Lessons learned in this pilot real-time coastal ocean forecasting project help develop and test metrics for forecast skill assessment for the West Coast Operational Forecast System (WCOFS), currently at testing and development phase at the National Oceanic and Atmospheric Administration (NOAA).
Vegetation Exposure to Ozone over the Continental United States: Assessment of Exposure Indices by the Eta-CMAQ Air Quality Forecast Model

EPA Science Inventory

This study presents the first evaluation of the performance of the Eta-CMAQ air quality forecast model to predict a variety of widely used seasonal mean and cumulative O₃ exposure indices associated with vegetation using the U.S. AIRNow O₃ observations.
California motor vehicle stock, travel and fuel forecast.

DOT National Transportation Integrated Search

2009-06-01

This is the twenty-fourth in a series of reports that forecasts Vehicle Miles of Travel (VMT) in California. This report is intended for transportation planning, travel forecasting, air quality modeling, and fuel tax revenue projection. : This report...
"Going the Extra Mile in Downscaling: Why Downscaling is not ...

EPA Pesticide Factsheets

This presentation provides an example of doing additional work for preprocessing global climate model data for use in regional climate modeling simulations with the Weather Research and Forecasting (WRF) model. In this presentation, results from 15 months of downscaling the Community Earth System Model (CESM) were shown, both using the out-of-the-box downscaling of CESM and also with a modification to setting the inland lake temperatures. The National Exposure Research Laboratory (NERL) Atmospheric Modeling and Analysis Division (AMAD) conducts research in support of EPA mission to protect human health and the environment. AMAD research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMAD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMAD are being used by EPA, NOAA, and the air pollution community in understanding and forecasting not only the magnitude of the air pollution problem, but also in developing emission control policies and regulations for air quality improvements.
Simple statistical bias correction techniques greatly improve moderate resolution air quality forecast at station level

NASA Astrophysics Data System (ADS)

Curci, Gabriele; Falasca, Serena

2017-04-01

Deterministic air quality forecast is routinely carried out at many local Environmental Agencies in Europe and throughout the world by means of eulerian chemistry-transport models. The skill of these models in predicting the ground-level concentrations of relevant pollutants (ozone, nitrogen dioxide, particulate matter) a few days ahead has greatly improved in recent years, but it is not yet always compliant with the required quality level for decision making (e.g. the European Commission has set a maximum uncertainty of 50% on daily values of relevant pollutants). Post-processing of deterministic model output is thus still regarded as a useful tool to make the forecast more reliable. In this work, we test several bias correction techniques applied to a long-term dataset of air quality forecasts over Europe and Italy. We used the WRF-CHIMERE modelling system, which provides operational experimental chemical weather forecast at CETEMPS (http://pumpkin.aquila.infn.it/forechem/), to simulate the years 2008-2012 at low resolution over Europe (0.5° x 0.5°) and moderate resolution over Italy (0.15° x 0.15°). We compared the simulated dataset with available observation from the European Environmental Agency database (AirBase) and characterized model skill and compliance with EU legislation using the Delta tool from FAIRMODE project (http://fairmode.jrc.ec.europa.eu/). The bias correction techniques adopted are, in order of complexity: (1) application of multiplicative factors calculated as the ratio of model-to-observed concentrations averaged over the previous days; (2) correction of the statistical distribution of model forecasts, in order to make it similar to that of the observations; (3) development and application of Model Output Statistics (MOS) regression equations. We illustrate differences and advantages/disadvantages of the three approaches. All the methods are relatively easy to implement for other modelling systems.
The use of quantile regression to forecast higher than expected respiratory deaths in a daily time series: a study of New York City data 1987-2000.

PubMed

Soyiri, Ireneous N; Reidpath, Daniel D

2013-01-01

Forecasting higher than expected numbers of health events provides potentially valuable insights in its own right, and may contribute to health services management and syndromic surveillance. This study investigates the use of quantile regression to predict higher than expected respiratory deaths. Data taken from 70,830 deaths occurring in New York were used. Temporal, weather and air quality measures were fitted using quantile regression at the 90th-percentile with half the data (in-sample). Four QR models were fitted: an unconditional model predicting the 90th-percentile of deaths (Model 1), a seasonal/temporal (Model 2), a seasonal, temporal plus lags of weather and air quality (Model 3), and a seasonal, temporal model with 7-day moving averages of weather and air quality. Models were cross-validated with the out of sample data. Performance was measured as proportionate reduction in weighted sum of absolute deviations by a conditional, over unconditional models; i.e., the coefficient of determination (R1). The coefficient of determination showed an improvement over the unconditional model between 0.16 and 0.19. The greatest improvement in predictive and forecasting accuracy of daily mortality was associated with the inclusion of seasonal and temporal predictors (Model 2). No gains were made in the predictive models with the addition of weather and air quality predictors (Models 3 and 4). However, forecasting models that included weather and air quality predictors performed slightly better than the seasonal and temporal model alone (i.e., Model 3 > Model 4 > Model 2) This study provided a new approach to predict higher than expected numbers of respiratory related-deaths. The approach, while promising, has limitations and should be treated at this stage as a proof of concept.
The Use of Quantile Regression to Forecast Higher Than Expected Respiratory Deaths in a Daily Time Series: A Study of New York City Data 1987-2000

PubMed Central

Soyiri, Ireneous N.; Reidpath, Daniel D.

2013-01-01

Forecasting higher than expected numbers of health events provides potentially valuable insights in its own right, and may contribute to health services management and syndromic surveillance. This study investigates the use of quantile regression to predict higher than expected respiratory deaths. Data taken from 70,830 deaths occurring in New York were used. Temporal, weather and air quality measures were fitted using quantile regression at the 90th-percentile with half the data (in-sample). Four QR models were fitted: an unconditional model predicting the 90th-percentile of deaths (Model 1), a seasonal / temporal (Model 2), a seasonal, temporal plus lags of weather and air quality (Model 3), and a seasonal, temporal model with 7-day moving averages of weather and air quality. Models were cross-validated with the out of sample data. Performance was measured as proportionate reduction in weighted sum of absolute deviations by a conditional, over unconditional models; i.e., the coefficient of determination (R1). The coefficient of determination showed an improvement over the unconditional model between 0.16 and 0.19. The greatest improvement in predictive and forecasting accuracy of daily mortality was associated with the inclusion of seasonal and temporal predictors (Model 2). No gains were made in the predictive models with the addition of weather and air quality predictors (Models 3 and 4). However, forecasting models that included weather and air quality predictors performed slightly better than the seasonal and temporal model alone (i.e., Model 3 > Model 4 > Model 2) This study provided a new approach to predict higher than expected numbers of respiratory related-deaths. The approach, while promising, has limitations and should be treated at this stage as a proof of concept. PMID:24147122
Developing air quality forecasts

NASA Astrophysics Data System (ADS)

Lee, Pius; Saylor, Rick; Meagher, James

2012-05-01

Third International Workshop on Air Quality Forecasting Research; Potomac, Maryland, 29 November to 1 December 2011 Elevated concentrations of both near-surface ozone (O3) and fine particulate matter smaller than 2.5 micrometers in diameter have been implicated in increased mortality and other human health impacts. In light of these known influences on human health, many governments around the world have instituted air quality forecasting systems to provide their citizens with advance warning of impending poor air quality so that they can take actions to limit exposure. In an effort to improve the performance of air quality forecasting systems and provide a forum for the exchange of the latest research in air quality modeling, the International Workshop on Air Quality Forecasting Research (IWAQFR) was established in 2009 and is cosponsored by the U.S. National Oceanic and Atmospheric Administration (NOAA), Environment Canada (EC), and the World Meteorological Organization (WMO). The steering committee for IWAQFR's establishment was composed of Véronique Bouchet, Mike Howe, and Craig Stoud (EC); Greg Carmichael (University of Iowa); Paula Davidson and Jim Meagher (NOAA); and Liisa Jalkanen (WMO). The most recent workshop took place in Maryland.
A FAST BAYESIAN METHOD FOR UPDATING AND FORECASTING HOURLY OZONE LEVELS

EPA Science Inventory

A Bayesian hierarchical space-time model is proposed by combining information from real-time ambient AIRNow air monitoring data, and output from a computer simulation model known as the Community Multi-scale Air Quality (Eta-CMAQ) forecast model. A model validation analysis shows...
The Betting Odds Rating System: Using soccer forecasts to forecast soccer.

PubMed

Wunderlich, Fabian; Memmert, Daniel

2018-01-01

Betting odds are frequently found to outperform mathematical models in sports related forecasting tasks, however the factors contributing to betting odds are not fully traceable and in contrast to rating-based forecasts no straightforward measure of team-specific quality is deducible from the betting odds. The present study investigates the approach of combining the methods of mathematical models and the information included in betting odds. A soccer forecasting model based on the well-known ELO rating system and taking advantage of betting odds as a source of information is presented. Data from almost 15.000 soccer matches (seasons 2007/2008 until 2016/2017) are used, including both domestic matches (English Premier League, German Bundesliga, Spanish Primera Division and Italian Serie A) and international matches (UEFA Champions League, UEFA Europe League). The novel betting odds based ELO model is shown to outperform classic ELO models, thus demonstrating that betting odds prior to a match contain more relevant information than the result of the match itself. It is shown how the novel model can help to gain valuable insights into the quality of soccer teams and its development over time, thus having a practical benefit in performance analysis. Moreover, it is argued that network based approaches might help in further improving rating and forecasting methods.
The Betting Odds Rating System: Using soccer forecasts to forecast soccer

PubMed Central

Memmert, Daniel

2018-01-01

Betting odds are frequently found to outperform mathematical models in sports related forecasting tasks, however the factors contributing to betting odds are not fully traceable and in contrast to rating-based forecasts no straightforward measure of team-specific quality is deducible from the betting odds. The present study investigates the approach of combining the methods of mathematical models and the information included in betting odds. A soccer forecasting model based on the well-known ELO rating system and taking advantage of betting odds as a source of information is presented. Data from almost 15.000 soccer matches (seasons 2007/2008 until 2016/2017) are used, including both domestic matches (English Premier League, German Bundesliga, Spanish Primera Division and Italian Serie A) and international matches (UEFA Champions League, UEFA Europe League). The novel betting odds based ELO model is shown to outperform classic ELO models, thus demonstrating that betting odds prior to a match contain more relevant information than the result of the match itself. It is shown how the novel model can help to gain valuable insights into the quality of soccer teams and its development over time, thus having a practical benefit in performance analysis. Moreover, it is argued that network based approaches might help in further improving rating and forecasting methods. PMID:29870554
Combination of synoptical-analogous and dynamical methods to increase skill score of monthly air temperature forecasts over Northern Eurasia

NASA Astrophysics Data System (ADS)

Khan, Valentina; Tscepelev, Valery; Vilfand, Roman; Kulikova, Irina; Kruglova, Ekaterina; Tischenko, Vladimir

2016-04-01

Long-range forecasts at monthly-seasonal time scale are in great demand of socio-economic sectors for exploiting climate-related risks and opportunities. At the same time, the quality of long-range forecasts is not fully responding to user application necessities. Different approaches, including combination of different prognostic models, are used in forecast centers to increase the prediction skill for specific regions and globally. In the present study, two forecasting methods are considered which are exploited in operational practice of Hydrometeorological Center of Russia. One of them is synoptical-analogous method of forecasting of surface air temperature at monthly scale. Another one is dynamical system based on the global semi-Lagrangian model SL-AV, developed in collaboration of Institute of Numerical Mathematics and Hydrometeorological Centre of Russia. The seasonal version of this model has been used to issue global and regional forecasts at monthly-seasonal time scales. This study presents results of the evaluation of surface air temperature forecasts generated with using above mentioned synoptical-statistical and dynamical models, and their combination to potentially increase skill score over Northern Eurasia. The test sample of operational forecasts is encompassing period from 2010 through 2015. The seasonal and interannual variability of skill scores of these methods has been discussed. It was noticed that the quality of all forecasts is highly dependent on the inertia of macro-circulation processes. The skill scores of forecasts are decreasing during significant alterations of synoptical fields for both dynamical and empirical schemes. Procedure of combination of forecasts from different methods, in some cases, has demonstrated its effectiveness. For this study the support has been provided by Grant of Russian Science Foundation (№14-37-00053).
Parametric decadal climate forecast recalibration (DeFoReSt 1.0)

NASA Astrophysics Data System (ADS)

Pasternack, Alexander; Bhend, Jonas; Liniger, Mark A.; Rust, Henning W.; Müller, Wolfgang A.; Ulbrich, Uwe

2018-01-01

Near-term climate predictions such as decadal climate forecasts are increasingly being used to guide adaptation measures. For near-term probabilistic predictions to be useful, systematic errors of the forecasting systems have to be corrected. While methods for the calibration of probabilistic forecasts are readily available, these have to be adapted to the specifics of decadal climate forecasts including the long time horizon of decadal climate forecasts, lead-time-dependent systematic errors (drift) and the errors in the representation of long-term changes and variability. These features are compounded by small ensemble sizes to describe forecast uncertainty and a relatively short period for which typically pairs of reforecasts and observations are available to estimate calibration parameters. We introduce the Decadal Climate Forecast Recalibration Strategy (DeFoReSt), a parametric approach to recalibrate decadal ensemble forecasts that takes the above specifics into account. DeFoReSt optimizes forecast quality as measured by the continuous ranked probability score (CRPS). Using a toy model to generate synthetic forecast observation pairs, we demonstrate the positive effect on forecast quality in situations with pronounced and limited predictability. Finally, we apply DeFoReSt to decadal surface temperature forecasts from the MiKlip prototype system and find consistent, and sometimes considerable, improvements in forecast quality compared with a simple calibration of the lead-time-dependent systematic errors.
Urban Air Quality Modelling with AURORA: Prague and Bratislava

NASA Astrophysics Data System (ADS)

Veldeman, N.; Viaene, P.; De Ridder, K.; Peelaerts, W.; Lauwaet, D.; Muhammad, N.; Blyth, L.

2012-04-01

The European Commission, in its strategy to protect the health of the European citizens, states that in order to assess the impact of air pollution on public health, information on long-term exposure to air pollution should be available. Currently, indicators of air quality are often being generated using measured pollutant concentrations. While air quality monitoring stations data provide accurate time series information at specific locations, air quality models have the advantage of being able to assess the spatial variability of air quality (for different resolutions) and predict air quality in the future based on different scenarios. When running such air quality models at a high spatial and temporal resolution, one can simulate the actual situation as closely as possible, allowing for a detailed assessment of the risk of exposure to citizens from different pollutants. AURORA (Air quality modelling in Urban Regions using an Optimal Resolution Approach), a prognostic 3-dimensional Eulerian chemistry-transport model, is designed to simulate urban- to regional-scale atmospheric pollutant concentration and exposure fields. The AURORA model also allows to calculate the impact of changes in land use (e.g. planting of trees) or of emission reduction scenario's on air quality. AURORA is currently being applied within the ESA atmospheric GMES service, PASODOBLE (http://www.myair-eu.org), that delivers information on air quality, greenhouse gases, stratospheric ozone, … At present there are two operational AURORA services within PASODOBLE. Within the "Air quality forecast service" VITO delivers daily air quality forecasts for Belgium at a resolution of 5 km and for the major Belgian cities: Brussels, Ghent, Antwerp, Liege and Charleroi. Furthermore forecast services are provided for Prague, Czech Republic and Bratislava, Slovakia, both at a resolution of 1 km. The "Urban/regional air quality assessment service" provides urban- and regional-scale maps (hourly resolution) for air pollution and human exposure statistics for an entire year. So far we concentrated on Brussels, Belgium and the Rotterdam harbour area, The Netherlands. In this contribution we focus on the operational forecast services. Reference Lefebvre W. et al. (2011) Validation of the MIMOSA-AURORA-IFDM model chain for policy support: Modeling concentrations of elemental carbon in Flanders, Atmospheric Environment 45, 6705-6713

Forecasting approaches to the Mekong River

NASA Astrophysics Data System (ADS)

Plate, E. J.

2009-04-01

Hydrologists distinguish between flood forecasts, which are concerned with events of the immediate future, and flood predictions, which are concerned with events that are possible, but whose date of occurrence is not determined. Although in principle both involve the determination of runoff from rainfall, the analytical approaches differ because of different objectives. The differences between the two approaches will be discussed, starting with an analysis of the forecasting process. The Mekong River in south-east Asia is used as an example. Prediction is defined as forecast for a hypothetical event, such as the 100-year flood, which is usually sufficiently specified by its magnitude and its probability of occurrence. It forms the basis for designing flood protection structures and risk management activities. The method for determining these quantities is hydrological modeling combined with extreme value statistics, today usually applied both to rainfall events and to observed river discharges. A rainfall-runoff model converts extreme rainfall events into extreme discharges, which at certain gage points along a river are calibrated against observed discharges. The quality of the model output is assessed against the mean value by means of the Nash-Sutcliffe quality criterion. The result of this procedure is a design hydrograph (or a family of design hydrographs) which are used as inputs into a hydraulic model, which converts the hydrograph into design water levels according to the hydraulic situation of the location. The accuracy of making a prediction in this sense is not particularly high: hydrologists know that the 100-year flood is a statistical quantity which can be estimated only within comparatively wide error bounds, and the hydraulics of a river site, in particular under conditions of heavy sediment loads has many uncertainties. Safety margins, such as additional freeboards are arranged to compensate for the uncertainty of the prediction. Forecasts, on the other hand, have as objective to obtain an accurate hydrograph of the near future. The method by means of which this is done is not as important as the accuracy of the forecast. A mathematical rainfall-runoff model is not necessarily a good forecast model. It has to be very carefully designed, and in many cases statistical models are found to give better results than mathematical models. Forecasters have the advantage of knowing the course of the hydrographs up to the point in time where forecasts have to be made. Therefore, models can be calibrated on line against the hydrograph of the immediate past. To assess the quality of a forecast, the quality criterion should not be based on the mean value, as does the Nash-Sutcliffe criterion, but should be based on the best forecast given the information up to the forecast time. Without any additional information, the best forecast when only the present day value is known is to assume a no-change scenario, i.e. to assume that the present value does not change in the immediate future. For the Mekong there exists a forecasting system which is based on a rainfall-runoff model operated by the Mekong River Commission. This model is found not to be adequate for forecasting for periods longer than one or two days ahead. Improvements are sought through two approaches: a strictly deterministic rainfall-runoff model, and a strictly statistical model based on regression with upstream stations. The two approaches are com-pared, and suggestions are made how to best combine the advantages of both approaches. This requires that due consideration is given to critical hydraulic conditions of the river at and in between the gauging stations. Critical situations occur in two ways: when the river overtops, in which case the rainfall-runoff model is incomplete unless overflow losses are considered, and at the confluence with tributaries. Of particular importance is the role of the large Tonle Sap Lake, which dampens the hydrograph downstream of Phnom Penh. The effect of these components of river hydraulics on forecasting accuracy will be assessed.
DEVELOPMENT OF NEAR-SHORE HYDRODYNAMIC MODELS FOR BEACH CLOSURE FORECASTING IN THE GREAT LAKES

EPA Science Inventory

Water quality managers and other planning and decision entities are increasingly calling for up-to-the-minute data on present water quality conditions or forecasts of these data that can be used to adjust or respond to quickly developing activities with environmental implications...
PREMAQ: A NEW PRE-PROCESSOR TO CMAQ FOR AIR-QUALITY FORECASTING

EPA Science Inventory

A new pre-processor to CMAQ (PREMAQ) has been developed as part of the national air-quality forecasting system. PREMAQ combines the functionality of MCIP and parts of SMOKE in a single real-time processor. PREMAQ was specifically designed to link NCEP's Eta model with CMAQ, and...
How Clean is your Local Air? Here's an app for that

NASA Astrophysics Data System (ADS)

Maskey, M.; Yang, E.; Christopher, S. A.; Keiser, K.; Nair, U. S.; Graves, S. J.

2011-12-01

Air quality is a vital element of our environment. Accurate and localized air quality information is critical for characterizing environmental impacts at the local and regional levels. Advances in location-aware handheld devices and air quality modeling have enabled a group of UAHuntsville scientists to develop a mobile app, LocalAQI, that informs users of current conditions and forecasts of up to twenty-four hours, of air quality indices. The air quality index is based on Community Multiscale Air Quality Modeling System (CMAQ). UAHuntsville scientists have used satellite remote sensing products as inputs to CMAQ, resulting in forecast guidance for particulate matter air quality. The CMAQ output is processed to compute a standardized air quality index. Currently, the air quality index is available for the eastern half of the United States. LocalAQI consists of two main views: air quality index view and map view. The air quality index view displays current air quality for the zip code of a location of interest. Air quality index value is translated into a color-coded advisory system. In addition, users are able to cycle through available hourly forecasts for a location. This location-aware app defaults to the current air quality of user's location. The map view displays color-coded air quality information for the eastern US with an ability to animate through the available forecasts. The app is developed using a cross-platform native application development tool, appcelerator; hence LocalAQI is available for iOS and Android-based phones and pads.
Assimilation of lightning data by nudging tropospheric water vapor and applications to numerical forecasts of convective events

NASA Astrophysics Data System (ADS)

Dixon, Kenneth

A lightning data assimilation technique is developed for use with observations from the World Wide Lightning Location Network (WWLLN). The technique nudges the water vapor mixing ratio toward saturation within 10 km of a lightning observation. This technique is applied to deterministic forecasts of convective events on 29 June 2012, 17 November 2013, and 19 April 2011 as well as an ensemble forecast of the 29 June 2012 event using the Weather Research and Forecasting (WRF) model. Lightning data are assimilated over the first 3 hours of the forecasts, and the subsequent impact on forecast quality is evaluated. The nudged deterministic simulations for all events produce composite reflectivity fields that are closer to observations. For the ensemble forecasts of the 29 June 2012 event, the improvement in forecast quality from lightning assimilation is more subtle than for the deterministic forecasts, suggesting that the lightning assimilation may improve ensemble convective forecasts where conventional observations (e.g., aircraft, surface, radiosonde, satellite) are less dense or unavailable.
Research and Application of an Air Quality Early Warning System Based on a Modified Least Squares Support Vector Machine and a Cloud Model.

PubMed

Wang, Jianzhou; Niu, Tong; Wang, Rui

2017-03-02

The worsening atmospheric pollution increases the necessity of air quality early warning systems (EWSs). Despite the fact that a massive amount of investigation about EWS in theory and practicality has been conducted by numerous researchers, studies concerning the quantification of uncertain information and comprehensive evaluation are still lacking, which impedes further development in the area. In this paper, firstly a comprehensive warning system is proposed, which consists of two vital indispensable modules, namely effective forecasting and scientific evaluation, respectively. For the forecasting module, a novel hybrid model combining the theory of data preprocessing and numerical optimization is first developed to implement effective forecasting for air pollutant concentration. Especially, in order to further enhance the accuracy and robustness of the warning system, interval forecasting is implemented to quantify the uncertainties generated by forecasts, which can provide significant risk signals by using point forecasting for decision-makers. For the evaluation module, a cloud model, based on probability and fuzzy set theory, is developed to perform comprehensive evaluations of air quality, which can realize the transformation between qualitative concept and quantitative data. To verify the effectiveness and efficiency of the warning system, extensive simulations based on air pollutants data from Dalian in China were effectively implemented, which illustrate that the warning system is not only remarkably high-performance, but also widely applicable.
Research and Application of an Air Quality Early Warning System Based on a Modified Least Squares Support Vector Machine and a Cloud Model

PubMed Central

Wang, Jianzhou; Niu, Tong; Wang, Rui

2017-01-01

The worsening atmospheric pollution increases the necessity of air quality early warning systems (EWSs). Despite the fact that a massive amount of investigation about EWS in theory and practicality has been conducted by numerous researchers, studies concerning the quantification of uncertain information and comprehensive evaluation are still lacking, which impedes further development in the area. In this paper, firstly a comprehensive warning system is proposed, which consists of two vital indispensable modules, namely effective forecasting and scientific evaluation, respectively. For the forecasting module, a novel hybrid model combining the theory of data preprocessing and numerical optimization is first developed to implement effective forecasting for air pollutant concentration. Especially, in order to further enhance the accuracy and robustness of the warning system, interval forecasting is implemented to quantify the uncertainties generated by forecasts, which can provide significant risk signals by using point forecasting for decision-makers. For the evaluation module, a cloud model, based on probability and fuzzy set theory, is developed to perform comprehensive evaluations of air quality, which can realize the transformation between qualitative concept and quantitative data. To verify the effectiveness and efficiency of the warning system, extensive simulations based on air pollutants data from Dalian in China were effectively implemented, which illustrate that the warning system is not only remarkably high-performance, but also widely applicable. PMID:28257122
Status of Air Quality in Central California and Needs for Further Study

NASA Astrophysics Data System (ADS)

Tanrikulu, S.; Beaver, S.; Soong, S.; Tran, C.; Jia, Y.; Matsuoka, J.; McNider, R. T.; Biazar, A. P.; Palazoglu, A.; Lee, P.; Wang, J.; Kang, D.; Aneja, V. P.

2012-12-01

Ozone and PM2.5 levels frequently exceed NAAQS in central California (CC). Additional emission reductions are needed to attain and maintain the standards there. Agencies are developing cost-effective emission control strategies along with complementary incentive programs to reduce emissions when exceedances are forecasted. These approaches require accurate modeling and forecasting capabilities. A variety of models have been rigorously applied (MM5, WRF, CMAQ, CAMx) over CC. Despite the vast amount of land-based measurements from special field programs and significant effort, models have historically exhibited marginal performance. Satellite data may improve model performance by: establishing IC/BC over outlying areas of the modeling domain having unknown conditions; enabling FDDA over the Pacific Ocean to characterize important marine inflows and pollutant outflows; and filling in the gaps of the land-based monitoring network. BAAQMD, in collaboration with the NASA AQAST, plans to conduct four studies that include satellite-based data in CC air quality analysis and modeling: The first project enhances and refines weather patterns, especially aloft, impacting summer ozone formation. Surface analyses were unable to characterize the strong attenuating effect of the complex terrain to steer marine winds impinging on the continent. The dense summer clouds and fog over the Pacific Ocean form spatial patterns that can be related to the downstream air flows through polluted areas. The goal of this project is to explore, characterize, and quantify these relationships using cloud cover data. Specifically, cloud agreement statistics will be developed using satellite data and model clouds. Model skin temperature predictions will be compared to both MODIS and GOES skin temperatures. The second project evaluates and improves the initial and simulated fields of meteorological models that provide inputs to air quality models. The study will attempt to determine whether a cloud dynamical adjustment developed by UAHuntsville can improve model performance for maritime stratus and whether a moisture adjustment scheme in the Pleim-Xiu boundary layer scheme can use satellite data in place of coarse surface air temperature measurements. The goal is to improve meteorological model performance that leads to improved air quality model performance. The third project evaluates and improves forecasting skills of the National Air Quality Forecasting Model in CC by using land-based routine measurements as well as satellite data. Local forecasts are mostly based on surface meteorological and air quality measurements and weather charts provided by NWS. The goal is to improve the average accuracy in forecasting exceedances, which is around 60%. The fourth project uses satellite data for monitoring trends in fine particulate matter (PM2.5) in the San Francisco Bay Area. It evaluates the effectiveness of a rule adopted in 2008 that restricts household wood burning on days forecasted to have high PM2.5 levels. The goal is to complement current analyses based on surface data covering the largest sub-regions and population centers. The overall goal is to use satellite data to overcome limitations of land-based measurements. The outcomes will be further conceptual understanding of pollutant formation, improved regulatory model performance, and better optimized forecasting programs.
Application Study of Comprehensive Forecasting Model Based on Entropy Weighting Method on Trend of PM2.5 Concentration in Guangzhou, China

PubMed Central

Liu, Dong-jun; Li, Li

2015-01-01

For the issue of haze-fog, PM2.5 is the main influence factor of haze-fog pollution in China. The trend of PM2.5 concentration was analyzed from a qualitative point of view based on mathematical models and simulation in this study. The comprehensive forecasting model (CFM) was developed based on the combination forecasting ideas. Autoregressive Integrated Moving Average Model (ARIMA), Artificial Neural Networks (ANNs) model and Exponential Smoothing Method (ESM) were used to predict the time series data of PM2.5 concentration. The results of the comprehensive forecasting model were obtained by combining the results of three methods based on the weights from the Entropy Weighting Method. The trend of PM2.5 concentration in Guangzhou China was quantitatively forecasted based on the comprehensive forecasting model. The results were compared with those of three single models, and PM2.5 concentration values in the next ten days were predicted. The comprehensive forecasting model balanced the deviation of each single prediction method, and had better applicability. It broadens a new prediction method for the air quality forecasting field. PMID:26110332
Application Study of Comprehensive Forecasting Model Based on Entropy Weighting Method on Trend of PM2.5 Concentration in Guangzhou, China.

PubMed

Liu, Dong-jun; Li, Li

2015-06-23

For the issue of haze-fog, PM2.5 is the main influence factor of haze-fog pollution in China. The trend of PM2.5 concentration was analyzed from a qualitative point of view based on mathematical models and simulation in this study. The comprehensive forecasting model (CFM) was developed based on the combination forecasting ideas. Autoregressive Integrated Moving Average Model (ARIMA), Artificial Neural Networks (ANNs) model and Exponential Smoothing Method (ESM) were used to predict the time series data of PM2.5 concentration. The results of the comprehensive forecasting model were obtained by combining the results of three methods based on the weights from the Entropy Weighting Method. The trend of PM2.5 concentration in Guangzhou China was quantitatively forecasted based on the comprehensive forecasting model. The results were compared with those of three single models, and PM2.5 concentration values in the next ten days were predicted. The comprehensive forecasting model balanced the deviation of each single prediction method, and had better applicability. It broadens a new prediction method for the air quality forecasting field.
Linking Air Quality and Human Health Effects Models: An Application to the Los Angeles Air Basin

PubMed Central

Stewart, Devoun R; Saunders, Emily; Perea, Roberto A; Fitzgerald, Rosa; Campbell, David E; Stockwell, William R

2017-01-01

Proposed emission control strategies for reducing ozone and particulate matter are evaluated better when air quality and health effects models are used together. The Community Multiscale Air Quality (CMAQ) model is the US Environmental Protection Agency’s model for determining public policy and forecasting air quality. CMAQ was used to forecast air quality changes due to several emission control strategies that could be implemented between 2008 and 2030 for the South Coast Air Basin that includes Los Angeles. The Environmental Benefits Mapping and Analysis Program—Community Edition (BenMAP-CE) was used to estimate health and economic impacts of the different emission control strategies based on CMAQ simulations. BenMAP-CE is a computer program based on epidemiologic studies that link human health and air quality. This modeling approach is better for determining optimum public policy than approaches that only examine concentration changes. PMID:29162976
Linking Air Quality and Human Health Effects Models: An Application to the Los Angeles Air Basin.

PubMed

Stewart, Devoun R; Saunders, Emily; Perea, Roberto A; Fitzgerald, Rosa; Campbell, David E; Stockwell, William R

2017-01-01

Proposed emission control strategies for reducing ozone and particulate matter are evaluated better when air quality and health effects models are used together. The Community Multiscale Air Quality (CMAQ) model is the US Environmental Protection Agency's model for determining public policy and forecasting air quality. CMAQ was used to forecast air quality changes due to several emission control strategies that could be implemented between 2008 and 2030 for the South Coast Air Basin that includes Los Angeles. The Environmental Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE) was used to estimate health and economic impacts of the different emission control strategies based on CMAQ simulations. BenMAP-CE is a computer program based on epidemiologic studies that link human health and air quality. This modeling approach is better for determining optimum public policy than approaches that only examine concentration changes.
A state of the art regarding urban air quality prediction models

NASA Astrophysics Data System (ADS)

Croitoru, Cristiana; Nastase, Ilinca

2018-02-01

Urban pollution represents an increasing risk to residents of urban regions, particularly in large, over-industrialized cities knowing that the traffic is responsible for more than 25% of air gaseous pollutants and dust particles. Air quality modelling plays an important role in addressing air pollution control and management approaches by providing guidelines for better and more efficient air quality forecasting, along with smart monitoring sensor networks. The advances in technology regarding simulations, forecasting and monitoring are part of the new smart cities which offers a healthy environment for their occupants.
Advances in air quality prediction with the use of integrated systems

NASA Astrophysics Data System (ADS)

Dragani, R.; Benedetti, A.; Engelen, R. J.; Peuch, V. H.

2017-12-01

Recent years have seen the rise of global operational atmospheric composition forecasting systems for several applications including climate monitoring, provision of boundary conditions for regional air quality forecasting, energy sector applications, to mention a few. Typically, global forecasts are provided in the medium-range up to five days ahead and are initialized with an analysis based on satellite data. In this work we present the latest advances in data assimilation using the ECMWF's 4D-Var system extended to atmospheric composition which is currently operational under the Copernicus Atmosphere Monitoring Service of the European Commission. The service is based on acquisition of all relevant data available in near-real-time, the processing of these datasets in the assimilation and the subsequent dissemination of global forecasts at ECMWF. The global forecasts are used by the CAMS regional models as boundary conditions for the European forecasts based on a multi-model ensemble. The global forecasts are also used to provide boundary conditions for other parts of the world (e.g., China) and are freely available to all interested entities. Some of the regional models also perform assimilation of satellite and ground-based observations. All products are assessed, validated and made publicly available on https://atmosphere.copernicus.eu/.
Quality Assessment of the Cobel-Isba Numerical Forecast System of Fog and Low Clouds

NASA Astrophysics Data System (ADS)

Bergot, Thierry

2007-06-01

Short-term forecasting of fog is a difficult issue which can have a large societal impact. Fog appears in the surface boundary layer and is driven by the interactions between land surface and the lower layers of the atmosphere. These interactions are still not well parameterized in current operational NWP models, and a new methodology based on local observations, an adaptive assimilation scheme and a local numerical model is tested. The proposed numerical forecast method of foggy conditions has been run during three years at Paris-CdG international airport. This test over a long-time period allows an in-depth evaluation of the forecast quality. This study demonstrates that detailed 1-D models, including detailed physical parameterizations and high vertical resolution, can reasonably represent the major features of the life cycle of fog (onset, development and dissipation) up to +6 h. The error on the forecast onset and burn-off time is typically 1 h. The major weakness of the methodology is related to the evolution of low clouds (stratus lowering). Even if the occurrence of fog is well forecasted, the value of the horizontal visibility is only crudely forecasted. Improvements in the microphysical parameterization and in the translation algorithm converting NWP prognostic variables into a corresponding horizontal visibility seems necessary to accurately forecast the value of the visibility.
A novel hybrid forecasting model for PM₁₀ and SO₂ daily concentrations.

PubMed

Wang, Ping; Liu, Yong; Qin, Zuodong; Zhang, Guisheng

2015-02-01

Air-quality forecasting in urban areas is difficult because of the uncertainties in describing both the emission and meteorological fields. The use of incomplete information in the training phase restricts practical air-quality forecasting. In this paper, we propose a hybrid artificial neural network and a hybrid support vector machine, which effectively enhance the forecasting accuracy of an artificial neural network (ANN) and support vector machine (SVM) by revising the error term of the traditional methods. The hybrid methodology can be described in two stages. First, we applied the ANN or SVM forecasting system with historical data and exogenous parameters, such as meteorological variables. Then, the forecasting target was revised by the Taylor expansion forecasting model using the residual information of the error term in the previous stage. The innovation involved in this approach is that it sufficiently and validly utilizes the useful residual information on an incomplete input variable condition. The proposed method was evaluated by experiments using a 2-year dataset of daily PM₁₀ (particles with a diameter of 10 μm or less) concentrations and SO₂ (sulfur dioxide) concentrations from four air pollution monitoring stations located in Taiyuan, China. The theoretical analysis and experimental results demonstrated that the forecasting accuracy of the proposed model is very promising. Copyright © 2014 Elsevier B.V. All rights reserved.
The FireWork air quality forecast system with near-real-time biomass burning emissions: Recent developments and evaluation of performance for the 2015 North American wildfire season.

PubMed

Pavlovic, Radenko; Chen, Jack; Anderson, Kerry; Moran, Michael D; Beaulieu, Paul-André; Davignon, Didier; Cousineau, Sophie

2016-09-01

Environment and Climate Change Canada's FireWork air quality (AQ) forecast system for North America with near-real-time biomass burning emissions has been running experimentally during the Canadian wildfire season since 2013. The system runs twice per day with model initializations at 00 UTC and 12 UTC, and produces numerical AQ forecast guidance with 48-hr lead time. In this work we describe the FireWork system, which incorporates near-real-time biomass burning emissions based on the Canadian Wildland Fire Information System (CWFIS) as an input to the operational Regional Air Quality Deterministic Prediction System (RAQDPS). To demonstrate the capability of the system we analyzed two forecast periods in 2015 (June 2-July 15, and August 15-31) when fire activity was high, and observed fire-smoke-impacted areas in western Canada and the western United States. Modeled PM2.5 surface concentrations were compared with surface measurements and benchmarked with results from the operational RAQDPS, which did not consider near-real-time biomass burning emissions. Model performance statistics showed that FireWork outperformed RAQDPS with improvements in forecast hourly PM2.5 across the region; the results were especially significant for stations near the path of fire plume trajectories. Although the hourly PM2.5 concentrations predicted by FireWork still displayed bias for areas with active fires for these two periods (mean bias [MB] of -7.3 µg m(-3) and 3.1 µg m(-3)), it showed better forecast skill than the RAQDPS (MB of -11.7 µg m(-3) and -5.8 µg m(-3)) and demonstrated a greater ability to capture temporal variability of episodic PM2.5 events (correlation coefficient values of 0.50 and 0.69 for FireWork compared to 0.03 and 0.11 for RAQDPS). A categorical forecast comparison based on an hourly PM2.5 threshold of 30 µg m(-3) also showed improved scores for probability of detection (POD), critical success index (CSI), and false alarm rate (FAR). Smoke from wildfires can have a large impact on regional air quality (AQ) and can expose populations to elevated pollution levels. Environment and Climate Change Canada has been producing operational air quality forecasts for all of Canada since 2009 and is now working to include near-real-time wildfire emissions (NRTWE) in its operational AQ forecasting system. An experimental forecast system named FireWork, which includes NRTWE, has been undergoing testing and evaluation since 2013. A performance analysis of FireWork forecasts for the 2015 wildfire season shows that FireWork provides significant improvements to surface PM2.5 forecasts and valuable guidance to regional forecasters and first responders.
Study on the influence of ground and satellite observations on the numerical air-quality for PM10 over Romanian territory

NASA Astrophysics Data System (ADS)

Dumitrache, Rodica Claudia; Iriza, Amalia; Maco, Bogdan Alexandru; Barbu, Cosmin Danut; Hirtl, Marcus; Mantovani, Simone; Nicola, Oana; Irimescu, Anisoara; Craciunescu, Vasile; Ristea, Alina; Diamandi, Andrei

2016-10-01

The numerical forecast of particulate matter concentrations in general, and PM10 in particular is a theme of high socio-economic relevance. The aim of this study was to investigate the impact of ground and satellite data assimilation of PM10 observations into the Weather Research and Forecasting model coupled with Chemistry (WRF-CHEM) numerical air quality model for Romanian territory. This is the first initiative of the kind for this domain of interest. Assimilation of satellite information - e.g. AOT's in air quality models is of interest due to the vast spatial coverage of the observations. Support Vector Regression (SVR) techniques are used to estimate the PM content from heterogeneous data sources, including EO products (Aerosol Optical Thickness), ground measurements and numerical model data (temperature, humidity, wind, etc.). In this study we describe the modeling framework employed and present the evaluation of the impact from the data assimilation of PM10 observations on the forecast of the WRF-CHEM model. Integrations of the WRF-CHEM model in data assimilation enabled/disabled configurations allowed the evaluation of satellite and ground data assimilation impact on the PM10 forecast performance for the Romanian territory. The model integration and evaluation were performed for two months, one in winter conditions (January 2013) and one in summer conditions (June 2013).
Evaluation of streamflow forecast for the National Water Model of U.S. National Weather Service

NASA Astrophysics Data System (ADS)

Rafieeinasab, A.; McCreight, J. L.; Dugger, A. L.; Gochis, D.; Karsten, L. R.; Zhang, Y.; Cosgrove, B.; Liu, Y.

2016-12-01

The National Water Model (NWM), an implementation of the community WRF-Hydro modeling system, is an operational hydrologic forecasting model for the contiguous United States. The model forecasts distributed hydrologic states and fluxes, including soil moisture, snowpack, ET, and ponded water. In particular, the NWM provides streamflow forecasts at more than 2.7 million river reaches for three forecast ranges: short (15 hr), medium (10 days), and long (30 days). In this study, we verify short and medium range streamflow forecasts in the context of the verification of their respective quantitative precipitation forecasts/forcing (QPF), the High Resolution Rapid Refresh (HRRR) and the Global Forecast System (GFS). The streamflow evaluation is performed for summer of 2016 at more than 6,000 USGS gauges. Both individual forecasts and forecast lead times are examined. Selected case studies of extreme events aim to provide insight into the quality of the NWM streamflow forecasts. A goal of this comparison is to address how much streamflow bias originates from precipitation forcing bias. To this end, precipitation verification is performed over the contributing areas above (and between assimilated) USGS gauge locations. Precipitation verification is based on the aggregated, blended StageIV/StageII data as the "reference truth". We summarize the skill of the streamflow forecasts, their skill relative to the QPF, and make recommendations for improving NWM forecast skill.
A YEAR-LONG MM5 EVALUATION USING A MODEL EVALUATION TOOLKIT

EPA Science Inventory

Air quality modeling has expanded in both sophistication and application over the past decade. Meteorological and air quality modeling tools are being used for research, forecasting, and regulatory related emission control strategies. Results from air quality simulations have far...

Operational hydrological forecasting in Bavaria. Part II: Ensemble forecasting

NASA Astrophysics Data System (ADS)

Ehret, U.; Vogelbacher, A.; Moritz, K.; Laurent, S.; Meyer, I.; Haag, I.

2009-04-01

In part I of this study, the operational flood forecasting system in Bavaria and an approach to identify and quantify forecast uncertainty was introduced. The approach is split into the calculation of an empirical 'overall error' from archived forecasts and the calculation of an empirical 'model error' based on hydrometeorological forecast tests, where rainfall observations were used instead of forecasts. The 'model error' can especially in upstream catchments where forecast uncertainty is strongly dependent on the current predictability of the atrmosphere be superimposed on the spread of a hydrometeorological ensemble forecast. In Bavaria, two meteorological ensemble prediction systems are currently tested for operational use: the 16-member COSMO-LEPS forecast and a poor man's ensemble composed of DWD GME, DWD Cosmo-EU, NCEP GFS, Aladin-Austria, MeteoSwiss Cosmo-7. The determination of the overall forecast uncertainty is dependent on the catchment characteristics: 1. Upstream catchment with high influence of weather forecast a) A hydrological ensemble forecast is calculated using each of the meteorological forecast members as forcing. b) Corresponding to the characteristics of the meteorological ensemble forecast, each resulting forecast hydrograph can be regarded as equally likely. c) The 'model error' distribution, with parameters dependent on hydrological case and lead time, is added to each forecast timestep of each ensemble member d) For each forecast timestep, the overall (i.e. over all 'model error' distribution of each ensemble member) error distribution is calculated e) From this distribution, the uncertainty range on a desired level (here: the 10% and 90% percentile) is extracted and drawn as forecast envelope. f) As the mean or median of an ensemble forecast does not necessarily exhibit meteorologically sound temporal evolution, a single hydrological forecast termed 'lead forecast' is chosen and shown in addition to the uncertainty bounds. This can be either an intermediate forecast between the extremes of the ensemble spread or a manually selected forecast based on a meteorologists advice. 2. Downstream catchments with low influence of weather forecast In downstream catchments with strong human impact on discharge (e.g. by reservoir operation) and large influence of upstream gauge observation quality on forecast quality, the 'overall error' may in most cases be larger than the combination of the 'model error' and an ensemble spread. Therefore, the overall forecast uncertainty bounds are calculated differently: a) A hydrological ensemble forecast is calculated using each of the meteorological forecast members as forcing. Here, additionally the corresponding inflow hydrograph from all upstream catchments must be used. b) As for an upstream catchment, the uncertainty range is determined by combination of 'model error' and the ensemble member forecasts c) In addition, the 'overall error' is superimposed on the 'lead forecast'. For reasons of consistency, the lead forecast must be based on the same meteorological forecast in the downstream and all upstream catchments. d) From the resulting two uncertainty ranges (one from the ensemble forecast and 'model error', one from the 'lead forecast' and 'overall error'), the envelope is taken as the most prudent uncertainty range. In sum, the uncertainty associated with each forecast run is calculated and communicated to the public in the form of 10% and 90% percentiles. As in part I of this study, the methodology as well as the useful- or uselessness of the resulting uncertainty ranges will be presented and discussed by typical examples.
INTEGRATION OF SATELLITE, MODELED, AND GROUND BASED AEROSOL DATA FOR USE IN AIR QUALITY AND PUBLIC HEALTH APPLICATIONS ( AGU-BALTIMORE )

EPA Science Inventory

Within the next several years NOAA and EPA will begin to issue PM_2.5 air quality forecasts over the entire domain of the eastern United States, eventually extending to national coverage. These forecasts will provide continuous estimated values of particulate matter on ...
Hybrid Forecasting of Daily River Discharges Considering Autoregressive Heteroscedasticity

NASA Astrophysics Data System (ADS)

Szolgayová, Elena Peksová; Danačová, Michaela; Komorniková, Magda; Szolgay, Ján

2017-06-01

It is widely acknowledged that in the hydrological and meteorological communities, there is a continuing need to improve the quality of quantitative rainfall and river flow forecasts. A hybrid (combined deterministic-stochastic) modelling approach is proposed here that combines the advantages offered by modelling the system dynamics with a deterministic model and a deterministic forecasting error series with a data-driven model in parallel. Since the processes to be modelled are generally nonlinear and the model error series may exhibit nonstationarity and heteroscedasticity, GARCH-type nonlinear time series models are considered here. The fitting, forecasting and simulation performance of such models have to be explored on a case-by-case basis. The goal of this paper is to test and develop an appropriate methodology for model fitting and forecasting applicable for daily river discharge forecast error data from the GARCH family of time series models. We concentrated on verifying whether the use of a GARCH-type model is suitable for modelling and forecasting a hydrological model error time series on the Hron and Morava Rivers in Slovakia. For this purpose we verified the presence of heteroscedasticity in the simulation error series of the KLN multilinear flow routing model; then we fitted the GARCH-type models to the data and compared their fit with that of an ARMA - type model. We produced one-stepahead forecasts from the fitted models and again provided comparisons of the model's performance.
ASSESSMENT OF AN ENSEMBLE OF SEVEN REAL-TIME OZONE FORECASTS OVER EASTERN NORTH AMERICA DURING THE SUMMER OF 2004

EPA Science Inventory

The real-time forecasts of ozone (O₃) from seven air quality forecast models (AQFMs) are statistically evaluated against observations collected during July and August of 2004 (53 days) through the Aerometric Information Retrieval Now (AIRNow) network at roughly 340 mon...
Comparisons of Three-Dimensional Variational Data Assimilation and Model Output Statistics in Improving Atmospheric Chemistry Forecasts

NASA Astrophysics Data System (ADS)

Ma, Chaoqun; Wang, Tijian; Zang, Zengliang; Li, Zhijin

2018-07-01

Atmospheric chemistry models usually perform badly in forecasting wintertime air pollution because of their uncertainties. Generally, such uncertainties can be decreased effectively by techniques such as data assimilation (DA) and model output statistics (MOS). However, the relative importance and combined effects of the two techniques have not been clarified. Here, a one-month air quality forecast with the Weather Research and Forecasting-Chemistry (WRF-Chem) model was carried out in a virtually operational setup focusing on Hebei Province, China. Meanwhile, three-dimensional variational (3DVar) DA and MOS based on one-dimensional Kalman filtering were implemented separately and simultaneously to investigate their performance in improving the model forecast. Comparison with observations shows that the chemistry forecast with MOS outperforms that with 3DVar DA, which could be seen in all the species tested over the whole 72 forecast hours. Combined use of both techniques does not guarantee a better forecast than MOS only, with the improvements and degradations being small and appearing rather randomly. Results indicate that the implementation of MOS is more suitable than 3DVar DA in improving the operational forecasting ability of WRF-Chem.
Multi-pollutant surface objective analyses and mapping of air quality health index over North America.

PubMed

Robichaud, Alain; Ménard, Richard; Zaïtseva, Yulia; Anselmo, David

2016-01-01

Air quality, like weather, can affect everyone, but responses differ depending on the sensitivity and health condition of a given individual. To help protect exposed populations, many countries have put in place real-time air quality nowcasting and forecasting capabilities. We present in this paper an optimal combination of air quality measurements and model outputs and show that it leads to significant improvements in the spatial representativeness of air quality. The product is referred to as multi-pollutant surface objective analyses (MPSOAs). Moreover, based on MPSOA, a geographical mapping of the Canadian Air Quality Health Index (AQHI) is also presented which provides users (policy makers, public, air quality forecasters, and epidemiologists) with a more accurate picture of the health risk anytime and anywhere in Canada and the USA. Since pollutants can also behave as passive atmospheric tracers, they provide information about transport and dispersion and, hence, reveal synoptic and regional meteorological phenomena. MPSOA could also be used to build air pollution climatology, compute local and national trends in air quality, and detect systematic biases in numerical air quality (AQ) models. Finally, initializing AQ models at regular time intervals with MPSOA can produce more accurate air quality forecasts. It is for these reasons that the Canadian Meteorological Centre (CMC) in collaboration with the Air Quality Research Division (AQRD) of Environment Canada has recently implemented MPSOA in their daily operations.
Evaluation and Quality Control for the Copernicus Seasonal Forecast Systems

NASA Astrophysics Data System (ADS)

Manubens, N.; Hunter, A.; Bedia, J.; Bretonnière, P. A.; Bhend, J.; Doblas-Reyes, F. J.

2017-12-01

The EU funded Copernicus Climate Change Service (C3S) will provide authoritative information about past, current and future climate for a wide range of users, from climate scientists to stakeholders from a wide range of sectors including insurance, energy or transport. It has been recognized that providing information about the products' quality and provenance is paramount to establish trust in the service and allow users to make best use of the available information. This presentation outlines the work being conducted within the Quality Assurance for Multi-model Seasonal Forecast Products project (QA4Seas). The aim of QA4Seas is to develop a strategy for the evaluation and quality control (EQC) of the multi-model seasonal forecasts provided by C3S. First, we present the set of guidelines the data providers must comply with, ensuring the data is fully traceable and harmonized across data sets. Second, we discuss the ongoing work on defining a provenance and metadata model that is able to encode such information, and that can be extended to describe the steps followed to obtain the final verification products such as maps and time series of forecast quality measures. The metadata model is based on the Resource Description Framework W3C standard, being thus extensible and reusable. It benefits from widely adopted vocabularies to describe data provenance and workflows, as well as from expert consensus and community-support for the development of the verification and downscaling specific ontologies. Third, we describe the open source software being developed to generate fully reproducible and certifiable seasonal forecast products, which also attaches provenance and metadata information to the verification measures and enables the user to visually inspect the quality of the C3S products. QA4Seas is seeking collaboration with similar initiatives, as well as extending the discussion to interested parties outside the C3S community to share experiences and establish global common guidelines or best practices regarding data provenance.
National Centers for Environmental Prediction

Science.gov Websites

Reference List Table of Contents NCEP OPERATIONAL MODEL FORECAST GRAPHICS PARALLEL/EXPERIMENTAL MODEL Developmental Air Quality Forecasts and Verification Back to Table of Contents 2. PARALLEL/EXPERIMENTAL GRAPHICS VERIFICATION (GRID VS.OBS) WEB PAGE (NCEP EXPERIMENTAL PAGE, INTERNAL USE ONLY) Interactive web page tool for
On The Usage Of Fire Smoke Emissions In An Air Quality Forecasting System To Reduce Particular Matter Forecasting Error

NASA Astrophysics Data System (ADS)

Huang, H. C.; Pan, L.; McQueen, J.; Lee, P.; ONeill, S. M.; Ruminski, M.; Shafran, P.; DiMego, G.; Huang, J.; Stajner, I.; Upadhayay, S.; Larkin, N. K.

2016-12-01

Wildfires contribute to air quality problems not only towards primary emissions of particular matters (PM) but also emitted ozone precursor gases that can lead to elevated ozone concentration. Wildfires are unpredictable and can be ignited by natural causes such as lightning or accidently by human negligent behavior such as live cigarette. Although wildfire impacts on the air quality can be studied by collecting fire information after events, it is extremely difficult to predict future occurrence and behavior of wildfires for real-time air quality forecasts. Because of the time constraints of operational air quality forecasting, assumption of future day's fire behavior often have to be made based on observed fire information in the past. The United States (U.S.) NOAA/NWS built the National Air Quality Forecast Capability (NAQFC) based on the U.S. EPA CMAQ to provide air quality forecast guidance (prediction) publicly. State and local forecasters use the forecast guidance to issue air quality alerts in their area. The NAQFC fine particulates (PM2.5) prediction includes emissions from anthropogenic and biogenic sources, as well as natural sources such as dust storms and fires. The fire emission input to the NAQFC is derived from the NOAA NESDIS HMS fire and smoke detection product and the emission module of the US Forest Service BlueSky Smoke Modeling Framework. This study focuses on the error estimation of NAQFC PM2.5 predictions resulting from fire emissions. The comparisons between the NAQFC modeled PM2.5 and the EPA AirNow surface observation show that present operational NAQFC fire emissions assumption can lead to a huge error in PM2.5 prediction as fire emissions are sometimes placed at wrong location and time. This PM2.5 prediction error can be propagated from the fire source in the Northwest U.S. to downstream areas as far as the Southeast U.S. From this study, a new procedure has been identified to minimize the aforementioned error. An additional 24 hours reanalysis-run of NAQFC using same-day observed fire emission are being tested. Preliminary results have shown that this procedure greatly improves the PM2.5 predictions at both nearby and downstream areas from fire sources. The 24 hours reanalysis-run is critical and necessary especially during extreme fire events to provide better PM2.5 predictions.
Impact of inherent meteorology uncertainty on air quality ...

EPA Pesticide Factsheets

It is well established that there are a number of different classifications and sources of uncertainties in environmental modeling systems. Air quality models rely on two key inputs, namely, meteorology and emissions. When using air quality models for decision making, it is important to understand how uncertainties in these inputs affect the simulated concentrations. Ensembles are one method to explore how uncertainty in meteorology affects air pollution concentrations. Most studies explore this uncertainty by running different meteorological models or the same model with different physics options and in some cases combinations of different meteorological and air quality models. While these have been shown to be useful techniques in some cases, we present a technique that leverages the initial condition perturbations of a weather forecast ensemble, namely, the Short-Range Ensemble Forecast system to drive the four-dimensional data assimilation in the Weather Research and Forecasting (WRF)-Community Multiscale Air Quality (CMAQ) model with a key focus being the response of ozone chemistry and transport. Results confirm that a sizable spread in WRF solutions, including common weather variables of temperature, wind, boundary layer depth, clouds, and radiation, can cause a relatively large range of ozone-mixing ratios. Pollutant transport can be altered by hundreds of kilometers over several days. Ozone-mixing ratios of the ensemble can vary as much as 10–20 ppb
Blending forest fire smoke forecasts with observed data can improve their utility for public health applications

NASA Astrophysics Data System (ADS)

Yuchi, Weiran; Yao, Jiayun; McLean, Kathleen E.; Stull, Roland; Pavlovic, Radenko; Davignon, Didier; Moran, Michael D.; Henderson, Sarah B.

2016-11-01

Fine particulate matter (PM2.5) generated by forest fires has been associated with a wide range of adverse health outcomes, including exacerbation of respiratory diseases and increased risk of mortality. Due to the unpredictable nature of forest fires, it is challenging for public health authorities to reliably evaluate the magnitude and duration of potential exposures before they occur. Smoke forecasting tools are a promising development from the public health perspective, but their widespread adoption is limited by their inherent uncertainties. Observed measurements from air quality monitoring networks and remote sensing platforms are more reliable, but they are inherently retrospective. It would be ideal to reduce the uncertainty in smoke forecasts by integrating any available observations. This study takes spatially resolved PM2.5 estimates from an empirical model that integrates air quality measurements with satellite data, and averages them with PM2.5 predictions from two smoke forecasting systems. Two different indicators of population respiratory health are then used to evaluate whether the blending improved the utility of the smoke forecasts. Among a total of six models, including two single forecasts and four blended forecasts, the blended estimates always performed better than the forecast values alone. Integrating measured observations into smoke forecasts could improve public health preparedness for smoke events, which are becoming more frequent and intense as the climate changes.
OPERATIONAL AND DIAGNOSTIC EVALUATION OF THE OZONE FORECASTS BY THE ETA-CMAQ MODEL SUITE DURING THE 2002 NEW ENGLAND AIR QUALITY STUDY (NEAQS)

EPA Science Inventory

Ozone (O₃), a secondary pollutant, is created in part by emissions from anthropogenic and biogenic sources. It is necessary for local air quality agencies to accurately forecast ozone concentrations to warn the public of unhealthy air and to encourage people to volunta...
Diagnostic Analysis of Ozone Concentrations Simulated by Two Regional-Scale Air Quality Models

EPA Science Inventory

Since the Community Multiscale Air Quality modeling system (CMAQ) and the Weather Research and Forecasting with Chemistry model (WRF/Chem) use different approaches to simulate the interaction of meteorology and chemistry, this study compares the CMAQ and WRF/Chem air quality simu...
Development of On-line Wildfire Emissions for the Operational Canadian Air Quality Forecast System

NASA Astrophysics Data System (ADS)

Pavlovic, R.; Menard, S.; Chen, J.; Anselmo, D.; Paul-Andre, B.; Gravel, S.; Moran, M. D.; Davignon, D.

2013-12-01

An emissions processing system has been developed to incorporate near-real-time emissions from wildfires and large prescribed burns into Environment Canada's real-time GEM-MACH air quality (AQ) forecast system. Since the GEM-MACH forecast domain covers Canada and most of the USA, including Alaska, fire location information is needed for both of these large countries. Near-real-time satellite data are obtained and processed separately for the two countries for organizational reasons. Fire location and fuel consumption data for Canada are provided by the Canadian Forest Service's Canadian Wild Fire Information System (CWFIS) while fire location and emissions data for the U.S. are provided by the SMARTFIRE (Satellite Mapping Automated Reanalysis Tool for Fire Incident Reconciliation) system via the on-line BlueSky Gateway. During AQ model runs, emissions from individual fire sources are injected into elevated model layers based on plume-rise calculations and then transport and chemistry calculations are performed. This 'on the fly' approach to the insertion of emissions provides greater flexibility since on-line meteorology is used and reduces computational overhead in emission pre-processing. An experimental wildfire version of GEM-MACH was run in real-time mode for the summers of 2012 and 2013. 48-hour forecasts were generated every 12 hours (at 00 and 12 UTC). Noticeable improvements in the AQ forecasts for PM2.5 were seen in numerous regions where fire activity was high. Case studies evaluating model performance for specific regions, computed objective scores, and subjective evaluations by AQ forecasters will be included in this presentation. Using the lessons learned from the last two summers, Environment Canada will continue to work towards the goal of incorporating near-real-time intermittent wildfire emissions within the operational air quality forecast system.
Urban air quality forecasting based on multi-dimensional collaborative Support Vector Regression (SVR): A case study of Beijing-Tianjin-Shijiazhuang

PubMed Central

Liu, Bing-Chun; Binaykia, Arihant; Chang, Pei-Chann; Tiwari, Manoj Kumar; Tsao, Cheng-Chin

2017-01-01

Today, China is facing a very serious issue of Air Pollution due to its dreadful impact on the human health as well as the environment. The urban cities in China are the most affected due to their rapid industrial and economic growth. Therefore, it is of extreme importance to come up with new, better and more reliable forecasting models to accurately predict the air quality. This paper selected Beijing, Tianjin and Shijiazhuang as three cities from the Jingjinji Region for the study to come up with a new model of collaborative forecasting using Support Vector Regression (SVR) for Urban Air Quality Index (AQI) prediction in China. The present study is aimed to improve the forecasting results by minimizing the prediction error of present machine learning algorithms by taking into account multiple city multi-dimensional air quality information and weather conditions as input. The results show that there is a decrease in MAPE in case of multiple city multi-dimensional regression when there is a strong interaction and correlation of the air quality characteristic attributes with AQI. Also, the geographical location is found to play a significant role in Beijing, Tianjin and Shijiazhuang AQI prediction. PMID:28708836
Three-model ensemble wind prediction in southern Italy

NASA Astrophysics Data System (ADS)

Torcasio, Rosa Claudia; Federico, Stefano; Calidonna, Claudia Roberta; Avolio, Elenio; Drofa, Oxana; Landi, Tony Christian; Malguzzi, Piero; Buzzi, Andrea; Bonasoni, Paolo

2016-03-01

Quality of wind prediction is of great importance since a good wind forecast allows the prediction of available wind power, improving the penetration of renewable energies into the energy market. Here, a 1-year (1 December 2012 to 30 November 2013) three-model ensemble (TME) experiment for wind prediction is considered. The models employed, run operationally at National Research Council - Institute of Atmospheric Sciences and Climate (CNR-ISAC), are RAMS (Regional Atmospheric Modelling System), BOLAM (BOlogna Limited Area Model), and MOLOCH (MOdello LOCale in H coordinates). The area considered for the study is southern Italy and the measurements used for the forecast verification are those of the GTS (Global Telecommunication System). Comparison with observations is made every 3 h up to 48 h of forecast lead time. Results show that the three-model ensemble outperforms the forecast of each individual model. The RMSE improvement compared to the best model is between 22 and 30 %, depending on the season. It is also shown that the three-model ensemble outperforms the IFS (Integrated Forecasting System) of the ECMWF (European Centre for Medium-Range Weather Forecast) for the surface wind forecasts. Notably, the three-model ensemble forecast performs better than each unbiased model, showing the added value of the ensemble technique. Finally, the sensitivity of the three-model ensemble RMSE to the length of the training period is analysed.
Canadian Operational Air Quality Forecasting Systems: Status, Recent Progress, and Challenges

NASA Astrophysics Data System (ADS)

Pavlovic, Radenko; Davignon, Didier; Ménard, Sylvain; Munoz-Alpizar, Rodrigo; Landry, Hugo; Beaulieu, Paul-André; Gilbert, Samuel; Moran, Michael; Chen, Jack

2017-04-01

ECCC's Canadian Meteorological Centre Operations (CMCO) division runs a number of operational air quality (AQ)-related systems that revolve around the Regional Air Quality Deterministic Prediction System (RAQDPS). The RAQDPS generates 48-hour AQ forecasts and outputs hourly concentration fields of O3, PM2.5, NO2, and other pollutants twice daily on a North-American domain with 10-km horizontal grid spacing and 80 vertical levels. A closely related AQ forecast system with near-real-time wildfire emissions, known as FireWork, has been run by CMCO during the Canadian wildfire season (April to October) since 2014. This system became operational in June 2016. The CMCO`s operational AQ forecast systems also benefit from several support systems, such as a statistical post-processing model called UMOS-AQ that is applied to enhance forecast reliability at point locations with AQ monitors. The Regional Deterministic Air Quality Analysis (RDAQA) system has also been connected to the RAQDPS since February 2013, and hourly surface objective analyses are now available for O3, PM2.5, NO2, PM10, SO2 and, indirectly, the Canadian Air Quality Health Index. As of June 2015, another version of the RDAQA has been connected to FireWork (RDAQA-FW). For verification purposes, CMCO developed a third support system called Verification for Air QUality Models (VAQUM), which has a geospatial relational database core and which enables continuous monitoring of the AQ forecast systems' performance. Urban environments are particularly subject to AQ pollution. In order to improve the services offered, ECCC has recently been investing efforts to develop a high resolution air quality prediction capability for urban areas in Canada. In this presentation, a comprehensive description of the ECCC AQ systems will be provided, along with a discussion on AQ systems performance. Recent improvements, current challenges, and future directions of the Canadian operational AQ program will also be discussed.
Air Quality Modeling and Forecasting over the United States Using WRF-Chem

NASA Astrophysics Data System (ADS)

Boxe, C.; Hafsa, U.; Blue, S.; Emmanuel, S.; Griffith, E.; Moore, J.; Tam, J.; Khan, I.; Cai, Z.; Bocolod, B.; Zhao, J.; Ahsan, S.; Gurung, D.; Tang, N.; Bartholomew, J.; Rafi, R.; Caltenco, K.; Rivas, M.; Ditta, H.; Alawlaqi, H.; Rowley, N.; Khatim, F.; Ketema, N.; Strothers, J.; Diallo, I.; Owens, C.; Radosavljevic, J.; Austin, S. A.; Johnson, L. P.; Zavala-Gutierrez, R.; Breary, N.; Saint-Hilaire, D.; Skeete, D.; Stock, J.; Salako, O.

2016-12-01

WRF-Chem is the Weather Research and Forecasting (WRF) model coupled with Chemistry. The model simulates the emission, transport, mixing, and chemical transformation of trace gases and aerosols simultaneously with the meteorology. The model is used for investigation of regional-scale air quality, field program analysis, and cloud-scale interactions between clouds and chemistry. The development of WRF-Chem is a collaborative effort among the community led by NOAA/ESRL scientists. The Official WRF-Chem web page is located at the NOAA web site. Our model development is closely linked with both NOAA/ESRL and DOE/PNNL efforts. Description of PNNL WRF-Chem model development is located at the PNNL web site as well as the PNNL Aerosol Modeling Testbed. High school and undergraduate students, representative of academic institutions throughout USA's Tri-State Area (New York, New Jersey, Connecticut), set up WRF-Chem on CUNY CSI's High Performance Computing Center. Students learned the back-end coding that governs WRF-Chems structure and the front-end coding that displays visually specified weather simulations and forecasts. Students also investigated the impact, to select baseline simulations/forecasts, due to the reaction, NO2 + OH + M → HOONO + M (k = 9.2 × 10-12 cm3 molecule-1 s-1, Mollner et al. 2010). The reaction of OH and NO2 to form gaseous nitric acid (HONO2) is among the most influential and in atmospheric chemistry. Till a few years prior, its rate coefficient remained poorly determined under tropospheric conditions because of difficulties in making laboratory measurements at 760 torr. These activities fosters student coding competencies and deep insights into weather forecast and air quality.
Application of Satellite and Ozonesonde Data to the Study of Nighttime Tropospheric Ozone Impacts and Relationship to Air Quality

NASA Astrophysics Data System (ADS)

Osterman, G. B.; Eldering, A.; Neu, J. L.; Tang, Y.; McQueen, J.; Pinder, R. W.

2011-12-01

To help protect human health and ecosystems, regional-scale atmospheric chemistry models are used to forecast high ozone events and to design emission control strategies to decrease the frequency and severity of ozone events. Despite the impact that nighttime aloft ozone can have on surface ozone, regional-scale atmospheric chemistry models often do not simulate the nighttime ozone concentrations well and nor do they sufficiently capture the ozone transport patterns. Fully characterizing the importance of the nighttime ozone has been hampered by limited measurements of the vertical distribution of ozone and ozone-precursors. The main focus of this work is to begin to utilize remote sensing data sets to characterize the impact of nighttime aloft ozone to air quality events. We will describe our plans to use NASA satellite data sets, transport models and air quality models to study ozone transport, focusing primarily on nighttime ozone and provide initial results. We will use satellite and ozonesonde data to help understand how well the air quality models are simulating ozone in the lower free troposphere and attempt to characterize the impact of nighttime ozone to air quality events. Our specific objectives are: 1) Characterize nighttime aloft ozone using remote sensing data and sondes. 2) Evaluate the ability of the Community Multi-scale Air Quality (CMAQ) model and the National Air Quality Forecast Capability (NAQFC) model to capture the nighttime aloft ozone and its relationship to air quality events. 3) Analyze a set of air quality events and determine the relationship of air quality events to the nighttime aloft ozone. We will achieve our objectives by utilizing the ozone profile data from the NASA Earth Observing System (EOS) Tropospheric Emission Spectrometer (TES) and other sensors, ozonesonde data collected during the Aura mission (IONS), EPA AirNow ground station ozone data, the CMAQ continental-scale air quality model, and the National Air Quality Forecast model.
Insights on the impact of systematic model errors on data assimilation performance in changing catchments

NASA Astrophysics Data System (ADS)

Pathiraja, S.; Anghileri, D.; Burlando, P.; Sharma, A.; Marshall, L.; Moradkhani, H.

2018-03-01

The global prevalence of rapid and extensive land use change necessitates hydrologic modelling methodologies capable of handling non-stationarity. This is particularly true in the context of Hydrologic Forecasting using Data Assimilation. Data Assimilation has been shown to dramatically improve forecast skill in hydrologic and meteorological applications, although such improvements are conditional on using bias-free observations and model simulations. A hydrologic model calibrated to a particular set of land cover conditions has the potential to produce biased simulations when the catchment is disturbed. This paper sheds new light on the impacts of bias or systematic errors in hydrologic data assimilation, in the context of forecasting in catchments with changing land surface conditions and a model calibrated to pre-change conditions. We posit that in such cases, the impact of systematic model errors on assimilation or forecast quality is dependent on the inherent prediction uncertainty that persists even in pre-change conditions. Through experiments on a range of catchments, we develop a conceptual relationship between total prediction uncertainty and the impacts of land cover changes on the hydrologic regime to demonstrate how forecast quality is affected when using state estimation Data Assimilation with no modifications to account for land cover changes. This work shows that systematic model errors as a result of changing or changed catchment conditions do not always necessitate adjustments to the modelling or assimilation methodology, for instance through re-calibration of the hydrologic model, time varying model parameters or revised offline/online bias estimation.

The FireWork air quality forecast system with near-real-time biomass burning emissions: Recent developments and evaluation of performance for the 2015 North American wildfire season

PubMed Central

Pavlovic, Radenko; Chen, Jack; Anderson, Kerry; Moran, Michael D.; Beaulieu, Paul-André; Davignon, Didier; Cousineau, Sophie

2016-01-01

ABSTRACT Environment and Climate Change Canada’s FireWork air quality (AQ) forecast system for North America with near-real-time biomass burning emissions has been running experimentally during the Canadian wildfire season since 2013. The system runs twice per day with model initializations at 00 UTC and 12 UTC, and produces numerical AQ forecast guidance with 48-hr lead time. In this work we describe the FireWork system, which incorporates near-real-time biomass burning emissions based on the Canadian Wildland Fire Information System (CWFIS) as an input to the operational Regional Air Quality Deterministic Prediction System (RAQDPS). To demonstrate the capability of the system we analyzed two forecast periods in 2015 (June 2–July 15, and August 15–31) when fire activity was high, and observed fire-smoke-impacted areas in western Canada and the western United States. Modeled PM2.5 surface concentrations were compared with surface measurements and benchmarked with results from the operational RAQDPS, which did not consider near-real-time biomass burning emissions. Model performance statistics showed that FireWork outperformed RAQDPS with improvements in forecast hourly PM2.5 across the region; the results were especially significant for stations near the path of fire plume trajectories. Although the hourly PM2.5 concentrations predicted by FireWork still displayed bias for areas with active fires for these two periods (mean bias [MB] of –7.3 µg m−3 and 3.1 µg m−3), it showed better forecast skill than the RAQDPS (MB of –11.7 µg m−3 and –5.8 µg m−3) and demonstrated a greater ability to capture temporal variability of episodic PM2.5 events (correlation coefficient values of 0.50 and 0.69 for FireWork compared to 0.03 and 0.11 for RAQDPS). A categorical forecast comparison based on an hourly PM2.5 threshold of 30 µg m−3 also showed improved scores for probability of detection (POD), critical success index (CSI), and false alarm rate (FAR). Implications: Smoke from wildfires can have a large impact on regional air quality (AQ) and can expose populations to elevated pollution levels. Environment and Climate Change Canada has been producing operational air quality forecasts for all of Canada since 2009 and is now working to include near-real-time wildfire emissions (NRTWE) in its operational AQ forecasting system. An experimental forecast system named FireWork, which includes NRTWE, has been undergoing testing and evaluation since 2013. A performance analysis of FireWork forecasts for the 2015 wildfire season shows that FireWork provides significant improvements to surface PM2.5 forecasts and valuable guidance to regional forecasters and first responders. PMID:26934496
Evaluation of Wind Power Forecasts from the Vermont Weather Analytics Center and Identification of Improvements

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

Optis, Michael; Scott, George N.; Draxl, Caroline

The goal of this analysis was to assess the wind power forecast accuracy of the Vermont Weather Analytics Center (VTWAC) forecast system and to identify potential improvements to the forecasts. Based on the analysis at Georgia Mountain, the following recommendations for improving forecast performance were made: 1. Resolve the significant negative forecast bias in February-March 2017 (50% underprediction on average) 2. Improve the ability of the forecast model to capture the strong diurnal cycle of wind power 3. Add ability for forecast model to assess internal wake loss, particularly at sites where strong diurnal shifts in wind direction are present.more » Data availability and quality limited the robustness of this forecast assessment. A more thorough analysis would be possible given a longer period of record for the data (at least one full year), detailed supervisory control and data acquisition data for each wind plant, and more detailed information on the forecast system input data and methodologies.« less
Assessing the Value of Frost Forecasts to Orchardists: A Dynamic Decision-Making Approach.

NASA Astrophysics Data System (ADS)

Katz, Richard W.; Murphy, Allan H.; Winkler, Robert L.

1982-04-01

The methodology of decision analysis is used to investigate the economic value of frost (i.e., minimum temperature) forecasts to orchardists. First, the fruit-frost situation and previous studies of the value of minimum temperature forecasts in this context are described. Then, after a brief overview of decision analysis, a decision-making model for the fruit-frost problem is presented. The model involves identifying the relevant actions and events (or outcomes), specifying the effect of taking protective action, and describing the relationships among temperature, bud loss, and yield loss. A bivariate normal distribution is used to model the relationship between forecast and observed temperatures, thereby characterizing the quality of different types of information. Since the orchardist wants to minimize expenses (or maximize payoffs) over the entire frost-protection season and since current actions and outcomes at any point in the season are related to both previous and future actions and outcomes, the decision-making problem is inherently dynamic in nature. As a result, a class of dynamic models known as Markov decision processes is considered. A computational technique called dynamic programming is used in conjunction with these models to determine the optimal actions and to estimate the value of meteorological information.Some results concerning the value of frost forecasts to orchardists in the Yakima Valley of central Washington are presented for the cases of red delicious apples, bartlett pears, and elberta peaches. Estimates of the parameter values in the Markov decision process are obtained from relevant physical and economic data. Twenty years of National Weather Service forecast and observed temperatures for the Yakima key station are used to estimate the quality of different types of information, including perfect forecasts, current forecasts, and climatological information. The orchardist's optimal actions over the frost-protection season and the expected expenses associated with the use of such information are determined using a dynamic programming algorithm. The value of meteorological information is defined as the difference between the expected expense for the information of interest and the expected expense for climatological information. Over the entire frost-protection season, the value estimates (in 1977 dollars) for current forecasts were $808 per acre for red delicious apples, $492 per acre for bartlett pears, and $270 per acre for elberta peaches. These amounts account for 66, 63, and 47%, respectively, of the economic value associated with decisions based on perfect forecasts. Varying the quality of the minimum temperature forecasts reveals that the relationship between the accuracy and value of such forecasts is nonlinear and that improvements in current forecasts would not be as significant in terms of economic value as were comparable improvements in the past.Several possible extensions of this study of the value of frost forecasts to orchardists are briefly described. Finally, the application of the dynamic model formulated in this paper to other decision-making problems involving the use of meteorological information is mentioned.
Dispersion Modeling Using Ensemble Forecasts Compared to ETEX Measurements.

NASA Astrophysics Data System (ADS)

Straume, Anne Grete; N'dri Koffi, Ernest; Nodop, Katrin

1998-11-01

Numerous numerical models are developed to predict long-range transport of hazardous air pollution in connection with accidental releases. When evaluating and improving such a model, it is important to detect uncertainties connected to the meteorological input data. A Lagrangian dispersion model, the Severe Nuclear Accident Program, is used here to investigate the effect of errors in the meteorological input data due to analysis error. An ensemble forecast, produced at the European Centre for Medium-Range Weather Forecasts, is then used as model input. The ensemble forecast members are generated by perturbing the initial meteorological fields of the weather forecast. The perturbations are calculated from singular vectors meant to represent possible forecast developments generated by instabilities in the atmospheric flow during the early part of the forecast. The instabilities are generated by errors in the analyzed fields. Puff predictions from the dispersion model, using ensemble forecast input, are compared, and a large spread in the predicted puff evolutions is found. This shows that the quality of the meteorological input data is important for the success of the dispersion model. In order to evaluate the dispersion model, the calculations are compared with measurements from the European Tracer Experiment. The model manages to predict the measured puff evolution concerning shape and time of arrival to a fairly high extent, up to 60 h after the start of the release. The modeled puff is still too narrow in the advection direction.
Modeling and forecasting the distribution of Vibrio vulnificus in Chesapeake Bay.

PubMed

Jacobs, J M; Rhodes, M; Brown, C W; Hood, R R; Leight, A; Long, W; Wood, R

2014-11-01

To construct statistical models to predict the presence, abundance and potential virulence of Vibrio vulnificus in surface waters of Chesapeake Bay for implementation in ecological forecasting systems. We evaluated and applied previously published qPCR assays to water samples (n = 1636) collected from Chesapeake Bay from 2007-2010 in conjunction with State water quality monitoring programmes. A variety of statistical techniques were used in concert to identify water quality parameters associated with V. vulnificus presence, abundance and virulence markers in the interest of developing strong predictive models for use in regional oceanographic modeling systems. A suite of models are provided to represent the best model fit and alternatives using environmental variables that allow them to be put to immediate use in current ecological forecasting efforts. Environmental parameters such as temperature, salinity and turbidity are capable of accurately predicting abundance and distribution of V. vulnificus in Chesapeake Bay. Forcing these empirical models with output from ocean modeling systems allows for spatially explicit forecasts for up to 48 h in the future. This study uses one of the largest data sets compiled to model Vibrio in an estuary, enhances our understanding of environmental correlates with abundance, distribution and presence of potentially virulent strains and offers a method to forecast these pathogens that may be replicated in other regions. This article has been contributed to by US Government employees and their work is in the public domain in the USA.
The Impact of Assimilation of GPM Clear Sky Radiance on HWRF Hurricane Track and Intensity Forecasts

NASA Astrophysics Data System (ADS)

Yu, C. L.; Pu, Z.

2016-12-01

The impact of GPM microwave imager (GMI) clear sky radiances on hurricane forecasting is examined by ingesting GMI level 1C recalibrated brightness temperature into the NCEP Gridpoint Statistical Interpolation (GSI)- based ensemble-variational hybrid data assimilation system for the operational Hurricane Weather Research and Forecast (HWRF) system. The GMI clear sky radiances are compared with the Community Radiative Transfer Model (CRTM) simulated radiances to closely study the quality of the radiance observations. The quality check result indicates the presence of bias in various channels. A static bias correction scheme, in which the appropriate bias correction coefficients for GMI data is evaluated by applying regression method on a sufficiently large sample of data representative to the observational bias in the regions of concern, is used to correct the observational bias in GMI clear sky radiances. Forecast results with and without assimilation of GMI radiance are compared using hurricane cases from recent hurricane seasons (e.g., Hurricane Joaquin in 2015). Diagnoses of data assimilation results show that the bias correction coefficients obtained from the regression method can correct the inherent biases in GMI radiance data, significantly reducing observational residuals. The removal of biases also allows more data to pass GSI quality control and hence to be assimilated into the model. Forecast results for hurricane Joaquin demonstrates that the quality of analysis from the data assimilation is sensitive to the bias correction, with positive impacts on the hurricane track forecast when systematic biases are removed from the radiance data. Details will be presented at the symposium.
Evaluation and economic value of winter weather forecasts

NASA Astrophysics Data System (ADS)

Snyder, Derrick W.

State and local highway agencies spend millions of dollars each year to deploy winter operation teams to plow snow and de-ice roadways. Accurate and timely weather forecast information is critical for effective decision making. Students from Purdue University partnered with the Indiana Department of Transportation to create an experimental winter weather forecast service for the 2012-2013 winter season in Indiana to assist in achieving these goals. One forecast product, an hourly timeline of winter weather hazards produced daily, was evaluated for quality and economic value. Verification of the forecasts was performed with data from the Rapid Refresh numerical weather model. Two objective verification criteria were developed to evaluate the performance of the timeline forecasts. Using both criteria, the timeline forecasts had issues with reliability and discrimination, systematically over-forecasting the amount of winter weather that was observed while also missing significant winter weather events. Despite these quality issues, the forecasts still showed significant, but varied, economic value compared to climatology. Economic value of the forecasts was estimated to be 29.5 million or 4.1 million, depending on the verification criteria used. Limitations of this valuation system are discussed and a framework is developed for more thorough studies in the future.
Forecasting relative impacts of land use on anadromous fish habitat to guide conservation planning.

PubMed

Lohse, Kathleen A; Newburn, David A; Opperman, Jeff J; Merenlender, Adina M

2008-03-01

Land use change can adversely affect water quality and freshwater ecosystems, yet our ability to predict how systems will respond to different land uses, particularly rural-residential development, is limited by data availability and our understanding of biophysical thresholds. In this study, we use spatially explicit parcel-level data to examine the influence of land use (including urban, rural-residential, and vineyard) on salmon spawning substrate quality in tributaries of the Russian River in California. We develop a land use change model to forecast the probability of losses in high-quality spawning habitat and recommend priority areas for incentive-based land conservation efforts. Ordinal logistic regression results indicate that all three land use types were negatively associated with spawning substrate quality, with urban development having the largest marginal impact. For two reasons, however, forecasted rural-residential and vineyard development have much larger influences on decreasing spawning substrate quality relative to urban development. First, the land use change model estimates 10 times greater land use conversion to both rural-residential and vineyard compared to urban. Second, forecasted urban development is concentrated in the most developed watersheds, which already have poor spawning substrate quality, such that the marginal response to future urban development is less significant. To meet the goals of protecting salmonid spawning habitat and optimizing investments in salmon recovery, we suggest investing in watersheds where future rural-residential development and vineyards threaten high-quality fish habitat, rather than the most developed watersheds, where land values are higher.
The air quality forecast in Beijing with Community Multi-scale Air Quality Modeling (CMAQ) System: model evaluation and improvement

NASA Astrophysics Data System (ADS)

Wu, Q.

2013-12-01

The MM5-SMOKE-CMAQ model system, which is developed by the United States Environmental Protection Agency(U.S. EPA) as the Models-3 system, has been used for the daily air quality forecast in the Beijing Municipal Environmental Monitoring Center(Beijing MEMC), as a part of the Ensemble Air Quality Forecast System for Beijing(EMS-Beijing) since the Olympic Games year 2008. In this study, we collect the daily forecast results of the CMAQ model in the whole year 2010 for the model evaluation. The results show that the model play a good model performance in most days but underestimate obviously in some air pollution episode. A typical air pollution episode from 11st - 20th January 2010 was chosen, which the air pollution index(API) of particulate matter (PM10) observed by Beijing MEMC reaches to 180 while the prediction of PM10-API is about 100. Taking in account all stations in Beijing, including urban and suburban stations, three numerical methods are used for model improvement: firstly, enhance the inner domain with 4km grids, the coverage from only Beijing to the area including its surrounding cities; secondly, update the Beijing stationary area emission inventory, from statistical county-level to village-town level, that would provide more detail spatial informance for area emissions; thirdly, add some industrial points emission in Beijing's surrounding cities, the latter two are both the improvement of emission. As the result, the peak of the nine national standard stations averaged PM10-API, which is simulated by CMAQ as daily hindcast PM10-API, reach to 160 and much near to the observation. The new results show better model performance, which the correlation coefficent is 0.93 in national standard stations average and 0.84 in all stations, the relative error is 15.7% in national standard stations averaged and 27% in all stations. The time series of 9 national standard in Beijing urban The scatter diagram of all stations in Beijing, the red is the forecast and the blue is new result.
A scoping review of malaria forecasting: past work and future directions

PubMed Central

Zinszer, Kate; Verma, Aman D; Charland, Katia; Brewer, Timothy F; Brownstein, John S; Sun, Zhuoyu; Buckeridge, David L

2012-01-01

Objectives There is a growing body of literature on malaria forecasting methods and the objective of our review is to identify and assess methods, including predictors, used to forecast malaria. Design Scoping review. Two independent reviewers searched information sources, assessed studies for inclusion and extracted data from each study. Information sources Search strategies were developed and the following databases were searched: CAB Abstracts, EMBASE, Global Health, MEDLINE, ProQuest Dissertations & Theses and Web of Science. Key journals and websites were also manually searched. Eligibility criteria for included studies We included studies that forecasted incidence, prevalence or epidemics of malaria over time. A description of the forecasting model and an assessment of the forecast accuracy of the model were requirements for inclusion. Studies were restricted to human populations and to autochthonous transmission settings. Results We identified 29 different studies that met our inclusion criteria for this review. The forecasting approaches included statistical modelling, mathematical modelling and machine learning methods. Climate-related predictors were used consistently in forecasting models, with the most common predictors being rainfall, relative humidity, temperature and the normalised difference vegetation index. Model evaluation was typically based on a reserved portion of data and accuracy was measured in a variety of ways including mean-squared error and correlation coefficients. We could not compare the forecast accuracy of models from the different studies as the evaluation measures differed across the studies. Conclusions Applying different forecasting methods to the same data, exploring the predictive ability of non-environmental variables, including transmission reducing interventions and using common forecast accuracy measures will allow malaria researchers to compare and improve models and methods, which should improve the quality of malaria forecasting. PMID:23180505
Impact of AIRS Thermodynamic Profile on Regional Weather Forecast

NASA Technical Reports Server (NTRS)

Chou, Shih-Hung; Zavodsky, Brad; Jedlovee, Gary

2010-01-01

Prudent assimilation of AIRS thermodynamic profiles and quality indicators can improve initial conditions for regional weather models. AIRS-enhanced analysis has warmer and moister PBL. Forecasts with AIRS profiles are generally closer to NAM analyses than CNTL. Assimilation of AIRS leads to an overall QPF improvement in 6-h accumulated precipitation forecasts. Including AIRS profiles in assimilation process enhances the moist instability and produces stronger updrafts and a better precipitation forecast than the CNTL run.
Impact of Atmospheric Infrared Sounder (AIRS) Thermodynamic Profiles on Regional Weather Forecasting

NASA Technical Reports Server (NTRS)

Chou, Shih-Hung; Zavodsky, Bradley T.; Jedlovee, Gary J.

2010-01-01

In data sparse regions, remotely-sensed observations can be used to improve analyses and lead to better forecasts. One such source comes from the Atmospheric Infrared Sounder (AIRS), which together with the Advanced Microwave Sounding Unit (AMSU), provides temperature and moisture profiles with accuracy comparable to that of radiosondes. The purpose of this paper is to describe a procedure to assimilate AIRS thermodynamic profile data into a regional configuration of the Advanced Research Weather Research and Forecasting (WRF-ARW) model using its three-dimension variational (3DVAR) analysis component (WRF-Var). Quality indicators are used to select only the highest quality temperature and moisture profiles for assimilation in both clear and partly cloudy regions. Separate error characteristics for land and water profiles are also used in the assimilation process. Assimilation results indicate that AIRS profiles produce an analysis closer to in situ observations than the background field. Forecasts from a 37-day case study period in the winter of 2007 show that AIRS profile data can lead to improvements in 6-h cumulative precipitation forecasts due to instability added in the forecast soundings by the AIRS profiles. Additionally, in a convective heavy rainfall event from February 2007, assimilation of AIRS profiles produces a more unstable boundary layer resulting in enhanced updrafts in the model. These updrafts produce a squall line and precipitation totals that more closely reflect ground-based observations than a no AIRS control forecast. The location of available high-quality AIRS profiles ahead of approaching storm systems is found to be of paramount importance to the amount of impact the observations will have on the resulting forecasts.
Linked Hydrologic-Hydrodynamic Model Framework to Forecast Impacts of Rivers on Beach Water Quality

NASA Astrophysics Data System (ADS)

Anderson, E. J.; Fry, L. M.; Kramer, E.; Ritzenthaler, A.

2014-12-01

The goal of NOAA's beach quality forecasting program is to use a multi-faceted approach to aid in detection and prediction of bacteria in recreational waters. In particular, our focus has been on the connection between tributary loads and bacteria concentrations at nearby beaches. While there is a clear link between stormwater runoff and beach water quality, quantifying the contribution of river loadings to nearshore bacterial concentrations is complicated due to multiple processes that drive bacterial concentrations in rivers as well as those processes affecting the fate and transport of bacteria upon exiting the rivers. In order to forecast potential impacts of rivers on beach water quality, we developed a linked hydrologic-hydrodynamic water quality framework that simulates accumulation and washoff of bacteria from the landscape, and then predicts the fate and transport of washed off bacteria from the watershed to the coastal zone. The framework includes a watershed model (IHACRES) to predict fecal indicator bacteria (FIB) loadings to the coastal environment (accumulation, wash-off, die-off) as a function of effective rainfall. These loadings are input into a coastal hydrodynamic model (FVCOM), including a bacteria transport model (Lagrangian particle), to simulate 3D bacteria transport within the coastal environment. This modeling system provides predictive tools to assist local managers in decision-making to reduce human health threats.
Remote-sensing based approach to forecast habitat quality under climate change scenarios.

PubMed

Requena-Mullor, Juan M; López, Enrique; Castro, Antonio J; Alcaraz-Segura, Domingo; Castro, Hermelindo; Reyes, Andrés; Cabello, Javier

2017-01-01

As climate change is expected to have a significant impact on species distributions, there is an urgent challenge to provide reliable information to guide conservation biodiversity policies. In addressing this challenge, we propose a remote sensing-based approach to forecast the future habitat quality for European badger, a species not abundant and at risk of local extinction in the arid environments of southeastern Spain, by incorporating environmental variables related with the ecosystem functioning and correlated with climate and land use. Using ensemble prediction methods, we designed global spatial distribution models for the distribution range of badger using presence-only data and climate variables. Then, we constructed regional models for an arid region in the southeast Spain using EVI (Enhanced Vegetation Index) derived variables and weighting the pseudo-absences with the global model projections applied to this region. Finally, we forecast the badger potential spatial distribution in the time period 2071-2099 based on IPCC scenarios incorporating the uncertainty derived from the predicted values of EVI-derived variables. By including remotely sensed descriptors of the temporal dynamics and spatial patterns of ecosystem functioning into spatial distribution models, results suggest that future forecast is less favorable for European badgers than not including them. In addition, change in spatial pattern of habitat suitability may become higher than when forecasts are based just on climate variables. Since the validity of future forecast only based on climate variables is currently questioned, conservation policies supported by such information could have a biased vision and overestimate or underestimate the potential changes in species distribution derived from climate change. The incorporation of ecosystem functional attributes derived from remote sensing in the modeling of future forecast may contribute to the improvement of the detection of ecological responses under climate change scenarios.
Remote-sensing based approach to forecast habitat quality under climate change scenarios

PubMed Central

Requena-Mullor, Juan M.; López, Enrique; Castro, Antonio J.; Alcaraz-Segura, Domingo; Castro, Hermelindo; Reyes, Andrés; Cabello, Javier

2017-01-01

As climate change is expected to have a significant impact on species distributions, there is an urgent challenge to provide reliable information to guide conservation biodiversity policies. In addressing this challenge, we propose a remote sensing-based approach to forecast the future habitat quality for European badger, a species not abundant and at risk of local extinction in the arid environments of southeastern Spain, by incorporating environmental variables related with the ecosystem functioning and correlated with climate and land use. Using ensemble prediction methods, we designed global spatial distribution models for the distribution range of badger using presence-only data and climate variables. Then, we constructed regional models for an arid region in the southeast Spain using EVI (Enhanced Vegetation Index) derived variables and weighting the pseudo-absences with the global model projections applied to this region. Finally, we forecast the badger potential spatial distribution in the time period 2071–2099 based on IPCC scenarios incorporating the uncertainty derived from the predicted values of EVI-derived variables. By including remotely sensed descriptors of the temporal dynamics and spatial patterns of ecosystem functioning into spatial distribution models, results suggest that future forecast is less favorable for European badgers than not including them. In addition, change in spatial pattern of habitat suitability may become higher than when forecasts are based just on climate variables. Since the validity of future forecast only based on climate variables is currently questioned, conservation policies supported by such information could have a biased vision and overestimate or underestimate the potential changes in species distribution derived from climate change. The incorporation of ecosystem functional attributes derived from remote sensing in the modeling of future forecast may contribute to the improvement of the detection of ecological responses under climate change scenarios. PMID:28257501
Evaluating the Vertical Distribution of Ozone and its Relationship to Pollution Events in Air Quality Models using Satellite Data

NASA Astrophysics Data System (ADS)

Osterman, G. B.; Neu, J. L.; Eldering, A.; Pinder, R. W.; Tang, Y.; McQueen, J.

2014-12-01

Most regional scale models that are used for air quality forecasts and ozone source attribution do not adequately capture the distribution of ozone in the mid- and upper troposphere, but it is unclear how this shortcoming relates to their ability to simulate surface ozone. We combine ozone profile data from the NASA Earth Observing System (EOS) Tropospheric Emission Spectrometer (TES) and a new joint product from TES and the Ozone Monitoring Instrument along with ozonesonde measurements and EPA AirNow ground station ozone data to examine air quality events during August 2006 in the Community Multi-Scale Air Quality (CMAQ) and National Air Quality Forecast Capability (NAQFC) models. We present both aggregated statistics and case-study analyses with the goal of assessing the relationship between the models' ability to reproduce surface air quality events and their ability to capture the vertical distribution of ozone. We find that the models lack the mid-tropospheric ozone variability seen in TES and the ozonesonde data, and discuss the conditions under which this variability appears to be important for surface air quality.
Moving beyond the cost-loss ratio: economic assessment of streamflow forecasts for a risk-averse decision maker

NASA Astrophysics Data System (ADS)

Matte, Simon; Boucher, Marie-Amélie; Boucher, Vincent; Fortier Filion, Thomas-Charles

2017-06-01

A large effort has been made over the past 10 years to promote the operational use of probabilistic or ensemble streamflow forecasts. Numerous studies have shown that ensemble forecasts are of higher quality than deterministic ones. Many studies also conclude that decisions based on ensemble rather than deterministic forecasts lead to better decisions in the context of flood mitigation. Hence, it is believed that ensemble forecasts possess a greater economic and social value for both decision makers and the general population. However, the vast majority of, if not all, existing hydro-economic studies rely on a cost-loss ratio framework that assumes a risk-neutral decision maker. To overcome this important flaw, this study borrows from economics and evaluates the economic value of early warning flood systems using the well-known Constant Absolute Risk Aversion (CARA) utility function, which explicitly accounts for the level of risk aversion of the decision maker. This new framework allows for the full exploitation of the information related to a forecasts' uncertainty, making it especially suited for the economic assessment of ensemble or probabilistic forecasts. Rather than comparing deterministic and ensemble forecasts, this study focuses on comparing different types of ensemble forecasts. There are multiple ways of assessing and representing forecast uncertainty. Consequently, there exist many different means of building an ensemble forecasting system for future streamflow. One such possibility is to dress deterministic forecasts using the statistics of past error forecasts. Such dressing methods are popular among operational agencies because of their simplicity and intuitiveness. Another approach is the use of ensemble meteorological forecasts for precipitation and temperature, which are then provided as inputs to one or many hydrological model(s). In this study, three concurrent ensemble streamflow forecasting systems are compared: simple statistically dressed deterministic forecasts, forecasts based on meteorological ensembles, and a variant of the latter that also includes an estimation of state variable uncertainty. This comparison takes place for the Montmorency River, a small flood-prone watershed in southern central Quebec, Canada. The assessment of forecasts is performed for lead times of 1 to 5 days, both in terms of forecasts' quality (relative to the corresponding record of observations) and in terms of economic value, using the new proposed framework based on the CARA utility function. It is found that the economic value of a forecast for a risk-averse decision maker is closely linked to the forecast reliability in predicting the upper tail of the streamflow distribution. Hence, post-processing forecasts to avoid over-forecasting could help improve both the quality and the value of forecasts.
Verification of Ensemble Forecasts for the New York City Operations Support Tool

NASA Astrophysics Data System (ADS)

Day, G.; Schaake, J. C.; Thiemann, M.; Draijer, S.; Wang, L.

2012-12-01

The New York City water supply system operated by the Department of Environmental Protection (DEP) serves nine million people. It covers 2,000 square miles of portions of the Catskill, Delaware, and Croton watersheds, and it includes nineteen reservoirs and three controlled lakes. DEP is developing an Operations Support Tool (OST) to support its water supply operations and planning activities. OST includes historical and real-time data, a model of the water supply system complete with operating rules, and lake water quality models developed to evaluate alternatives for managing turbidity in the New York City Catskill reservoirs. OST will enable DEP to manage turbidity in its unfiltered system while satisfying its primary objective of meeting the City's water supply needs, in addition to considering secondary objectives of maintaining ecological flows, supporting fishery and recreation releases, and mitigating downstream flood peaks. The current version of OST relies on statistical forecasts of flows in the system based on recent observed flows. To improve short-term decision making, plans are being made to transition to National Weather Service (NWS) ensemble forecasts based on hydrologic models that account for short-term weather forecast skill, longer-term climate information, as well as the hydrologic state of the watersheds and recent observed flows. To ensure that the ensemble forecasts are unbiased and that the ensemble spread reflects the actual uncertainty of the forecasts, a statistical model has been developed to post-process the NWS ensemble forecasts to account for hydrologic model error as well as any inherent bias and uncertainty in initial model states, meteorological data and forecasts. The post-processor is designed to produce adjusted ensemble forecasts that are consistent with the DEP historical flow sequences that were used to develop the system operating rules. A set of historical hindcasts that is representative of the real-time ensemble forecasts is needed to verify that the post-processed forecasts are unbiased, statistically reliable, and preserve the skill inherent in the "raw" NWS ensemble forecasts. A verification procedure and set of metrics will be presented that provide an objective assessment of ensemble forecasts. The procedure will be applied to both raw ensemble hindcasts and to post-processed ensemble hindcasts. The verification metrics will be used to validate proper functioning of the post-processor and to provide a benchmark for comparison of different types of forecasts. For example, current NWS ensemble forecasts are based on climatology, using each historical year to generate a forecast trace. The NWS Hydrologic Ensemble Forecast System (HEFS) under development will utilize output from both the National Oceanic Atmospheric Administration (NOAA) Global Ensemble Forecast System (GEFS) and the Climate Forecast System (CFS). Incorporating short-term meteorological forecasts and longer-term climate forecast information should provide sharper, more accurate forecasts. Hindcasts from HEFS will enable New York City to generate verification results to validate the new forecasts and further fine-tune system operating rules. Project verification results will be presented for different watersheds across a range of seasons, lead times, and flow levels to assess the quality of the current ensemble forecasts.
The impact of satellite temperature soundings on the forecasts of a small national meteorological service

NASA Technical Reports Server (NTRS)

Wolfson, N.; Thomasell, A.; Alperson, Z.; Brodrick, H.; Chang, J. T.; Gruber, A.; Ohring, G.

1984-01-01

The impact of introducing satellite temperature sounding data on a numerical weather prediction model of a national weather service is evaluated. A dry five level, primitive equation model which covers most of the Northern Hemisphere, is used for these experiments. Series of parallel forecast runs out to 48 hours are made with three different sets of initial conditions: (1) NOSAT runs, only conventional surface and upper air observations are used; (2) SAT runs, satellite soundings are added to the conventional data over oceanic regions and North Africa; and (3) ALLSAT runs, the conventional upper air observations are replaced by satellite soundings over the entire model domain. The impact on the forecasts is evaluated by three verification methods: the RMS errors in sea level pressure forecasts, systematic errors in sea level pressure forecasts, and errors in subjective forecasts of significant weather elements for a selected portion of the model domain. For the relatively short range of the present forecasts, the major beneficial impacts on the sea level pressure forecasts are found precisely in those areas where the satellite sounding are inserted and where conventional upper air observations are sparse. The RMS and systematic errors are reduced in these regions. The subjective forecasts of significant weather elements are improved with the use of the satellite data. It is found that the ALLSAT forecasts are of a quality comparable to the SAR forecasts.
LINKING ETA MODEL WITH THE COMMUNITY MULTISCALE AIR QUALITY (CMAQ) MODELING SYSTEM: OZONE BOUNDARY CONDITIONS

EPA Science Inventory

A prototype surface ozone concentration forecasting model system for the Eastern U.S. has been developed. The model system is consisting of a regional meteorological and a regional air quality model. It demonstrated a strong prediction dependence on its ozone boundary conditions....

The Past, Present and Future of the Meteorological Phenomena Identification Near the Ground (mPING) Project

NASA Astrophysics Data System (ADS)

Elmore, K. L.

2016-12-01

The Metorological Phenomemna Identification NeartheGround (mPING) project is an example of a crowd-sourced, citizen science effort to gather data of sufficeint quality and quantity needed by new post processing methods that use machine learning. Transportation and infrastructure are particularly sensitive to precipitation type in winter weather. We extract attributes from operational numerical forecast models and use them in a random forest to generate forecast winter precipitation types. We find that random forests applied to forecast soundings are effective at generating skillful forecasts of surface ptype with consideralbly more skill than the current algorithms, especuially for ice pellets and freezing rain. We also find that three very different forecast models yuield similar overall results, showing that random forests are able to extract essentially equivalent information from different forecast models. We also show that the random forest for each model, and each profile type is unique to the particular forecast model and that the random forests developed using a particular model suffer significant degradation when given attributes derived from a different model. This implies that no single algorithm can perform well across all forecast models. Clearly, random forests extract information unavailable to "physically based" methods because the physical information in the models does not appear as we expect. One intersting result is that results from the classic "warm nose" sounding profile are, by far, the most sensitive to the particular forecast model, but this profile is also the one for which random forests are most skillful. Finally, a method for calibrarting probabilties for each different ptype using multinomial logistic regression is shown.
AQA - Air Quality model for Austria - Evaluation and Developments

NASA Astrophysics Data System (ADS)

Hirtl, M.; Krüger, B. C.; Baumann-Stanzer, K.; Skomorowski, P.

2009-04-01

The regional weather forecast model ALADIN of the Central Institute for Meteorology and Geodynamics (ZAMG) is used in combination with the chemical transport model CAMx (www.camx.com) to conduct forecasts of gaseous and particulate air pollution over Europe. The forecasts which are done in cooperation with the University of Natural Resources and Applied Life Sciences in Vienna (BOKU) are supported by the regional governments since 2005 with the main interest on the prediction of tropospheric ozone. The daily ozone forecasts are evaluated for the summer 2008 with the observations of about 150 air quality stations in Austria. In 2008 the emission-model SMOKE was integrated into the modelling system to calculate the biogenic emissions. The anthropogenic emissions are based on the newest EMEP data set as well as on regional inventories for the core domain. The performance of SMOKE is shown for a summer period in 2007. In the frame of the COST-action 728 „Enhancing mesoscale meteorological modelling capabilities for air pollution and dispersion applications", multi-model ensembles are used to conduct an international model evaluation. The model calculations of meteorological- and concentration fields are compared to measurements on the ensemble platform at the Joint Research Centre (JRC) in Ispra. The results for 2 episodes in 2006 show the performance of the different models as well as of the model ensemble.
Operational skill assessment of the IBI-MFC Ocean Forecasting System within the frame of the CMEMS.

NASA Astrophysics Data System (ADS)

Lorente Jimenez, Pablo; Garcia-Sotillo, Marcos; Amo-Balandron, Arancha; Aznar Lecocq, Roland; Perez Gomez, Begoña; Levier, Bruno; Alvarez-Fanjul, Enrique

2016-04-01

Since operational ocean forecasting systems (OOFSs) are increasingly used as tools to support high-stakes decision-making for coastal management, a rigorous skill assessment of model performance becomes essential. In this context, the IBI-MFC (Iberia-Biscay-Ireland Monitoring & Forecasting Centre) has been providing daily ocean model estimates and forecasts for the IBI regional seas since 2011, first in the frame of MyOcean projects and later as part of the Copernicus Marine Environment Monitoring Service (CMEMS). A comprehensive web validation tool named NARVAL (North Atlantic Regional VALidation) has been developed to routinely monitor IBI performance and to evaluate model's veracity and prognostic capabilities. Three-dimensional comparisons are carried out on a different time basis ('online mode' - daily verifications - and 'delayed mode' - for longer time periods -) using a broad variety of in-situ (buoys, tide-gauges, ARGO-floats, drifters and gliders) and remote-sensing (satellite and HF radars) observational sources as reference fields to validate against the NEMO model solution. Product quality indicators and meaningful skill metrics are automatically computed not only averaged over the entire IBI domain but also over specific sub-regions of particular interest from a user perspective (i.e. coastal or shelf areas) in order to determine IBI spatial and temporal uncertainty levels. A complementary aspect of NARVAL web tool is the intercomparison of different CMEMS forecast model solutions in overlapping areas. Noticeable efforts are in progress in order to quantitatively assess the quality and consistency of nested system outputs by setting up specific intercomparison exercises on different temporal and spatial scales, encompassing global configurations (CMEMS Global system), regional applications (NWS and MED ones) and local high-resolution coastal models (i.e. the PdE SAMPA system in the Gibraltar Strait). NARVAL constitutes a powerful approach to increase our knowledge on the IBI-MFC forecast system and aids us to inform CMEMS end users about the provided ocean forecasting products' confidence level by routinely delivering QUality Information Documents (QUIDs). It allows the detection of strengths and weaknesses in the modeling of several key physical processes and the understanding of potential sources of discrepancies in IBI predictions. Once the numerical model shortcomings are identified, potential improvements can be achieved thanks to reliable upgrades, making evolve IBI OOFS towards more refined and advanced versions.
Development of a multi-ensemble Prediction Model for China

NASA Astrophysics Data System (ADS)

Brasseur, G. P.; Bouarar, I.; Petersen, A. K.

2016-12-01

As part of the EU-sponsored Panda and MarcoPolo Projects, a multi-model prediction system including 7 models has been developed. Most regional models use global air quality predictions provided by the Copernicus Atmospheric Monitoring Service and downscale the forecast at relatively high spatial resolution in eastern China. The paper will describe the forecast system and show examples of forecasts produced for several Chinese urban areas and displayed on a web site developed by the Dutch Meteorological service. A discussion on the accuracy of the predictions based on a detailed validation process using surface measurements from the Chinese monitoring network will be presented.
The Value of Humans in the Operational River Forecasting Enterprise

NASA Astrophysics Data System (ADS)

Pagano, T. C.

2012-04-01

The extent of human control over operational river forecasts, such as by adjusting model inputs and outputs, varies from nearly completely automated systems to those where forecasts are generated after discussion among a group of experts. Historical and realtime data availability, the complexity of hydrologic processes, forecast user needs, and forecasting institution support/resource availability (e.g. computing power, money for model maintenance) influence the character and effectiveness of operational forecasting systems. Automated data quality algorithms, if used at all, are typically very basic (e.g. checks for impossible values); substantial human effort is devoted to cleaning up forcing data using subjective methods. Similarly, although it is an active research topic, nearly all operational forecasting systems struggle to make quantitative use of Numerical Weather Prediction model-based precipitation forecasts, instead relying on the assessment of meteorologists. Conversely, while there is a strong tradition in meteorology of making raw model outputs available to forecast users via the Internet, this is rarely done in hydrology; Operational river forecasters express concerns about exposing users to raw guidance, due to the potential for misinterpretation and misuse. However, this limits the ability of users to build their confidence in operational products through their own value-added analyses. Forecasting agencies also struggle with provenance (i.e. documenting the production process and archiving the pieces that went into creating a forecast) although this is necessary for quantifying the benefits of human involvement in forecasting and diagnosing weak links in the forecasting chain. In hydrology, the space between model outputs and final operational products is nearly unstudied by the academic community, although some studies exist in other fields such as meteorology.
Chesapeake Bay Forecast System: Oxygen Prediction for the Sustainable Ecosystem Management

NASA Astrophysics Data System (ADS)

Mathukumalli, B.; Long, W.; Zhang, X.; Wood, R.; Murtugudde, R. G.

2010-12-01

The Chesapeake Bay Forecast System (CBFS) is a flexible, end-to-end expert prediction tool for decision makers that will provide customizable, user-specified predictions and projections of the region’s climate, air and water quality, local chemistry, and ecosystems at days to decades. As a part of CBFS, the long-term water quality data were collected and assembled to develop ecological models for the sustainable management of the Chesapeake Bay. Cultural eutrophication depletes oxygen levels in this ecosystem particularly in summer which has several negative implications on the structure and function of ecosystem. In order to understand dynamics and prediction of spatially-explicit oxygen levels in the Bay, an empirical process based ecological model is developed with long-term control variables (water temperature, salinity, nitrogen and phosphorus). Statistical validation methods were employed to demonstrate usability of predictions for management purposes and the predicted oxygen levels are quite faithful to observations. The predicted oxygen values and other physical outputs from downscaling of regional weather and climate predictions, or forecasts from hydrodynamic models can be used to forecast various ecological components. Such forecasts would be useful for both recreational and commercial users of the bay (for example, bass fishing). Furthermore, this work can also be used to predict extent of hypoxia/anoxia not only from anthropogenic nutrient pollution, but also from global warming. Some hindcasts and forecasts are discussed along with the ongoing efforts at a mechanistic ecosystem model to provide prognostic oxygen predictions and projections and upper trophic modeling using an energetics approach.
Integrated Water Vapour Retrieval From Irish GPS Network: Results From Validation With Radiosondes And Microwave Profiler And Assimilation Into HIRLAM 7.2 Operational Forecasting Model

NASA Astrophysics Data System (ADS)

Hanafin, J. A.; Whelan, E.; McGrath, R.; Jennings, S. G.; O'Dowd, C.

2009-12-01

Retrieval of atmospheric integrated water vapour (IWV) from ground-based GPS receivers and provision of this data product for meteorological applications is the focus of the European EUMETNET GPS water vapour programme. The results presented here are the first from a project to provide such information about the state of the atmosphere around Ireland for climate monitoring and improved numerical weather prediction. Two geodetic reference GPS receivers have been deployed at Valentia Observatory in Co. Kerry and Mace Head Atmospheric Research Station in Co. Galway, Ireland. A system to retrieve column-integrated atmospheric water vapour from the data they provide has been developed. Data quality has been assessed using co-located radiosondes at Valentia and observations from a microwave profiling radiometer at Mace Head. Results from the data processing and comparisons with independent observations will be presented. Water vapour retrievals from such sensors can provide good quality observations at hourly intervals of this essential climate variable for assimilation into numerical nowcast and forecast systems. Previous studies have shown that using these data to constrain initial model conditions can improve the accuracy of precipitation forecasts, particularly for heavy rainfall. The current operational forecast model in use at Met Éireann for the region is the new version 7.2 HIRLAM (High-Resolution Limited Area Model). The effects on the forecast for Ireland have been evaluated by assimilating the data into 48-hour forecast runs of this model and results of this study will also be presented.
The Simulations of Wildland Fire Smoke PM25 in the NWS Air Quality Forecasting Systems

NASA Astrophysics Data System (ADS)

Huang, H. C.; Pan, L.; McQueen, J.; Lee, P.; ONeill, S. M.; Ruminski, M.; Shafran, P.; Huang, J.; Stajner, I.; Upadhayay, S.; Larkin, N. K.

2017-12-01

The increase of wildland fire intensity and frequency in the United States (U.S.) has led to property loss, human fatality, and poor air quality due to elevated particulate matters and surface ozone concentrations. The NOAA/National Weather Service (NWS) built the National Air Quality Forecast Capability (NAQFC) based on the U.S. Environmental Protection Agency (EPA) Community Multi-scale Air Quality (CMAQ) Modeling System driven by the NCEP North American Mesoscale Forecast System meteorology to provide ozone and fine particulate matter (PM2.5) forecast guidance publicly. State and local forecasters use the NWS air quality forecast guidance to issue air quality alerts in their area. The NAQFC PM2.5 predictions include emissions from anthropogenic and biogenic sources, as well as natural sources such as dust storms and wildland fires. The wildland fire emission inputs to the NAQFC is derived from the NOAA National Environmental Satellite, Data, and Information Service Hazard Mapping System fire and smoke detection product and the emission module of the U.S. Forest Service (USFS) BlueSky Smoke Modeling Framework. Wildland fires are unpredictable and can be ignited by natural causes such as lightning or be human-caused. It is extremely difficult to predict future occurrences and behavior of wildland fires, as is the available bio-fuel to be burned for real-time air quality predictions. Assumptions of future day's wildland fire behavior often have to be made from older observed wildland fire information. The comparisons between the NAQFC modeled PM2.5 and the EPA AirNow surface observation show that large errors in PM2.5 prediction can occur if fire smoke emissions are sometimes placed at the wrong location and/or time. A configuration of NAQFC CMAQ-system to re-run previous 24 hours, during which wildland fires were observed from satellites has been included recently. This study focuses on the effort performed to minimize the error in NAQFC PM2.5 predictions resulting from incorporating fire smoke emissions into the NAQFC from a recently updated newer version of USFS BlueSky system. This study will show how new approaches has improved the PM2.5 predictions at both nearby and downstream areas from fire sources. Furthermore, Environment and Climate Change Canada (ECCC) fire emissions data are being tested.
Experiment evaluates ocean models and data assimiliation in the Gulf Stream

NASA Astrophysics Data System (ADS)

Willems, Robert C.; Glenn, S. M.; Crowley, M. F.; Malanotte-Rizzoli, P.; Young, R. E.; Ezer, T.; Mellor, G. L.; Arango, H. G.; Robinson, A. R.; Lai, C.-C. A.

Using data sets of known quality as the basis for comparison, a recent experiment explored the Gulf Stream Region at 27°-47°N and 80°-50°W to assess the nowcast/forecast capability of specific ocean models and the impact of data assimilation. Scientists from five universities and the Naval Research Laboratory/Stennis Space Center participated in the Data Assimilation and Model Evaluation Experiment (DAMEÉ-GSR).DAMEÉ-GSR was based on case studies, each successively more complex, and was divided into three phases using case studies (data) from 1987 and 1988. Phase I evaluated models' forecast capability using common initial conditions and comparing model forecast fields with observational data at forecast time over a 2-week period. Phase II added data assimilation and assessed its impact on forecast capability, using the same case studies as in phase I, and phase III added a 2-month case study overlapping some periods in Phases I and II.
How accurate are the weather forecasts for Bierun (southern Poland)?

NASA Astrophysics Data System (ADS)

Gawor, J.

2012-04-01

Weather forecast accuracy has increased in recent times mainly thanks to significant development of numerical weather prediction models. Despite the improvements, the forecasts should be verified to control their quality. The evaluation of forecast accuracy can also be an interesting learning activity for students. It joins natural curiosity about everyday weather and scientific process skills: problem solving, database technologies, graph construction and graphical analysis. The examination of the weather forecasts has been taken by a group of 14-year-old students from Bierun (southern Poland). They participate in the GLOBE program to develop inquiry-based investigations of the local environment. For the atmospheric research the automatic weather station is used. The observed data were compared with corresponding forecasts produced by two numerical weather prediction models, i.e. COAMPS (Coupled Ocean/Atmosphere Mesoscale Prediction System) developed by Naval Research Laboratory Monterey, USA; it runs operationally at the Interdisciplinary Centre for Mathematical and Computational Modelling in Warsaw, Poland and COSMO (The Consortium for Small-scale Modelling) used by the Polish Institute of Meteorology and Water Management. The analysed data included air temperature, precipitation, wind speed, wind chill and sea level pressure. The prediction periods from 0 to 24 hours (Day 1) and from 24 to 48 hours (Day 2) were considered. The verification statistics that are commonly used in meteorology have been applied: mean error, also known as bias, for continuous data and a 2x2 contingency table to get the hit rate and false alarm ratio for a few precipitation thresholds. The results of the aforementioned activity became an interesting basis for discussion. The most important topics are: 1) to what extent can we rely on the weather forecasts? 2) How accurate are the forecasts for two considered time ranges? 3) Which precipitation threshold is the most predictable? 4) Why are some weather elements easier to verify than others? 5) What factors may contribute to the quality of the weather forecast?
DEVELOPING EMISSION INVENTORIES FOR BIOMASS BURNING FOR REAL-TIME AND RETROSPECTIVE MODELING

EPA Science Inventory

The EPA uses chemical transport models to simulate historic meteorological episodes for developing air quality management strategies. In addition, chemical transport models are now being used operationally to create air quality forecasts. There are currently a number of methods a...
Regional Precipitation Forecast with Atmospheric InfraRed Sounder (AIRS) Profile Assimilation

NASA Technical Reports Server (NTRS)

Chou, S.-H.; Zavodsky, B. T.; Jedloved, G. J.

2010-01-01

Advanced technology in hyperspectral sensors such as the Atmospheric InfraRed Sounder (AIRS; Aumann et al. 2003) on NASA's polar orbiting Aqua satellite retrieve higher vertical resolution thermodynamic profiles than their predecessors due to increased spectral resolution. Although these capabilities do not replace the robust vertical resolution provided by radiosondes, they can serve as a complement to radiosondes in both space and time. These retrieved soundings can have a significant impact on weather forecasts if properly assimilated into prediction models. Several recent studies have evaluated the performance of specific operational weather forecast models when AIRS data are included in the assimilation process. LeMarshall et al. (2006) concluded that AIRS radiances significantly improved 500 hPa anomaly correlations in medium-range forecasts of the Global Forecast System (GFS) model. McCarty et al. (2009) demonstrated similar forecast improvement in 0-48 hour forecasts in an offline version of the operational North American Mesoscale (NAM) model when AIRS radiances were assimilated at the regional scale. Reale et al. (2008) showed improvements to Northern Hemisphere 500 hPa height anomaly correlations in NASA's Goddard Earth Observing System Model, Version 5 (GEOS-5) global system with the inclusion of partly cloudy AIRS temperature profiles. Singh et al. (2008) assimilated AIRS temperature and moisture profiles into a regional modeling system for a study of a heavy rainfall event during the summer monsoon season in Mumbai, India. This paper describes an approach to assimilate AIRS temperature and moisture profiles into a regional configuration of the Advanced Research Weather Research and Forecasting (WRF-ARW) model using its three-dimensional variational (3DVAR) assimilation system (WRF-Var; Barker et al. 2004). Section 2 describes the AIRS instrument and how the quality indicators are used to intelligently select the highest-quality data for assimilation. Section 3 presents an overall precipitation improvement with AIRS assimilation during a 37-day case study period, and Section 4 focuses on a single case study to further investigate the meteorological impact of AIRS profiles on synoptic scale models. Finally, Section 5 provides a summary of the paper.
Influence of Boundary Conditions on Simulated U.S. Air Quality

EPA Science Inventory

One of the key inputs to regional-scale photochemical models frequently used in air quality planning and forecasting applications are chemical boundary conditions representing background pollutant concentrations originating outside the regional modeling domain. A number of studie...
A Bayesian joint probability modeling approach for seasonal forecasting of streamflows at multiple sites

NASA Astrophysics Data System (ADS)

Wang, Q. J.; Robertson, D. E.; Chiew, F. H. S.

2009-05-01

Seasonal forecasting of streamflows can be highly valuable for water resources management. In this paper, a Bayesian joint probability (BJP) modeling approach for seasonal forecasting of streamflows at multiple sites is presented. A Box-Cox transformed multivariate normal distribution is proposed to model the joint distribution of future streamflows and their predictors such as antecedent streamflows and El Niño-Southern Oscillation indices and other climate indicators. Bayesian inference of model parameters and uncertainties is implemented using Markov chain Monte Carlo sampling, leading to joint probabilistic forecasts of streamflows at multiple sites. The model provides a parametric structure for quantifying relationships between variables, including intersite correlations. The Box-Cox transformed multivariate normal distribution has considerable flexibility for modeling a wide range of predictors and predictands. The Bayesian inference formulated allows the use of data that contain nonconcurrent and missing records. The model flexibility and data-handling ability means that the BJP modeling approach is potentially of wide practical application. The paper also presents a number of statistical measures and graphical methods for verification of probabilistic forecasts of continuous variables. Results for streamflows at three river gauges in the Murrumbidgee River catchment in southeast Australia show that the BJP modeling approach has good forecast quality and that the fitted model is consistent with observed data.
Eta-CMAQ Air Quality Forecasts for O3 and Related Species Using Three Different Photochemical Mechanisms (CB4, CB05, SAPRC-99): Comparisons with Measurements During the 2004 ICARTT Study

EPA Science Inventory

In this study, we compare the CB4, CB05 and SAPRC-99 mechanisms by examining the impact of these different chemical mechanisms on the Eta-CMAQ air quality forecast model simulations for O₃ and its related precursors over the eastern US through comparisons with the inte...
Forecasting in the presence of expectations

NASA Astrophysics Data System (ADS)

Allen, R.; Zivin, J. G.; Shrader, J.

2016-05-01

Physical processes routinely influence economic outcomes, and actions by economic agents can, in turn, influence physical processes. This feedback creates challenges for forecasting and inference, creating the potential for complementarity between models from different academic disciplines. Using the example of prediction of water availability during a drought, we illustrate the potential biases in forecasts that only take part of a coupled system into account. In particular, we show that forecasts can alter the feedbacks between supply and demand, leading to inaccurate prediction about future states of the system. Although the example is specific to drought, the problem of feedback between expectations and forecast quality is not isolated to the particular model-it is relevant to areas as diverse as population assessments for conservation, balancing the electrical grid, and setting macroeconomic policy.
PROJECT SUMMARY: DEVELOPMENT OF THE VIRTUAL BEACH MODEL, PHASE I: AN EMPIRICAL MODEL

EPA Science Inventory

Mathematical models based on water-quality and other environmental surrogates may help to provide water quality assessment within a few hours and potentially provide one to three day forecasts, providing beach managers and public-health officials a tool for developing beach-speci...
Impact of atmosphere and land surface initial conditions on seasonal forecast of global surface temperature

NASA Astrophysics Data System (ADS)

Materia, Stefano; Borrelli, Andrea; Bellucci, Alessio; Alessandri, Andrea; Di Pietro, Pierluigi; Athanasiadis, Panagiotis; Navarra, Antonio; Gualdi, Silvio

2014-05-01

The impact of land surface and atmosphere initialization on the forecast skill of a seasonal prediction system is investigated, and an effort to disentangle the role played by the individual components to the global predictability is done, via a hierarchy of seasonal forecast experiments performed under different initialization strategies. A realistic atmospheric initial state allows an improved equilibrium between the ocean and overlying atmosphere, mitigating the coupling shock and possibly increasing the model predictive skill in the ocean. In fact, in a few regions characterized by strong air-sea coupling, the atmosphere initial condition affects the forecast skill for several months. In particular, the ENSO region, the eastern tropical Atlantic and the North Pacific benefit significantly from the atmosphere initialization. On mainland, the impact of atmospheric initial conditions is detected in the early phase of the forecast, while the quality of land surface initialization affects the forecast skill in the following lead seasons. The winter forecast in the high latitude plains of Siberia and Canada benefit from the snow initialization, while the impact of soil moisture initial state is particularly effective in the Mediterranean region, in central Asia and Australia. However, initialization through land surface reanalysis does not systematically guarantee an enhancement of the predictive skill: the quality of the forecast is sometimes higher for the non-constrained model. Overall, the introduction of a realistic initialization of land surface and atmosphere substantially increases skill and accuracy. However, further developments in the operating procedure for land surface initialization are required for more accurate seasonal forecasts.
Shared investment projects and forecasting errors: setting framework conditions for coordination and sequencing data quality activities.

PubMed

Leitner, Stephan; Brauneis, Alexander; Rausch, Alexandra

2015-01-01

In this paper, we investigate the impact of inaccurate forecasting on the coordination of distributed investment decisions. In particular, by setting up a computational multi-agent model of a stylized firm, we investigate the case of investment opportunities that are mutually carried out by organizational departments. The forecasts of concern pertain to the initial amount of money necessary to launch and operate an investment opportunity, to the expected intertemporal distribution of cash flows, and the departments' efficiency in operating the investment opportunity at hand. We propose a budget allocation mechanism for coordinating such distributed decisions The paper provides guidance on how to set framework conditions, in terms of the number of investment opportunities considered in one round of funding and the number of departments operating one investment opportunity, so that the coordination mechanism is highly robust to forecasting errors. Furthermore, we show that-in some setups-a certain extent of misforecasting is desirable from the firm's point of view as it supports the achievement of the corporate objective of value maximization. We then address the question of how to improve forecasting quality in the best possible way, and provide policy advice on how to sequence activities for improving forecasting quality so that the robustness of the coordination mechanism to errors increases in the best possible way. At the same time, we show that wrong decisions regarding the sequencing can lead to a decrease in robustness. Finally, we conduct a comprehensive sensitivity analysis and prove that-in particular for relatively good forecasters-most of our results are robust to changes in setting the parameters of our multi-agent simulation model.
Forecasting the Emergency Department Patients Flow.

PubMed

Afilal, Mohamed; Yalaoui, Farouk; Dugardin, Frédéric; Amodeo, Lionel; Laplanche, David; Blua, Philippe

2016-07-01

Emergency department (ED) have become the patient's main point of entrance in modern hospitals causing it frequent overcrowding, thus hospital managers are increasingly paying attention to the ED in order to provide better quality service for patients. One of the key elements for a good management strategy is demand forecasting. In this case, forecasting patients flow, which will help decision makers to optimize human (doctors, nurses…) and material(beds, boxs…) resources allocation. The main interest of this research is forecasting daily attendance at an emergency department. The study was conducted on the Emergency Department of Troyes city hospital center, France, in which we propose a new practical ED patients classification that consolidate the CCMU and GEMSA categories into one category and innovative time-series based models to forecast long and short term daily attendance. The models we developed for this case study shows very good performances (up to 91,24 % for the annual Total flow forecast) and robustness to epidemic periods.

Uncertainty, ensembles and air quality dispersion modeling: applications and challenges

NASA Astrophysics Data System (ADS)

Dabberdt, Walter F.; Miller, Erik

The past two decades have seen significant advances in mesoscale meteorological modeling research and applications, such as the development of sophisticated and now widely used advanced mesoscale prognostic models, large eddy simulation models, four-dimensional data assimilation, adjoint models, adaptive and targeted observational strategies, and ensemble and probabilistic forecasts. Some of these advances are now being applied to urban air quality modeling and applications. Looking forward, it is anticipated that the high-priority air quality issues for the near-to-intermediate future will likely include: (1) routine operational forecasting of adverse air quality episodes; (2) real-time high-level support to emergency response activities; and (3) quantification of model uncertainty. Special attention is focused here on the quantification of model uncertainty through the use of ensemble simulations. Application to emergency-response dispersion modeling is illustrated using an actual event that involved the accidental release of the toxic chemical oleum. Both surface footprints of mass concentration and the associated probability distributions at individual receptors are seen to provide valuable quantitative indicators of the range of expected concentrations and their associated uncertainty.
Support vector machine-an alternative to artificial neuron network for water quality forecasting in an agricultural nonpoint source polluted river?

PubMed

Liu, Mei; Lu, Jun

2014-09-01

Water quality forecasting in agricultural drainage river basins is difficult because of the complicated nonpoint source (NPS) pollution transport processes and river self-purification processes involved in highly nonlinear problems. Artificial neural network (ANN) and support vector model (SVM) were developed to predict total nitrogen (TN) and total phosphorus (TP) concentrations for any location of the river polluted by agricultural NPS pollution in eastern China. River flow, water temperature, flow travel time, rainfall, dissolved oxygen, and upstream TN or TP concentrations were selected as initial inputs of the two models. Monthly, bimonthly, and trimonthly datasets were selected to train the two models, respectively, and the same monthly dataset which had not been used for training was chosen to test the models in order to compare their generalization performance. Trial and error analysis and genetic algorisms (GA) were employed to optimize the parameters of ANN and SVM models, respectively. The results indicated that the proposed SVM models performed better generalization ability due to avoiding the occurrence of overtraining and optimizing fewer parameters based on structural risk minimization (SRM) principle. Furthermore, both TN and TP SVM models trained by trimonthly datasets achieved greater forecasting accuracy than corresponding ANN models. Thus, SVM models will be a powerful alternative method because it is an efficient and economic tool to accurately predict water quality with low risk. The sensitivity analyses of two models indicated that decreasing upstream input concentrations during the dry season and NPS emission along the reach during average or flood season should be an effective way to improve Changle River water quality. If the necessary water quality and hydrology data and even trimonthly data are available, the SVM methodology developed here can easily be applied to other NPS-polluted rivers.
Season-ahead water quality forecasts for the Schuylkill River, Pennsylvania

NASA Astrophysics Data System (ADS)

Block, P. J.; Leung, K.

2013-12-01

Anticipating and preparing for elevated water quality parameter levels in critical water sources, using weather forecasts, is not uncommon. In this study, we explore the feasibility of extending this prediction scale to a season-ahead for the Schuylkill River in Philadelphia, utilizing both statistical and dynamical prediction models, to characterize the season. This advance information has relevance for recreational activities, ecosystem health, and water treatment, as the Schuylkill provides 40% of Philadelphia's water supply. The statistical model associates large-scale climate drivers with streamflow and water quality parameter levels; numerous variables from NOAA's CFSv2 model are evaluated for the dynamical approach. A multi-model combination is also assessed. Results indicate moderately skillful prediction of average summertime total coliform and wintertime turbidity, using season-ahead oceanic and atmospheric variables, predominantly from the North Atlantic Ocean. Models predicting the number of elevated turbidity events across the wintertime season are also explored.
Observations and modeling of air quality trends over 1990-2010 across the northern hemisphere: China, the United States and Europe

EPA Science Inventory

Trends in air quality across the Northern Hemisphere over a 21-year period (1990–2010) were simulated using the Community Multiscale Air Quality (CMAQ) multiscale chemical transport model driven by meteorology from Weather Research and Forecasting WRF) simulations and internally ...
The Mauna Kea Weather Center: Custom Atmospheric Forecasting Support for Mauna Kea

NASA Astrophysics Data System (ADS)

Businger, Steven

2011-03-01

The success of operations at Mauna Kea Observatories is strongly influenced by weather conditions. The Mauna Kea Weather Center, an interdisciplinary research program, was established in 1999 to develop and provide custom weather support for Mauna Kea Observatories. The operational forecasting goals of the program are to facilitate the best possible use of favorable atmospheric conditions for scientific benefit and to ensure operational safety. During persistent clear periods, astronomical observing quality varies substantially due to changes in the vertical profiles of temperature, wind, moisture, and turbulence. Cloud and storm systems occasionally cause adverse or even hazardous conditions. A dedicated, daily, real-time mesoscale numerical modeling effort provides crucial forecast guidance in both cases. Several key atmospheric variables are forecast with sufficient skill to be of operational and scientific benefit to the telescopes on Mauna Kea. Summit temperature forecasts allow mirrors to be set to the ambient temperature to reduce image distortion. Precipitable water forecasts allow infrared observations to be prioritized according to atmospheric opacity. Forecasts of adverse and hazardous conditions protect the safety of personnel and allow for scheduling of maintenance when observing is impaired by cloud. The research component of the project continues to improve the accuracy and content of the forecasts. In particular, case studies have resulted in operational forecasts of astronomical observing quality, or seeing.
Sensitivity of an Integrated Mesoscale Atmosphere and Agriculture Land Modeling System (WRF/CMAQ-EPIC) to MODIS Vegetation and Lightning Assimilation

EPA Science Inventory

The combined meteorology and air quality modeling system composed of the Weather Research and Forecast (WRF) model and Community Multiscale Air Quality (CMAQ) model is an important decision support tool that is used in research and regulatory decisions related to emissions, meteo...
Real-time forecasting of an epidemic using a discrete time stochastic model: a case study of pandemic influenza (H1N1-2009).

PubMed

Nishiura, Hiroshi

2011-02-16

Real-time forecasting of epidemics, especially those based on a likelihood-based approach, is understudied. This study aimed to develop a simple method that can be used for the real-time epidemic forecasting. A discrete time stochastic model, accounting for demographic stochasticity and conditional measurement, was developed and applied as a case study to the weekly incidence of pandemic influenza (H1N1-2009) in Japan. By imposing a branching process approximation and by assuming the linear growth of cases within each reporting interval, the epidemic curve is predicted using only two parameters. The uncertainty bounds of the forecasts are computed using chains of conditional offspring distributions. The quality of the forecasts made before the epidemic peak appears largely to depend on obtaining valid parameter estimates. The forecasts of both weekly incidence and final epidemic size greatly improved at and after the epidemic peak with all the observed data points falling within the uncertainty bounds. Real-time forecasting using the discrete time stochastic model with its simple computation of the uncertainty bounds was successful. Because of the simplistic model structure, the proposed model has the potential to additionally account for various types of heterogeneity, time-dependent transmission dynamics and epidemiological details. The impact of such complexities on forecasting should be explored when the data become available as part of the disease surveillance.
Impact of Ozone Radiative Feedbacks on Global Weather Forecasting

NASA Astrophysics Data System (ADS)

Ivanova, I.; de Grandpré, J.; Rochon, Y. J.; Sitwell, M.

2017-12-01

A coupled Chemical Data Assimilation system for ozone is being developed at Environment and Climate Change Canada (ECCC) with the goals to improve the forecasting of UV index and the forecasting of air quality with the Global Environmental Multi-scale (GEM) Model for Air quality and Chemistry (MACH). Furthermore, this system provides an opportunity to evaluate the benefit of ozone assimilation for improving weather forecasting with the ECCC Global Deterministic Prediction System (GDPS) for Numerical Weather Prediction (NWP). The present UV index forecasting system uses a statistical approach for evaluating the impact of ozone in clear-sky and cloudy conditions, and the use of real-time ozone analysis and ozone forecasts is highly desirable. Improving air quality forecasting with GEM-MACH further necessitates the development of integrated dynamical-chemical assimilation system. Upon its completion, real-time ozone analysis and ozone forecasts will also be available for piloting the regional air quality system, and for the computation of ozone heating rates, in replacement of the monthly mean ozone distribution currently used in the GDPS. Experiments with ozone radiative feedbacks were run with the GDPS at 25km resolution and 84 levels with a lid at 0.1 hPa and were initialized with ozone analysis that has assimilated total ozone column from OMI, OMPS, and GOME satellite instruments. The results show that the use of prognostic ozone for the computation of the heating/cooling rates has a significant impact on the temperature distribution throughout the stratosphere and upper troposphere regions. The impact of ozone assimilation is especially significant in the tropopause region, where ozone heating in the infrared wavelengths is important and ozone lifetime is relatively long. The implementation of the ozone radiative feedback in the GDPS requires addressing various issues related to model biases (temperature and humidity) and biases in equilibrium state (ozone mixing ratio, air temperature and overhead column ozone) used for the calculation of the linearized photochemical production and loss of ozone. Furthermore the radiative budget in the tropopause region is strongly affected by water vapor cooling, which impact requires further evaluation for the use in chemically coupled operational NWP systems.
Forecasting volcanic eruptions and other material failure phenomena: An evaluation of the failure forecast method

NASA Astrophysics Data System (ADS)

Bell, Andrew F.; Naylor, Mark; Heap, Michael J.; Main, Ian G.

2011-08-01

Power-law accelerations in the mean rate of strain, earthquakes and other precursors have been widely reported prior to material failure phenomena, including volcanic eruptions, landslides and laboratory deformation experiments, as predicted by several theoretical models. The Failure Forecast Method (FFM), which linearizes the power-law trend, has been routinely used to forecast the failure time in retrospective analyses; however, its performance has never been formally evaluated. Here we use synthetic and real data, recorded in laboratory brittle creep experiments and at volcanoes, to show that the assumptions of the FFM are inconsistent with the error structure of the data, leading to biased and imprecise forecasts. We show that a Generalized Linear Model method provides higher-quality forecasts that converge more accurately to the eventual failure time, accounting for the appropriate error distributions. This approach should be employed in place of the FFM to provide reliable quantitative forecasts and estimate their associated uncertainties.
Evaluation of Day and Nighttime Lower Tropospheric Ozone from Air Quality Models using TES and Ozonesondes

NASA Astrophysics Data System (ADS)

Osterman, G. B.; Neu, J. L.; Eldering, A.; Pinder, R. W.; Tang, Y.; McQueen, J.

2012-12-01

At night, ozone can be transported long distances above the surface inversion layer without chemical destruction or deposition. As the boundary layer breaks up in the morning, this nocturnal ozone can be mixed down to the surface and rapidly increase ozone concentrations at a rate that can rival chemical ozone production. Most regional scale models that are used for air quality forecasts and ozone source attribution do not adequately capture nighttime ozone concentrations and transport. We combine ozone profile data from the NASA Earth Observing System (EOS) Tropospheric Emission Spectrometer (TES) and other sensors, ozonesonde data collected during the INTEX Ozonesonde Network Study (IONS), EPA AirNow ground station ozone data, the Community Multi-Scale Air Quality (CMAQ) model, and the National Air Quality Forecast Capability (NAQFC) model to examine air quality events during August 2006. We present both aggregated statistics and case-study analyses that assess the relationship between the models' ability to reproduce surface air quality events and their ability to capture the vertical distribution of ozone both during the day and at night. We perform the comparisons looking at the geospatial dependence in the differences between the measurements and models under different surface ozone conditions.
The Influence of Observation Errors on Analysis Error and Forecast Skill Investigated with an Observing System Simulation Experiment

NASA Technical Reports Server (NTRS)

Prive, N. C.; Errico, R. M.; Tai, K.-S.

2013-01-01

The Global Modeling and Assimilation Office (GMAO) observing system simulation experiment (OSSE) framework is used to explore the response of analysis error and forecast skill to observation quality. In an OSSE, synthetic observations may be created that have much smaller error than real observations, and precisely quantified error may be applied to these synthetic observations. Three experiments are performed in which synthetic observations with magnitudes of applied observation error that vary from zero to twice the estimated realistic error are ingested into the Goddard Earth Observing System Model (GEOS-5) with Gridpoint Statistical Interpolation (GSI) data assimilation for a one-month period representing July. The analysis increment and observation innovation are strongly impacted by observation error, with much larger variances for increased observation error. The analysis quality is degraded by increased observation error, but the change in root-mean-square error of the analysis state is small relative to the total analysis error. Surprisingly, in the 120 hour forecast increased observation error only yields a slight decline in forecast skill in the extratropics, and no discernable degradation of forecast skill in the tropics.
Impact of Atmospheric Infrared Sounder (AIRS) Thermodynamic Profiles on Regional Precipitation Forecasting

NASA Technical Reports Server (NTRS)

Chou, S.-H.; Zavodsky, B. T.; Jedloved, G. J.

2010-01-01

In data sparse regions, remotely-sensed observations can be used to improve analyses and lead to better forecasts. One such source comes from the Atmospheric Infrared Sounder (AIRS), which together with the Advanced Microwave Sounding Unit (AMSU), provides temperature and moisture profiles in clear and cloudy regions with accuracy which approaches that of radiosondes. The purpose of this paper is to describe an approach to assimilate AIRS thermodynamic profile data into a regional configuration of the Advanced Research WRF (ARW) model using WRF-Var. Quality indicators are used to select only the highest quality temperature and moisture profiles for assimilation in clear and partly cloudy regions, and uncontaminated portions of retrievals above clouds in overcast regions. Separate error characteristics for land and water profiles are also used in the assimilation process. Assimilation results indicate that AIRS profiles produce an analysis closer to in situ observations than the background field. Forecasts from a 37-day case study period in the winter of 2007 show that AIRS profile data can lead to improvements in 6-h cumulative precipitation forecasts resulting from improved thermodynamic fields. Additionally, in a convective heavy rainfall event from February 2007, assimilation of AIRS profiles produces a more unstable boundary layer resulting in enhanced updrafts in the model. These updrafts produce a squall line and precipitation totals that more closely reflect ground-based observations than a no AIRS control forecast. The location of available high-quality AIRS profiles ahead of approaching storm systems is found to be of paramount importance to the amount of impact the observations will have on the resulting forecasts.
A coupled human-natural system to assess the operational value of weather and climate services for agriculture

NASA Astrophysics Data System (ADS)

Li, Yu; Giuliani, Matteo; Castelletti, Andrea

2017-09-01

Recent advances in weather and climate (W&C) services are showing increasing forecast skills over seasonal and longer timescales, potentially providing valuable support in informing decisions in a variety of economic sectors. Quantifying this value, however, might not be straightforward as better forecast quality does not necessarily imply better decisions by the end users, especially when forecasts do not reach their final users, when providers are not trusted, or when forecasts are not appropriately understood. In this study, we contribute an assessment framework to evaluate the operational value of W&C services for informing agricultural practices by complementing traditional forecast quality assessments with a coupled human-natural system behavioural model which reproduces farmers' decisions. This allows a more critical assessment of the forecast value mediated by the end users' perspective, including farmers' risk attitudes and behavioural factors. The application to an agricultural area in northern Italy shows that the quality of state-of-the-art W&C services is still limited in predicting the weather and the crop yield of the incoming agricultural season, with ECMWF annual products simulated by the IFS/HOPE model resulting in the most skillful product in the study area. However, we also show that the accuracy of estimating crop yield and the probability of making optimal decisions are not necessarily linearly correlated, with the overall assessment procedure being strongly impacted by the behavioural attitudes of farmers, which can produce rank reversals in the quantification of the W&C services operational value depending on the different perceptions of risk and uncertainty.
Hydrologic Forecasting in the 21st Century: Challenges and Directions of Research

NASA Astrophysics Data System (ADS)

Restrepo, P.; Schaake, J.

2009-04-01

Traditionally, the role of the Hydrology program of the National Weather Service has been centered around forecasting floods, in order to minimize loss of lives and damage to property as a result of floods as well as water levels for navigable rivers, and water supply in some areas of the country. A number of factors, including shifting population patterns, widespread drought and concerns about climate change have made it imperative to widen the focus to cover forecasting flows ranging from drought to floods and anything in between. Because of these concerns, it is imperative to develop models that rely more on the physical characteristics of the watershed for parameterization and less on historical observations. Furthermore, it is also critical to consider explicitly the sources of uncertainty in the forecasting process, including parameter values, model structure, forcings (both observations and forecasts), initial conditions, and streamflow observations. A consequence of more widespread occurrence of low flows as a result either of the already evident earlier snowmelt in the Western United States, or of the predicted changes in precipitation patterns, is the issue of water quality: lower flows will have higher concentrations of certain pollutants. This paper describes the current projects and future directions of research for hydrologic forecasting in the United States. Ongoing projects on quantitative precipitation and temperature estimates and forecasts, uncertainty modeling by the use of ensembles, data assimilation, verification, distributed conceptual modeling will be reviewed. Broad goals of the research directions are: 1) reliable modeling of the different sources of uncertainty. 2) a more expeditious and cost-effective approach by reducing the effort required in model calibration; 3) improvements in forecast lead-time and accuracy; 4) an approach for rapid adjustment of model parameters to account for changes in the watershed, both rapid as the result from forest fires or levee breaches, and slow, as the result of watershed reforestation, reforestation or urban development; 5) an expanded suite of products, including soil moisture and temperature forecasts, and water quality constituents; and 6) a comprehensive verification system to assess the effectiveness of the other 5 goals. To this end, the research plan places an emphasis on research of models with parameters that can be derived from physical watershed characteristics. Purely physically based models may be unattainable or impractical, and, therefore, models resulting from a combination of physically and conceptually approached processes may be required With respect to the hydrometeorological forcings the research plan emphasizes the development of improved precipitation estimation techniques through the synthesis of radar, rain gauge, satellite, and numerical weather prediction model output, particularly in those areas where ground-based sensors are inadequate to detect spatial variability in precipitation. Better estimation and forecasting of precipitation are most likely to be achieved by statistical merging of remote-sensor observations and forecasts from high-resolution numerical prediction models. Enhancements to the satellite-based precipitation products will include use of TRMM precipitation data in preparation for information to be supplied by the Global Precipitation Mission satellites not yet deployed. Because of a growing need for services in water resources, including low-flow forecasts for water supply customers, we will be directing research into coupled surface-groundwater models that will eventually replace the groundwater component of the existing models, and will be part of the new generation of models. Finally, the research plan covers the directions of research for probabilistic forecasting using ensembles, data assimilation and the verification and validation of both deterministic and probabilistic forecasts.
Improved Space-Time Forecasting of next Day Ozone Concentrations in the Eastern U.S.

EPA Science Inventory

There is an urgent need to provide accurate air quality information and forecasts to the general public and environmental health decision-makers. This paper develops a hierarchical space-time model for daily 8-hour maximum ozone concentration (O3) data covering much of the easter...
Multi-Scale Enviro-HIRLAM Forecasting of Weather and Atmospheric Composition over China and its Megacities

NASA Astrophysics Data System (ADS)

Mahura, Alexander; Amstrup, Bjarne; Nuterman, Roman; Yang, Xiaohua; Baklanov, Alexander

2017-04-01

Air pollution is a serious problem in different regions of China and its continuously growing megacities. Information on air quality, and especially, in urbanized areas is important for decision making, emergency response and population. In particular, the metropolitan areas of Shanghai, Beijing, and Pearl River Delta are well known as main regions having serious air pollution problems. The on-line integrated meteorology-chemistry-aerosols Enviro-HIRLAM (Environment - HIgh Resolution Limited Area Model) model adapted for China and selected megacities is applied for forecasting of weather and atmospheric composition (with focus on aerosols). The model system is running in downscaling chain from regional to urban scales at subsequent horizontal resolutions of 15-5-2.5 km. The model setup includes also the urban Building Effects Parameterization module, describing different types of urban districts (industrial commercial, city center, high density and residential) with its own morphological and aerodynamical characteristics. The effects of urbanization are important for atmospheric transport, dispersion, deposition, and chemical transformations, in addition to better quality emission inventories for China and selected urban areas. The Enviro-HIRLAM system provides meteorology and air quality forecasts at regional-subregional-urban scales (China - East China - selected megacities). In particular, such forecasting is important for metropolitan areas, where formation and development of meteorological and chemical/aerosol patterns are especially complex. It also provides information for evaluation impact on selected megacities of China as well as for investigation relationship between air pollution and meteorology.
"Updates to Model Algorithms & Inputs for the Biogenic ...

EPA Pesticide Factsheets

We have developed new canopy emission algorithms and land use data for BEIS. Simulations with BEIS v3.4 and these updates in CMAQ v5.0.2 are compared these changes to the Model of Emissions of Gases and Aerosols from Nature (MEGAN) and evaluated the simulations against observations. This has resulted in improvements in model evaluations of modeled isoprene, NOx, and O3. The National Exposure Research Laboratory (NERL) Atmospheric Modeling and Analysis Division (AMAD) conducts research in support of EPA mission to protect human health and the environment. AMAD research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMAD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMAD are being used by EPA, NOAA, and the air pollution community in understanding and forecasting not only the magnitude of the air pollution problem, but also in developing emission control policies and regulations for air quality improvements.
Sampling strategies based on singular vectors for assimilated models in ocean forecasting systems

NASA Astrophysics Data System (ADS)

Fattorini, Maria; Brandini, Carlo; Ortolani, Alberto

2016-04-01

Meteorological and oceanographic models do need observations, not only as a ground truth element to verify the quality of the models, but also to keep model forecast error acceptable: through data assimilation techniques which merge measured and modelled data, natural divergence of numerical solutions from reality can be reduced / controlled and a more reliable solution - called analysis - is computed. Although this concept is valid in general, its application, especially in oceanography, raises many problems due to three main reasons: the difficulties that have ocean models in reaching an acceptable state of equilibrium, the high measurements cost and the difficulties in realizing them. The performances of the data assimilation procedures depend on the particular observation networks in use, well beyond the background quality and the used assimilation method. In this study we will present some results concerning the great impact of the dataset configuration, in particular measurements position, on the evaluation of the overall forecasting reliability of an ocean model. The aim consists in identifying operational criteria to support the design of marine observation networks at regional scale. In order to identify the observation network able to minimize the forecast error, a methodology based on Singular Vectors Decomposition of the tangent linear model is proposed. Such a method can give strong indications on the local error dynamics. In addition, for the purpose of avoiding redundancy of information contained in the data, a minimal distance among data positions has been chosen on the base of a spatial correlation analysis of the hydrodynamic fields under investigation. This methodology has been applied for the choice of data positions starting from simplified models, like an ideal double-gyre model and a quasi-geostrophic one. Model configurations and data assimilation are based on available ROMS routines, where a variational assimilation algorithm (4D-var) is included as part of the code These first applications have provided encouraging results in terms of increased predictability time and reduced forecast error, also improving the quality of the analysis used to recover the real circulation patterns from a first guess quite far from the real state.
Operational forecast products and applications based on WRF/Chem

NASA Astrophysics Data System (ADS)

Hirtl, Marcus; Flandorfer, Claudia; Langer, Matthias; Mantovani, Simone; Olefs, Marc; Schellander-Gorgas, Theresa

2015-04-01

The responsibilities of the national weather service of Austria (ZAMG) include the support of the federal states and the public in questions connected to the protection of the environment in the frame of advisory and counseling services as well as expert opinions. The ZAMG conducts daily Air-Quality forecasts using the on-line coupled model WRF/Chem. The mother domain expands over Europe, North Africa and parts of Russia. The nested domain includes the alpine region and has a horizontal resolution of 4 km. Local emissions (Austria) are used in combination with European inventories (TNO and EMEP) for the simulations. The modeling system is presented and the results from the evaluation of the assimilation of pollutants using the 3D-VAR software GSI is shown. Currently observational data (PM10 and O3) from the Austrian Air-Quality network and from European stations (EEA) are assimilated into the model on an operational basis. In addition PM maps are produced using Aerosol Optical Thickness (AOT) observations from MODIS in combination with model data using machine learning techniques. The modeling system is operationally evaluated with different data sets. The emphasis of the application is on the forecast of pollutants which are compared to the hourly values (PM10, O3 and NO2) of the Austrian Air-Quality network. As the meteorological conditions are important for transport and chemical processes, some parameters like wind and precipitation are automatically evaluated (SAL diagrams, maps, …) with other models (e.g. ECMWF, AROME, …) and ground stations via web interface. The prediction of the AOT is also important for operators of solar power plants. In the past Numerical Weather Prediction (NWP) models were used to predict the AOT based on cloud forecasts at the ZAMG. These models do not consider the spatial and temporal variation of the aerosol distribution in the atmosphere with a consequent impact on the accuracy of forecasts especially during clear-sky days when the influence of the aerosols can have a strong impact on the AOT. WRF/Chem forecasts of the atmospheric optical properties are used to add information on the incoming radiation during these days. The evaluation of the model with satellite data for different episodes with clear-sky conditions is presented.
Post-processing method for wind speed ensemble forecast using wind speed and direction

NASA Astrophysics Data System (ADS)

Sofie Eide, Siri; Bjørnar Bremnes, John; Steinsland, Ingelin

2017-04-01

Statistical methods are widely applied to enhance the quality of both deterministic and ensemble NWP forecasts. In many situations, like wind speed forecasting, most of the predictive information is contained in one variable in the NWP models. However, in statistical calibration of deterministic forecasts it is often seen that including more variables can further improve forecast skill. For ensembles this is rarely taken advantage of, mainly due to that it is generally not straightforward how to include multiple variables. In this study, it is demonstrated how multiple variables can be included in Bayesian model averaging (BMA) by using a flexible regression method for estimating the conditional means. The method is applied to wind speed forecasting at 204 Norwegian stations based on wind speed and direction forecasts from the ECMWF ensemble system. At about 85 % of the sites the ensemble forecasts were improved in terms of CRPS by adding wind direction as predictor compared to only using wind speed. On average the improvements were about 5 %, but mainly for moderate to strong wind situations. For weak wind speeds adding wind direction had more or less neutral impact.

USA Nutrient managment forecasting via the "Fertilizer Forecaster": linking surface runnof, nutrient application and ecohydrology.

NASA Astrophysics Data System (ADS)

Drohan, Patrick; Buda, Anthony; Kleinman, Peter; Miller, Douglas; Lin, Henry; Beegle, Douglas; Knight, Paul

2017-04-01

USA and state nutrient management planning offers strategic guidance that strives to educate farmers and those involved in nutrient management to make wise management decisions. A goal of such programs is to manage hotspots of water quality degradation that threaten human and ecosystem health, water and food security. The guidance provided by nutrient management plans does not provide the day-to-day support necessary to make operational decisions, particularly when and where to apply nutrients over the short term. These short-term decisions on when and where to apply nutrients often make the difference between whether the nutrients impact water quality or are efficiently utilized by crops. Infiltrating rainfall events occurring shortly after broadcast nutrient applications are beneficial, given they will wash soluble nutrients into the soil where they are used by crops. Rainfall events that generate runoff shortly after nutrients are broadcast may wash off applied nutrients, and produce substantial nutrient losses from that site. We are developing a model and data based support tool for nutrient management, the Fertilizer Forecaster, which identifies the relative probability of runoff or infiltrating events in Pennsylvania (PA) landscapes in order to improve water quality. This tool will support field specific decisions by farmers and land managers on when and where to apply fertilizers and manures over 24, 48 and 72 hour periods. Our objectives are to: (1) monitor agricultural hillslopes in watersheds representing four of the five Physiographic Provinces of the Chesapeake Bay basin; (2) validate a high resolution mapping model that identifies soils prone to runoff; (3) develop an empirically based approach to relate state-of-the-art weather forecast variables to site-specific rainfall infiltration or runoff occurrence; (4) test the empirical forecasting model against alternative approaches to forecasting runoff occurrence; and (5) recruit farmers from the four watersheds to use web-based forecast maps in daily manure and fertilizer application decisions. Data from on-farm trials is being used to assess farmer fertilizer, manure, and tillage management decisions before and after use of the Fertilizer Forecaster. This data will help us understand not only the effectiveness of the tool, but also characteristics of farmers with the greatest potential to benefit from such a tool. Feedback from on-farm trials will be used to refine a final tool for field deployment. We hope that the Fertilizer Forecaster will serve as the basis for state (USA-PA), regional (Chesapeake Bay), and national changes in nutrient management planning. This Fertilizer Forecaster is an innovative management practice that is designed to enhance the services of aquatic ecosystems by improving water quality and enhance the services of terrestrial ecosystems by increasing the efficiency of nutrient use by targeted crops.
Air quality early-warning system for cities in China

NASA Astrophysics Data System (ADS)

Xu, Yunzhen; Yang, Wendong; Wang, Jianzhou

2017-01-01

Air pollution has become a serious issue in many developing countries, especially in China, and could generate adverse effects on human beings. Air quality early-warning systems play an increasingly significant role in regulatory plans that reduce and control emissions of air pollutants and inform the public in advance when harmful air pollution is foreseen. However, building a robust early-warning system that will improve the ability of early-warning is not only a challenge but also a critical issue for the entire society. Relevant research is still poor in China and cannot always satisfy the growing requirements of regulatory planning, despite the issue's significance. Therefore, in this paper, a hybrid air quality early-warning system was successfully developed, composed of forecasting and evaluation. First, a hybrid forecasting model was proposed as an important part of this system based on the theory of "decomposition and ensemble" and combined with the advanced data processing technique, support vector machine, the latest bio-inspired optimization algorithm and the leave-one-out strategy for deciding weights. Afterwards, to intensify the research, fuzzy evaluation was performed, which also plays an indispensable role in the early-warning system. The forecasting model and fuzzy evaluation approaches are complementary. Case studies using daily air pollution concentrations of six air pollutants from three cities in China (i.e., Taiyuan, Harbin and Chongqing) are used as examples to evaluate the efficiency and effectiveness of the developed air quality early-warning system. Experimental results demonstrate that both the accuracy and the effectiveness of the developed system are greatly superior for air quality early warning. Furthermore, the application of forecasting and evaluation enables the informative and effective quantification of future air quality, offering a significant advantage, and can be employed to develop rapid air quality early-warning systems.
Forecasting models for flow and total dissolved solids in Karoun river-Iran

NASA Astrophysics Data System (ADS)

Salmani, Mohammad Hassan; Salmani Jajaei, Efat

2016-04-01

Water quality is one of the most important factors contributing to a healthy life. From the water quality management point of view, TDS (total dissolved solids) is the most important factor and many water developing plans have been implemented in recognition of this factor. However, these plans have not been perfect and very successful in overcoming the poor water quality problem, so there are a good volume of related studies in the literature. We study TDS and the water flow of the Karoun river in southwest Iran. We collected the necessary time series data from the Harmaleh station located in the river. We present two Univariate Seasonal Autoregressive Integrated Movement Average (ARIMA) models to forecast TDS and water flow in this river. Then, we build up a Transfer Function (TF) model to formulate the TDS as a function of water flow volume. A performance comparison between the Seasonal ARIMA and the TF models are presented.
Should we use seasonnal meteorological ensemble forecasts for hydrological forecasting? A case study for nordic watersheds in Canada.

NASA Astrophysics Data System (ADS)

Bazile, Rachel; Boucher, Marie-Amélie; Perreault, Luc; Leconte, Robert; Guay, Catherine

2017-04-01

Hydro-electricity is a major source of energy for many countries throughout the world, including Canada. Long lead-time streamflow forecasts are all the more valuable as they help decision making and dam management. Different techniques exist for long-term hydrological forecasting. Perhaps the most well-known is 'Extended Streamflow Prediction' (ESP), which considers past meteorological scenarios as possible, often equiprobable, future scenarios. In the ESP framework, those past-observed meteorological scenarios (climatology) are used in turn as the inputs of a chosen hydrological model to produce ensemble forecasts (one member corresponding to each year in the available database). Many hydropower companies, including Hydro-Québec (province of Quebec, Canada) use variants of the above described ESP system operationally for long-term operation planning. The ESP system accounts for the hydrological initial conditions and for the natural variability of the meteorological variables. However, it cannot consider the current initial state of the atmosphere. Climate models can help remedy this drawback. In the context of a changing climate, dynamical forecasts issued from climate models seem to be an interesting avenue to improve upon the ESP method and could help hydropower companies to adapt their management practices to an evolving climate. Long-range forecasts from climate models can also be helpful for water management at locations where records of past meteorological conditions are short or nonexistent. In this study, we compare 7-month hydrological forecasts obtained from climate model outputs to an ESP system. The ESP system mimics the one used operationally at Hydro-Québec. The dynamical climate forecasts are produced by the European Center for Medium range Weather Forecasts (ECMWF) System4. Forecasts quality is assessed using numerical scores such as the Continuous Ranked Probability Score (CRPS) and the Ignorance score and also graphical tools such as the reliability diagram. This study covers 10 nordic watersheds. We show that forecast performance according to the CRPS varies with lead-time but also with the period of the year. The raw forecasts from the ECMWF System4 display important biases for both temperature and precipitation, which need to be corrected. The linear scaling method is used for this purpose and is found effective. Bias correction improves forecasts performance, especially during the summer when the precipitations are over-estimated. According to the CRPS, bias corrected forecasts from System4 show performances comparable to those of the ESP system. However, the Ignorance score, which penalizes the lack of calibration (under-dispersive forecasts in this case) more severely than the CRPS, provides a different outlook for the comparison of the two systems. In fact, according to the Ignorance score, the ESP system outperforms forecasts based on System4 in most cases. This illustrates that the joint use of several metrics is crucial to assess the quality of a forecasts system thoroughly. Globally, ESP provide reliable forecasts which can be over-dispersed whereas bias corrected ECMWF System4 forecasts are sharper but at the risk of missing events.
A new air quality monitoring and early warning system: Air quality assessment and air pollutant concentration prediction.

PubMed

Yang, Zhongshan; Wang, Jian

2017-10-01

Air pollution in many countries is worsening with industrialization and urbanization, resulting in climate change and affecting people's health, thus, making the work of policymakers more difficult. It is therefore both urgent and necessary to establish amore scientific air quality monitoring and early warning system to evaluate the degree of air pollution objectively, and predict pollutant concentrations accurately. However, the integration of air quality assessment and air pollutant concentration prediction to establish an air quality system is not common. In this paper, we propose a new air quality monitoring and early warning system, including an assessment module and forecasting module. In the air quality assessment module, fuzzy comprehensive evaluation is used to determine the main pollutants and evaluate the degree of air pollution more scientifically. In the air pollutant concentration prediction module, a novel hybridization model combining complementary ensemble empirical mode decomposition, a modified cuckoo search and differential evolution algorithm, and an Elman neural network, is proposed to improve the forecasting accuracy of six main air pollutant concentrations. To verify the effectiveness of this system, pollutant data for two cities in China are used. The result of the fuzzy comprehensive evaluation shows that the major air pollutants in Xi'an and Jinan are PM 10 and PM 2.5 respectively, and that the air quality of Xi'an is better than that of Jinan. The forecasting results indicate that the proposed hybrid model is remarkably superior to all benchmark models on account of its higher prediction accuracy and stability. Copyright © 2017 Elsevier Inc. All rights reserved.
The forecasting research of early warning systems for atmospheric pollutants: A case in Yangtze River Delta region

NASA Astrophysics Data System (ADS)

Song, Yiliao; Qin, Shanshan; Qu, Jiansheng; Liu, Feng

2015-10-01

The issue of air quality regarding PM pollution levels in China is a focus of public attention. To address that issue, to date, a series of studies is in progress, including PM monitoring programs, PM source apportionment, and the enactment of new ambient air quality index standards. However, related research concerning computer modeling for PM future trends estimation is rare, despite its significance to forecasting and early warning systems. Thereby, a study regarding deterministic and interval forecasts of PM is performed. In this study, data on hourly and 12 h-averaged air pollutants are applied to forecast PM concentrations within the Yangtze River Delta (YRD) region of China. The characteristics of PM emissions have been primarily examined and analyzed using different distribution functions. To improve the distribution fitting that is crucial for estimating PM levels, an artificial intelligence algorithm is incorporated to select the optimal parameters. Following that step, an ANF model is used to conduct deterministic forecasts of PM. With the identified distributions and deterministic forecasts, different levels of PM intervals are estimated. The results indicate that the lognormal or gamma distributions are highly representative of the recorded PM data with a goodness-of-fit R2 of approximately 0.998. Furthermore, the results of the evaluation metrics (MSE, MAPE and CP, AW) also show high accuracy within the deterministic and interval forecasts of PM, indicating that this method enables the informative and effective quantification of future PM trends.
An evaluation of the real-time tropical cyclone forecast skill of the Navy Operational Global Atmospheric Prediction System in the western North Pacific

NASA Technical Reports Server (NTRS)

Fiorino, Michael; Goerss, James S.; Jensen, Jack J.; Harrison, Edward J., Jr.

1993-01-01

The paper evaluates the meteorological quality and operational utility of the Navy Operational Global Atmospheric Prediction System (NOGAPS) in forecasting tropical cyclones. It is shown that the model can provide useful predictions of motion and formation on a real-time basis in the western North Pacific. The meterological characteristics of the NOGAPS tropical cyclone predictions are evaluated by examining the formation of low-level cyclone systems in the tropics and vortex structure in the NOGAPS analysis and verifying 72-h forecasts. The adjusted NOGAPS track forecasts showed equitable skill to the baseline aid and the dynamical model. NOGAPS successfully predicted unusual equatorward turns for several straight-running cyclones.
Evaluation of the Community Multiscale Air Quality Model for Simulating Winter Ozone Formation in the Uinta Basin.

EPA Science Inventory

The Weather Research and Forecasting (WRF) and Community Multiscale Air Quality (CMAQ) models were used to simulate a 10 day high‐ozone episode observed during the 2013 Uinta Basin Winter Ozone Study (UBWOS). The baseline model had a large negative bias when compared to ozo...
Replacement Beef Cow Valuation under Data Availability Constraints

PubMed Central

Hagerman, Amy D.; Thompson, Jada M.; Ham, Charlotte; Johnson, Kamina K.

2017-01-01

Economists are often tasked with estimating the benefits or costs associated with livestock production losses; however, lack of available data or absence of consistent reporting can reduce the accuracy of these valuations. This work looks at three potential estimation techniques for determining the value for replacement beef cows with varying types of market data to proxy constrained data availability and discusses the potential margin of error for each technique. Oklahoma bred replacement cows are valued using hedonic pricing based on Oklahoma bred cow data—a best case scenario—vector error correction modeling (VECM) based on national cow sales data and cost of production (COP) based on just a representative enterprise budget and very limited sales data. Each method was then used to perform a within-sample forecast of 2016 January to December, and forecasts are compared with the 2016 monthly observed market prices in Oklahoma using the mean absolute percent error (MAPE). Hedonic pricing methods tend to overvalue for within-sample forecasting but performed best, as measured by MAPE for high quality cows. The VECM tended to undervalue cows but performed best for younger animals. COP performed well, compared with the more data intensive methods. Examining each method individually across eight representative replacement beef female types, the VECM forecast resulted in a MAPE under 10% for 33% of forecasted months, followed by hedonic pricing at 24% of the forecasted months and COP at 14% of the forecasted months for average quality beef females. For high quality females, the hedonic pricing method worked best producing a MAPE under 10% in 36% of the forecasted months followed by the COP method at 21% of months and the VECM at 14% of the forecasted months. These results suggested that livestock valuation method selection was not one-size-fits-all and may need to vary based not only on the data available but also on the characteristics (e.g., quality or age) of the livestock being valued. PMID:29164141
Short-term forecasting of emergency inpatient flow.

PubMed

Abraham, Gad; Byrnes, Graham B; Bain, Christopher A

2009-05-01

Hospital managers have to manage resources effectively, while maintaining a high quality of care. For hospitals where admissions from the emergency department to the wards represent a large proportion of admissions, the ability to forecast these admissions and the resultant ward occupancy is especially useful for resource planning purposes. Since emergency admissions often compete with planned elective admissions, modeling emergency demand may result in improved elective planning as well. We compare several models for forecasting daily emergency inpatient admissions and occupancy. The models are applied to three years of daily data. By measuring their mean square error in a cross-validation framework, we find that emergency admissions are largely random, and hence, unpredictable, whereas emergency occupancy can be forecasted using a model combining regression and autoregressive integrated moving average (ARIMA) model, or a seasonal ARIMA model, for up to one week ahead. Faced with variable admissions and occupancy, hospitals must prepare a reserve capacity of beds and staff. Our approach allows estimation of the required reserve capacity.
Using a Software Tool in Forecasting: a Case Study of Sales Forecasting Taking into Account Data Uncertainty

NASA Astrophysics Data System (ADS)

Fabianová, Jana; Kačmáry, Peter; Molnár, Vieroslav; Michalik, Peter

2016-10-01

Forecasting is one of the logistics activities and a sales forecast is the starting point for the elaboration of business plans. Forecast accuracy affects the business outcomes and ultimately may significantly affect the economic stability of the company. The accuracy of the prediction depends on the suitability of the use of forecasting methods, experience, quality of input data, time period and other factors. The input data are usually not deterministic but they are often of random nature. They are affected by uncertainties of the market environment, and many other factors. Taking into account the input data uncertainty, the forecast error can by reduced. This article deals with the use of the software tool for incorporating data uncertainty into forecasting. Proposals are presented of a forecasting approach and simulation of the impact of uncertain input parameters to the target forecasted value by this case study model. The statistical analysis and risk analysis of the forecast results is carried out including sensitivity analysis and variables impact analysis.
Challenges and potential solutions for European coastal ocean modelling

NASA Astrophysics Data System (ADS)

She, Jun; Stanev, Emil

2017-04-01

Coastal operational oceanography is a science and technological platform to integrate and transform the outcomes in marine monitoring, new knowledge generation and innovative technologies into operational information products and services in the coastal ocean. It has been identified as one of the four research priorities by EuroGOOS (She et al. 2016). Coastal modelling plays a central role in such an integration and transformation. A next generation coastal ocean forecasting system should have following features: i) being able to fully exploit benefits from future observations, ii) generate meaningful products in finer scales e.g., sub-mesoscale and in estuary-coast-sea continuum, iii) efficient parallel computing and model grid structure, iv) provide high quality forecasts as forcing to NWP and coastal climate models, v) resolving correctly inter-basin and inter-sub-basin water exchange, vi) resolving synoptic variability and predictability in marine ecosystems, e.g., for algae bloom, vi) being able to address critical and relevant issues in coastal applications, e.g., marine spatial planning, maritime safety, marine pollution protection, disaster prevention, offshore wind energy, climate change adaptation and mitigation, ICZM (integrated coastal zone management), the WFD (Water Framework Directive), and the MSFD (Marine Strategy Framework Directive), especially on habitat, eutrophication, and hydrographic condition descriptors. This presentation will address above challenges, identify limits of current models and propose correspondent research needed. The proposed roadmap will address an integrated monitoring-modelling approach and developing Unified European Coastal Ocean Models. In the coming years, a few new developments in European Sea observations can expected, e.g., more near real time delivering on profile observations made by research vessels, more shallow water Argo floats and bio-Argo floats deployed, much more high resolution sea level data from SWOT and on-going altimetry missions, contributing to resolving (sub-)mesoscale eddies, more currents measurements from ADCPs and HF radars, geostationary data for suspended sediment and diurnal observations from satellite SST products. These developments will make it possible to generate new knowledge and build up new capacities for modelling and forecasting systems, e.g., improved currents forecast, improved water skin temperature and surface winds forecast, improved modelling and forecast of (sub) mesoscale activities and drift forecast, new forecast capabilities on SPM (Suspended Particle Matter) and algae bloom. There will be much more in-situ and satellite data available for assimilation. The assimilation of sea level, chl-a, ferrybox and profile observations will greatly improves the ocean-ice-ecosystem forecast quality.
ASSESSMENT OF THE WINTER-TIME PERFORMANCE OF DEVELOPMENTAL PARTICULATE MATTER FORECASTS WITH THE ETA-CMAQ MODELING SYSTEM

EPA Science Inventory

It is desirable for local air quality agencies to accurately forecast tropospheric PM_2.5 concentrations to alert the sensitive population of the onset, severity and duration of unhealthy air, and to encourage the public and industry to reduce emissions-producing activi...
ETA-CMAQ MODELING SYSTEM'S CAPABILITY TO PROVIDE PM 2.5 AND AEROSOL OPTICAL THICKNESS FORECAST

EPA Science Inventory

In 2003, NOAA and the U.S. EPA signed a Memorandum of Agreement to work together to develop a National Air Quality Forecasting (AQF) capability. To meet this goal, NOAA's National Weather Service (NWS), the Office of Atmospheric Research (OAR) and the U.S. EPA developed and eval...
Application of Wavelet Filters in an Evaluation of ...

EPA Pesticide Factsheets

Air quality model evaluation can be enhanced with time-scale specific comparisons of outputs and observations. For example, high-frequency (hours to one day) time scale information in observed ozone is not well captured by deterministic models and its incorporation into model performance metrics lead one to devote resources to stochastic variations in model outputs. In this analysis, observations are compared with model outputs at seasonal, weekly, diurnal and intra-day time scales. Filters provide frequency specific information that can be used to compare the strength (amplitude) and timing (phase) of observations and model estimates. The National Exposure Research Laboratory′s (NERL′s) Atmospheric Modeling and Analysis Division (AMAD) conducts research in support of EPA′s mission to protect human health and the environment. AMAD′s research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the Nation′s air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMAD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMAD are being used by EPA, NOAA, and the air pollution community in understanding and forecasting not only the magnitude of the air pollu
Eco-morphological Real-time Forecasting tool to predict hydrodynamic, sediment and nutrient dynamic in Coastal Louisiana

NASA Astrophysics Data System (ADS)

Messina, F.; Meselhe, E. A.; Buckman, L.; Twight, D.

2017-12-01

Louisiana coastal zone is one of the most productive and dynamic eco-geomorphic systems in the world. This unique natural environment has been alternated by human activities and natural processes such as sea level rise, subsidence, dredging of canals for oil and gas production, the Mississippi River levees which don't allow the natural river sediment. As a result of these alterations land loss, erosion and flood risk are becoming real issues for Louisiana. Costal authorities have been studying the benefits and effects of several restoration projects, e.g. freshwater and sediment diversions. The protection of communities, wildlife and of the unique environments is a high priority in this region. The Water Institute of the Gulf, together with Deltares, has developed a forecasting and information system for a pilot location in Coastal Louisiana, specifically for Barataria Bay and Breton Sound Basins in the Mississippi River Deltaic Plain. The system provides a 7-day forecast of water level, salinity, and temperature, under atmospheric and coastal forecasted conditions, such as freshwater riverine inflow, rainfall, evaporation, wind, and tide. The system also forecasts nutrient distribution (e.g., Chla and dissolved oxygen) and sediment transport. The Flood Early Warning System FEWS is used as a platform to import multivariate data from several sources, use them to monitor the pilot location and to provide boundary conditions to the model. A hindcast model is applied to compare the model results to the observed data, and to provide the initial condition to the forecast model. This system represents a unique tool which provides valuable information regarding the overall conditions of the basins. It offers the opportunity to adaptively manage existing and planned diversions to meet certain salinity and water level targets or thresholds while maximizing land-building goals. Moreover, water quality predictions provide valuable information on the current ecological conditions of the area. Real time observations and model predictions can be used as guidance to decision makers regarding the operation of control structures in response to forecasted weather or river flood events. Coastal communities can benefit from water level, salinity and water quality forecast to manage their activities.
Real-time forecasting of an epidemic using a discrete time stochastic model: a case study of pandemic influenza (H1N1-2009)

PubMed Central

2011-01-01

Background Real-time forecasting of epidemics, especially those based on a likelihood-based approach, is understudied. This study aimed to develop a simple method that can be used for the real-time epidemic forecasting. Methods A discrete time stochastic model, accounting for demographic stochasticity and conditional measurement, was developed and applied as a case study to the weekly incidence of pandemic influenza (H1N1-2009) in Japan. By imposing a branching process approximation and by assuming the linear growth of cases within each reporting interval, the epidemic curve is predicted using only two parameters. The uncertainty bounds of the forecasts are computed using chains of conditional offspring distributions. Results The quality of the forecasts made before the epidemic peak appears largely to depend on obtaining valid parameter estimates. The forecasts of both weekly incidence and final epidemic size greatly improved at and after the epidemic peak with all the observed data points falling within the uncertainty bounds. Conclusions Real-time forecasting using the discrete time stochastic model with its simple computation of the uncertainty bounds was successful. Because of the simplistic model structure, the proposed model has the potential to additionally account for various types of heterogeneity, time-dependent transmission dynamics and epidemiological details. The impact of such complexities on forecasting should be explored when the data become available as part of the disease surveillance. PMID:21324153
Chemical weather forecasting for the Yangtze River Delta

NASA Astrophysics Data System (ADS)

Xie, Y.; Xu, J.; Zhou, G.; Chang, L.; Chen, B.

2016-12-01

Shanghai is one of the largest megacities in the world. With rapid economic growth of the city and its surrounding areas in recent years, air pollution has posed adverse effects on public health and ecosystem. In winter heavy pollution episodes are often associated with PM exceedances under stagnant conditions or transport events, whereas in summer the region frequently experiences elevated O3 levels. Chemical weather prediction systems with the WRF-Chem and CMAQ models are being developed to support air quality and haze forecasting for Shanghai and the Yangtze River Delta region. We will present main components of the modeling system, forecasting products, as well as evaluation results. Evaluation of the WRF-Chem forecasts show the model has generally good ability to capture the temporal variations of O3 and PM2.5. Substantial regional differences exist, with the best performance in Shanghai. Meanwhile, the forecasts tend to degrade during highly polluted episodes and transitional time periods, which highlights the need to improve model representation of key process (e.g. meteorological fields and formation of secondary pollutants). Recent work includes using the ECMWF global model forecasts as chemical boundary conditions for our regional model. We investigate the impact of chemical downscaling, and also compare the results from different models participated in the PANDA (PArtnership with chiNa on space Data) project. Results from ongoing efforts (e.g. chemical weather forecasting driven by SMS regional high resolution NWP) will also be presented.
Diagnosis of North American Multi-Model Ensemble (NMME) skill for predicting floods and droughts over the continental USA

NASA Astrophysics Data System (ADS)

Slater, L. J.; Villarini, G.; Bradley, A.

2015-12-01

Model predictions of precipitation and temperature are crucial to mitigate the impacts of major flood and drought events through informed planning and response. However, the potential value and applicability of these predictions is inescapably linked to their forecast quality. The North-American Multi-Model Ensemble (NMME) is a multi-agency supported forecasting system for intraseasonal to interannual (ISI) climate predictions. Retrospective forecasts and real-time information are provided by each agency free of charge to facilitate collaborative research efforts for predicting future climate conditions as well as extreme weather events such as floods and droughts. Using the PRISM climate mapping system as the reference data, we examine the skill of five General Circulation Models (GCMs) from the NMME project to forecast monthly and seasonal precipitation and temperature over seven sub-regions of the continental United States. For each model, we quantify the seasonal accuracy of the forecast relative to observed precipitation using the mean square error skill score. This score is decomposed to assess the accuracy of the forecast in the absence of biases (potential skill), and in the presence of conditional (slope reliability) and unconditional (standardized mean error) biases. The quantification of these biases allows us to diagnose each model's skill over a full range temporal and spatial scales. Finally, we test each model's forecasting skill by evaluating its ability to predict extended periods of extreme temperature and precipitation that were conducive to 'billion-dollar' historical flood and drought events in different regions of the continental USA. The forecasting skill of the individual climate models is summarized and presented along with a discussion of different multi-model averaging techniques for predicting such events.
Seasonal Water Balance Forecasts for Drought Early Warning in Ethiopia

NASA Astrophysics Data System (ADS)

Spirig, Christoph; Bhend, Jonas; Liniger, Mark

2016-04-01

Droughts severely impact Ethiopian agricultural production. Successful early warning for drought conditions in the upcoming harvest season therefore contributes to better managing food shortages arising from adverse climatic conditions. So far, however, meteorological seasonal forecasts have not been used in Ethiopia's national food security early warning system (i.e. the LEAP platform). Here we analyse the forecast quality of seasonal forecasts of total rainfall and of the meteorological water balance as a proxy for plant available water. We analyse forecast skill of June to September rainfall and water balance from dynamical seasonal forecast systems, the ECMWF System4 and EC-EARTH global forecasting systems. Rainfall forecasts outperform forecasts assuming a stationary climate mainly in north-eastern Ethiopia - an area that is particularly vulnerable to droughts. Forecasts of the water balance index seem to be even more skilful and thus more useful than pure rainfall forecasts. The results vary though for different lead times and skill measures employed. We further explore the potential added value of dynamically downscaling the forecasts through several dynamical regional climate models made available through the EU FP7 project EUPORIAS. Preliminary results suggest that dynamically downscaled seasonal forecasts are not significantly better compared with seasonal forecasts from the global models. We conclude that seasonal forecasts of a simple climate index such as the water balance have the potential to benefit drought early warning in Ethiopia, both due to its positive predictive skill and higher usefulness than seasonal mean quantities.

Update on the NASA GEOS-5 Aerosol Forecasting and Data Assimilation System

NASA Technical Reports Server (NTRS)

Colarco, Peter; da Silva, Arlindo; Aquila, Valentina; Bian, Huisheng; Buchard, Virginie; Castellanos, Patricia; Darmenov, Anton; Follette-Cook, Melanie; Govindaraju, Ravi; Keller, Christoph;

2017-01-01

GEOS-5 is the Goddard Earth Observing System model. GEOS-5 is maintained by the NASA Global Modeling and Assimilation Office. Core development is within GMAO,Goddard Atmospheric Chemistry and Dynamics Laboratory, and with external partners. Primary GEOS-5 functions: Earth system model for studying climate variability and change, provide research quality reanalyses for supporting NASA instrument teams and scientific community, provide near-real time forecasts of meteorology,aerosols, and other atmospheric constituents to support NASA airborne campaigns.

Shared Investment Projects and Forecasting Errors: Setting Framework Conditions for Coordination and Sequencing Data Quality Activities

PubMed Central

Leitner, Stephan; Brauneis, Alexander; Rausch, Alexandra

2015-01-01

In this paper, we investigate the impact of inaccurate forecasting on the coordination of distributed investment decisions. In particular, by setting up a computational multi-agent model of a stylized firm, we investigate the case of investment opportunities that are mutually carried out by organizational departments. The forecasts of concern pertain to the initial amount of money necessary to launch and operate an investment opportunity, to the expected intertemporal distribution of cash flows, and the departments’ efficiency in operating the investment opportunity at hand. We propose a budget allocation mechanism for coordinating such distributed decisions The paper provides guidance on how to set framework conditions, in terms of the number of investment opportunities considered in one round of funding and the number of departments operating one investment opportunity, so that the coordination mechanism is highly robust to forecasting errors. Furthermore, we show that—in some setups—a certain extent of misforecasting is desirable from the firm’s point of view as it supports the achievement of the corporate objective of value maximization. We then address the question of how to improve forecasting quality in the best possible way, and provide policy advice on how to sequence activities for improving forecasting quality so that the robustness of the coordination mechanism to errors increases in the best possible way. At the same time, we show that wrong decisions regarding the sequencing can lead to a decrease in robustness. Finally, we conduct a comprehensive sensitivity analysis and prove that—in particular for relatively good forecasters—most of our results are robust to changes in setting the parameters of our multi-agent simulation model. PMID:25803736
Communicating uncertainty in hydrological forecasts: mission impossible?

NASA Astrophysics Data System (ADS)

Ramos, Maria-Helena; Mathevet, Thibault; Thielen, Jutta; Pappenberger, Florian

2010-05-01

Cascading uncertainty in meteo-hydrological modelling chains for forecasting and integrated flood risk assessment is an essential step to improve the quality of hydrological forecasts. Although the best methodology to quantify the total predictive uncertainty in hydrology is still debated, there is a common agreement that one must avoid uncertainty misrepresentation and miscommunication, as well as misinterpretation of information by users. Several recent studies point out that uncertainty, when properly explained and defined, is no longer unwelcome among emergence response organizations, users of flood risk information and the general public. However, efficient communication of uncertain hydro-meteorological forecasts is far from being a resolved issue. This study focuses on the interpretation and communication of uncertain hydrological forecasts based on (uncertain) meteorological forecasts and (uncertain) rainfall-runoff modelling approaches to decision-makers such as operational hydrologists and water managers in charge of flood warning and scenario-based reservoir operation. An overview of the typical flow of uncertainties and risk-based decisions in hydrological forecasting systems is presented. The challenges related to the extraction of meaningful information from probabilistic forecasts and the test of its usefulness in assisting operational flood forecasting are illustrated with the help of two case-studies: 1) a study on the use and communication of probabilistic flood forecasting within the European Flood Alert System; 2) a case-study on the use of probabilistic forecasts by operational forecasters from the hydroelectricity company EDF in France. These examples show that attention must be paid to initiatives that promote or reinforce the active participation of expert forecasters in the forecasting chain. The practice of face-to-face forecast briefings, focusing on sharing how forecasters interpret, describe and perceive the model output forecasted scenarios, is essential. We believe that the efficient communication of uncertainty in hydro-meteorological forecasts is not a mission impossible. Questions remaining unanswered in probabilistic hydrological forecasting should not neutralize the goal of such a mission, and the suspense kept should instead act as a catalyst for overcoming the remaining challenges.
Challenges in Understanding and Forecasting Winds in Complex Terrain.

NASA Astrophysics Data System (ADS)

Mann, J.; Fernando, J.; Wilczak, J. M.

2017-12-01

An overview will be given of some of the challenges in understanding and forecasting winds in complex terrain. These challenges can occur for several different reasons including 1) gaps in our understanding of fundamental physical boundary layer processes occurring in complex terrain; 2) a lack of adequate parameterizations and/or numerical schemes in NWP models; and 3) inadequate observations for initialization of NWP model forecasts. Specific phenomena that will be covered include topographic wakes/vortices, cold pools, gap flows, and mountain-valley winds, with examples taken from several air quality and wind energy related field programs in California as well as from the recent Second Wind Forecast Improvement Program (WFIP2) field campaign in the Columbia River Gorge/Basin area of Washington and Oregon States. Recent parameterization improvements discussed will include those for boundary layer turbulence, including 3D turbulence schemes, and gravity wave drag. Observational requirements for improving wind forecasting in complex terrain will be discussed, especially in the context of forecasting pressure gradient driven gap flow events.
Modeling and forecasting the distribution of Vibrio vulnificus in Chesapeake Bay

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

Jacobs, John M.; Rhodes, M.; Brown, C. W.

The aim is to construct statistical models to predict the presence, abundance and potential virulence of Vibrio vulnificus in surface waters. A variety of statistical techniques were used in concert to identify water quality parameters associated with V. vulnificus presence, abundance and virulence markers in the interest of developing strong predictive models for use in regional oceanographic modeling systems. A suite of models are provided to represent the best model fit and alternatives using environmental variables that allow them to be put to immediate use in current ecological forecasting efforts. Conclusions: Environmental parameters such as temperature, salinity and turbidity aremore » capable of accurately predicting abundance and distribution of V. vulnificus in Chesapeake Bay. Forcing these empirical models with output from ocean modeling systems allows for spatially explicit forecasts for up to 48 h in the future. This study uses one of the largest data sets compiled to model Vibrio in an estuary, enhances our understanding of environmental correlates with abundance, distribution and presence of potentially virulent strains and offers a method to forecast these pathogens that may be replicated in other regions.« less
THE SCIENTIFIC BASIS OF NOAA'S AIR QUALITY FORECASTING PROGRAM

EPA Science Inventory

For many years, the National Oceanic and Atmospheric Administration (NOAA) has conducted atmospheric research, including chemical and physical measurements, process studies, and the development and evaluation of experimental meteorological and photochemical air quality models. ...
How is the weather? Forecasting inpatient glycemic control

PubMed Central

Saulnier, George E; Castro, Janna C; Cook, Curtiss B; Thompson, Bithika M

2017-01-01

Aim: Apply methods of damped trend analysis to forecast inpatient glycemic control. Method: Observed and calculated point-of-care blood glucose data trends were determined over 62 weeks. Mean absolute percent error was used to calculate differences between observed and forecasted values. Comparisons were drawn between model results and linear regression forecasting. Results: The forecasted mean glucose trends observed during the first 24 and 48 weeks of projections compared favorably to the results provided by linear regression forecasting. However, in some scenarios, the damped trend method changed inferences compared with linear regression. In all scenarios, mean absolute percent error values remained below the 10% accepted by demand industries. Conclusion: Results indicate that forecasting methods historically applied within demand industries can project future inpatient glycemic control. Additional study is needed to determine if forecasting is useful in the analyses of other glucometric parameters and, if so, how to apply the techniques to quality improvement. PMID:29134125
The North American Multi-Model Ensemble (NMME): Phase-1 Seasonal to Interannual Prediction, Phase-2 Toward Developing Intra-Seasonal Prediction

NASA Technical Reports Server (NTRS)

Kirtman, Ben P.; Min, Dughong; Infanti, Johnna M.; Kinter, James L., III; Paolino, Daniel A.; Zhang, Qin; vandenDool, Huug; Saha, Suranjana; Mendez, Malaquias Pena; Becker, Emily;

2013-01-01

The recent US National Academies report "Assessment of Intraseasonal to Interannual Climate Prediction and Predictability" was unequivocal in recommending the need for the development of a North American Multi-Model Ensemble (NMME) operational predictive capability. Indeed, this effort is required to meet the specific tailored regional prediction and decision support needs of a large community of climate information users. The multi-model ensemble approach has proven extremely effective at quantifying prediction uncertainty due to uncertainty in model formulation, and has proven to produce better prediction quality (on average) then any single model ensemble. This multi-model approach is the basis for several international collaborative prediction research efforts, an operational European system and there are numerous examples of how this multi-model ensemble approach yields superior forecasts compared to any single model. Based on two NOAA Climate Test Bed (CTB) NMME workshops (February 18, and April 8, 2011) a collaborative and coordinated implementation strategy for a NMME prediction system has been developed and is currently delivering real-time seasonal-to-interannual predictions on the NOAA Climate Prediction Center (CPC) operational schedule. The hindcast and real-time prediction data is readily available (e.g., http://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME/) and in graphical format from CPC (http://origin.cpc.ncep.noaa.gov/products/people/wd51yf/NMME/index.html). Moreover, the NMME forecast are already currently being used as guidance for operational forecasters. This paper describes the new NMME effort, presents an overview of the multi-model forecast quality, and the complementary skill associated with individual models.

Extending the Applicability of the Community Multiscale Air Quality Model to Hemispheric Scales: Motivation, Challenges, and Progress

EPA Science Inventory

The adaptation of the Community Multiscale Air Quality (CMAQ) modeling system to simulate O₃, particulate matter, and related precursor distributions over the northern hemisphere is presented. Hemispheric simulations with CMAQ and the Weather Research and Forecasting (...
AROME-Arctic: New operational NWP model for the Arctic region

NASA Astrophysics Data System (ADS)

Süld, Jakob; Dale, Knut S.; Myrland, Espen; Batrak, Yurii; Homleid, Mariken; Valkonen, Teresa; Seierstad, Ivar A.; Randriamampianina, Roger

2016-04-01

In the frame of the EU-funded project ACCESS (Arctic Climate Change, Economy and Society), MET Norway aimed 1) to describe the present monitoring and forecasting capabilities in the Arctic; and 2) to identify the key factors limiting the forecasting capabilities and to give recommendations on key areas to improve the forecasting capabilities in the Arctic. We have observed that the NWP forecast quality is lower in the Arctic than in the regions further south. Earlier research indicated that one of the factors behind this is the composition of the observing system in the Arctic, in particular the scarceness of conventional observations. To further assess possible strategies for alleviating the situation and propose scenarios for a future Arctic observing system, we have performed a set of experiments to gain a more detailed insight in the contribution of the components of the present observing system in a regional state-of-the-art non-hydrostatic NWP model using the AROME physics (Seity et al, 2011) at 2.5 km horizontal resolution - AROME-Arctic. Our observing system experiment studies showed that conventional observations (Synop, Buoys) can play an important role in correcting the surface state of the model, but prove that the present upper-air conventional (Radiosondes, Aircraft) observations in the area are too scarce to have a significant effect on forecasts. We demonstrate that satellite sounding data play an important role in improving forecast quality. This is the case with satellite temperature sounding data (AMSU-A, IASI), as well as with the satellite moisture sounding data (AMSU-B/MHS, IASI). With these sets of observations, the AROME-Arctic clearly performs better in forecasting extreme events, like for example polar lows. For more details see presentation by Randriamampianina et al. in this session. The encouraging performance of AROME-Arctic lead us to implement it with more observations and improved settings into daily runs with the objective to substitute our actual operational Arctic mesoscale HIRLAM (High Resolution Limited Area Model) NWP model. This presentation will discuss in detail the operational implementation of the AROME-Arctic model together with post-processing methods. Aimed services in the Arctic region covered by the model, such as online weather forecasting (yr.no) and tracking of polar lows (barentswatch.no), is also included.
Sensitivity of the Community Multiscale Air Quality (CMAQ) Model v4.7 Results for the Eastern United States to MM5 and WRF Meteorological Drivers

EPA Science Inventory

This paper presents a comparison of the operational performance of two Community Multiscale Air Quality (CMAQ) model v4.7 simulations that utilize input data from the 5th generation Mesoscale Model MM5 and the Weather Research and Forecasting (WRF) meteorological models.
Validation of Seasonal Forecast of Indian Summer Monsoon Rainfall

NASA Astrophysics Data System (ADS)

Das, Sukanta Kumar; Deb, Sanjib Kumar; Kishtawal, C. M.; Pal, Pradip Kumar

2015-06-01

The experimental seasonal forecast of Indian summer monsoon (ISM) rainfall during June through September using Community Atmosphere Model (CAM) version 3 has been carried out at the Space Applications Centre Ahmedabad since 2009. The forecasts, based on a number of ensemble members (ten minimum) of CAM, are generated in several phases and updated on regular basis. On completion of 5 years of experimental seasonal forecasts in operational mode, it is required that the overall validation or correctness of the forecast system is quantified and that the scope is assessed for further improvements of the forecast over time, if any. The ensemble model climatology generated by a set of 20 identical CAM simulations is considered as the model control simulation. The performance of the forecast has been evaluated by assuming the control simulation as the model reference. The forecast improvement factor shows positive improvements, with higher values for the recent forecasted years as compared to the control experiment over the Indian landmass. The Taylor diagram representation of the Pearson correlation coefficient (PCC), standard deviation and centered root mean square difference has been used to demonstrate the best PCC, in the order of 0.74-0.79, recorded for the seasonal forecast made during 2013. Further, the bias score of different phases of experiment revealed the fact that the ISM rainfall forecast is affected by overestimation in predicting the low rain-rate (less than 7 mm/day), but by underestimation in the medium and high rain-rate (higher than 11 mm/day). Overall, the analysis shows significant improvement of the ISM forecast over the last 5 years, viz. 2009-2013, due to several important modifications that have been implemented in the forecast system. The validation exercise has also pointed out a number of shortcomings in the forecast system; these will be addressed in the upcoming years of experiments to improve the quality of the ISM prediction.
Assessing the viability of `over-the-loop' real-time short-to-medium range ensemble streamflow forecasts

NASA Astrophysics Data System (ADS)

Wood, A. W.; Clark, E.; Mendoza, P. A.; Nijssen, B.; Newman, A. J.; Clark, M. P.; Arnold, J.; Nowak, K. C.

2016-12-01

Many if not most national operational short-to-medium range streamflow prediction systems rely on a forecaster-in-the-loop approach in which some parts of the forecast workflow are automated, but others require the hands-on-effort of an experienced human forecaster. This approach evolved out of the need to correct for deficiencies in the models and datasets that were available for forecasting, and often leads to skillful predictions despite the use of relatively simple, conceptual models. On the other hand, the process is not reproducible, which limits opportunities to assess and incorporate process variations, and the effort required to make forecasts in this way is an obstacle to expanding forecast services - e.g., though adding new forecast locations or more frequent forecast updates, running more complex models, or producing forecast ensembles and hindcasts that can support verification. In the last decade, the hydrologic forecasting community has begun to develop more centralized, `over-the-loop' systems. The quality of these new forecast products will depend on their ability to leverage research in areas including earth system modeling, parameter estimation, data assimilation, statistical post-processing, weather and climate prediction, verification, and uncertainty estimation through the use of ensembles. Currently, the operational streamflow forecasting and water management communities have little experience with the strengths and weaknesses of over-the-loop approaches, even as the systems are being rolled out in major operational forecasting centers. There is thus a need both to evaluate these forecasting advances and to demonstrate their potential in a public arena, raising awareness in forecast user communities and development programs alike. To address this need, the National Center for Atmospheric Research is collaborating with the University of Washington, the Bureau of Reclamation and the US Army Corps of Engineers, using the NCAR 'System for Hydromet Analysis, Research, and Prediction' (SHARP) to implement, assess and demonstrate real-time over-the-loop forecasts. We present early hindcast and verification results from SHARP for short to medium range streamflow forecasts in a number of US case study watersheds.
CCPP-ARM Parameterization Testbed Model Forecast Data

DOE Data Explorer

Klein, Stephen

2008-01-15

Dataset contains the NCAR CAM3 (Collins et al., 2004) and GFDL AM2 (GFDL GAMDT, 2004) forecast data at locations close to the ARM research sites. These data are generated from a series of multi-day forecasts in which both CAM3 and AM2 are initialized at 00Z every day with the ECMWF reanalysis data (ERA-40), for the year 1997 and 2000 and initialized with both the NASA DAO Reanalyses and the NCEP GDAS data for the year 2004. The DOE CCPP-ARM Parameterization Testbed (CAPT) project assesses climate models using numerical weather prediction techniques in conjunction with high quality field measurements (e.g. ARM data).
"Total Deposition (TDEP) Maps" | Science Inventory | US EPA

EPA Pesticide Factsheets

The presentation provides an update on the use of a hybrid methodology that relies on measured values from national monitoring networks and modeled values from CMAQ to produce of maps of total deposition for use in critical loads and other ecological assessments. Additionally, comparisons of the deposition values from the hybrid approach are compared with deposition estimates from other methodologies. The National Exposure Research Laboratory (NERL) Atmospheric Modeling and Analysis Division (AMAD) conducts research in support of EPA mission to protect human health and the environment. AMAD research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMAD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMAD are being used by EPA, NOAA, and the air pollution community in understanding and forecasting not only the magnitude of the air pollution problem, but also in developing emission control policies and regulations for air quality improvements.
Improving short-term air quality predictions over the U.S. using chemical data assimilation

NASA Astrophysics Data System (ADS)

Kumar, R.; Delle Monache, L.; Alessandrini, S.; Saide, P.; Lin, H. C.; Liu, Z.; Pfister, G.; Edwards, D. P.; Baker, B.; Tang, Y.; Lee, P.; Djalalova, I.; Wilczak, J. M.

2017-12-01

State and local air quality forecasters across the United States use air quality forecasts from the National Air Quality Forecasting Capability (NAQFC) at the National Oceanic and Atmospheric Administration (NOAA) as one of the key tools to protect the public from adverse air pollution related health effects by dispensing timely information about air pollution episodes. This project funded by the National Aeronautics and Space Administration (NASA) aims to enhance the decision-making process by improving the accuracy of NAQFC short-term predictions of ground-level particulate matter of less than 2.5 µm in diameter (PM2.5) by exploiting NASA Earth Science Data with chemical data assimilation. The NAQFC is based on the Community Multiscale Air Quality (CMAQ) model. To improve the initialization of PM2.5 in CMAQ, we developed a new capability in the community Gridpoint Statistical Interpolation (GSI) system to assimilate Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) retrievals in CMAQ. Specifically, we developed new capabilities within GSI to read/write CMAQ data, a forward operator that calculates AOD at 550 nm from CMAQ aerosol chemical composition and an adjoint of the forward operator that translates the changes in AOD to aerosol chemical composition. A generalized background error covariance program called "GEN_BE" has been extended to calculate background error covariance using CMAQ output. The background error variances are generated using a combination of both emissions and meteorological perturbations to better capture sources of uncertainties in PM2.5 simulations. The newly developed CMAQ-GSI system is used to perform daily 24-h PM2.5 forecasts with and without data assimilation from 15 July to 14 August 2014, and the resulting forecasts are compared against AirNOW PM2.5 measurements at 550 stations across the U. S. We find that the assimilation of MODIS AOD retrievals improves initialization of the CMAQ model in terms of improved correlation coefficient and reduced bias. However, we notice a large bias in nighttime PM2.5 simulations which is primarily associated with very shallow boundary layer in the model. The developments and results will be discussed in detail during the presentation.
New smoke predictions for Alaska in NOAA’s National Air Quality Forecast Capability

NASA Astrophysics Data System (ADS)

Davidson, P. M.; Ruminski, M.; Draxler, R.; Kondragunta, S.; Zeng, J.; Rolph, G.; Stajner, I.; Manikin, G.

2009-12-01

Smoke from wildfire is an important component of fine particle pollution, which is responsible for tens of thousands of premature deaths each year in the US. In Alaska, wildfire smoke is the leading cause of poor air quality in summer. Smoke forecast guidance helps air quality forecasters and the public take steps to limit exposure to airborne particulate matter. A new smoke forecast guidance tool, built by a cross-NOAA team, leverages efforts of NOAA’s partners at the USFS on wildfire emissions information, and with EPA, in coordinating with state/local air quality forecasters. Required operational deployment criteria, in categories of objective verification, subjective feedback, and production readiness, have been demonstrated in experimental testing during 2008-2009, for addition to the operational products in NOAA's National Air Quality Forecast Capability. The Alaska smoke forecast tool is an adaptation of NOAA’s smoke predictions implemented operationally for the lower 48 states (CONUS) in 2007. The tool integrates satellite information on location of wildfires with weather (North American mesoscale model) and smoke dispersion (HYSPLIT) models to produce daily predictions of smoke transport for Alaska, in binary and graphical formats. Hour-by hour predictions at 12km grid resolution of smoke at the surface and in the column are provided each day by 13 UTC, extending through midnight next day. Forecast accuracy and reliability are monitored against benchmark criteria for accuracy and reliability. While wildfire activity in the CONUS is year-round, the intense wildfire activity in AK is limited to the summer. Initial experimental testing during summer 2008 was hindered by unusually limited wildfire activity and very cloudy conditions. In contrast, heavier than average wildfire activity during summer 2009 provided a representative basis (more than 60 days of wildfire smoke) for demonstrating required prediction accuracy. A new satellite observation product was developed for routine near-real time verification of these predictions. The footprint of the predicted smoke from identified fires is verified with satellite observations of the spatial extent of smoke aerosols (5km resolution). Based on geostationary aerosol optical depth measurements that provide good time resolution of the horizontal spatial extent of the plumes, these observations do not yield quantitative concentrations of smoke particles at the surface. Predicted surface smoke concentrations are consistent with the limited number of in situ observations of total fine particle mass from all sources; however they are much higher than predicted for most CONUS fires. To assess uncertainty associated with fire emissions estimates, sensitivity analyses are in progress.
Performance and diagnostic evaluation of ozone predictions by the Eta-Community Multiscale Air Quality Forecast System during the 2002 New England Air Quality Study.

PubMed

Yu, Shaocai; Mathur, Rohit; Kang, Daiwen; Schere, Kenneth; Eder, Brian; Pleim, Jonathan

2006-10-01

A real-time air quality forecasting system (Eta-Community Multiscale Air Quality [CMAQ] model suite) has been developed by linking the National Centers for Environmental Estimation Eta model to the U.S. Environmental Protection Agency (EPA) CMAQ model. This work presents results from the application of the Eta-CMAQ modeling system for forecasting ozone (O3) over the Northeastern United States during the 2002 New England Air Quality Study (NEAQS). Spatial and temporal performance of the Eta-CMAQ model for O3 was evaluated by comparison with observations from the EPA Air Quality System (AQS) network. This study also examines the ability of the model to simulate the processes governing the distributions of tropospheric O3 on the basis of the intensive datasets obtained at the four Atmospheric Investigation, Regional Modeling, Analysis, and Estimation (AIRMAP) and Harvard Forest (HF) surface sites. The episode analysis reveals that the model captured the buildup of O3 concentrations over the northeastern domain from August 11 and reproduced the spatial distributions of observed O3 very well for the daytime (8:00 p.m.) of both August 8 and 12 with most of normalized mean bias (NMB) within +/- 20%. The model reproduced 53.3% of the observed hourly O3 within a factor of 1.5 with NMB of 29.7% and normalized mean error of 46.9% at the 342 AQS sites. The comparison of modeled and observed lidar O3 vertical profiles shows that whereas the model reproduced the observed vertical structure, it tended to overestimate at higher altitude. The model reproduced 64-77% of observed NO2 photolysis rate values within a factor of 1.5 at the AIRMAP sites. At the HF site, comparison of modeled and observed O3/nitrogen oxide (NOx) ratios suggests that the site is mainly under strongly NOx-sensitive conditions (>53%). It was found that the modeled lower limits of the O3 production efficiency values (inferred from O3-CO correlation) are close to the observations.
Cardiac catheterization laboratory inpatient forecast tool: a prospective evaluation

PubMed Central

Flanagan, Eleni; Siddiqui, Sauleh; Appelbaum, Jeff; Kasper, Edward K; Levin, Scott

2016-01-01

Objective To develop and prospectively evaluate a web-based tool that forecasts the daily bed need for admissions from the cardiac catheterization laboratory using routinely available clinical data within electronic medical records (EMRs). Methods The forecast model was derived using a 13-month retrospective cohort of 6384 catheterization patients. Predictor variables such as demographics, scheduled procedures, and clinical indicators mined from free-text notes were input to a multivariable logistic regression model that predicted the probability of inpatient admission. The model was embedded into a web-based application connected to the local EMR system and used to support bed management decisions. After implementation, the tool was prospectively evaluated for accuracy on a 13-month test cohort of 7029 catheterization patients. Results The forecast model predicted admission with an area under the receiver operating characteristic curve of 0.722. Daily aggregate forecasts were accurate to within one bed for 70.3% of days and within three beds for 97.5% of days during the prospective evaluation period. The web-based application housing the forecast model was used by cardiology providers in practice to estimate daily admissions from the catheterization laboratory. Discussion The forecast model identified older age, male gender, invasive procedures, coronary artery bypass grafts, and a history of congestive heart failure as qualities indicating a patient was at increased risk for admission. Diagnostic procedures and less acute clinical indicators decreased patients’ risk of admission. Despite the site-specific limitations of the model, these findings were supported by the literature. Conclusion Data-driven predictive analytics may be used to accurately forecast daily demand for inpatient beds for cardiac catheterization patients. Connecting these analytics to EMR data sources has the potential to provide advanced operational decision support. PMID:26342217
“Nitrogen Budgets for the Mississippi River Basin using the ...

EPA Pesticide Factsheets

Presentation on the results from the 3 linked models, EPIC (USDA), CMAQ and NEWS to analyze a scenario of increased corn production related to biofuels together with Clean Air Act emission reductions across the US and the resultant effect on nitrogen loading to the Gulf of Mexico from the Mississippi River Basin. This is a demonstration of a capability to connect the N cascade bringing air, land, water together. EPIC = Environmental Policy Integrated Climate model, CMAQ = Community Multiscale Air Quality model, NEWS = Nutrient Export of WaterSheds model. The National Exposure Research Laboratory (NERL) Atmospheric Modeling and Analysis Division (AMAD) conducts research in support of EPA mission to protect human health and the environment. AMAD research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMAD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMAD are being used by EPA, NOAA, and the air pollution community in understanding and forecasting not only the magnitude of the air pollution problem, but also in developing emission control policies and regulations for air quality improvements.

A quantitative comparison of precipitation forecasts between the storm-scale numerical weather prediction model and auto-nowcast system in Jiangsu, China

NASA Astrophysics Data System (ADS)

Wang, Gaili; Yang, Ji; Wang, Dan; Liu, Liping

2016-11-01

Extrapolation techniques and storm-scale Numerical Weather Prediction (NWP) models are two primary approaches for short-term precipitation forecasts. The primary objective of this study is to verify precipitation forecasts and compare the performances of two nowcasting schemes: a Beijing Auto-Nowcast system (BJ-ANC) based on extrapolation techniques and a storm-scale NWP model called the Advanced Regional Prediction System (ARPS). The verification and comparison takes into account six heavy precipitation events that occurred in the summer of 2014 and 2015 in Jiangsu, China. The forecast performances of the two schemes were evaluated for the next 6 h at 1-h intervals using gridpoint-based measures of critical success index, bias, index of agreement, root mean square error, and using an object-based verification method called Structure-Amplitude-Location (SAL) score. Regarding gridpoint-based measures, BJ-ANC outperforms ARPS at first, but then the forecast accuracy decreases rapidly with lead time and performs worse than ARPS after 4-5 h of the initial forecast. Regarding the object-based verification method, most forecasts produced by BJ-ANC focus on the center of the diagram at the 1-h lead time and indicate high-quality forecasts. As the lead time increases, BJ-ANC overestimates precipitation amount and produces widespread precipitation, especially at a 6-h lead time. The ARPS model overestimates precipitation at all lead times, particularly at first.
A Study on the Potential Applications of Satellite Data in Air Quality Monitoring and Forecasting

NASA Technical Reports Server (NTRS)

Li, Can; Hsu, N. Christina; Tsay, Si-Chee

2011-01-01

In this study we explore the potential applications of MODIS (Moderate Resolution Imaging Spectroradiometer) -like satellite sensors in air quality research for some Asian regions. The MODIS aerosol optical thickness (AOT), NCEP global reanalysis meteorological data, and daily surface PM(sub 10) concentrations over China and Thailand from 2001 to 2009 were analyzed using simple and multiple regression models. The AOT-PM(sub 10) correlation demonstrates substantial seasonal and regional difference, likely reflecting variations in aerosol composition and atmospheric conditions, Meteorological factors, particularly relative humidity, were found to influence the AOT-PM(sub 10) relationship. Their inclusion in regression models leads to more accurate assessment of PM(sub 10) from space borne observations. We further introduced a simple method for employing the satellite data to empirically forecast surface particulate pollution, In general, AOT from the previous day (day 0) is used as a predicator variable, along with the forecasted meteorology for the following day (day 1), to predict the PM(sub 10) level for day 1. The contribution of regional transport is represented by backward trajectories combined with AOT. This method was evaluated through PM(sub 10) hindcasts for 2008-2009, using ohservations from 2005 to 2007 as a training data set to obtain model coefficients. For five big Chinese cities, over 50% of the hindcasts have percentage error less than or equal to 30%. Similar performance was achieved for cities in northern Thailand. The MODIS AOT data are responsible for at least part of the demonstrated forecasting skill. This method can be easily adapted for other regions, but is probably most useful for those having sparse ground monitoring networks or no access to sophisticated deterministic models. We also highlight several existing issues, including some inherent to a regression-based approach as exemplified by a case study for Beijing, Further studies will be necessa1Y before satellite data can see more extensive applications in the operational air quality monitoring and forecasting.
Obtaining high-resolution stage forecasts by coupling large-scale hydrologic models with sensor data

NASA Astrophysics Data System (ADS)

Fries, K. J.; Kerkez, B.

2017-12-01

We investigate how "big" quantities of distributed sensor data can be coupled with a large-scale hydrologic model, in particular the National Water Model (NWM), to obtain hyper-resolution forecasts. The recent launch of the NWM provides a great example of how growing computational capacity is enabling a new generation of massive hydrologic models. While the NWM spans an unprecedented spatial extent, there remain many questions about how to improve forecast at the street-level, the resolution at which many stakeholders make critical decisions. Further, the NWM runs on supercomputers, so water managers who may have access to their own high-resolution measurements may not readily be able to assimilate them into the model. To that end, we ask the question: how can the advances of the large-scale NWM be coupled with new local observations to enable hyper-resolution hydrologic forecasts? A methodology is proposed whereby the flow forecasts of the NWM are directly mapped to high-resolution stream levels using Dynamical System Identification. We apply the methodology across a sensor network of 182 gages in Iowa. Of these sites, approximately one third have shown to perform well in high-resolution flood forecasting when coupled with the outputs of the NWM. The quality of these forecasts is characterized using Principal Component Analysis and Random Forests to identify where the NWM may benefit from new sources of local observations. We also discuss how this approach can help municipalities identify where they should place low-cost sensors to most benefit from flood forecasts of the NWM.
Air pollution and climate response to aerosol direct radiative effects: A modeling study of decadal trends across the northern hemisphere

EPA Science Inventory

Decadal hemispheric Weather Research and Forecast-Community Multiscale Air Quality simulations from 1990 to 2010 were conducted to examine the meteorology and air quality responses to the aerosol direct radiative effects. The model's performance for the simulation of hourly surfa...
Dynamic Evaluation of Two Decades of WRF-CMAQ Ozone Simulations over the Contiguous United States (2017 CMAS)

EPA Science Inventory

Weather Research and Forecasting (WRF)–Community Multi-scale Air Quality (CMAQ) model over the contiguous United States is conducted to assess how well the changes in observed ozone air quality are simulated by the model. The changes induced by variations in meteorology and...
Advancing Data assimilation for Baltic Monitoring and Forecasting Center: implementation and evaluation of HBP-PDAF system

NASA Astrophysics Data System (ADS)

Korabel, Vasily; She, Jun; Huess, Vibeke; Woge Nielsen, Jacob; Murawsky, Jens; Nerger, Lars

2017-04-01

The potential of an efficient data assimilation (DA) scheme to improve model forecast skill was successfully demonstrated by many operational centres around the world. The Baltic-North Sea region is one of the most heavily monitored seas. Ferryboxes, buoys, ADCP moorings, shallow water Argo floats, and research vessels are providing more and more near-real time observations. Coastal altimetry has now providing increasing amount of high resolution sea level observations, which will be significantly expanded by the launch of SWOT satellite in next years. This will turn operational DA into a valuable tool for improving forecast quality in the region. This motivated us to focus on advancing DA for the Baltic Monitoring and Forecasting Centre (BAL MFC) in order to create a common framework for operational data assimilation in the Baltic Sea. We have implemented HBM-PDAF system based on the Parallel Data Assimilation Framework (PDAF), a highly versatile and optimised parallel suit with a choice of sequential schemes originally developed at AWI, and a hydrodynamic HIROMB-BOOS Model (HBM). At initial phase, only the satellite Sea Surface Temperature (SST) Level 3 data has been assimilated. Several related aspects are discussed, including improvements of the forecast quality for both surface and subsurface fields, the estimation of ensemble-based forecast error covariance, as well as possibilities of assimilating new types of observations, such as in-situ salinity and temperature profiles, coastal altimetry, and ice concentration.
Personalized glucose forecasting for type 2 diabetes using data assimilation

PubMed Central

Albers, David J.; Gluckman, Bruce; Ginsberg, Henry; Hripcsak, George; Mamykina, Lena

2017-01-01

Type 2 diabetes leads to premature death and reduced quality of life for 8% of Americans. Nutrition management is critical to maintaining glycemic control, yet it is difficult to achieve due to the high individual differences in glycemic response to nutrition. Anticipating glycemic impact of different meals can be challenging not only for individuals with diabetes, but also for expert diabetes educators. Personalized computational models that can accurately forecast an impact of a given meal on an individual’s blood glucose levels can serve as the engine for a new generation of decision support tools for individuals with diabetes. However, to be useful in practice, these computational engines need to generate accurate forecasts based on limited datasets consistent with typical self-monitoring practices of individuals with type 2 diabetes. This paper uses three forecasting machines: (i) data assimilation, a technique borrowed from atmospheric physics and engineering that uses Bayesian modeling to infuse data with human knowledge represented in a mechanistic model, to generate real-time, personalized, adaptable glucose forecasts; (ii) model averaging of data assimilation output; and (iii) dynamical Gaussian process model regression. The proposed data assimilation machine, the primary focus of the paper, uses a modified dual unscented Kalman filter to estimate states and parameters, personalizing the mechanistic models. Model selection is used to make a personalized model selection for the individual and their measurement characteristics. The data assimilation forecasts are empirically evaluated against actual postprandial glucose measurements captured by individuals with type 2 diabetes, and against predictions generated by experienced diabetes educators after reviewing a set of historical nutritional records and glucose measurements for the same individual. The evaluation suggests that the data assimilation forecasts compare well with specific glucose measurements and match or exceed in accuracy expert forecasts. We conclude by examining ways to present predictions as forecast-derived range quantities and evaluate the comparative advantages of these ranges. PMID:28448498
Portals for Real-Time Earthquake Data and Forecasting: Challenge and Promise (Invited)

NASA Astrophysics Data System (ADS)

Rundle, J. B.; Holliday, J. R.; Graves, W. R.; Feltstykket, R.; Donnellan, A.; Glasscoe, M. T.

2013-12-01

Earthquake forecasts have been computed by a variety of countries world-wide for over two decades. For the most part, forecasts have been computed for insurance, reinsurance and underwriters of catastrophe bonds. However, recent events clearly demonstrate that mitigating personal risk is becoming the responsibility of individual members of the public. Open access to a variety of web-based forecasts, tools, utilities and information is therefore required. Portals for data and forecasts present particular challenges, and require the development of both apps and the client/server architecture to deliver the basic information in real time. The basic forecast model we consider is the Natural Time Weibull (NTW) method (JBR et al., Phys. Rev. E, 86, 021106, 2012). This model uses small earthquakes (';seismicity-based models') to forecast the occurrence of large earthquakes, via data-mining algorithms combined with the ANSS earthquake catalog. This method computes large earthquake probabilities using the number of small earthquakes that have occurred in a region since the last large earthquake. Localizing these forecasts in space so that global forecasts can be computed in real time presents special algorithmic challenges, which we describe in this talk. Using 25 years of data from the ANSS California-Nevada catalog of earthquakes, we compute real-time global forecasts at a grid scale of 0.1o. We analyze and monitor the performance of these models using the standard tests, which include the Reliability/Attributes and Receiver Operating Characteristic (ROC) tests. It is clear from much of the analysis that data quality is a major limitation on the accurate computation of earthquake probabilities. We discuss the challenges of serving up these datasets over the web on web-based platforms such as those at www.quakesim.org , www.e-decider.org , and www.openhazards.com.
The use of satellite data assimilation methods in regional NWP for solar irradiance forecasting

NASA Astrophysics Data System (ADS)

Kurzrock, Frederik; Cros, Sylvain; Chane-Ming, Fabrice; Potthast, Roland; Linguet, Laurent; Sébastien, Nicolas

2016-04-01

As an intermittent energy source, the injection of solar power into electricity grids requires irradiance forecasting in order to ensure grid stability. On time scales of more than six hours ahead, numerical weather prediction (NWP) is recognized as the most appropriate solution. However, the current representation of clouds in NWP models is not sufficiently precise for an accurate forecast of solar irradiance at ground level. Dynamical downscaling does not necessarily increase the quality of irradiance forecasts. Furthermore, incorrectly simulated cloud evolution is often the cause of inaccurate atmospheric analyses. In non-interconnected tropical areas, the large amplitudes of solar irradiance variability provide abundant solar yield but present significant problems for grid safety. Irradiance forecasting is particularly important for solar power stakeholders in these regions where PV electricity penetration is increasing. At the same time, NWP is markedly more challenging in tropic areas than in mid-latitudes due to the special characteristics of tropical homogeneous convective air masses. Numerous data assimilation methods and strategies have evolved and been applied to a large variety of global and regional NWP models in the recent decades. Assimilating data from geostationary meteorological satellites is an appropriate approach. Indeed, models converting radiances measured by satellites into cloud properties already exist. Moreover, data are available at high temporal frequencies, which enable a pertinent cloud cover evolution modelling for solar energy forecasts. In this work, we present a survey of different approaches which aim at improving cloud cover forecasts using the assimilation of geostationary meteorological satellite data into regional NWP models. Various approaches have been applied to a variety of models and satellites and in different regions of the world. Current methods focus on the assimilation of cloud-top information, derived from infrared channels. For example, those information have been directly assimilated by modifying the water vapour profile in the initial conditions of the WRF model in California using GOES satellite imagery. In Europe, the assimilation of cloud-top height and relative humidity has been performed in an indirect approach using an ensemble Kalman filter. In this case Meteosat SEVIRI cloud information has been assimilated in the COSMO model. Although such methods generally provide improved cloud cover forecasts in mid-latitudes, the major limitation is that only clear-sky or completely cloudy cases can be considered. Indeed, fractional clouds cause a measured signal mixing cold clouds and warmer Earth surface. If the model's initial state is directly forced by cloud properties observed by satellite, the changed model fields have to be smoothed in order to avoid numerical instability. Other crucial aspects which influence forecast quality in the case of satellite radiance assimilation are channel selection, bias and error treatment. The overall promising satellite data assimilation methods in regional NWP have not yet been explicitly applied and tested under tropical conditions. Therefore, a deeper understanding on the benefits of such methods is necessary to improve irradiance forecast schemes.
DYNAMIC ELECTRICITY GENERATION FOR ADDRESSING DAILY AIR QUALITY EXCEEDANCES IN THE US

EPA Science Inventory

We will design, demonstrate, and evaluate a dynamic management system for managing daily air quality, exploring different elements of the design of this system such as how air quality forecasts can best be used, and decision rules for the electrical dispatch model. We will ...
Applications of Satellite Remote Sensing Products to Enhance and Evaluate the AIRPACT Regional Air Quality Modeling System

NASA Astrophysics Data System (ADS)

Herron-Thorpe, F. L.; Mount, G. H.; Emmons, L. K.; Lamb, B. K.; Jaffe, D. A.; Wigder, N. L.; Chung, S. H.; Zhang, R.; Woelfle, M.; Vaughan, J. K.; Leung, F. T.

2013-12-01

The WSU AIRPACT air quality modeling system for the Pacific Northwest forecasts hourly levels of aerosols and atmospheric trace gases for use in determining potential health and ecosystem impacts by air quality managers. AIRPACT uses the WRF/SMOKE/CMAQ modeling framework, derives dynamic boundary conditions from MOZART-4 forecast simulations with assimilated MOPITT CO, and uses the BlueSky framework to derive fire emissions. A suite of surface measurements and satellite-based remote sensing data products across the AIRPACT domain are used to evaluate and improve model performance. Specific investigations include anthropogenic emissions, wildfire simulations, and the effects of long-range transport on surface ozone. In this work we synthesize results for multiple comparisons of AIRPACT with satellite products such as IASI ammonia, AIRS carbon monoxide, MODIS AOD, OMI tropospheric ozone and nitrogen dioxide, and MISR plume height. Features and benefits of the newest version of AIRPACT's web-interface are also presented.
Application of Recent Advances in Forward Modeling of Emissions from Boreal and Temperate Wildfires to Real-time Forecasting of Aerosol and Trace Gas Concentrations

NASA Astrophysics Data System (ADS)

Hyer, E. J.; Reid, J. S.; Kasischke, E. S.; Allen, D. J.

2005-12-01

The magnitude of trace gas and aerosol emissions from wildfires is a scientific problem with important implications for atmospheric composition, and is also integral to understanding carbon cycling in terrestrial ecosystems. Recent ecological research on modeling wildfire emissions has integrated theoretical advances derived from ecological fieldwork with improved spatial and temporal databases to produce "post facto" estimates of emissions with high spatial and temporal resolution. These advances have been shown to improve agreement with atmospheric observations at coarse scales, but can in principle be applied to applications, such as forecasting, at finer scales. However, several of the approaches employed in these forward models are incompatible with the requirements of real-time forecasting, requiring modification of data inputs and calculation methods. Because of the differences in data inputs used for real-time and "post-facto" emissions modeling, the key uncertainties in the forward problem are not necessarily the same for these two applications. However, adaptation of these advances in forward modeling to forecasting applications has the potential to improve air quality forecasts, and also to provide a large body of experimental data which can be used to constrain crucial uncertainties in current conceptual models of wildfire emissions. This talk describes a forward modeling method developed at the University of Maryland and its application to the Fire Locating and Modeling of Burning Emissions (FLAMBE) system at the Naval Research Laboratory. Methods for applying the outputs of the NRL aerosol forecasting system to the inverse problem of constraining emissions will also be discussed. The system described can use the feedback supplied by atmospheric observations to improve the emissions source description in the forecasting model, and can also be used for hypothesis testing regarding fire behavior and data inputs.
Near-real-time Estimation and Forecast of Total Precipitable Water in Europe

NASA Astrophysics Data System (ADS)

Bartholy, J.; Kern, A.; Barcza, Z.; Pongracz, R.; Ihasz, I.; Kovacs, R.; Ferencz, C.

2013-12-01

Information about the amount and spatial distribution of atmospheric water vapor (or total precipitable water) is essential for understanding weather and the environment including the greenhouse effect, the climate system with its feedbacks and the hydrological cycle. Numerical weather prediction (NWP) models need accurate estimations of water vapor content to provide realistic forecasts including representation of clouds and precipitation. In the present study we introduce our research activity for the estimation and forecast of atmospheric water vapor in Central Europe using both observations and models. The Eötvös Loránd University (Hungary) operates a polar orbiting satellite receiving station in Budapest since 2002. This station receives Earth observation data from polar orbiting satellites including MODerate resolution Imaging Spectroradiometer (MODIS) Direct Broadcast (DB) data stream from satellites Terra and Aqua. The received DB MODIS data are automatically processed using freely distributed software packages. Using the IMAPP Level2 software total precipitable water is calculated operationally using two different methods. Quality of the TPW estimations is a crucial question for further application of the results, thus validation of the remotely sensed total precipitable water fields is presented using radiosonde data. In a current research project in Hungary we aim to compare different estimations of atmospheric water vapor content. Within the frame of the project we use a NWP model (DBCRAS; Direct Broadcast CIMSS Regional Assimilation System numerical weather prediction software developed by the University of Wisconsin, Madison) to forecast TPW. DBCRAS uses near real time Level2 products from the MODIS data processing chain. From the wide range of the derived Level2 products the MODIS TPW parameter found within the so-called mod07 results (Atmospheric Profiles Product) and the cloud top pressure and cloud effective emissivity parameters from the so-called mod06 results (Cloud Product) are assimilated twice a day (at 00 and 12 UTC) by DBCRAS. DBCRAS creates 72 hours long weather forecasts with 48 km horizontal resolution. DBCRAS is operational at the University since 2009 which means that by now sufficient data is available for the verification of the model. In the present study verification results for the DBCRAS total precipitable water forecasts are presented based on analysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF). Numerical indices are calculated to quantify the performance of DBCRAS. During a limited time period DBCRAS was also ran without assimilating MODIS products which means that there is possibility to quantify the effect of assimilating MODIS physical products on the quality of the forecasts. For this limited time period verification indices are compared to decide whether MODIS data improves forecast quality or not.
Convective Weather Forecast Accuracy Analysis at Center and Sector Levels

NASA Technical Reports Server (NTRS)

Wang, Yao; Sridhar, Banavar

2010-01-01

This paper presents a detailed convective forecast accuracy analysis at center and sector levels. The study is aimed to provide more meaningful forecast verification measures to aviation community, as well as to obtain useful information leading to the improvements in the weather translation capacity models. In general, the vast majority of forecast verification efforts over past decades have been on the calculation of traditional standard verification measure scores over forecast and observation data analyses onto grids. These verification measures based on the binary classification have been applied in quality assurance of weather forecast products at the national level for many years. Our research focuses on the forecast at the center and sector levels. We calculate the standard forecast verification measure scores for en-route air traffic centers and sectors first, followed by conducting the forecast validation analysis and related verification measures for weather intensities and locations at centers and sectors levels. An approach to improve the prediction of sector weather coverage by multiple sector forecasts is then developed. The weather severe intensity assessment was carried out by using the correlations between forecast and actual weather observation airspace coverage. The weather forecast accuracy on horizontal location was assessed by examining the forecast errors. The improvement in prediction of weather coverage was determined by the correlation between actual sector weather coverage and prediction. observed and forecasted Convective Weather Avoidance Model (CWAM) data collected from June to September in 2007. CWAM zero-minute forecast data with aircraft avoidance probability of 60% and 80% are used as the actual weather observation. All forecast measurements are based on 30-minute, 60- minute, 90-minute, and 120-minute forecasts with the same avoidance probabilities. The forecast accuracy analysis for times under one-hour showed that the errors in intensity and location for center forecast are relatively low. For example, 1-hour forecast intensity and horizontal location errors for ZDC center were about 0.12 and 0.13. However, the correlation between sector 1-hour forecast and actual weather coverage was weak, for sector ZDC32, about 32% of the total variation of observation weather intensity was unexplained by forecast; the sector horizontal location error was about 0.10. The paper also introduces an approach to estimate the sector three-dimensional actual weather coverage by using multiple sector forecasts, which turned out to produce better predictions. Using Multiple Linear Regression (MLR) model for this approach, the correlations between actual observation and the multiple sector forecast model prediction improved by several percents at 95% confidence level in comparison with single sector forecast.
Assessing probabilistic predictions of ENSO phase and intensity from the North American Multimodel Ensemble

NASA Astrophysics Data System (ADS)

Tippett, Michael K.; Ranganathan, Meghana; L'Heureux, Michelle; Barnston, Anthony G.; DelSole, Timothy

2017-05-01

Here we examine the skill of three, five, and seven-category monthly ENSO probability forecasts (1982-2015) from single and multi-model ensemble integrations of the North American Multimodel Ensemble (NMME) project. Three-category forecasts are typical and provide probabilities for the ENSO phase (El Niño, La Niña or neutral). Additional forecast categories indicate the likelihood of ENSO conditions being weak, moderate or strong. The level of skill observed for differing numbers of forecast categories can help to determine the appropriate degree of forecast precision. However, the dependence of the skill score itself on the number of forecast categories must be taken into account. For reliable forecasts with same quality, the ranked probability skill score (RPSS) is fairly insensitive to the number of categories, while the logarithmic skill score (LSS) is an information measure and increases as categories are added. The ignorance skill score decreases to zero as forecast categories are added, regardless of skill level. For all models, forecast formats and skill scores, the northern spring predictability barrier explains much of the dependence of skill on target month and forecast lead. RPSS values for monthly ENSO forecasts show little dependence on the number of categories. However, the LSS of multimodel ensemble forecasts with five and seven categories show statistically significant advantages over the three-category forecasts for the targets and leads that are least affected by the spring predictability barrier. These findings indicate that current prediction systems are capable of providing more detailed probabilistic forecasts of ENSO phase and amplitude than are typically provided.
Air quality real-time forecast before and during the G-20 ...

EPA Pesticide Factsheets

The 2016 G-20 Hangzhou summit, the eleventh annual meeting of the G-20 heads of government, will be held during September 3-5, 2016 in Hangzhou, China. For a successful summit, it is important to ensure good air quality. To achieve this goal, governments of Hangzhou and its surrounding provinces will enforce a series of emission reductions, such as a forced closure of major highly-polluting industries and also limiting car and construction emissions in the cities and surroundings during the 2016 G-20 Hangzhou summit. Air quality forecast systems consisting of the two-way coupled WRF-CMAQ and online-coupled WRF-Chem have been applied to forecast air quality in Hangzhou regularly. This study will present the results of real-time forecasts of air quality over eastern China using 12-km grid spacing and for Hangzhou area using 4-km grid spacing with these two modeling systems using emission inventories for base and 2016 G-20 scenarios before and during the 2016 G-20 Hangzhou summit. Evaluations of models’ performance for both cases for PM2.5, PM10, O3, SO2, NO2, CO, air quality index (AQI), and aerosol optical depth (AOD) are carried out by comparing them with observations obtained from satellites, such as MODIS, and surface monitoring networks. The effects of the emission reduction efforts on expected air quality improvements during the2016 G-20 Hangzhou summit will be studied in depth. This study provides insights on how air quality will be improved by a plan
Korea-United States Air Quality (KORUS-AQ) Campaign

NASA Technical Reports Server (NTRS)

Castellanos, Patricia; Da Silva, Arlindo; Longo-De Freitas, Karla

2017-01-01

The Korea-United States Air Quality (KORUS-AQ) campaign was an international cooperative field study based out of Osan Air Base, Songtan, South Korea (about 60 kilometers south of Seoul) in April-June 2016. A comprehensive suite of instruments capable of measuring atmospheric composition was deployed around the Korean peninsula on aircrafts, ships, and at ground sites in order to characterize local and transboundary pollution. The NASA Goddard Earth Observing System, version 5 (GEOS-5) forecast model was used for near real time meteorological and aerosol forecasting and flight planning during the KORUS-AQ campaign. Evaluation of GEOS-5 against observations from the campaign will help to identify inaccuracies in the models physical and chemical processes in this region within East Asia and lead to further developments of the modeling system.
High-resolution visibility and air quality forecasting using multi-layer urban canopy model for highly urbanized Hong Kong and the Pearl River Delta

NASA Astrophysics Data System (ADS)

Piu NG, Chak; HAO, Song; Fat LAM, Yun

2015-04-01

Visibility is a universally critical element which affects the public in many aspects, including economic activities, health of local citizens and safety of marine transportation and aviation. The Interagency Monitoring of Protected Visual Environments (IMPROVE) visibility equation, an empirical equation developed by USEPA, has been modified by various studies to fit into the application upon the Asian continent including Hong Kong and China. Often these studies focused on the improvement of the existing IMPROVE equation by modifying its particulate speciation using local observation data. In this study, we developed an Integrated Forecast System (IFS) to predict the next-day air quality and visibility using Weather Research and Forecasting model with Building Energy Parameterization and Building Energy Model (WRF-BEP+BEM) and Community Multi-scale Air Quality Model (CMAQ). Unlike the other studies, the core of this study is to include detailed urbanization impacts with calibrated "IMPROVE equation for PRD" into the modeling system for Hong Kong's environs. The ultra-high resolution land cover information (~1km x 1km) from Google images, was digitized into the Geographic Information System (GIS) for preparing the model-ready input for IFS. The NCEP FNL (Final) Operation Global Analysis (FNL) and the Global Forecasting System (GFS) datasets were tested for both hind-cast and forecast cases, in order to calibrate the input of urban parameters in the WRF-BEP+BEM model. The evaluation of model performance with sensitivity cases was performed on sea surface temperature (SST), surface temperature (T), wind speed/direction with the major pollutants (i.e., PM10, PM2.5, NOx, SO2 and O3) using local observation and will be presented/discussed in this paper. References: 1. Y. L. Lee, R. Sequeira, Visibility degradation across Hong Kong its components and their relative contribution. Atmospheric Environment 2001, 35, 5861-5872. doi:10.1016/S1352-2310(01)00395-8 2. R. Zhang, Q. Bian, J. C. H. Fung, A. K. H. Lau, Mathematical modeling of seasonal variations in visibility in Hong Kong and the Pearl River Delta region. Atmospheric Environment 2013, 77, 803-816. http://dx.doi.org/10.1016/j.atmosenv.2013.05.048
P.88 Regional Precipitation Forecast with Atmospheric Infrared Sounder (AIRS) Profiles

NASA Technical Reports Server (NTRS)

Chou, Shih-Hung; Zavodsky, Bradley; Jedlovec, Gary

2010-01-01

Prudent assimulation of AIRS thermodynamic profiles and quality indicators can improve initial conditions for regional weather models. In general, AIRS-enhanced analysis more closely resembles radiosondes than the CNTL; forecasts with AIRS profiles are generally closer to NAM analyses than CNTL for sensible weather parameters (not shown here). Assimilation of AIRS leads to an overall QPF improvement in 6-h accumulated precipitation forecases. Including AIRS profiles in assimilation process enhances the low-level instability and produces stronger updrafts and a better precipitation forecast than the CNTL run.
Great Lakes water quality scenario models: Operational feasibility -Lake Michigan Mass Balance models

EPA Science Inventory

An overview of the Lake Michigan Mass Balance models were provided (eutrophication/nutrients, atrazine, mercury, and PCBs) with emphasis on the PCB model post-audit and forecast for Lake Trout. Provided were modeling construct, model description, and primary results. An assessm...

Randomly correcting model errors in the ARPEGE-Climate v6.1 component of CNRM-CM: applications for seasonal forecasts

NASA Astrophysics Data System (ADS)

Batté, Lauriane; Déqué, Michel

2016-06-01

Stochastic methods are increasingly used in global coupled model climate forecasting systems to account for model uncertainties. In this paper, we describe in more detail the stochastic dynamics technique introduced by Batté and Déqué (2012) in the ARPEGE-Climate atmospheric model. We present new results with an updated version of CNRM-CM using ARPEGE-Climate v6.1, and show that the technique can be used both as a means of analyzing model error statistics and accounting for model inadequacies in a seasonal forecasting framework.The perturbations are designed as corrections of model drift errors estimated from a preliminary weakly nudged re-forecast run over an extended reference period of 34 boreal winter seasons. A detailed statistical analysis of these corrections is provided, and shows that they are mainly made of intra-month variance, thereby justifying their use as in-run perturbations of the model in seasonal forecasts. However, the interannual and systematic error correction terms cannot be neglected. Time correlation of the errors is limited, but some consistency is found between the errors of up to 3 consecutive days.These findings encourage us to test several settings of the random draws of perturbations in seasonal forecast mode. Perturbations are drawn randomly but consistently for all three prognostic variables perturbed. We explore the impact of using monthly mean perturbations throughout a given forecast month in a first ensemble re-forecast (SMM, for stochastic monthly means), and test the use of 5-day sequences of perturbations in a second ensemble re-forecast (S5D, for stochastic 5-day sequences). Both experiments are compared in the light of a REF reference ensemble with initial perturbations only. Results in terms of forecast quality are contrasted depending on the region and variable of interest, but very few areas exhibit a clear degradation of forecasting skill with the introduction of stochastic dynamics. We highlight some positive impacts of the method, mainly on Northern Hemisphere extra-tropics. The 500 hPa geopotential height bias is reduced, and improvements project onto the representation of North Atlantic weather regimes. A modest impact on ensemble spread is found over most regions, which suggests that this method could be complemented by other stochastic perturbation techniques in seasonal forecasting mode.
Integrating Measurement Based New Knowledge on Wildland Fire Emissions and Chemistry into the AIRPACT Air Quality Forecasting for the Pacific Northwest

NASA Astrophysics Data System (ADS)

Nergui, T.; Lee, Y.; Chung, S. H.; Lamb, B. K.; Yokelson, R. J.; Barsanti, K.

2017-12-01

A number of chamber and field measurements have shown that atmospheric organic aerosols and their precursors produced from wildfires are significantly underestimated in the emission inventories used for air quality models for various applications such as regulatory strategy development, impact assessments of air pollutants, and air quality forecasting for public health. The AIRPACT real-time air quality forecasting system consistently underestimates surface level fine particulate matter (PM2.5) concentrations in the summer at both urban and rural locations in the Pacific Northwest, primarily result of errors in organic particulate matter. In this work, we implement updated chemical speciation and emission factors based on FLAME-IV (Fourth Fire Lab at Missoula Experiment) and other measurements in the Blue-Sky fire emission model and the SMOKE emission preprocessor; and modified parameters for the secondary organic aerosol (SOA) module in CMAQ chemical transport model of the AIRPACT modeling system. Simulation results from CMAQ version 5.2 which has a better treatment for anthropogenic SOA formation (as a base case) and modified parameterization used for fire emissions and chemistry in the model (fire-soa case) are evaluated against airborne measurements downwind of the Big Windy Complex Fire and the Colockum Tarps Fire, both of which occurred in the Pacific Northwest in summer 2013. Using the observed aerosol chemical composition and mass loadings for organics, nitrate, sulfate, ammonium, and chloride from aircraft measurements during the Studies of Emissions and Atmospheric Composition, Clouds, and Climate Coupling by Regional Surveys (SEAC4RS) and the Biomass Burning Observation Project (BBOP), we assess how new knowledge gained from wildfire measurements improve model predictions for SOA and its contribution to the total mass of PM2.5 concentrations.
A real-time evaluation and demonstration of strategies for 'Over-The-Loop' ensemble streamflow forecasting in US watersheds

NASA Astrophysics Data System (ADS)

Wood, Andy; Clark, Elizabeth; Mendoza, Pablo; Nijssen, Bart; Newman, Andy; Clark, Martyn; Nowak, Kenneth; Arnold, Jeffrey

2017-04-01

Many if not most national operational streamflow prediction systems rely on a forecaster-in-the-loop approach that require the hands-on-effort of an experienced human forecaster. This approach evolved from the need to correct for long-standing deficiencies in the models and datasets used in forecasting, and the practice often leads to skillful flow predictions despite the use of relatively simple, conceptual models. Yet the 'in-the-loop' forecast process is not reproducible, which limits opportunities to assess and incorporate new techniques systematically, and the effort required to make forecasts in this way is an obstacle to expanding forecast services - e.g., though adding new forecast locations or more frequent forecast updates, running more complex models, or producing forecast and hindcasts that can support verification. In the last decade, the hydrologic forecasting community has begun develop more centralized, 'over-the-loop' systems. The quality of these new forecast products will depend on their ability to leverage research in areas including earth system modeling, parameter estimation, data assimilation, statistical post-processing, weather and climate prediction, verification, and uncertainty estimation through the use of ensembles. Currently, many national operational streamflow forecasting and water management communities have little experience with the strengths and weaknesses of over-the-loop approaches, even as such systems are beginning to be deployed operationally in centers such as ECMWF. There is thus a need both to evaluate these forecasting advances and to demonstrate their potential in a public arena, raising awareness in forecast user communities and development programs alike. To address this need, the US National Center for Atmospheric Research is collaborating with the University of Washington, the Bureau of Reclamation and the US Army Corps of Engineers, using the NCAR 'System for Hydromet Analysis Research and Prediction Applications' (SHARP) to implement, assess and demonstrate real-time over-the-loop ensemble flow forecasts in a range of US watersheds. The system relies on fully ensemble techniques, including: an 100-member ensemble of meteorological model forcings and an ensemble particle filter data assimilation for initializing watershed states; analog/regression-based downscaling of ensemble weather forecasts from GEFS; and statistical post-processing of ensemble forecast outputs, all of which run in real-time within a workflow managed by ECWMF's ecFlow libraries over large US regional domains. We describe SHARP and present early hindcast and verification results for short to seasonal range streamflow forecasts in a number of US case study watersheds.
Comparative evaluation of the impact of GRAPES and MM5 meteorology on CMAQ prediction over Pearl River Delta, China

NASA Astrophysics Data System (ADS)

Deng, T.; Chen, Y.; Wan, Q.

2017-12-01

The Community Multiscale Air Quality (CMAQ) model was utilized for forecasting air quality over the Pearl River Delta (PRD) region from December 2013 to January 2014. The pollution forecasting performance of CMAQ coupled with the two different meteorological models, the Global/Regional Assimilation and Prediction System (GRAPES) and the 5th-generation Mesoscale Model (MM5), was assessed by combining observational data. The effect of meteorological factors and physical-chemical processes on forecast results was discussed through process analysis. The results showed that both models have similar good performance with better performance by GRAPES-CMAQ. GRAPES was superior in predicting the overall meteorological element variation tendencies but showed large deviations in atmospheric pressure and wind speed. It contributed to higher correlation coefficients of the pollutants with GRAPES-CMAQ, but with greater deviation. The underestimations of nitrate and ammonium salt contributed to the underestimations of Particle Matter (PM) and extinction coefficients. Surface layer SO2, CO and NO source emissions made the sole positive contribution. O3 originated mainly from horizontal and vertical transport and chemical processes were the main consumption item. On the contrary, NO2 derived mainly from chemical production.
Multiannual forecasting of seasonal influenza dynamics reveals climatic and evolutionary drivers.

PubMed

Axelsen, Jacob Bock; Yaari, Rami; Grenfell, Bryan T; Stone, Lewi

2014-07-01

Human influenza occurs annually in most temperate climatic zones of the world, with epidemics peaking in the cold winter months. Considerable debate surrounds the relative role of epidemic dynamics, viral evolution, and climatic drivers in driving year-to-year variability of outbreaks. The ultimate test of understanding is prediction; however, existing influenza models rarely forecast beyond a single year at best. Here, we use a simple epidemiological model to reveal multiannual predictability based on high-quality influenza surveillance data for Israel; the model fit is corroborated by simple metapopulation comparisons within Israel. Successful forecasts are driven by temperature, humidity, antigenic drift, and immunity loss. Essentially, influenza dynamics are a balance between large perturbations following significant antigenic jumps, interspersed with nonlinear epidemic dynamics tuned by climatic forcing.
Developing a calibrated CONUS-wide watershed-scale simulation platform for quantifying the influence of different sources of uncertainty on streamflow forecast skill

NASA Astrophysics Data System (ADS)

Newman, A. J.; Sampson, K. M.; Wood, A. W.; Hopson, T. M.; Brekke, L. D.; Arnold, J.; Raff, D. A.; Clark, M. P.

2013-12-01

Skill in model-based hydrologic forecasting depends on the ability to estimate a watershed's initial moisture and energy conditions, to forecast future weather and climate inputs, and on the quality of the hydrologic model's representation of watershed processes. The impact of these factors on prediction skill varies regionally, seasonally, and by model. We are investigating these influences using a watershed simulation platform that spans the continental US (CONUS), encompassing a broad range of hydroclimatic variation, and that uses the current simulation models of National Weather Service streamflow forecasting operations. The first phase of this effort centered on the implementation and calibration of the SNOW-17 and Sacramento soil moisture accounting (SAC-SMA) based hydrologic modeling system for a range of watersheds. The base configuration includes 630 basins in the United States Geological Survey's Hydro-Climatic Data Network 2009 (HCDN-2009, Lins 2012) conterminous U.S. basin subset. Retrospective model forcings were derived from Daymet (http://daymet.ornl.gov/), and where available, a priori parameter estimates were based on or compared with the operational NWS model parameters. Model calibration was accomplished by several objective, automated strategies, including the shuffled complex evolution (SCE) optimization approach developed within the NWS in the early 1990s (Duan et al. 1993). This presentation describes outcomes from this effort, including insights about measuring simulation skill, and on relationships between simulation skill and model parameters, basin characteristics (climate, topography, vegetation, soils), and the quality of forcing inputs. References: %Z Thornton, P.; Thornton, M.; Mayer, B.; Wilhelmi, N.; Wei, Y.; Devarakonda, R; Cook, R. Daymet: Daily Surface Weather on a 1 km Grid for North America. 1980-2008; Oak Ridge National Laboratory Distributed Active Archive Center: Oak Ridge, TN, USA, 2012; Volume 10.
[Medical human resources planning in Europe: A literature review of the forecasting models].

PubMed

Benahmed, N; Deliège, D; De Wever, A; Pirson, M

2018-02-01

Healthcare is a labor-intensive sector in which half of the expenses are dedicated to human resources. Therefore, policy makers, at national and internal levels, attend to the number of practicing professionals and the skill mix. This paper aims to analyze the European forecasting model for supply and demand of physicians. To describe the forecasting tools used for physician planning in Europe, a grey literature search was done in the OECD, WHO, and European Union libraries. Electronic databases such as Pubmed, Medine, Embase and Econlit were also searched. Quantitative methods for forecasting medical supply rely mainly on stock-and-flow simulations and less often on systemic dynamics. Parameters included in forecasting models exhibit wide variability for data availability and quality. The forecasting of physician needs is limited to healthcare consumption and rarely considers overall needs and service targets. Besides quantitative methods, horizon scanning enables an evaluation of the changes in supply and demand in an uncertain future based on qualitative techniques such as semi-structured interviews, Delphi Panels, or focus groups. Finally, supply and demand forecasting models should be regularly updated. Moreover, post-hoc analyze is also needed but too rarely implemented. Medical human resource planning in Europe is inconsistent. Political implementation of the results of forecasting projections is essential to insure efficient planning. However, crucial elements such as mobility data between Member States are poorly understood, impairing medical supply regulation policies. These policies are commonly limited to training regulations, while horizontal and vertical substitution is less frequently taken into consideration. Copyright © 2017 Elsevier Masson SAS. All rights reserved.
THE NEW ENGLAND AIR QUALITY FORECASTING PILOT PROGRAM: DEVELOPMENT OF AN EVALUATION PROTOCOL AND PERFORMANCE BENCHMARK

EPA Science Inventory

The National Oceanic and Atmospheric Administration recently sponsored the New England Forecasting Pilot Program to serve as a "test bed" for chemical forecasting by providing all of the elements of a National Air Quality Forecasting System, including the development and implemen...
Operational on-line coupled chemical weather forecasts for Europe with WRF/Chem

NASA Astrophysics Data System (ADS)

Hirtl, Marcus; Mantovani, Simone; Krüger, Bernd C.; Flandorfer, Claudia; Langer, Matthias

2014-05-01

Air quality is a key element for the well-being and quality of life of European citizens. Air pollution measurements and modeling tools are essential for the assessment of air quality according to EU legislation. The responsibilities of ZAMG as the national weather service of Austria include the support of the federal states and the public in questions connected to the protection of the environment in the frame of advisory and counseling services as well as expert opinions. ZAMG conducts daily Air-Quality forecasts using the on-line coupled model WRF/Chem. Meteorology is simulated simultaneously with the emissions, turbulent mixing, transport, transformation, and fate of trace gases and aerosols. The emphasis of the application is on predicting pollutants over Austria. Two domains are used for the simulations: the mother domain covers Europe with a resolution of 12 km, the inner domain includes the alpine region with a horizontal resolution of 4 km; 45 model levels are used in the vertical direction. The model runs 2 times per day for a period of 72 hours and is initialized with ECMWF forecasts. On-line coupled models allow considering two-way interactions between different atmospheric processes including chemistry (both gases and aerosols), clouds, radiation, boundary layer, emissions, meteorology and climate. In the operational set-up direct-, indirect and semi-direct effects between meteorology and air chemistry are enabled. The model is running on the HPCF (High Performance Computing Facility) of the ZAMG. In the current set-up 1248 CPUs are used. As the simulations need a big amount of computing resources, a method to safe I/O-time was implemented. Every MPI task writes all its output into the shared memory filesystem of the compute nodes. Once the WRF/Chem integration is finished, all split NetCDF-files are merged and saved on the global file system. The merge-routine is based on parallel-NetCDF. With this method the model runs about 30% faster on the SGI-ICEX. Different additional external data sources can be used to improve the forecasts. Satellite measurements of the Aerosol Optical Thickness (AOT) and ground-based PM10-measurements are combined to highly-resolved initial fields using regression- and assimilation techniques. The available local emission inventories provided by the different Austrian regional governments were harmonized and are used for the model simulations. A model evaluation for a selected episode in February 2010 is presented with respect to PM10 forecasts. During that month exceedances of PM10-thresholds occurred at many measurement stations of the Austrian network. Different model runs (only model/only ground stations assimilated/satellite and ground stations assimilated) are compared to the respective measurements.
Probabilistic Forecasting of Surface Ozone with a Novel Statistical Approach

NASA Technical Reports Server (NTRS)

Balashov, Nikolay V.; Thompson, Anne M.; Young, George S.

2017-01-01

The recent change in the Environmental Protection Agency's surface ozone regulation, lowering the surface ozone daily maximum 8-h average (MDA8) exceedance threshold from 75 to 70 ppbv, poses significant challenges to U.S. air quality (AQ) forecasters responsible for ozone MDA8 forecasts. The forecasters, supplied by only a few AQ model products, end up relying heavily on self-developed tools. To help U.S. AQ forecasters, this study explores a surface ozone MDA8 forecasting tool that is based solely on statistical methods and standard meteorological variables from the numerical weather prediction (NWP) models. The model combines the self-organizing map (SOM), which is a clustering technique, with a step wise weighted quadratic regression using meteorological variables as predictors for ozone MDA8. The SOM method identifies different weather regimes, to distinguish between various modes of ozone variability, and groups them according to similarity. In this way, when a regression is developed for a specific regime, data from the other regimes are also used, with weights that are based on their similarity to this specific regime. This approach, regression in SOM (REGiS), yields a distinct model for each regime taking into account both the training cases for that regime and other similar training cases. To produce probabilistic MDA8 ozone forecasts, REGiS weighs and combines all of the developed regression models on the basis of the weather patterns predicted by an NWP model. REGiS is evaluated over the San Joaquin Valley in California and the northeastern plains of Colorado. The results suggest that the model performs best when trained and adjusted separately for an individual AQ station and its corresponding meteorological site.
Different types of drifts in two seasonal forecast systems and their dependence on ENSO

NASA Astrophysics Data System (ADS)

Hermanson, L.; Ren, H.-L.; Vellinga, M.; Dunstone, N. D.; Hyder, P.; Ineson, S.; Scaife, A. A.; Smith, D. M.; Thompson, V.; Tian, B.; Williams, K. D.

2017-11-01

Seasonal forecasts using coupled ocean-atmosphere climate models are increasingly employed to provide regional climate predictions. For the quality of forecasts to improve, regional biases in climate models must be diagnosed and reduced. The evolution of biases as initialized forecasts drift away from the observations is poorly understood, making it difficult to diagnose the causes of climate model biases. This study uses two seasonal forecast systems to examine drifts in sea surface temperature (SST) and precipitation, and compares them to the long-term bias in the free-running version of each model. Drifts are considered from daily to multi-annual time scales. We define three types of drift according to their relation with the long-term bias in the free-running model: asymptoting, overshooting and inverse drift. We find that precipitation almost always has an asymptoting drift. SST drifts on the other hand, vary between forecasting systems, where one often overshoots and the other often has an inverse drift. We find that some drifts evolve too slowly to have an impact on seasonal forecasts, even though they are important for climate projections. The bias found over the first few days can be very different from that in the free-running model, so although daily weather predictions can sometimes provide useful information on the causes of climate biases, this is not always the case. We also find that the magnitude of equatorial SST drifts, both in the Pacific and other ocean basins, depends on the El Niño Southern Oscillation (ENSO) phase. Averaging over all hindcast years can therefore hide the details of ENSO state dependent drifts and obscure the underlying physical causes. Our results highlight the need to consider biases across a range of timescales in order to understand their causes and develop improved climate models.
Hybrid vs Adaptive Ensemble Kalman Filtering for Storm Surge Forecasting

NASA Astrophysics Data System (ADS)

Altaf, M. U.; Raboudi, N.; Gharamti, M. E.; Dawson, C.; McCabe, M. F.; Hoteit, I.

2014-12-01

Recent storm surge events due to Hurricanes in the Gulf of Mexico have motivated the efforts to accurately forecast water levels. Toward this goal, a parallel architecture has been implemented based on a high resolution storm surge model, ADCIRC. However the accuracy of the model notably depends on the quality and the recentness of the input data (mainly winds and bathymetry), model parameters (e.g. wind and bottom drag coefficients), and the resolution of the model grid. Given all these uncertainties in the system, the challenge is to build an efficient prediction system capable of providing accurate forecasts enough ahead of time for the authorities to evacuate the areas at risk. We have developed an ensemble-based data assimilation system to frequently assimilate available data into the ADCIRC model in order to improve the accuracy of the model. In this contribution we study and analyze the performances of different ensemble Kalman filter methodologies for efficient short-range storm surge forecasting, the aim being to produce the most accurate forecasts at the lowest possible computing time. Using Hurricane Ike meteorological data to force the ADCIRC model over a domain including the Gulf of Mexico coastline, we implement and compare the forecasts of the standard EnKF, the hybrid EnKF and an adaptive EnKF. The last two schemes have been introduced as efficient tools for enhancing the behavior of the EnKF when implemented with small ensembles by exploiting information from a static background covariance matrix. Covariance inflation and localization are implemented in all these filters. Our results suggest that both the hybrid and the adaptive approach provide significantly better forecasts than those resulting from the standard EnKF, even when implemented with much smaller ensembles.
Short-term Forecasting Tools for Agricultural Nutrient Management.

PubMed

Easton, Zachary M; Kleinman, Peter J A; Buda, Anthony R; Goering, Dustin; Emberston, Nichole; Reed, Seann; Drohan, Patrick J; Walter, M Todd; Guinan, Pat; Lory, John A; Sommerlot, Andrew R; Sharpley, Andrew

2017-11-01

The advent of real-time, short-term farm management tools is motivated by the need to protect water quality above and beyond the general guidance offered by existing nutrient management plans. Advances in high-performance computing and hydrologic or climate modeling have enabled rapid dissemination of real-time information that can assist landowners and conservation personnel with short-term management planning. This paper reviews short-term decision support tools for agriculture that are under various stages of development and implementation in the United States: (i) Wisconsin's Runoff Risk Advisory Forecast (RRAF) System, (ii) New York's Hydrologically Sensitive Area Prediction Tool, (iii) Virginia's Saturated Area Forecast Model, (iv) Pennsylvania's Fertilizer Forecaster, (v) Washington's Application Risk Management (ARM) System, and (vi) Missouri's Design Storm Notification System. Although these decision support tools differ in their underlying model structure, the resolution at which they are applied, and the hydroclimates to which they are relevant, all provide forecasts (range 24-120 h) of runoff risk or soil moisture saturation derived from National Weather Service Forecast models. Although this review highlights the need for further development of robust and well-supported short-term nutrient management tools, their potential for adoption and ultimate utility requires an understanding of the appropriate context of application, the strategic and operational needs of managers, access to weather forecasts, scales of application (e.g., regional vs. field level), data requirements, and outreach communication structure. Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.
Assimilation of Quality Controlled AIRS Temperature Profiles using the NCEP GFS

NASA Technical Reports Server (NTRS)

Susskind, Joel; Reale, Oreste; Iredell, Lena; Rosenberg, Robert

2013-01-01

We have previously conducted a number of data assimilation experiments using AIRS Version-5 quality controlled temperature profiles as a step toward finding an optimum balance of spatial coverage and sounding accuracy with regard to improving forecast skill. The data assimilation and forecast system we used was the Goddard Earth Observing System Model , Version-5 (GEOS-5) Data Assimilation System (DAS), which represents a combination of the NASA GEOS-5 forecast model with the National Centers for Environmental Prediction (NCEP) operational Grid Point Statistical Interpolation (GSI) global analysis scheme. All analyses and forecasts were run at a 0.5deg x 0.625deg spatial resolution. Data assimilation experiments were conducted in four different seasons, each in a different year. Three different sets of data assimilation experiments were run during each time period: Control; AIRS T(p); and AIRS Radiance. In the "Control" analysis, all the data used operationally by NCEP was assimilated, but no AIRS data was assimilated. Radiances from the Aqua AMSU-A instrument were also assimilated operationally by NCEP and are included in the "Control". The AIRS Radiance assimilation adds AIRS observed radiance observations for a select set of channels to the data set being assimilated, as done operationally by NCEP. In the AIRS T(p) assimilation, all information used in the Control was assimilated as well as Quality Controlled AIRS Version-5 temperature profiles, i.e., AIRS T(p) information was substituted for AIRS radiance information. The AIRS Version-5 temperature profiles were presented to the GSI analysis as rawinsonde profiles, assimilated down to a case-by-case appropriate pressure level p(sub best) determined using the Quality Control procedure. Version-5 also determines case-by-case, level-by-level error estimates of the temperature profiles, which were used as the uncertainty of each temperature measurement. These experiments using GEOS-5 have shown that forecasts resulting from analyses using the AIRS T(p) assimilation system were superior to those from the Radiance assimilation system, both with regard to global 7 day forecast skill and also the ability to predict storm tracks and intensity.
Operational hydrological forecasting in Bavaria. Part I: Forecast uncertainty

NASA Astrophysics Data System (ADS)

Ehret, U.; Vogelbacher, A.; Moritz, K.; Laurent, S.; Meyer, I.; Haag, I.

2009-04-01

In Bavaria, operational flood forecasting has been established since the disastrous flood of 1999. Nowadays, forecasts based on rainfall information from about 700 raingauges and 600 rivergauges are calculated and issued for nearly 100 rivergauges. With the added experience of the 2002 and 2005 floods, awareness grew that the standard deterministic forecast, neglecting the uncertainty associated with each forecast is misleading, creating a false feeling of unambiguousness. As a consequence, a system to identify, quantify and communicate the sources and magnitude of forecast uncertainty has been developed, which will be presented in part I of this study. In this system, the use of ensemble meteorological forecasts plays a key role which will be presented in part II. Developing the system, several constraints stemming from the range of hydrological regimes and operational requirements had to be met: Firstly, operational time constraints obviate the variation of all components of the modeling chain as would be done in a full Monte Carlo simulation. Therefore, an approach was chosen where only the most relevant sources of uncertainty were dynamically considered while the others were jointly accounted for by static error distributions from offline analysis. Secondly, the dominant sources of uncertainty vary over the wide range of forecasted catchments: In alpine headwater catchments, typically of a few hundred square kilometers in size, rainfall forecast uncertainty is the key factor for forecast uncertainty, with a magnitude dynamically changing with the prevailing predictability of the atmosphere. In lowland catchments encompassing several thousands of square kilometers, forecast uncertainty in the desired range (usually up to two days) is mainly dependent on upstream gauge observation quality, routing and unpredictable human impact such as reservoir operation. The determination of forecast uncertainty comprised the following steps: a) From comparison of gauge observations and several years of archived forecasts, overall empirical error distributions termed 'overall error' were for each gauge derived for a range of relevant forecast lead times. b) The error distributions vary strongly with the hydrometeorological situation, therefore a subdivision into the hydrological cases 'low flow, 'rising flood', 'flood', flood recession' was introduced. c) For the sake of numerical compression, theoretical distributions were fitted to the empirical distributions using the method of moments. Here, the normal distribution was generally best suited. d) Further data compression was achieved by representing the distribution parameters as a function (second-order polynome) of lead time. In general, the 'overall error' obtained from the above procedure is most useful in regions where large human impact occurs and where the influence of the meteorological forecast is limited. In upstream regions however, forecast uncertainty is strongly dependent on the current predictability of the atmosphere, which is contained in the spread of an ensemble forecast. Including this dynamically in the hydrological forecast uncertainty estimation requires prior elimination of the contribution of the weather forecast to the 'overall error'. This was achieved by calculating long series of hydrometeorological forecast tests, where rainfall observations were used instead of forecasts. The resulting error distribution is termed 'model error' and can be applied on hydrological ensemble forecasts, where ensemble rainfall forecasts are used as forcing. The concept will be illustrated by examples (good and bad ones) covering a wide range of catchment sizes, hydrometeorological regimes and quality of hydrological model calibration. The methodology to combine the static and dynamic shares of uncertainty will be presented in part II of this study.
The Impact of Atmospheric InfraRed Sounder (AIRS) Profiles on Short-term Weather Forecasts

NASA Technical Reports Server (NTRS)

Chou, Shih-Hung; Zavodsky, Brad; Jedlovec, Gary J.; Lapenta, William

2007-01-01

The Atmospheric Infrared Sounder (AIRS), together with the Advanced Microwave Sounding Unit (AMSU), represents one of the most advanced spacebased atmospheric sounding systems. The combined AlRS/AMSU system provides radiance measurements used to retrieve temperature profiles with an accuracy of 1 K over 1 km layers under both clear and partly cloudy conditions, while the accuracy of the derived humidity profiles is 15% in 2 km layers. Critical to the successful use of AIRS profiles for weather and climate studies is the use of profile quality indicators and error estimates provided with each profile Aside form monitoring changes in Earth's climate, one of the objectives of AIRS is to provide sounding information of sufficient accuracy such that the assimilation of the new observations, especially in data sparse region, will lead to an improvement in weather forecasts. The purpose of this paper is to describe a procedure to optimally assimilate highresolution AIRS profile data in a regional analysis/forecast model. The paper will focus on the impact of AIRS profiles on a rapidly developing east coast storm and will also discuss preliminary results for a 30-day forecast period, simulating a quasi-operation environment. Temperature and moisture profiles were obtained from the prototype version 5.0 EOS science team retrieval algorithm which includes explicit error information for each profile. The error profile information was used to select the highest quality temperature and moisture data for every profile location and pressure level for assimilation into the ARPS Data Analysis System (ADAS). The AIRS-enhanced analyses were used as initial fields for the Weather Research and Forecast (WRF) system used by the SPORT project for regional weather forecast studies. The ADASWRF system will be run on CONUS domain with an emphasis on the east coast. The preliminary assessment of the impact of the AIRS profiles will focus on quality control issues associated with AIRS, intelligent use of the quality indicators, and forecast verification.
Toward a US National Air Quality Forecast Capability: Current and Planned Capabilities

EPA Science Inventory

As mandated by Congress, NOAA is establishing a US national air quality forecast capability. This capability is being built with EPA, to provide air quality forecast information with enough accuracy and lead-time so that people can take actions to limit harmful effects of poor a...
Sensitivity of WRF-chem predictions to dust source function specification in West Asia

NASA Astrophysics Data System (ADS)

Nabavi, Seyed Omid; Haimberger, Leopold; Samimi, Cyrus

2017-02-01

Dust storms tend to form in sparsely populated areas covered by only few observations. Dust source maps, known as source functions, are used in dust models to allocate a certain potential of dust release to each place. Recent research showed that the well known Ginoux source function (GSF), currently used in Weather Research and Forecasting Model coupled with Chemistry (WRF-chem), exhibits large errors over some regions in West Asia, particularly near the IRAQ/Syrian border. This study aims to improve the specification of this critical part of dust forecasts. A new source function based on multi-year analysis of satellite observations, called West Asia source function (WASF), is therefore proposed to raise the quality of WRF-chem predictions in the region. WASF has been implemented in three dust schemes of WRF-chem. Remotely sensed and ground-based observations have been used to verify the horizontal and vertical extent and location of simulated dust clouds. Results indicate that WRF-chem performance is significantly improved in many areas after the implementation of WASF. The modified runs (long term simulations over the summers 2008-2012, using nudging) have yielded an average increase of Spearman correlation between observed and forecast aerosol optical thickness by 12-16 percent points compared to control runs with standard source functions. They even outperform MACC and DREAM dust simulations over many dust source regions. However, the quality of the forecasts decreased with distance from sources, probably due to deficiencies in the transport and deposition characteristics of the forecast model in these areas.
Integrating Satellite Measurements from Polar-orbiting instruments into Smoke Disperson Forecasts

NASA Astrophysics Data System (ADS)

Smith, N.; Pierce, R. B.; Barnet, C.; Gambacorta, A.; Davies, J. E.; Strabala, K.

2015-12-01

The IDEA-I (Infusion of Satellite Data into Environmental Applications-International) is a real-time system that currently generates trajectory-based forecasts of aerosol dispersion and stratospheric intrusions. Here we demonstrate new capabilities that use satellite measurements from the Joint Polar Satellite System (JPSS) Suomi-NPP (S-NPP) instruments (operational since 2012) in the generation of trajectory-based predictions of smoke dispersion from North American wildfires. Two such data products are used, namely the Visible Infrared Imaging Radiometer Suite (VIIRS) Aerosol Optical Depth (AOD) and the combined Cross-track Infrared Sounder (CrIS) and Advanced Technology Microwave Sounder (ATMS) NOAA-Unique CrIS-ATMS Processing System (NUCAPS) carbon monoxide (CO) retrievals. The latter is a new data product made possible by the release of full spectral-resolution CrIS measurements since December 2014. Once NUCAPS CO becomes operationally available it will be used in real-time applications such as IDEA-I along with VIIRS AOD and meteorological forecast fields to support National Weather Service (NWS) Incident Meteorologist (IMET) and air quality management decision making. By combining different measurements, the information content of the IDEA-I transport and dispersion forecast is improved within the complex terrain features that dominate the Western US and Alaska. The primary user community of smoke forecasts is the Western regions of the National Weather Service (NWS) and US Environmental Protection Agency (EPA) due to the significant impacts of wildfires in these regions. With this we demonstrate the quality of the smoke dispersion forecasts that can be achieved by integrating polar-orbiting satellite measurements with forecast models to enable on-site decision support services for fire incident management teams and other real-time air quality agencies.
Data Assimilation and Regional Forecasts Using Atmospheric InfraRed Sounder (AIRS) Profiles

NASA Technical Reports Server (NTRS)

Chou, Shih-Hung; Zavodsky, Bradley; Jedlovec, Gary

2009-01-01

In data sparse regions, remotely-sensed observations can be used to improve analyses, which in turn should lead to better forecasts. One such source comes from the Atmospheric Infrared Sounder (AIRS), which together with the Advanced Microwave Sounding Unit (AMSU), provides temperature and moisture profiles with an accuracy comparable to that of radiosondes. The purpose of this paper is to describe a procedure to optimally assimilate AIRS thermodynamic profiles--obtained from the version 5.0 Earth Observing System (EOS) science team retrieval algorithm-into a regional configuration of the Weather Research and Forecasting (WRF) model using WRF-Var. The paper focuses on development of background error covariances for the regional domain and background field type, a methodology for ingesting AIRS profiles as separate over-land and over-water retrievals with different error characteristics, and utilization of level-by-level quality indicators to select only the highest quality data. The assessment of the impact of the AIRS profiles on WRF-Var analyses will focus on intelligent use of the quality indicators, optimized tuning of the WRF-Var, and comparison of analysis soundings to radiosondes. The analyses will be used to conduct a month-long series of regional forecasts over the continental U.S. The long-tern1 impact of AIRS profiles on forecast will be assessed against verifying radiosonde and stage IV precipitation data.

Data Assimilation and Regional Forecasts using Atmospheric InfraRed Sounder (AIRS) Profiles

NASA Technical Reports Server (NTRS)

Zabodsky, Brad; Chou, Shih-Hung; Jedlovec, Gary J.

2009-01-01

In data sparse regions, remotely-sensed observations can be used to improve analyses, which in turn should lead to better forecasts. One such source comes from the Atmospheric Infrared Sounder (AIRS), which, together with the Advanced Microwave Sounding Unit (AMSU), provides temperature and moisture profiles with an accuracy comparable to that of radionsondes. The purpose of this poster is to describe a procedure to optimally assimilate AIRS thermodynamic profiles, obtained from the version 5.0 Earth Observing System (EOS) science team retrieval algorithm, into a regional configuration of the Weather Research and Forecasting (WRF) model using WRF-Var. The poster focuses on development of background error covariances for the regional domain and background field type, a methodology for ingesting AIRS profiles as separate over-land and over-water retrievals with different error characteristics, and utilization of level-by-level quality indicators to select only the highest quality data. The assessment of the impact of the AIRS profiles on WRF-Var analyses will focus on intelligent use of the quality indicators, optimized tuning of the WRF-Var, and comparison of analysis soundings to radiosondes. The analyses are used to conduct a month-long series of regional forecasts over the continental U.S. The long-term impact of AIRS profiles on forecast will be assessed against NAM analyses and stage IV precipitation data.
Forecasting the quality of water-suppressed 1 H MR spectra based on a single-shot water scan.

PubMed

Kyathanahally, Sreenath P; Kreis, Roland

2017-08-01

To investigate whether an initial non-water-suppressed acquisition that provides information about the signal-to-noise ratio (SNR) and linewidth is enough to forecast the maximally achievable final spectral quality and thus inform the operator whether the foreseen number of averages and achieved field homogeneity is adequate. A large range of spectra with varying SNR and linewidth was simulated and fitted with popular fitting programs to determine the dependence of fitting errors on linewidth and SNR. A tool to forecast variance based on a single acquisition was developed and its performance evaluated on simulated and in vivo data obtained at 3 Tesla from various brain regions and acquisition settings. A strong correlation to real uncertainties in estimated metabolite contents was found for the forecast values and the Cramer-Rao lower bounds obtained from the water-suppressed spectra. It appears to be possible to forecast the best-case errors associated with specific metabolites to be found in model fits of water-suppressed spectra based on a single water scan. Thus, nonspecialist operators will be able to judge ahead of time whether the planned acquisition can possibly be of sufficient quality to answer the targeted clinical question or whether it needs more averages or improved shimming. Magn Reson Med 78:441-451, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.
Performance and Quality Assessment of the Forthcoming Copernicus Marine Service Global Ocean Monitoring and Forecasting Real-Time System

NASA Astrophysics Data System (ADS)

Lellouche, J. M.; Le Galloudec, O.; Greiner, E.; Garric, G.; Regnier, C.; Drillet, Y.

2016-02-01

Mercator Ocean currently delivers in real-time daily services (weekly analyses and daily forecast) with a global 1/12° high resolution system. The model component is the NEMO platform driven at the surface by the IFS ECMWF atmospheric analyses and forecasts. Observations are assimilated by means of a reduced-order Kalman filter with a 3D multivariate modal decomposition of the forecast error. It includes an adaptive-error estimate and a localization algorithm. Along track altimeter data, satellite Sea Surface Temperature and in situ temperature and salinity vertical profiles are jointly assimilated to estimate the initial conditions for numerical ocean forecasting. A 3D-Var scheme provides a correction for the slowly-evolving large-scale biases in temperature and salinity.Since May 2015, Mercator Ocean opened the Copernicus Marine Service (CMS) and is in charge of the global ocean analyses and forecast, at eddy resolving resolution. In this context, R&D activities have been conducted at Mercator Ocean these last years in order to improve the real-time 1/12° global system for the next CMS version in 2016. The ocean/sea-ice model and the assimilation scheme benefit among others from the following improvements: large-scale and objective correction of atmospheric quantities with satellite data, new Mean Dynamic Topography taking into account the last version of GOCE geoid, new adaptive tuning of some observational errors, new Quality Control on the assimilated temperature and salinity vertical profiles based on dynamic height criteria, assimilation of satellite sea-ice concentration, new freshwater runoff from ice sheets melting …This presentation doesn't focus on the impact of each update, but rather on the overall behavior of the system integrating all updates. This assessment reports on the products quality improvements, highlighting the level of performance and the reliability of the new system.
FULLY COUPLED "ONLINE" CHEMISTRY WITHIN THE WRF MODEL

EPA Science Inventory

A fully coupled "online" Weather Research and Forecasting/Chemistry (WRF/Chem) model has been developed. The air quality component of the model is fully consistent with the meteorological component; both components use the same transport scheme (mass and scalar preserving), the s...
Improved meteorology from an updated WRF/CMAQ modeling system with MODIS vegetation and albedo

EPA Science Inventory

Realistic vegetation characteristics and phenology from the Moderate Resolution Imaging Spectroradiometer (MODIS) products improve the simulation for the meteorology and air quality modeling system WRF/CMAQ (Weather Research and Forecasting model and Community Multiscale Air Qual...
Do quantitative decadal forecasts from GCMs provide decision relevant skill?

NASA Astrophysics Data System (ADS)

Suckling, E. B.; Smith, L. A.

2012-04-01

It is widely held that only physics-based simulation models can capture the dynamics required to provide decision-relevant probabilistic climate predictions. This fact in itself provides no evidence that predictions from today's GCMs are fit for purpose. Empirical (data-based) models are employed to make probability forecasts on decadal timescales, where it is argued that these 'physics free' forecasts provide a quantitative 'zero skill' target for the evaluation of forecasts based on more complicated models. It is demonstrated that these zero skill models are competitive with GCMs on decadal scales for probability forecasts evaluated over the last 50 years. Complications of statistical interpretation due to the 'hindcast' nature of this experiment, and the likely relevance of arguments that the lack of hindcast skill is irrelevant as the signal will soon 'come out of the noise' are discussed. A lack of decision relevant quantiative skill does not bring the science-based insights of anthropogenic warming into doubt, but it does call for a clear quantification of limits, as a function of lead time, for spatial and temporal scales on which decisions based on such model output are expected to prove maladaptive. Failing to do so may risk the credibility of science in support of policy in the long term. The performance amongst a collection of simulation models is evaluated, having transformed ensembles of point forecasts into probability distributions through the kernel dressing procedure [1], according to a selection of proper skill scores [2] and contrasted with purely data-based empirical models. Data-based models are unlikely to yield realistic forecasts for future climate change if the Earth system moves away from the conditions observed in the past, upon which the models are constructed; in this sense the empirical model defines zero skill. When should a decision relevant simulation model be expected to significantly outperform such empirical models? Probability forecasts up to ten years ahead (decadal forecasts) are considered, both on global and regional spatial scales for surface air temperature. Such decadal forecasts are not only important in terms of providing information on the impacts of near-term climate change, but also from the perspective of climate model validation, as hindcast experiments and a sufficient database of historical observations allow standard forecast verification methods to be used. Simulation models from the ENSEMBLES hindcast experiment [3] are evaluated and contrasted with static forecasts of the observed climatology, persistence forecasts and against simple statistical models, called dynamic climatology (DC). It is argued that DC is a more apropriate benchmark in the case of a non-stationary climate. It is found that the ENSEMBLES models do not demonstrate a significant increase in skill relative to the empirical models even at global scales over any lead time up to a decade ahead. It is suggested that the contsruction and co-evaluation with the data-based models become a regular component of the reporting of large simulation model forecasts. The methodology presented may easily be adapted to other forecasting experiments and is expected to influence the design of future experiments. The inclusion of comparisons with dynamic climatology and other data-based approaches provide important information to both scientists and decision makers on which aspects of state-of-the-art simulation forecasts are likely to be fit for purpose. [1] J. Bröcker and L. A. Smith. From ensemble forecasts to predictive distributions, Tellus A, 60(4), 663-678 (2007). [2] J. Bröcker and L. A. Smith. Scoring probabilistic forecasts: The importance of being proper, Weather and Forecasting, 22, 382-388 (2006). [3] F. J. Doblas-Reyes, A. Weisheimer, T. N. Palmer, J. M. Murphy and D. Smith. Forecast quality asessment of the ENSEMBLES seasonal-to-decadal stream 2 hindcasts, ECMWF Technical Memorandum, 621 (2010).
Multivariate Bias Correction Procedures for Improving Water Quality Predictions from the SWAT Model

NASA Astrophysics Data System (ADS)

Arumugam, S.; Libera, D.

2017-12-01

Water quality observations are usually not available on a continuous basis for longer than 1-2 years at a time over a decadal period given the labor requirements making calibrating and validating mechanistic models difficult. Further, any physical model predictions inherently have bias (i.e., under/over estimation) and require post-simulation techniques to preserve the long-term mean monthly attributes. This study suggests a multivariate bias-correction technique and compares to a common technique in improving the performance of the SWAT model in predicting daily streamflow and TN loads across the southeast based on split-sample validation. The approach is a dimension reduction technique, canonical correlation analysis (CCA) that regresses the observed multivariate attributes with the SWAT model simulated values. The common approach is a regression based technique that uses an ordinary least squares regression to adjust model values. The observed cross-correlation between loadings and streamflow is better preserved when using canonical correlation while simultaneously reducing individual biases. Additionally, canonical correlation analysis does a better job in preserving the observed joint likelihood of observed streamflow and loadings. These procedures were applied to 3 watersheds chosen from the Water Quality Network in the Southeast Region; specifically, watersheds with sufficiently large drainage areas and number of observed data points. The performance of these two approaches are compared for the observed period and over a multi-decadal period using loading estimates from the USGS LOADEST model. Lastly, the CCA technique is applied in a forecasting sense by using 1-month ahead forecasts of P & T from ECHAM4.5 as forcings in the SWAT model. Skill in using the SWAT model for forecasting loadings and streamflow at the monthly and seasonal timescale is also discussed.
Skill assessment of the coupled physical-biogeochemical operational Mediterranean Forecasting System

NASA Astrophysics Data System (ADS)

Cossarini, Gianpiero; Clementi, Emanuela; Salon, Stefano; Grandi, Alessandro; Bolzon, Giorgio; Solidoro, Cosimo

2016-04-01

The Mediterranean Monitoring and Forecasting Centre (Med-MFC) is one of the regional production centres of the European Marine Environment Monitoring Service (CMEMS-Copernicus). Med-MFC operatively manages a suite of numerical model systems (3DVAR-NEMO-WW3 and 3DVAR-OGSTM-BFM) that provides gridded datasets of physical and biogeochemical variables for the Mediterranean marine environment with a horizontal resolution of about 6.5 km. At the present stage, the operational Med-MFC produces ten-day forecast: daily for physical parameters and bi-weekly for biogeochemical variables. The validation of the coupled model system and the estimate of the accuracy of model products are key issues to ensure reliable information to the users and the downstream services. Product quality activities at Med-MFC consist of two levels of validation and skill analysis procedures. Pre-operational qualification activities focus on testing the improvement of the quality of a new release of the model system and relays on past simulation and historical data. Then, near real time (NRT) validation activities aim at the routinely and on-line skill assessment of the model forecast and relays on the NRT available observations. Med-MFC validation framework uses both independent (i.e. Bio-Argo float data, in-situ mooring and vessel data of oxygen, nutrients and chlorophyll, moored buoys, tide-gauges and ADCP of temperature, salinity, sea level and velocity) and semi-independent data (i.e. data already used for assimilation, such as satellite chlorophyll, Satellite SLA and SST and in situ vertical profiles of temperature and salinity from XBT, Argo and Gliders) We give evidence that different variables (e.g. CMEMS-products) can be validated at different levels (i.e. at the forecast level or at the level of model consistency) and at different spatial and temporal scales. The fundamental physical parameters temperature, salinity and sea level are routinely validated on daily, weekly and quarterly base at regional and sub-regional scale and along specific vertical layers (temperature and salinity); while velocity fields are daily validated against in situ coastal moorings. Since the velocity skill cannot be accurately assessed through coastal measurements due to the actual model horizontal resolution (~6.5 km), new validation metrics and procedures are under investigation. Chlorophyll is the only biogeochemical variable that can be validated routinely at the temporal and spatial scale of the weekly forecast, while nutrients and oxygen predictions can be validated locally or at sub-basin and seasonal scales. For the other biogeochemical variables (i.e. primary production, carbonate system variables) only the accuracy of the average dynamics and model consistency can be evaluated. Then, we discuss the limiting factors of the present validation framework, and the quality and extension of the observing system that would be needed for improving the reliability of the physical and biogeochemical Mediterranean forecast services.
Ozone distributions over southern Lake Michigan: comparisons between ferry-based observations, shoreline-based DOAS observations and model forecasts

NASA Astrophysics Data System (ADS)

Cleary, P. A.; Fuhrman, N.; Schulz, L.; Schafer, J.; Fillingham, J.; Bootsma, H.; McQueen, J.; Tang, Y.; Langel, T.; McKeen, S.; Williams, E. J.; Brown, S. S.

2015-05-01

Air quality forecast models typically predict large summertime ozone abundances over water relative to land in the Great Lakes region. While each state bordering Lake Michigan has dedicated monitoring systems, offshore measurements have been sparse, mainly executed through specific short-term campaigns. This study examines ozone abundances over Lake Michigan as measured on the Lake Express ferry, by shoreline differential optical absorption spectroscopy (DOAS) observations in southeastern Wisconsin and as predicted by the Community Multiscale Air Quality (CMAQ) model. From 2008 to 2009 measurements of O3, SO2, NO2 and formaldehyde were made in the summertime by DOAS at a shoreline site in Kenosha, WI. From 2008 to 2010 measurements of ambient ozone were conducted on the Lake Express, a high-speed ferry that travels between Milwaukee, WI, and Muskegon, MI, up to six times daily from spring to fall. Ferry ozone observations over Lake Michigan were an average of 3.8 ppb higher than those measured at shoreline in Kenosha, with little dependence on position of the ferry or temperature and with greatest differences during evening and night. Concurrent 1-48 h forecasts from the CMAQ model in the upper Midwestern region surrounding Lake Michigan were compared to ferry ozone measurements, shoreline DOAS measurements and Environmental Protection Agency (EPA) station measurements. The bias of the model O3 forecast was computed and evaluated with respect to ferry-based measurements. Trends in the bias with respect to location and time of day were explored showing non-uniformity in model bias over the lake. Model ozone bias was consistently high over the lake in comparison to land-based measurements, with highest biases for 25-48 h after initialization.
“AQMEII Status Update” | Science Inventory | US EPA

EPA Pesticide Factsheets

“AQMEII Status Update”This presentation provided an overview and status update of the Air Quality Model Evaluation International Initative (AQMEII) to participants of a workshop of the Task Force on Hemispheric Transport of Air Pollution (TF-HTAP) . In addition, the presentation also outlines the objectives and potential timeline for a possible next phase of AQMEII that would involve a collaboration with the current modeling activities of TF-HTAP. The purpose of the presentation was to provide participants at the HTAP meeting with an overview of current AQMEII activities and timelines and to obtain feedback from HTAP workshop participants regarding HTAP timelines. The National Exposure Research Laboratory (NERL) Atmospheric Modeling and Analysis Division (AMAD) conducts research in support of EPA mission to protect human health and the environment. AMAD research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMAD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMAD are being used by EPA, NOAA, and the air pollution community in understanding and forecasting not only the magnitude of the air po
Web-Based Real Time Earthquake Forecasting and Personal Risk Management

NASA Astrophysics Data System (ADS)

Rundle, J. B.; Holliday, J. R.; Graves, W. R.; Turcotte, D. L.; Donnellan, A.

2012-12-01

Earthquake forecasts have been computed by a variety of countries and economies world-wide for over two decades. For the most part, forecasts have been computed for insurance, reinsurance and underwriters of catastrophe bonds. One example is the Working Group on California Earthquake Probabilities that has been responsible for the official California earthquake forecast since 1988. However, in a time of increasingly severe global financial constraints, we are now moving inexorably towards personal risk management, wherein mitigating risk is becoming the responsibility of individual members of the public. Under these circumstances, open access to a variety of web-based tools, utilities and information is a necessity. Here we describe a web-based system that has been operational since 2009 at www.openhazards.com and www.quakesim.org. Models for earthquake physics and forecasting require input data, along with model parameters. The models we consider are the Natural Time Weibull (NTW) model for regional earthquake forecasting, together with models for activation and quiescence. These models use small earthquakes ('seismicity-based models") to forecast the occurrence of large earthquakes, either through varying rates of small earthquake activity, or via an accumulation of this activity over time. These approaches use data-mining algorithms combined with the ANSS earthquake catalog. The basic idea is to compute large earthquake probabilities using the number of small earthquakes that have occurred in a region since the last large earthquake. Each of these approaches has computational challenges associated with computing forecast information in real time. Using 25 years of data from the ANSS California-Nevada catalog of earthquakes, we show that real-time forecasting is possible at a grid scale of 0.1o. We have analyzed the performance of these models using Reliability/Attributes and standard Receiver Operating Characteristic (ROC) tests. We show how the Reliability and ROC tests allow us to judge data completeness and estimate error. It is clear from much of the analysis that data quality is a major limitation on the accurate computation of earthquake probabilities. We discuss the challenges and pitfalls in serving up these datasets over the web.
Meteorological air quality forecasting using the WRF-Chem model during the LMOS2017 field campaign

NASA Astrophysics Data System (ADS)

Stanier, C. O.; Abdioskouei, M.; Carmichael, G. R.; Christiansen, M.; Sobhani, N.

2017-12-01

The Lake Michigan Ozone Study (LMOS 2017) occurred during May and June 2017 to address the high ozone episodes in coastal communities surrounding Lake Michigan. Aircraft, ship, mobile lab, and ground-based stations were used in this campaign to build an extensive dataset regarding ozone, its precursors, and particulate matter. The University of Iowa produced high-resolution (4x4 km2 horizontal resolution and 53 vertical levels) forecast products using the WRF-Chem modeling system in support of experimental planning during LMOS 2017. The base forecast system used WRF-Chem 3.6.1 and updated National Emission Inventory (NEI-2011v2). In the updated NEI-2011v2, we reduced the NOx emissions by 28% based on EPA's estimated NOx trends from 2011 to 2017. We ran another daily forecast (perturbed forecast) with 50% reduced NOx emission to capture the sensitivity of ozone to NOx emission and account for the impact of weekend emissions on ozone values. Preliminary in-field evaluation of model performance for clouds, on-shore flows, and surface and aircraft sampled ozone and NOx concentrations found that the model successfully captured much of the observed synoptic variability of onshore flows. The model captured the variability of O3 well, but underpredicted peak ozone during high O3 episodes. In post-campaign WRF-Chem simulations, we investigated the sensitivity of the model to the hydrocarbon emission.
Dynamic Forecasting of Zika Epidemics Using Google Trends

PubMed Central

Jin, Yuan; Huang, Yong; Lin, Baihan; An, Xiaoping; Feng, Dan; Tong, Yigang

2017-01-01

We developed a dynamic forecasting model for Zika virus (ZIKV), based on real-time online search data from Google Trends (GTs). It was designed to provide Zika virus disease (ZVD) surveillance and detection for Health Departments, and predictive numbers of infection cases, which would allow them sufficient time to implement interventions. In this study, we found a strong correlation between Zika-related GTs and the cumulative numbers of reported cases (confirmed, suspected and total cases; p<0.001). Then, we used the correlation data from Zika-related online search in GTs and ZIKV epidemics between 12 February and 20 October 2016 to construct an autoregressive integrated moving average (ARIMA) model (0, 1, 3) for the dynamic estimation of ZIKV outbreaks. The forecasting results indicated that the predicted data by ARIMA model, which used the online search data as the external regressor to enhance the forecasting model and assist the historical epidemic data in improving the quality of the predictions, are quite similar to the actual data during ZIKV epidemic early November 2016. Integer-valued autoregression provides a useful base predictive model for ZVD cases. This is enhanced by the incorporation of GTs data, confirming the prognostic utility of search query based surveillance. This accessible and flexible dynamic forecast model could be used in the monitoring of ZVD to provide advanced warning of future ZIKV outbreaks. PMID:28060809
Dynamic Forecasting of Zika Epidemics Using Google Trends.

PubMed

Teng, Yue; Bi, Dehua; Xie, Guigang; Jin, Yuan; Huang, Yong; Lin, Baihan; An, Xiaoping; Feng, Dan; Tong, Yigang

2017-01-01

We developed a dynamic forecasting model for Zika virus (ZIKV), based on real-time online search data from Google Trends (GTs). It was designed to provide Zika virus disease (ZVD) surveillance and detection for Health Departments, and predictive numbers of infection cases, which would allow them sufficient time to implement interventions. In this study, we found a strong correlation between Zika-related GTs and the cumulative numbers of reported cases (confirmed, suspected and total cases; p<0.001). Then, we used the correlation data from Zika-related online search in GTs and ZIKV epidemics between 12 February and 20 October 2016 to construct an autoregressive integrated moving average (ARIMA) model (0, 1, 3) for the dynamic estimation of ZIKV outbreaks. The forecasting results indicated that the predicted data by ARIMA model, which used the online search data as the external regressor to enhance the forecasting model and assist the historical epidemic data in improving the quality of the predictions, are quite similar to the actual data during ZIKV epidemic early November 2016. Integer-valued autoregression provides a useful base predictive model for ZVD cases. This is enhanced by the incorporation of GTs data, confirming the prognostic utility of search query based surveillance. This accessible and flexible dynamic forecast model could be used in the monitoring of ZVD to provide advanced warning of future ZIKV outbreaks.
Real-time numerical forecast of global epidemic spreading: case study of 2009 A/H1N1pdm.

PubMed

Tizzoni, Michele; Bajardi, Paolo; Poletto, Chiara; Ramasco, José J; Balcan, Duygu; Gonçalves, Bruno; Perra, Nicola; Colizza, Vittoria; Vespignani, Alessandro

2012-12-13

Mathematical and computational models for infectious diseases are increasingly used to support public-health decisions; however, their reliability is currently under debate. Real-time forecasts of epidemic spread using data-driven models have been hindered by the technical challenges posed by parameter estimation and validation. Data gathered for the 2009 H1N1 influenza crisis represent an unprecedented opportunity to validate real-time model predictions and define the main success criteria for different approaches. We used the Global Epidemic and Mobility Model to generate stochastic simulations of epidemic spread worldwide, yielding (among other measures) the incidence and seeding events at a daily resolution for 3,362 subpopulations in 220 countries. Using a Monte Carlo Maximum Likelihood analysis, the model provided an estimate of the seasonal transmission potential during the early phase of the H1N1 pandemic and generated ensemble forecasts for the activity peaks in the northern hemisphere in the fall/winter wave. These results were validated against the real-life surveillance data collected in 48 countries, and their robustness assessed by focusing on 1) the peak timing of the pandemic; 2) the level of spatial resolution allowed by the model; and 3) the clinical attack rate and the effectiveness of the vaccine. In addition, we studied the effect of data incompleteness on the prediction reliability. Real-time predictions of the peak timing are found to be in good agreement with the empirical data, showing strong robustness to data that may not be accessible in real time (such as pre-exposure immunity and adherence to vaccination campaigns), but that affect the predictions for the attack rates. The timing and spatial unfolding of the pandemic are critically sensitive to the level of mobility data integrated into the model. Our results show that large-scale models can be used to provide valuable real-time forecasts of influenza spreading, but they require high-performance computing. The quality of the forecast depends on the level of data integration, thus stressing the need for high-quality data in population-based models, and of progressive updates of validated available empirical knowledge to inform these models.
Application of The Rainfall-runoff Model Topkapi For The Entire Basin of The Po River As Part of The European Project Effs

NASA Astrophysics Data System (ADS)

Todini, E.; Bartholmes, J.

The project EFFS (European Flood Forecasting System) aims at developing a flood forecasting system for the major river basins all over Europe. To extend the forecast- ing and thus the warning time in a significant way (up to 10 days) meteorological forecasting data from the ECMWF will be used as input to hydrological models. For this purpose it is fundamental to have a reliable rainfall-runoff model. For the river Po basin we chose the TOPKAPI model (Ciarapica, Todini 1998). TOPKAPI is a physi- cally based rainfall-runoff model that maintains its physical significance passing from hillslope to large basin scale. The aim of the distributed version is to reproduce the spatial variability and to lead to a better understanding of scaling effects on meteo- rological data used as well as of physical phenomena and parameters. By now the TOPKAPI model has been applied successfully to basins of smaller and medium size (up to 8000 km2). The present work also proves that TOPKAPI is a valuable flood forecasting tool for larger basins such as the Po river. An advantage of the TOPKAPI model is its physical basis. It doesn't need a "real" calibration in the common sense of the expression. The calibration work that has to be done is due to the unavoidable averaging and approximation in the input data representing various phenomena. This reduces the calibration work as well as the length of data required. The model was implemented on the Po river at spatial steps of 1km and time steps of 1 hour using available data during the year 1994. After the calibration phase, mesoscale forecasts (from ECMWF) as well as forecasts of LAM models (DWD,DMI) will be used as input to the Po river models and their behaviour will be studied as a function of the prediction quality and of the coarseness of the spatial discretisation.
Operational monitoring and forecasting of bathing water quality through exploiting satellite Earth observation and models: The AlgaRisk demonstration service

NASA Astrophysics Data System (ADS)

Shutler, J. D.; Warren, M. A.; Miller, P. I.; Barciela, R.; Mahdon, R.; Land, P. E.; Edwards, K.; Wither, A.; Jonas, P.; Murdoch, N.; Roast, S. D.; Clements, O.; Kurekin, A.

2015-04-01

Coastal zones and shelf-seas are important for tourism, commercial fishing and aquaculture. As a result the importance of good water quality within these regions to support life is recognised worldwide and a number of international directives for monitoring them now exist. This paper describes the AlgaRisk water quality monitoring demonstration service that was developed and operated for the UK Environment Agency in response to the microbiological monitoring needs within the revised European Union Bathing Waters Directive. The AlgaRisk approach used satellite Earth observation to provide a near-real time monitoring of microbiological water quality and a series of nested operational models (atmospheric and hydrodynamic-ecosystem) provided a forecast capability. For the period of the demonstration service (2008-2013) all monitoring and forecast datasets were processed in near-real time on a daily basis and disseminated through a dedicated web portal, with extracted data automatically emailed to agency staff. Near-real time data processing was achieved using a series of supercomputers and an Open Grid approach. The novel web portal and java-based viewer enabled users to visualise and interrogate current and historical data. The system description, the algorithms employed and example results focussing on a case study of an incidence of the harmful algal bloom Karenia mikimotoi are presented. Recommendations and the potential exploitation of web services for future water quality monitoring services are discussed.
NEW CATEGORICAL METRICS FOR AIR QUALITY MODEL EVALUATION

EPA Science Inventory

Traditional categorical metrics used in model evaluations are "clear-cut" measures in that the model's ability to predict an exceedance is defined by a fixed threshold concentration and the metrics are defined by observation-forecast sets that are paired both in space and time. T...
Intermittent Demand Forecasting in a Tertiary Pediatric Intensive Care Unit.

PubMed

Cheng, Chen-Yang; Chiang, Kuo-Liang; Chen, Meng-Yin

2016-10-01

Forecasts of the demand for medical supplies both directly and indirectly affect the operating costs and the quality of the care provided by health care institutions. Specifically, overestimating demand induces an inventory surplus, whereas underestimating demand possibly compromises patient safety. Uncertainty in forecasting the consumption of medical supplies generates intermittent demand events. The intermittent demand patterns for medical supplies are generally classified as lumpy, erratic, smooth, and slow-moving demand. This study was conducted with the purpose of advancing a tertiary pediatric intensive care unit's efforts to achieve a high level of accuracy in its forecasting of the demand for medical supplies. On this point, several demand forecasting methods were compared in terms of the forecast accuracy of each. The results confirm that applying Croston's method combined with a single exponential smoothing method yields the most accurate results for forecasting lumpy, erratic, and slow-moving demand, whereas the Simple Moving Average (SMA) method is the most suitable for forecasting smooth demand. In addition, when the classification of demand consumption patterns were combined with the demand forecasting models, the forecasting errors were minimized, indicating that this classification framework can play a role in improving patient safety and reducing inventory management costs in health care institutions.
How Hydroclimate Influences the Effectiveness of Particle Filter Data Assimilation of Streamflow in Initializing Short- to Medium-range Streamflow Forecasts

NASA Astrophysics Data System (ADS)

Clark, E.; Wood, A.; Nijssen, B.; Clark, M. P.

2017-12-01

Short- to medium-range (1- to 7-day) streamflow forecasts are important for flood control operations and in issuing potentially life-save flood warnings. In the U.S., the National Weather Service River Forecast Centers (RFCs) issue such forecasts in real time, depending heavily on a manual data assimilation (DA) approach. Forecasters adjust model inputs, states, parameters and outputs based on experience and consideration of a range of supporting real-time information. Achieving high-quality forecasts from new automated, centralized forecast systems will depend critically on the adequacy of automated DA approaches to make analogous corrections to the forecasting system. Such approaches would further enable systematic evaluation of real-time flood forecasting methods and strategies. Toward this goal, we have implemented a real-time Sequential Importance Resampling particle filter (SIR-PF) approach to assimilate observed streamflow into simulated initial hydrologic conditions (states) for initializing ensemble flood forecasts. Assimilating streamflow alone in SIR-PF improves simulated streamflow and soil moisture during the model spin up period prior to a forecast, with consequent benefits for forecasts. Nevertheless, it only consistently limits error in simulated snow water equivalent during the snowmelt season and in basins where precipitation falls primarily as snow. We examine how the simulated initial conditions with and without SIR-PF propagate into 1- to 7-day ensemble streamflow forecasts. Forecasts are evaluated in terms of reliability and skill over a 10-year period from 2005-2015. The focus of this analysis is on how interactions between hydroclimate and SIR-PF performance impact forecast skill. To this end, we examine forecasts for 5 hydroclimatically diverse basins in the western U.S. Some of these basins receive most of their precipitation as snow, others as rain. Some freeze throughout the mid-winter while others experience significant mid-winter melt events. We describe the methodology and present seasonal and inter-basin variations in DA-enhanced forecast skill.

Forecasting hourly PM(10) concentration in Cyprus through artificial neural networks and multiple regression models: implications to local environmental management.

PubMed

Paschalidou, Anastasia K; Karakitsios, Spyridon; Kleanthous, Savvas; Kassomenos, Pavlos A

2011-02-01

In the present work, two types of artificial neural network (NN) models using the multilayer perceptron (MLP) and the radial basis function (RBF) techniques, as well as a model based on principal component regression analysis (PCRA), are employed to forecast hourly PM(10) concentrations in four urban areas (Larnaca, Limassol, Nicosia and Paphos) in Cyprus. The model development is based on a variety of meteorological and pollutant parameters corresponding to the 2-year period between July 2006 and June 2008, and the model evaluation is achieved through the use of a series of well-established evaluation instruments and methodologies. The evaluation reveals that the MLP NN models display the best forecasting performance with R (2) values ranging between 0.65 and 0.76, whereas the RBF NNs and the PCRA models reveal a rather weak performance with R (2) values between 0.37-0.43 and 0.33-0.38, respectively. The derived MLP models are also used to forecast Saharan dust episodes with remarkable success (probability of detection ranging between 0.68 and 0.71). On the whole, the analysis shows that the models introduced here could provide local authorities with reliable and precise predictions and alarms about air quality if used on an operational basis.
An Approach to Remove the Systematic Bias from the Storm Surge forecasts in the Venice Lagoon

NASA Astrophysics Data System (ADS)

Canestrelli, A.

2017-12-01

In this work a novel approach is proposed for removing the systematic bias from the storm surge forecast computed by a two-dimensional shallow-water model. The model covers both the Adriatic and Mediterranean seas and provides the forecast at the entrance of the Venice Lagoon. The wind drag coefficient at the water-air interface is treated as a calibration parameter, with a different value for each range of wind velocities and wind directions. This sums up to a total of 16-64 parameters to be calibrated, depending on the chosen resolution. The best set of parameters is determined by means of an optimization procedure, which minimizes the RMS error between measured and modeled water level in Venice for the period 2011-2015. It is shown that a bias is present, for which the peaks of wind velocities provided by the weather forecast are largely underestimated, and that the calibration procedure removes this bias. When the calibrated model is used to reproduce events not included in the calibration dataset, the forecast error is strongly reduced, thus confirming the quality of our procedure. The proposed approach it is not site-specific and could be applied to different situations, such as storm surges caused by intense hurricanes.
Operational air quality forecasting system for Spain: CALIOPE

NASA Astrophysics Data System (ADS)

Baldasano, J. M.; Piot, M.; Jorba, O.; Goncalves, M.; Pay, M.; Pirez, C.; Lopez, E.; Gasso, S.; Martin, F.; García-Vivanco, M.; Palomino, I.; Querol, X.; Pandolfi, M.; Dieguez, J. J.; Padilla, L.

2009-12-01

The European Commission (EC) and the United States Environmental Protection Agency (US-EPA) have shown great concerns to understand the transport and dynamics of pollutants in the atmosphere. According to the European directives (1996/62/EC, 2002/3/EC, 2008/50/EC), air quality modeling, if accurately applied, is a useful tool to understand the dynamics of air pollutants, to analyze and forecast the air quality, and to develop programs reducing emissions and alert the population when health-related issues occur. The CALIOPE project, funded by the Spanish Ministry of the Environment, has the main objective to establish an air quality forecasting system for Spain. A partnership of four research institutions composes the CALIOPE project: the Barcelona Supercomputing Center (BSC), the center of investigation CIEMAT, the Earth Sciences Institute ‘Jaume Almera’ (IJA-CSIC) and the CEAM Foundation. CALIOPE will become the official Spanish air quality operational system. This contribution focuses on the recent developments and implementation of the integrated modelling system for the Iberian Peninsula (IP) and Canary Islands (CI) with a high spatial and temporal resolution (4x4 sq. km for IP and 2x2 sq. km for CI, 1 hour), namely WRF-ARW/HERMES04/CMAQ/BSC-DREAM. The HERMES04 emission model has been specifically developed as a high-resolution (1x1 sq. km, 1 hour) emission model for Spain. It includes biogenic and anthropogenic emissions such as on-road and paved-road resuspension production, power plant generation, ship and plane traffic, airports and ports activities, industrial and agricultural sectors as well as domestic and commercial emissions. The qualitative and quantitative evaluation of the model was performed for a reference year (2004) using data from ground-based measurement networks. The products of the CALIOPE system will provide 24h and 48h forecasts for O3, NO2, SO2, CO, PM10 and PM2.5 at surface level. An operational evaluation system has been developed to provide near real-time evaluation products for the Spanish territory. For this purpose, more than 130 surface stations, 2 ozonesondes and the OMI satellite retrieval information are introduced to the system on a daily basis. A web-based visualization system allows a straightforward access to all the evaluation products. The present contribution will describe the main characteristics of the operational system and results of the operational evaluation.
Stream-flow forecasting using extreme learning machines: A case study in a semi-arid region in Iraq

NASA Astrophysics Data System (ADS)

Yaseen, Zaher Mundher; Jaafar, Othman; Deo, Ravinesh C.; Kisi, Ozgur; Adamowski, Jan; Quilty, John; El-Shafie, Ahmed

2016-11-01

Monthly stream-flow forecasting can yield important information for hydrological applications including sustainable design of rural and urban water management systems, optimization of water resource allocations, water use, pricing and water quality assessment, and agriculture and irrigation operations. The motivation for exploring and developing expert predictive models is an ongoing endeavor for hydrological applications. In this study, the potential of a relatively new data-driven method, namely the extreme learning machine (ELM) method, was explored for forecasting monthly stream-flow discharge rates in the Tigris River, Iraq. The ELM algorithm is a single-layer feedforward neural network (SLFNs) which randomly selects the input weights, hidden layer biases and analytically determines the output weights of the SLFNs. Based on the partial autocorrelation functions of historical stream-flow data, a set of five input combinations with lagged stream-flow values are employed to establish the best forecasting model. A comparative investigation is conducted to evaluate the performance of the ELM compared to other data-driven models: support vector regression (SVR) and generalized regression neural network (GRNN). The forecasting metrics defined as the correlation coefficient (r), Nash-Sutcliffe efficiency (ENS), Willmott's Index (WI), root-mean-square error (RMSE) and mean absolute error (MAE) computed between the observed and forecasted stream-flow data are employed to assess the ELM model's effectiveness. The results revealed that the ELM model outperformed the SVR and the GRNN models across a number of statistical measures. In quantitative terms, superiority of ELM over SVR and GRNN models was exhibited by ENS = 0.578, 0.378 and 0.144, r = 0.799, 0.761 and 0.468 and WI = 0.853, 0.802 and 0.689, respectively and the ELM model attained lower RMSE value by approximately 21.3% (relative to SVR) and by approximately 44.7% (relative to GRNN). Based on the findings of this study, several recommendations were suggested for further exploration of the ELM model in hydrological forecasting problems.
Forecasting Space Weather-Induced GPS Performance Degradation Using Random Forest

NASA Astrophysics Data System (ADS)

Filjar, R.; Filic, M.; Milinkovic, F.

2017-12-01

Space weather and ionospheric dynamics have a profound effect on positioning performance of the Global Satellite Navigation System (GNSS). However, the quantification of that effect is still the subject of scientific activities around the world. In the latest contribution to the understanding of the space weather and ionospheric effects on satellite-based positioning performance, we conducted a study of several candidates for forecasting method for space weather-induced GPS positioning performance deterioration. First, a 5-days set of experimentally collected data was established, encompassing the space weather and ionospheric activity indices (including: the readings of the Sudden Ionospheric Disturbance (SID) monitors, components of geomagnetic field strength, global Kp index, Dst index, GPS-derived Total Electron Content (TEC) samples, standard deviation of TEC samples, and sunspot number) and observations of GPS positioning error components (northing, easting, and height positioning error) derived from the Adriatic Sea IGS reference stations' RINEX raw pseudorange files in quiet space weather periods. This data set was split into the training and test sub-sets. Then, a selected set of supervised machine learning methods based on Random Forest was applied to the experimentally collected data set in order to establish the appropriate regional (the Adriatic Sea) forecasting models for space weather-induced GPS positioning performance deterioration. The forecasting models were developed in the R/rattle statistical programming environment. The forecasting quality of the regional forecasting models developed was assessed, and the conclusions drawn on the advantages and shortcomings of the regional forecasting models for space weather-caused GNSS positioning performance deterioration.
Forecasting urban PM10 and PM2.5 pollution episodes in very stable nocturnal conditions and complex terrain using WRF-Chem CO tracer model

NASA Astrophysics Data System (ADS)

Saide, Pablo E.; Carmichael, Gregory R.; Spak, Scott N.; Gallardo, Laura; Osses, Axel E.; Mena-Carrasco, Marcelo A.; Pagowski, Mariusz

2011-05-01

This study presents a system to predict high pollution events that develop in connection with enhanced subsidence due to coastal lows, particularly in winter over Santiago de Chile. An accurate forecast of these episodes is of interest since the local government is entitled by law to take actions in advance to prevent public exposure to PM10 concentrations in excess of 150 μg m -3 (24 h running averages). The forecasting system is based on accurately simulating carbon monoxide (CO) as a PM10/PM2.5 surrogate, since during episodes and within the city there is a high correlation (over 0.95) among these pollutants. Thus, by accurately forecasting CO, which behaves closely to a tracer on this scale, a PM estimate can be made without involving aerosol-chemistry modeling. Nevertheless, the very stable nocturnal conditions over steep topography associated with maxima in concentrations are hard to represent in models. Here we propose a forecast system based on the WRF-Chem model with optimum settings, determined through extensive testing, that best describe both meteorological and air quality available measurements. Some of the important configurations choices involve the boundary layer (PBL) scheme, model grid resolution (both vertical and horizontal), meteorological initial and boundary conditions and spatial and temporal distribution of the emissions. A forecast for the 2008 winter is performed showing that this forecasting system is able to perform similarly to the authority decision for PM10 and better than persistence when forecasting PM10 and PM2.5 high pollution episodes. Problems regarding false alarm predictions could be related to different uncertainties in the model such as day to day emission variability, inability of the model to completely resolve the complex topography and inaccuracy in meteorological initial and boundary conditions. Finally, according to our simulations, emissions from previous days dominate episode concentrations, which highlights the need for 48 h forecasts that can be achieved by the system presented here. This is in fact the largest advantage of the proposed system.
Effects of 4D-Var data assimilation using remote sensing precipitation products in a WRF over the complex Heihe River Basin

NASA Astrophysics Data System (ADS)

Pan, Xiaoduo; Li, Xin; Cheng, Guodong

2017-04-01

Traditionally, ground-based, in situ observations, remote sensing, and regional climate modeling, individually, cannot provide the high-quality precipitation data required for hydrological prediction, especially over complex terrain. Data assimilation techniques are often used to assimilate ground observations and remote sensing products into models for dynamic downscaling. In this study, the Weather Research and Forecasting (WRF) model was used to assimilate two satellite precipitation products (TRMM 3B42 and FY-2D) using the 4D-Var data assimilation method. The results show that the assimilation of remote sensing precipitation products can improve the initial WRF fields of humidity and temperature, thereby improving precipitation forecasting and decreasing the spin-up time. Hence, assimilating TRMM and FY-2D remote sensing precipitation products using WRF 4D-Var can be viewed as a positive step toward improving the accuracy and lead time of numerical weather prediction models, particularly for short-term weather forecasting. Future work is proposed to assimilate a suite of remote sensing data, e.g., the combination of precipitation and soil moisture data, into a WRF model to improve 7-8 day forecasts of precipitation and other atmospheric variables.
Representing the Effects of Long-Range Transport and Lateral Boundary Conditions in Regional Air Pollution Models

EPA Science Inventory

The Community Multiscale Air Quality (CMAQ) modeling system was applied to a domain covering the northern hemisphere; meteorological information was derived from the Weather Research and Forecasting (WRF) model run on identical grid and projection configuration, while the emissio...
Global analysis of seasonal streamflow predictability using an ensemble prediction system and observations from 6192 small catchments worldwide

NASA Astrophysics Data System (ADS)

van Dijk, Albert I. J. M.; Peña-Arancibia, Jorge L.; Wood, Eric F.; Sheffield, Justin; Beck, Hylke E.

2013-05-01

Ideally, a seasonal streamflow forecasting system would ingest skilful climate forecasts and propagate these through calibrated hydrological models initialized with observed catchment conditions. At global scale, practical problems exist in each of these aspects. For the first time, we analyzed theoretical and actual skill in bimonthly streamflow forecasts from a global ensemble streamflow prediction (ESP) system. Forecasts were generated six times per year for 1979-2008 by an initialized hydrological model and an ensemble of 1° resolution daily climate estimates for the preceding 30 years. A post-ESP conditional sampling method was applied to 2.6% of forecasts, based on predictive relationships between precipitation and 1 of 21 climate indices prior to the forecast date. Theoretical skill was assessed against a reference run with historic forcing. Actual skill was assessed against streamflow records for 6192 small (<10,000 km2) catchments worldwide. The results show that initial catchment conditions provide the main source of skill. Post-ESP sampling enhanced skill in equatorial South America and Southeast Asia, particularly in terms of tercile probability skill, due to the persistence and influence of the El Niño Southern Oscillation. Actual skill was on average 54% of theoretical skill but considerably more for selected regions and times of year. The realized fraction of the theoretical skill probably depended primarily on the quality of precipitation estimates. Forecast skill could be predicted as the product of theoretical skill and historic model performance. Increases in seasonal forecast skill are likely to require improvement in the observation of precipitation and initial hydrological conditions.
Performance assessment of a Bayesian Forecasting System (BFS) for real-time flood forecasting

NASA Astrophysics Data System (ADS)

Biondi, D.; De Luca, D. L.

2013-02-01

SummaryThe paper evaluates, for a number of flood events, the performance of a Bayesian Forecasting System (BFS), with the aim of evaluating total uncertainty in real-time flood forecasting. The predictive uncertainty of future streamflow is estimated through the Bayesian integration of two separate processors. The former evaluates the propagation of input uncertainty on simulated river discharge, the latter computes the hydrological uncertainty of actual river discharge associated with all other possible sources of error. A stochastic model and a distributed rainfall-runoff model were assumed, respectively, for rainfall and hydrological response simulations. A case study was carried out for a small basin in the Calabria region (southern Italy). The performance assessment of the BFS was performed with adequate verification tools suited for probabilistic forecasts of continuous variables such as streamflow. Graphical tools and scalar metrics were used to evaluate several attributes of the forecast quality of the entire time-varying predictive distributions: calibration, sharpness, accuracy, and continuous ranked probability score (CRPS). Besides the overall system, which incorporates both sources of uncertainty, other hypotheses resulting from the BFS properties were examined, corresponding to (i) a perfect hydrological model; (ii) a non-informative rainfall forecast for predicting streamflow; and (iii) a perfect input forecast. The results emphasize the importance of using different diagnostic approaches to perform comprehensive analyses of predictive distributions, to arrive at a multifaceted view of the attributes of the prediction. For the case study, the selected criteria revealed the interaction of the different sources of error, in particular the crucial role of the hydrological uncertainty processor when compensating, at the cost of wider forecast intervals, for the unreliable and biased predictive distribution resulting from the Precipitation Uncertainty Processor.
Multi-model assessment of the impact of soil moisture initialization on mid-latitude summer predictability

NASA Astrophysics Data System (ADS)

Ardilouze, Constantin; Batté, L.; Bunzel, F.; Decremer, D.; Déqué, M.; Doblas-Reyes, F. J.; Douville, H.; Fereday, D.; Guemas, V.; MacLachlan, C.; Müller, W.; Prodhomme, C.

2017-12-01

Land surface initial conditions have been recognized as a potential source of predictability in sub-seasonal to seasonal forecast systems, at least for near-surface air temperature prediction over the mid-latitude continents. Yet, few studies have systematically explored such an influence over a sufficient hindcast period and in a multi-model framework to produce a robust quantitative assessment. Here, a dedicated set of twin experiments has been carried out with boreal summer retrospective forecasts over the 1992-2010 period performed by five different global coupled ocean-atmosphere models. The impact of a realistic versus climatological soil moisture initialization is assessed in two regions with high potential previously identified as hotspots of land-atmosphere coupling, namely the North American Great Plains and South-Eastern Europe. Over the latter region, temperature predictions show a significant improvement, especially over the Balkans. Forecast systems better simulate the warmest summers if they follow pronounced dry initial anomalies. It is hypothesized that models manage to capture a positive feedback between high temperature and low soil moisture content prone to dominate over other processes during the warmest summers in this region. Over the Great Plains, however, improving the soil moisture initialization does not lead to any robust gain of forecast quality for near-surface temperature. It is suggested that models biases prevent the forecast systems from making the most of the improved initial conditions.
Improvement of Meteorological Inputs for TexAQS-II Air Quality Simulations

NASA Astrophysics Data System (ADS)

Ngan, F.; Byun, D.; Kim, H.; Cheng, F.; Kim, S.; Lee, D.

2008-12-01

An air quality forecasting system (UH-AQF) for Eastern Texas, which is in operation by the Institute for Multidimensional Air Quality Studies (IMAQS) at the University of Houston, uses the Fifth-Generation PSU/NCAR Mesoscale Model MM5 model as the meteorological driver for modeling air quality with the Community Multiscale Air Quality (CMAQ) model. While the forecasting system was successfully used for the planning and implementation of various measurement activities, evaluations of the forecasting results revealed a few systematic problems in the numerical simulations. From comparison with observations, we observe some times over-prediction of northerly winds caused by inaccurate synoptic inputs and other times too strong southerly winds caused by local sea breeze development. Discrepancies in maximum and minimum temperature are also seen for certain days. Precipitation events, as well as clouds, are simulated at the incorrect locations and times occasionally. Model simulatednrealistic thunderstorms are simulated, causing sometimes cause unrealistically strong outflows. To understand physical and chemical processes influencing air quality measures, a proper description of real world meteorological conditions is essential. The objective of this study is to generate better meteorological inputs than the AQF results to support the chemistry modeling. We utilized existing objective analysis and nudging tools in the MM5 system to develop the MUltiscale Nest-down Data Assimilation System (MUNDAS), which incorporates extensive meteorological observations available in the simulated domain for the retrospective simulation of the TexAQS-II period. With the re-simulated meteorological input, we are able to better predict ozone events during TexAQS-II period. In addition, base datasets in MM5 such as land use/land cover, vegetation fraction, soil type and sea surface temperature are updated by satellite data to represent the surface features more accurately. They are key physical parameters inputs affecting transfer of heat, momentum and soil moisture in land-surface process in MM5. Using base the accurate input datasets, we are able to have improved see the differences of predictions of ground temperatures, winds and even thunderstorm activities within boundary layer.
Improving Regional Forecast by Assimilating Atmospheric InfraRed Sounder (AIRS) Profiles into WRF Model

NASA Technical Reports Server (NTRS)

Chou, Shih-Hung; Zavodsky, Brad; Jedlovec, Gary J.

2009-01-01

In data sparse regions, remotely-sensed observations can be used to improve analyses and produce improved forecasts. One such source comes from the Atmospheric InfraRed Sounder (AIRS), which together with the Advanced Microwave Sounding Unit (AMSU), represents one of the most advanced space-based atmospheric sounding systems. The purpose of this paper is to describe a procedure to optimally assimilate high resolution AIRS profile data into a regional configuration of the Advanced Research WRF (ARW) version 2.2 using WRF-Var. The paper focuses on development of background error covariances for the regional domain and background type, and an optimal methodology for ingesting AIRS temperature and moisture profiles as separate overland and overwater retrievals with different error characteristics. The AIRS thermodynamic profiles are derived from the version 5.0 Earth Observing System (EOS) science team retrieval algorithm and contain information about the quality of each temperature layer. The quality indicators were used to select the highest quality temperature and moisture data for each profile location and pressure level. The analyses were then used to conduct a month-long series of regional forecasts over the continental U.S. The long-term impacts of AIRS profiles on forecast were assessed against verifying NAM analyses and stage IV precipitation data.
Comparison of Satellite Observations of Aerosol Optical Depth to Surface Monitor Fine Particle Concentration

NASA Technical Reports Server (NTRS)

Kleb, Mary M.; AlSaadi, Jassim A.; Neil, Doreen O.; Pierce, Robert B.; Pippin, Margartet R.; Roell, Marilee M.; Kittaka, Chieko; Szykman, James J.

2004-01-01

Under NASA's Earth Science Applications Program, the Infusing satellite Data into Environmental Applications (IDEA) project examined the relationship between satellite observations and surface monitors of air pollutants to facilitate a more capable and integrated observing network. This report provides a comparison of satellite aerosol optical depth to surface monitor fine particle concentration observations for the month of September 2003 at more than 300 individual locations in the continental US. During September 2003, IDEA provided prototype, near real-time data-fusion products to the Environmental Protection Agency (EPA) directed toward improving the accuracy of EPA s next-day Air Quality Index (AQI) forecasts. Researchers from NASA Langley Research Center and EPA used data from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument combined with EPA ground network data to create a NASA-data-enhanced Forecast Tool. Air quality forecasters used this tool to prepare their forecasts of particle pollution, or particulate matter less than 2.5 microns in diameter (PM2.5), for the next-day AQI. The archived data provide a rich resource for further studies and analysis. The IDEA project uses data sets and models developed for tropospheric chemistry research to assist federal, state, and local agencies in making decisions concerning air quality management to protect public health.
Evaluation of TIGGE Ensemble Forecasts of Precipitation in Distinct Climate Regions in Iran

NASA Astrophysics Data System (ADS)

Aminyavari, Saleh; Saghafian, Bahram; Delavar, Majid

2018-04-01

The application of numerical weather prediction (NWP) products is increasing dramatically. Existing reports indicate that ensemble predictions have better skill than deterministic forecasts. In this study, numerical ensemble precipitation forecasts in the TIGGE database were evaluated using deterministic, dichotomous (yes/no), and probabilistic techniques over Iran for the period 2008-16. Thirteen rain gauges spread over eight homogeneous precipitation regimes were selected for evaluation. The Inverse Distance Weighting and Kriging methods were adopted for interpolation of the prediction values, downscaled to the stations at lead times of one to three days. To enhance the forecast quality, NWP values were post-processed via Bayesian Model Averaging. The results showed that ECMWF had better scores than other products. However, products of all centers underestimated precipitation in high precipitation regions while overestimating precipitation in other regions. This points to a systematic bias in forecasts and demands application of bias correction techniques. Based on dichotomous evaluation, NCEP did better at most stations, although all centers overpredicted the number of precipitation events. Compared to those of ECMWF and NCEP, UKMO yielded higher scores in mountainous regions, but performed poorly at other selected stations. Furthermore, the evaluations showed that all centers had better skill in wet than in dry seasons. The quality of post-processed predictions was better than those of the raw predictions. In conclusion, the accuracy of the NWP predictions made by the selected centers could be classified as medium over Iran, while post-processing of predictions is recommended to improve the quality.
Goals of Quality in Doctoral Studies and Forecasted Outcomes

ERIC Educational Resources Information Center

Zelvys, Rimantas

2007-01-01

This article discusses the quality assurance policy of doctoral studies implemented in Lithuania and its probable outcomes are forecasted. In scientific literature the quality of education is commonly defined as a holistic phenomenon composed of the quality of initial conditions, quality of process and quality of outputs. The accomplished document…
Sensitivity of monthly streamflow forecasts to the quality of rainfall forcing: When do dynamical climate forecasts outperform the Ensemble Streamflow Prediction (ESP) method?

NASA Astrophysics Data System (ADS)

Tanguy, M.; Prudhomme, C.; Harrigan, S.; Smith, K. A.; Parry, S.

2017-12-01

Forecasting hydrological extremes is challenging, especially at lead times over 1 month for catchments with limited hydrological memory and variable climates. One simple way to derive monthly or seasonal hydrological forecasts is to use historical climate data to drive hydrological models using the Ensemble Streamflow Prediction (ESP) method. This gives a range of possible future streamflow given known initial hydrologic conditions alone. The degree of skill of ESP depends highly on the forecast initialisation month and catchment type. Using dynamic rainfall forecasts as driving data instead of historical data could potentially improve streamflow predictions. A lot of effort is being invested within the meteorological community to improve these forecasts. However, while recent progress shows promise (e.g. NAO in winter), the skill of these forecasts at monthly to seasonal timescales is generally still limited, and the extent to which they might lead to improved hydrological forecasts is an area of active research. Additionally, these meteorological forecasts are currently being produced at 1 month or seasonal time-steps in the UK, whereas hydrological models require forcings at daily or sub-daily time-steps. Keeping in mind these limitations of available rainfall forecasts, the objectives of this study are to find out (i) how accurate monthly dynamical rainfall forecasts need to be to outperform ESP, and (ii) how the method used to disaggregate monthly rainfall forecasts into daily rainfall time series affects results. For the first objective, synthetic rainfall time series were created by increasingly degrading observed data (proxy for a `perfect forecast') from 0 % to +/-50 % error. For the second objective, three different methods were used to disaggregate monthly rainfall data into daily time series. These were used to force a simple lumped hydrological model (GR4J) to generate streamflow predictions at a one-month lead time for over 300 catchments representative of the range of UK's hydro-climatic conditions. These forecasts were then benchmarked against the traditional ESP method. It is hoped that the results of this work will help the meteorological community to identify where to focus their efforts in order to increase the usefulness of their forecasts within hydrological forecasting systems.
DEVELOPMENT AND EVALUATION OF PM 2.5 SOURCE APPORTIONMENT METHODOLOGIES

EPA Science Inventory

The receptor model called Positive Matrix Factorization (PMF) has been extensively used to apportion sources of ambient fine particulate matter (PM_2.5), but the accuracy of source apportionment results currently remains unknown. In addition, air quality forecast model...
Sedimentation Solutions for Military Ocean Terminal Sunny Point (MOTSU), North Carolina

DTIC Science & Technology

2012-07-01

quality at MOTSU at the request of US Army Engineer District–Wilmington (USAED-SAW). The objective was achieved through numerical modeling ...literature review, and sediment forecasting. This report documents the results of the numerical modeling study only. Two advantageous approaches for...data .............................................................................................................. 25 4 Model Development
Assessing the value of post-processed state-of-the-art long-term weather forecast ensembles for agricultural water management mediated by farmers' behaviours

NASA Astrophysics Data System (ADS)

Li, Yu; Giuliani, Matteo; Castelletti, Andrea

2016-04-01

Recent advances in modelling of coupled ocean-atmosphere dynamics significantly improved skills of long-term climate forecast from global circulation models (GCMs). These more accurate weather predictions are supposed to be a valuable support to farmers in optimizing farming operations (e.g. crop choice, cropping and watering time) and for more effectively coping with the adverse impacts of climate variability. Yet, assessing how actually valuable this information can be to a farmer is not straightforward and farmers' response must be taken into consideration. Indeed, in the context of agricultural systems potentially useful forecast information should alter stakeholders' expectation, modify their decisions, and ultimately produce an impact on their performance. Nevertheless, long-term forecast are mostly evaluated in terms of accuracy (i.e., forecast quality) by comparing hindcast and observed values and only few studies investigated the operational value of forecast looking at the gain of utility within the decision-making context, e.g. by considering the derivative of forecast information, such as simulated crop yields or simulated soil moisture, which are essential to farmers' decision-making process. In this study, we contribute a step further in the assessment of the operational value of long-term weather forecasts products by embedding these latter into farmers' behavioral models. This allows a more critical assessment of the forecast value mediated by the end-users' perspective, including farmers' risk attitudes and behavioral patterns. Specifically, we evaluate the operational value of thirteen state-of-the-art long-range forecast products against climatology forecast and empirical prediction (i.e. past year climate and historical average) within an integrated agronomic modeling framework embedding an implicit model of the farmers' decision-making process. Raw ensemble datasets are bias-corrected and downscaled using a stochastic weather generator, in order to address the mismatch of the spatio-temporal scale between forecast data from GCMs and our model. For each product, the experiment is composed by two cascade simulations: 1) an ex-ante simulation using forecast data, and 2) an ex-post simulation with observations. Multi-year simulations are performed to account for climate variability, and the operational value of the different forecast products is evaluated against the perfect foresight on the basis of expected crop productivity as well as the final decisions under different decision-making criterions. Our results show that not all products generate beneficial effects to farmers' performance, and the forecast errors might be amplified due to farmers' decision-making process and risk attitudes, yielding little or even worse performance compared with the empirical approaches.

IMPROVING NATIONAL AIR QUALITY FORECASTS WITH SATELLITE AEROSOL OBSERVATIONS

EPA Science Inventory

Air quality forecasts for major US metropolitan areas have been provided to the public through a partnership between the US Environmental Protection Agency and state and local air agencies since 1997. Recent years have witnessed improvement in forecast skill and expansion of fore...
The Application of Satellite-Derived, High-Resolution Land Use/Land Cover Data to Improve Urban Air Quality Model Forecasts

NASA Technical Reports Server (NTRS)

Quattrochi, D. A.; Lapenta, W. M.; Crosson, W. L.; Estes, M. G., Jr.; Limaye, A.; Kahn, M.

2006-01-01

Local and state agencies are responsible for developing state implementation plans to meet National Ambient Air Quality Standards. Numerical models used for this purpose simulate the transport and transformation of criteria pollutants and their precursors. The specification of land use/land cover (LULC) plays an important role in controlling modeled surface meteorology and emissions. NASA researchers have worked with partners and Atlanta stakeholders to incorporate an improved high-resolution LULC dataset for the Atlanta area within their modeling system and to assess meteorological and air quality impacts of Urban Heat Island (UHI) mitigation strategies. The new LULC dataset provides a more accurate representation of land use, has the potential to improve model accuracy, and facilitates prediction of LULC changes. Use of the new LULC dataset for two summertime episodes improved meteorological forecasts, with an existing daytime cold bias of approx. equal to 3 C reduced by 30%. Model performance for ozone prediction did not show improvement. In addition, LULC changes due to Atlanta area urbanization were predicted through 2030, for which model simulations predict higher urban air temperatures. The incorporation of UHI mitigation strategies partially offset this warming trend. The data and modeling methods used are generally applicable to other U.S. cities.
Diagnostic Analysis of the Three-Dimensional Sulfur Distributions over the Eastern United States Using the CMAQ Model and Measurements from the ICARTT Field Experiment

EPA Science Inventory

Previous comparisons of air quality modeling results from various forecast models with aircraft measurements of sulfate aerosol collected during the ICARTT field experiment indicated that models that included detailed treatment of gas- and aqueous-phase atmospheric sulfate format...
Pathways to designing and running an operational flood forecasting system: an adventure game!

NASA Astrophysics Data System (ADS)

Arnal, Louise; Pappenberger, Florian; Ramos, Maria-Helena; Cloke, Hannah; Crochemore, Louise; Giuliani, Matteo; Aalbers, Emma

2017-04-01

In the design and building of an operational flood forecasting system, a large number of decisions have to be taken. These include technical decisions related to the choice of the meteorological forecasts to be used as input to the hydrological model, the choice of the hydrological model itself (its structure and parameters), the selection of a data assimilation procedure to run in real-time, the use (or not) of a post-processor, and the computing environment to run the models and display the outputs. Additionally, a number of trans-disciplinary decisions are also involved in the process, such as the way the needs of the users will be considered in the modelling setup and how the forecasts (and their quality) will be efficiently communicated to ensure usefulness and build confidence in the forecasting system. We propose to reflect on the numerous, alternative pathways to designing and running an operational flood forecasting system through an adventure game. In this game, the player is the protagonist of an interactive story driven by challenges, exploration and problem-solving. For this presentation, you will have a chance to play this game, acting as the leader of a forecasting team at an operational centre. Your role is to manage the actions of your team and make sequential decisions that impact the design and running of the system in preparation to and during a flood event, and that deal with the consequences of the forecasts issued. Your actions are evaluated by how much they cost you in time, money and credibility. Your aim is to take decisions that will ultimately lead to a good balance between time and money spent, while keeping your credibility high over the whole process. This game was designed to highlight the complexities behind decision-making in an operational forecasting and emergency response context, in terms of the variety of pathways that can be selected as well as the timescale, cost and timing of effective actions.
Towards uncertainty estimates in global operational forecasts of trace gases in the Copernicus Atmosphere Monitoring System

NASA Astrophysics Data System (ADS)

Huijnen, V.; Bouarar, I.; Chabrillat, S. H.; Christophe, Y.; Thierno, D.; Karydis, V.; Marecal, V.; Pozzer, A.; Flemming, J.

2017-12-01

Operational atmospheric composition analyses and forecasts such as developed in the Copernicus Atmosphere Monitoring Service (CAMS) rely on modules describing emissions, chemical conversion, transport and removal processing, as well as data assimilation methods. The CAMS forecasts can be used to drive regional air quality models across the world. Critical analyses of uncertainties in any of these processes are continuously needed to advance the quality of such systems on a global scale, ranging from the surface up to the stratosphere. With regard to the atmospheric chemistry to describe the fate of trace gases, the operational system currently relies on a modified version of the CB05 chemistry scheme for the troposphere combined with the Cariolle scheme to describe stratospheric ozone, as integrated in ECMWF's Integrated Forecasting System (IFS). It is further constrained by assimilation of satellite observations of CO, O3 and NO2. As part of CAMS we have recently developed three fully independent schemes to describe the chemical conversion throughout the atmosphere. These parameterizations originate from parent model codes in MOZART, MOCAGE and a combination of TM5/BASCOE. In this contribution we evaluate the correspondence and elemental differences in the performance of the three schemes in an otherwise identical model configuration (excluding data-assimilation) against a large range of in-situ and satellite-based observations of ozone, CO, VOC's and chlorine-containing trace gases for both troposphere and stratosphere. This analysis aims to provide a measure of model uncertainty in the operational system for tracers that are not, or poorly, constrained by data assimilation. It aims also to provide guidance on the directions for further model improvement with regard to the chemical conversion module.
Objective calibration of numerical weather prediction models

NASA Astrophysics Data System (ADS)

Voudouri, A.; Khain, P.; Carmona, I.; Bellprat, O.; Grazzini, F.; Avgoustoglou, E.; Bettems, J. M.; Kaufmann, P.

2017-07-01

Numerical weather prediction (NWP) and climate models use parameterization schemes for physical processes, which often include free or poorly confined parameters. Model developers normally calibrate the values of these parameters subjectively to improve the agreement of forecasts with available observations, a procedure referred as expert tuning. A practicable objective multi-variate calibration method build on a quadratic meta-model (MM), that has been applied for a regional climate model (RCM) has shown to be at least as good as expert tuning. Based on these results, an approach to implement the methodology to an NWP model is presented in this study. Challenges in transferring the methodology from RCM to NWP are not only restricted to the use of higher resolution and different time scales. The sensitivity of the NWP model quality with respect to the model parameter space has to be clarified, as well as optimize the overall procedure, in terms of required amount of computing resources for the calibration of an NWP model. Three free model parameters affecting mainly turbulence parameterization schemes were originally selected with respect to their influence on the variables associated to daily forecasts such as daily minimum and maximum 2 m temperature as well as 24 h accumulated precipitation. Preliminary results indicate that it is both affordable in terms of computer resources and meaningful in terms of improved forecast quality. In addition, the proposed methodology has the advantage of being a replicable procedure that can be applied when an updated model version is launched and/or customize the same model implementation over different climatological areas.
Transitioning the Rice Realtime Forecast Models to DSCOVR

NASA Astrophysics Data System (ADS)

Bala, R.; Reiff, P. H.

2016-12-01

The Rice realtime forecast models of global magnetospheric indices Kp, Dst and AE have been actively running at mms.rice.edu/realtime/forecast.html for nearly a decade now. These neural network models were trained using the ACE archival solar wind data while the near-realtime forecasts are provided using instantaneous upwind solar wind data stream measured at the L1 point through ACE. Additionally, the webpage also provide status of the current space weather condition as an additional resource, updating every ten minutes. Furthermore, the subscribers of our space weather alert system, called `spacalrt', have been receiving email notices based on predefined thresholds. One of the gaps that is currently seen in the Rice neural network models lies in the density dependent models using variants of the solar wind pressure. The anomalous behavior in reporting densities in ACE has been a common issue for some time now. Often such behavior is observed when the solar energetic particle that are associated with solar flares or CMEs are Earth directed. Therefore, it is understood that the subsequent measures of the density reported by ACE will be either very low or, at a minimum, contaminated. Under these circumstances, the density-based Rice models typically underpredict. However, the newly launched DSCOVR satellite will help enhance our prediction models with high-quality data; it has real time space weather data available through the NOAA's Space Weather Prediction Center as of July, 2016. We are in the process of transitioning our forecast operations to include data from DSCOVR while running the original ACE data stream in parallel until it lasts. This paper will compare and contrast the forecasted values from the two satellites. Finally, we will discuss our efforts in providing the forecast products for the Rice space weather website that will be a part of the book on "Machine Learning Techniques for Space Weather" to be published by Elsiever.
Towards guided data assimilation for operational hydrologic forecasting in the US Tennessee River basin

NASA Astrophysics Data System (ADS)

Weerts, A.; Wood, A. W.; Clark, M. P.; Carney, S.; Day, G. N.; Lemans, M.; Sumihar, J.; Newman, A. J.

2014-12-01

In the US, the forecasting approach used by the NWS River Forecast Centers and other regional organizations such as the Bonneville Power Administration (BPA) or Tennessee Valley Authority (TVA) has traditionally involved manual model input and state modifications made by forecasters in real-time. This process is time consuming and requires expert knowledge and experience. The benefits of automated data assimilation (DA) as a strategy for avoiding manual modification approaches have been demonstrated in research studies (eg. Seo et al., 2009). This study explores the usage of various ensemble DA algorithms within the operational platform used by TVA. The final goal is to identify a DA algorithm that will guide the manual modification process used by TVA forecasters and realize considerable time gains (without loss of quality or even enhance the quality) within the forecast process. We evaluate the usability of various popular algorithms for DA that have been applied on a limited basis for operational hydrology. To this end, Delft-FEWS was wrapped (via piwebservice) in OpenDA to enable execution of FEWS workflows (and the chained models within these workflows, including SACSMA, UNITHG and LAGK) in a DA framework. Within OpenDA, several filter methods are available. We considered 4 algorithms: particle filter (RRF), Ensemble Kalman Filter and Asynchronous Ensemble Kalman and Particle filter. Retrospective simulation results for one location and algorithm (AEnKF) are illustrated in Figure 1. The initial results are promising. We will present verification results for these methods (and possible more) for a variety of sub basins in the Tennessee River basin. Finally, we will offer recommendations for guided DA based on our results. References Seo, D.-J., L. Cajina, R. Corby and T. Howieson, 2009: Automatic State Updating for Operational Streamflow Forecasting via Variational Data Assimilation, 367, Journal of Hydrology, 255-275. Figure 1. Retrospectively simulated streamflow for the headwater basin above Powell River at Jonesville (red is observed flow, blue is simulated flow without DA, black is simulated flow with DA)
How does informational heterogeneity affect the quality of forecasts?

NASA Astrophysics Data System (ADS)

Gualdi, S.; De Martino, A.

2010-01-01

We investigate a toy model of inductive interacting agents aiming to forecast a continuous, exogenous random variable E. Private information on E is spread heterogeneously across agents. Herding turns out to be the preferred forecasting mechanism when heterogeneity is maximal. However in such conditions aggregating information efficiently is hard even in the presence of learning, as the herding ratio rises significantly above the efficient market expectation of 1 and remarkably close to the empirically observed values. We also study how different parameters (interaction range, learning rate, cost of information and score memory) may affect this scenario and improve efficiency in the hard phase.
A PERFORMANCE EVALUATION OF THE ETA- CMAQ AIR QUALITY FORECAST SYSTEM FOR THE SUMMER OF 2005

EPA Science Inventory

This poster presents an evaluation of the Eta-CMAQ Air Quality Forecast System's experimental domain using O₃ observations obtained from EPA's AIRNOW program and a suite of statistical metrics examining both discrete and categorical forecasts.
Daily Peak Load Forecasting of Next Day using Weather Distribution and Comparison Value of Each Nearby Date Data

NASA Astrophysics Data System (ADS)

Ito, Shigenobu; Yukita, Kazuto; Goto, Yasuyuki; Ichiyanagi, Katsuhiro; Nakano, Hiroyuki

By the development of industry, in recent years; dependence to electric energy is growing year by year. Therefore, reliable electric power supply is in need. However, to stock a huge amount of electric energy is very difficult. Also, there is a necessity to keep balance between the demand and supply, which changes hour after hour. Consequently, to supply the high quality and highly dependable electric power supply, economically, and with high efficiency, there is a need to forecast the movement of the electric power demand carefully in advance. And using that forecast as the source, supply and demand management plan should be made. Thus load forecasting is said to be an important job among demand investment of electric power companies. So far, forecasting method using Fuzzy logic, Neural Net Work, Regression model has been suggested for the development of forecasting accuracy. Those forecasting accuracy is in a high level. But to invest electric power in higher accuracy more economically, a new forecasting method with higher accuracy is needed. In this paper, to develop the forecasting accuracy of the former methods, the daily peak load forecasting method using the weather distribution of highest and lowest temperatures, and comparison value of each nearby date data is suggested.
Development of a stacked ensemble model for forecasting and analyzing daily average PM2.5 concentrations in Beijing, China.

PubMed

Zhai, Binxu; Chen, Jianguo

2018-04-18

A stacked ensemble model is developed for forecasting and analyzing the daily average concentrations of fine particulate matter (PM 2.5 ) in Beijing, China. Special feature extraction procedures, including those of simplification, polynomial, transformation and combination, are conducted before modeling to identify potentially significant features based on an exploratory data analysis. Stability feature selection and tree-based feature selection methods are applied to select important variables and evaluate the degrees of feature importance. Single models including LASSO, Adaboost, XGBoost and multi-layer perceptron optimized by the genetic algorithm (GA-MLP) are established in the level 0 space and are then integrated by support vector regression (SVR) in the level 1 space via stacked generalization. A feature importance analysis reveals that nitrogen dioxide (NO 2 ) and carbon monoxide (CO) concentrations measured from the city of Zhangjiakou are taken as the most important elements of pollution factors for forecasting PM 2.5 concentrations. Local extreme wind speeds and maximal wind speeds are considered to extend the most effects of meteorological factors to the cross-regional transportation of contaminants. Pollutants found in the cities of Zhangjiakou and Chengde have a stronger impact on air quality in Beijing than other surrounding factors. Our model evaluation shows that the ensemble model generally performs better than a single nonlinear forecasting model when applied to new data with a coefficient of determination (R 2 ) of 0.90 and a root mean squared error (RMSE) of 23.69μg/m 3 . For single pollutant grade recognition, the proposed model performs better when applied to days characterized by good air quality than when applied to days registering high levels of pollution. The overall classification accuracy level is 73.93%, with most misclassifications made among adjacent categories. The results demonstrate the interpretability and generalizability of the stacked ensemble model. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.
Hydrodynamics and Water Quality forecasting over a Cloud Computing environment: INDIGO-DataCloud

NASA Astrophysics Data System (ADS)

Aguilar Gómez, Fernando; de Lucas, Jesús Marco; García, Daniel; Monteoliva, Agustín

2017-04-01

Algae Bloom due to eutrophication is an extended problem for water reservoirs and lakes that impacts directly in water quality. It can create a dead zone that lacks enough oxygen to support life and it can also be human harmful, so it must be controlled in water masses for supplying, bathing or other uses. Hydrodynamic and Water Quality modelling can contribute to forecast the status of the water system in order to alert authorities before an algae bloom event occurs. It can be used to predict scenarios and find solutions to reduce the harmful impact of the blooms. High resolution models need to process a big amount of data using a robust enough computing infrastructure. INDIGO-DataCloud (https://www.indigo-datacloud.eu/) is an European Commission funded project that aims at developing a data and computing platform targeting scientific communities, deployable on multiple hardware and provisioned over hybrid (private or public) e-infrastructures. The project addresses the development of solutions for different Case Studies using different Cloud-based alternatives. In the first INDIGO software release, a set of components are ready to manage the deployment of services to perform N number of Delft3D simulations (for calibrating or scenario definition) over a Cloud Computing environment, using the Docker technology: TOSCA requirement description, Docker repository, Orchestrator, AAI (Authorization, Authentication) and OneData (Distributed Storage System). Moreover, the Future Gateway portal based on Liferay, provides an user-friendly interface where the user can configure the simulations. Due to the data approach of INDIGO, the developed solutions can contribute to manage the full data life cycle of a project, thanks to different tools to manage datasets or even metadata. Furthermore, the cloud environment contributes to provide a dynamic, scalable and easy-to-use framework for non-IT experts users. This framework is potentially capable to automatize the processing of forecasting applying periodic tasks. For instance, a user can forecast every month the hydrodynamics and water quality status of a reservoir starting from a base model and supplying new data gathered from the instrumentation or observations. This interactive presentation aims to show the use of INDIGO solutions in a particular forecasting use case and to inspire others in the use of a Cloud framework for their applications.
Improving wind energy forecasts using an Ensemble Kalman Filter data assimilation technique in a fully coupled hydrologic and atmospheric model

NASA Astrophysics Data System (ADS)

Williams, J. L.; Maxwell, R. M.; Delle Monache, L.

2012-12-01

Wind power is rapidly gaining prominence as a major source of renewable energy. Harnessing this promising energy source is challenging because of the chaotic nature of wind and its propensity to change speed and direction over short time scales. Accurate forecasting tools are critical to support the integration of wind energy into power grids and to maximize its impact on renewable energy portfolios. Numerous studies have shown that soil moisture distribution and land surface vegetative processes profoundly influence atmospheric boundary layer development and weather processes on local and regional scales. Using the PF.WRF model, a fully-coupled hydrologic and atmospheric model employing the ParFlow hydrologic model with the Weather Research and Forecasting model coupled via mass and energy fluxes across the land surface, we have explored the connections between the land surface and the atmosphere in terms of land surface energy flux partitioning and coupled variable fields including hydraulic conductivity, soil moisture and wind speed, and demonstrated that reductions in uncertainty in these coupled fields propagate through the hydrologic and atmospheric system. We have adapted the Data Assimilation Research Testbed (DART), an implementation of the robust Ensemble Kalman Filter data assimilation algorithm, to expand our capability to nudge forecasts produced with the PF.WRF model using observational data. Using a semi-idealized simulation domain, we examine the effects of assimilating observations of variables such as wind speed and temperature collected in the atmosphere, and land surface and subsurface observations such as soil moisture on the quality of forecast outputs. The sensitivities we find in this study will enable further studies to optimize observation collection to maximize the utility of the PF.WRF-DART forecasting system.
Verification of CMAQ modeling with Discover-AQ campaigns against measurements and efficacy of emission inversion modeling

NASA Astrophysics Data System (ADS)

Lee, P.; Tang, Y.; Pan, L.; Szykman, J.; Plessel, T.; Tong, D.; Liu, Q.

2015-12-01

NASA has led a few DISCOVER-AQ campaigns in recent years: (1) Baltimore/Washington in July 2011, (2) Central valley, CA in January - February 2013, (3) Houston, TX in September 2013, and co-led with NCAR (4) Front Range, CO in July - August 2014. NOAA Air Resources Laboratory has participated in all these campaigns in the role of air quality forecasting support. For some of these campaigns post analyses were performed with the possible help of after-the-fact observed data from satellite retrieved radiances to constrain emissions through the Community Radiative Transfer Model (CRTM) developed at the Joint Center for Satellite Data Assimilation. It is our experience that despite the vastly different chemical regime, season, terrain, and meteorological conditions of the domain for the campaigns, we found that the emissions input and the U.S. EPA Community Air Quality model (CMAQ), the forecasting chemical transport model used to generate the forecast had severely under-estimated formaldehyde (HCHO), and carbon monoxide (CO) aloft between surface and the middle of the free troposphere - 500 hPa. Post analyses point to two strong suspects of these deficiencies: (a) emission projection fed into CMAQ, and/or (b) erroneously fast removal of the species. We investigate both of these potential deficiencies and for the former possible reason we looked into data assimilation and possible inverse modeling to adjust emission projection for CMAQ. We will elaborate more on the CRTM which plays a critical role in this aspect of remedying erroneous inputs to CMAQ. In addition, we will utilize some satellite products to improve initial fields of aerosols and CO for air quality forecasting. Suomi NPP VIIRS aerosol optical depth (AOD) environmental data record (EDR) delivers global aerosol information daily. The Unique CrIS/ATMS Processing System (NUCAPS) operationally generates vertical profiles of atmospheric carbonate EDRs (CO, CO2, and CH4) and ozone during day and night. The AOD can be assimilated by using the CRTM. The carbonate EDRs and ozone EDR can be directly assimilated.
Alternative configurations of Quantile Regression for estimating predictive uncertainty in water level forecasts for the Upper Severn River: a comparison

NASA Astrophysics Data System (ADS)

Lopez, Patricia; Verkade, Jan; Weerts, Albrecht; Solomatine, Dimitri

2014-05-01

Hydrological forecasting is subject to many sources of uncertainty, including those originating in initial state, boundary conditions, model structure and model parameters. Although uncertainty can be reduced, it can never be fully eliminated. Statistical post-processing techniques constitute an often used approach to estimate the hydrological predictive uncertainty, where a model of forecast error is built using a historical record of past forecasts and observations. The present study focuses on the use of the Quantile Regression (QR) technique as a hydrological post-processor. It estimates the predictive distribution of water levels using deterministic water level forecasts as predictors. This work aims to thoroughly verify uncertainty estimates using the implementation of QR that was applied in an operational setting in the UK National Flood Forecasting System, and to inter-compare forecast quality and skill in various, differing configurations of QR. These configurations are (i) 'classical' QR, (ii) QR constrained by a requirement that quantiles do not cross, (iii) QR derived on time series that have been transformed into the Normal domain (Normal Quantile Transformation - NQT), and (iv) a piecewise linear derivation of QR models. The QR configurations are applied to fourteen hydrological stations on the Upper Severn River with different catchments characteristics. Results of each QR configuration are conditionally verified for progressively higher flood levels, in terms of commonly used verification metrics and skill scores. These include Brier's probability score (BS), the continuous ranked probability score (CRPS) and corresponding skill scores as well as the Relative Operating Characteristic score (ROCS). Reliability diagrams are also presented and analysed. The results indicate that none of the four Quantile Regression configurations clearly outperforms the others.
Speed and Delay Prediction Models for Planning Applications

DOT National Transportation Integrated Search

1999-01-01

Estimation of vehicle speed and delay is fundamental to many forms of : transportation planning analyses including air quality, long-range travel : forecasting, major investment studies, and congestion management systems. : However, existing planning...
Air Quality Forecasting through Different Statistical and Artificial Intelligence Techniques

NASA Astrophysics Data System (ADS)

Mishra, D.; Goyal, P.

2014-12-01

Urban air pollution forecasting has emerged as an acute problem in recent years because there are sever environmental degradation due to increase in harmful air pollutants in the ambient atmosphere. In this study, there are different types of statistical as well as artificial intelligence techniques are used for forecasting and analysis of air pollution over Delhi urban area. These techniques are principle component analysis (PCA), multiple linear regression (MLR) and artificial neural network (ANN) and the forecasting are observed in good agreement with the observed concentrations through Central Pollution Control Board (CPCB) at different locations in Delhi. But such methods suffers from disadvantages like they provide limited accuracy as they are unable to predict the extreme points i.e. the pollution maximum and minimum cut-offs cannot be determined using such approach. Also, such methods are inefficient approach for better output forecasting. But with the advancement in technology and research, an alternative to the above traditional methods has been proposed i.e. the coupling of statistical techniques with artificial Intelligence (AI) can be used for forecasting purposes. The coupling of PCA, ANN and fuzzy logic is used for forecasting of air pollutant over Delhi urban area. The statistical measures e.g., correlation coefficient (R), normalized mean square error (NMSE), fractional bias (FB) and index of agreement (IOA) of the proposed model are observed in better agreement with the all other models. Hence, the coupling of statistical and artificial intelligence can be use for the forecasting of air pollutant over urban area.
Impact of GFZ's Effective Angular Momentum Forecasts on Polar Motion Prediction

NASA Astrophysics Data System (ADS)

Dill, Robert; Dobslaw, Henryk

2017-04-01

The Earth System Modelling group at GeoForschungsZentrum (GFZ) Potsdam offers now 6-day forecasts of Earth rotation excitation due to atmospheric, oceanic, and hydrologic angular momentum changes that are consistent with its 40 years-long EAM series. Those EAM forecasts are characterized by an improved long-term consistency due to the introduction of a time-invariant high-resolution reference topography into the AAM processing that accounts for occasional NWP model changes. In addition, all tidal signals from both atmosphere and ocean have been separated, and the temporal resolution of both AAM and OAM has been increased to 3 hours. Analysis of an extended set of EAM short-term hindcasts revealed positive prediction skills for up to 6 days into the future when compared to a persistent forecast. Whereas UT1 predictions in particular rely on an accurate AAM forecast, skillfull polar motion prediction requires high-quality OAM forecasts as well. We will present in this contribution the results from a multi-year hindcast experiment, demonstrating that the polar motion prediction as currently available from Bulletin A can be improved in particular for lead-times between 2 and 5 days by incorporating OAM forecasts. We will also report about early results obtained at Observatoire de Paris to predict polar motion from the integration of GFZ's 6-day EAM forecasts into the Liouville equation in a routine setting, that fully takes into account the operational latencies of all required input products.
Prediction of PM10 grades in Seoul, Korea using a neural network model based on synoptic patterns

NASA Astrophysics Data System (ADS)

Hur, S. K.; Oh, H. R.; Ho, C. H.; Kim, J.; Song, C. K.; Chang, L. S.; Lee, J. B.

2016-12-01

As of November 2014, the Korean Ministry of Environment (KME) started forecasting the level of ambient particulate matter with diameters ≤ 10 μm (PM10) as four grades: low (PM10 ≤ 30 μg m-3), moderate (30 < PM10 ≤ 80 μg m-3), high (80 < PM10 ≤ 150 μg m-3), and very high (PM10 > 150 μg m-3). Due to short history of forecast, overall performance of the operational forecasting system and its hit rate for the four PM10 grades are difficult to evaluate. In attempt to provide a statistical reference for the current air quality forecasting system, we hindcasted the four PM10 grades for the cold seasons (October-March) of 2001-2014 in Seoul, Korea using a neural network model based on the synoptic patterns of meteorological fields such as geopotential height, air temperature, relative humidity, and wind. In the form of cosine similarity, the distinctive synoptic patterns for each PM10 grades are well quantified as predictors to train the neural network model. Using these fields as predictors and considering the PM10 concentration in Seoul from the day before prediction as an additional predictor, an overall hit rate of 69% was achieved; the hit rates for the low, moderate, high, and very high PM10 grades were 33%, 83%, 45%, and 33%, respectively. This study reveals that the synoptic patterns of meteorological fields are useful predictors for the identification of favorable conditions for each PM10 grade, and the associated transboundary transport and local accumulation of PM10 from the industrialized regions of China. Consequently, the assessments of predictability obtained from the neural network model in this study are reliable to use as a statistical reference for the current air quality forecasting system.

Consequences Identification in Forecasting and Ethical Decision-making

PubMed Central

Stenmark, Cheryl K.; Antes, Alison L.; Thiel, Chase E.; Caughron, Jared J.; Wang, Xiaoqian; Mumford, Michael D.

2015-01-01

Forecasting involves predicting outcomes based on observations of the situation at hand. We examined the impact of the number and types of consequences considered on the quality of ethical decision-making. Undergraduates role-played several ethical problems in which they forecast potential outcomes and made decisions. Performance pressure (difficult demands placed on the situation) and interpersonal conflict (clashes among people in the problem situation) were manipulated within each problem scenario. The results indicated that the identification of potential consequences was positively associated with both higher quality forecasts and more ethical decisions. Neither performance pressure nor interpersonal conflict affected the quality of forecasts or decisions. Theoretical and practical implications of these findings and the use of this research approach are discussed. PMID:21460584
New Approach To Hour-By-Hour Weather Forecast

NASA Astrophysics Data System (ADS)

Liao, Q. Q.; Wang, B.

2017-12-01

Fine hourly forecast in single station weather forecast is required in many human production and life application situations. Most previous MOS (Model Output Statistics) which used a linear regression model are hard to solve nonlinear natures of the weather prediction and forecast accuracy has not been sufficient at high temporal resolution. This study is to predict the future meteorological elements including temperature, precipitation, relative humidity and wind speed in a local region over a relatively short period of time at hourly level. By means of hour-to-hour NWP (Numeral Weather Prediction)meteorological field from Forcastio (https://darksky.net/dev/docs/forecast) and real-time instrumental observation including 29 stations in Yunnan and 3 stations in Tianjin of China from June to October 2016, predictions are made of the 24-hour hour-by-hour ahead. This study presents an ensemble approach to combine the information of instrumental observation itself and NWP. Use autoregressive-moving-average (ARMA) model to predict future values of the observation time series. Put newest NWP products into the equations derived from the multiple linear regression MOS technique. Handle residual series of MOS outputs with autoregressive (AR) model for the linear property presented in time series. Due to the complexity of non-linear property of atmospheric flow, support vector machine (SVM) is also introduced . Therefore basic data quality control and cross validation makes it able to optimize the model function parameters , and do 24 hours ahead residual reduction with AR/SVM model. Results show that AR model technique is better than corresponding multi-variant MOS regression method especially at the early 4 hours when the predictor is temperature. MOS-AR combined model which is comparable to MOS-SVM model outperform than MOS. Both of their root mean square error and correlation coefficients for 2 m temperature are reduced to 1.6 degree Celsius and 0.91 respectively. The forecast accuracy of 24- hour forecast deviation no more than 2 degree Celsius is 78.75 % for MOS-AR model and 81.23 % for AR model.
Improving Forecast Skill by Assimilation of Quality-controlled AIRS Temperature Retrievals under Partially Cloudy Conditions

NASA Technical Reports Server (NTRS)

Reale, O.; Susskind, J.; Rosenberg, R.; Brin, E.; Riishojgaard, L.; Liu, E.; Terry, J.; Jusem, J. C.

2007-01-01

The National Aeronautics and Space Administration (NASA) Atmospheric Infrared Sounder (AIRS) on board the Aqua satellite has been long recognized as an important contributor towards the improvement of weather forecasts. At this time only a small fraction of the total data produced by AIRS is being used by operational weather systems. In fact, in addition to effects of thinning and quality control, the only AIRS data assimilated are radiance observations of channels unaffected by clouds. Observations in mid-lower tropospheric sounding AIRS channels are assimilated primarily under completely clear-sky conditions, thus imposing a very severe limitation on the horizontal distribution of the AIRS-derived information. In this work it is shown that the ability to derive accurate temperature profiles from AIRS observations in partially cloud-contaminated areas can be utilized to further improve the impact of AIRS observations in a global model and forecasting system. The analyses produced by assimilating AIRS temperature profiles obtained under partial cloud cover result in a substantially colder representation of the northern hemisphere lower midtroposphere at higher latitudes. This temperature difference has a strong impact, through hydrostatic adjustment, in the midtropospheric geopotential heights, which causes a different representation of the polar vortex especially over northeastern Siberia and Alaska. The AIRS-induced anomaly propagates through the model's dynamics producing improved 5-day forecasts.
Inductive reasoning and forecasting of population dynamics of Cylindrospermopsis raciborskii in three sub-tropical reservoirs by evolutionary computation.

PubMed

Recknagel, Friedrich; Orr, Philip T; Cao, Hongqing

2014-01-01

Seven-day-ahead forecasting models of Cylindrospermopsis raciborskii in three warm-monomictic and mesotrophic reservoirs in south-east Queensland have been developed by means of water quality data from 1999 to 2010 and the hybrid evolutionary algorithm HEA. Resulting models using all measured variables as inputs as well as models using electronically measurable variables only as inputs forecasted accurately timing of overgrowth of C. raciborskii and matched well high and low magnitudes of observed bloom events with 0.45≤r 2 >0.61 and 0.4≤r 2 >0.57, respectively. The models also revealed relationships and thresholds triggering bloom events that provide valuable information on synergism between water quality conditions and population dynamics of C. raciborskii. Best performing models based on using all measured variables as inputs indicated electrical conductivity (EC) within the range of 206-280mSm -1 as threshold above which fast growth and high abundances of C. raciborskii have been observed for the three lakes. Best models based on electronically measurable variables for the Lakes Wivenhoe and Somerset indicated a water temperature (WT) range of 25.5-32.7°C within which fast growth and high abundances of C. raciborskii can be expected. By contrast the model for Lake Samsonvale highlighted a turbidity (TURB) level of 4.8 NTU as indicator for mass developments of C. raciborskii. Experiments with online measured water quality data of the Lake Wivenhoe from 2007 to 2010 resulted in predictive models with 0.61≤r 2 >0.65 whereby again similar levels of EC and WT have been discovered as thresholds for outgrowth of C. raciborskii. The highest validity of r 2 =0.75 for an in situ data-based model has been achieved after considering time lags for EC by 7 days and dissolved oxygen by 1 day. These time lags have been discovered by a systematic screening of all possible combinations of time lags between 0 and 10 days for all electronically measurable variables. The so-developed model performs seven-day-ahead forecasts and is currently implemented and tested for early warning of C. raciborskii blooms in the Wivenhoe reservoir. Copyright © 2013 Elsevier B.V. All rights reserved.
Combining empirical approaches and error modelling to enhance predictive uncertainty estimation in extrapolation for operational flood forecasting. Tests on flood events on the Loire basin, France.

NASA Astrophysics Data System (ADS)

Berthet, Lionel; Marty, Renaud; Bourgin, François; Viatgé, Julie; Piotte, Olivier; Perrin, Charles

2017-04-01

An increasing number of operational flood forecasting centres assess the predictive uncertainty associated with their forecasts and communicate it to the end users. This information can match the end-users needs (i.e. prove to be useful for an efficient crisis management) only if it is reliable: reliability is therefore a key quality for operational flood forecasts. In 2015, the French flood forecasting national and regional services (Vigicrues network; www.vigicrues.gouv.fr) implemented a framework to compute quantitative discharge and water level forecasts and to assess the predictive uncertainty. Among the possible technical options to achieve this goal, a statistical analysis of past forecasting errors of deterministic models has been selected (QUOIQUE method, Bourgin, 2014). It is a data-based and non-parametric approach based on as few assumptions as possible about the forecasting error mathematical structure. In particular, a very simple assumption is made regarding the predictive uncertainty distributions for large events outside the range of the calibration data: the multiplicative error distribution is assumed to be constant, whatever the magnitude of the flood. Indeed, the predictive distributions may not be reliable in extrapolation. However, estimating the predictive uncertainty for these rare events is crucial when major floods are of concern. In order to improve the forecasts reliability for major floods, an attempt at combining the operational strength of the empirical statistical analysis and a simple error modelling is done. Since the heteroscedasticity of forecast errors can considerably weaken the predictive reliability for large floods, this error modelling is based on the log-sinh transformation which proved to reduce significantly the heteroscedasticity of the transformed error in a simulation context, even for flood peaks (Wang et al., 2012). Exploratory tests on some operational forecasts issued during the recent floods experienced in France (major spring floods in June 2016 on the Loire river tributaries and flash floods in fall 2016) will be shown and discussed. References Bourgin, F. (2014). How to assess the predictive uncertainty in hydrological modelling? An exploratory work on a large sample of watersheds, AgroParisTech Wang, Q. J., Shrestha, D. L., Robertson, D. E. and Pokhrel, P (2012). A log-sinh transformation for data normalization and variance stabilization. Water Resources Research, , W05514, doi:10.1029/2011WR010973
Impact of Lidar Wind Sounding on Mesoscale Forecast

NASA Technical Reports Server (NTRS)

Miller, Timothy L.; Chou, Shih-Hung; Goodman, H. Michael (Technical Monitor)

2001-01-01

An Observing System Simulation Experiment (OSSE) was conducted to study the impact of airborne lidar wind sounding on mesoscale weather forecast. A wind retrieval scheme, which interpolates wind data from a grid data system, simulates the retrieval of wind profile from a satellite lidar system. A mesoscale forecast system based on the PSU/NCAR MM5 model is developed and incorporated the assimilation of the retrieved line-of-sight wind. To avoid the "identical twin" problem, the NCEP reanalysis data is used as our reference "nature" atmosphere. The simulated space-based lidar wind observations were retrieved by interpolating the NCEP values to the observation locations. A modified dataset obtained by smoothing the NCEP dataset was used as the initial state whose forecast was sought to be improved by assimilating the retrieved lidar observations. Forecasts using wind profiles with various lidar instrument parameters has been conducted. The results show that to significantly improve the mesoscale forecast the satellite should fly near the storm center with large scanning radius. Increasing lidar firing rate also improves the forecast. Cloud cover and lack of aerosol degrade the quality of the lidar wind data and, subsequently, the forecast.
Volcanic ash modeling with the NMMB-MONARCH-ASH model: quantification of offline modeling errors

NASA Astrophysics Data System (ADS)

Marti, Alejandro; Folch, Arnau

2018-03-01

Volcanic ash modeling systems are used to simulate the atmospheric dispersion of volcanic ash and to generate forecasts that quantify the impacts from volcanic eruptions on infrastructures, air quality, aviation, and climate. The efficiency of response and mitigation actions is directly associated with the accuracy of the volcanic ash cloud detection and modeling systems. Operational forecasts build on offline coupled modeling systems in which meteorological variables are updated at the specified coupling intervals. Despite the concerns from other communities regarding the accuracy of this strategy, the quantification of the systematic errors and shortcomings associated with the offline modeling systems has received no attention. This paper employs the NMMB-MONARCH-ASH model to quantify these errors by employing different quantitative and categorical evaluation scores. The skills of the offline coupling strategy are compared against those from an online forecast considered to be the best estimate of the true outcome. Case studies are considered for a synthetic eruption with constant eruption source parameters and for two historical events, which suitably illustrate the severe aviation disruptive effects of European (2010 Eyjafjallajökull) and South American (2011 Cordón Caulle) volcanic eruptions. Evaluation scores indicate that systematic errors due to the offline modeling are of the same order of magnitude as those associated with the source term uncertainties. In particular, traditional offline forecasts employed in operational model setups can result in significant uncertainties, failing to reproduce, in the worst cases, up to 45-70 % of the ash cloud of an online forecast. These inconsistencies are anticipated to be even more relevant in scenarios in which the meteorological conditions change rapidly in time. The outcome of this paper encourages operational groups responsible for real-time advisories for aviation to consider employing computationally efficient online dispersal models.
A Hybrid Model for Predicting the Prevalence of Schistosomiasis in Humans of Qianjiang City, China

PubMed Central

Wang, Ying; Lu, Zhouqin; Tian, Lihong; Tan, Li; Shi, Yun; Nie, Shaofa; Liu, Li

2014-01-01

Backgrounds/Objective Schistosomiasis is still a major public health problem in China, despite the fact that the government has implemented a series of strategies to prevent and control the spread of the parasitic disease. Advanced warning and reliable forecasting can help policymakers to adjust and implement strategies more effectively, which will lead to the control and elimination of schistosomiasis. Our aim is to explore the application of a hybrid forecasting model to track the trends of the prevalence of schistosomiasis in humans, which provides a methodological basis for predicting and detecting schistosomiasis infection in endemic areas. Methods A hybrid approach combining the autoregressive integrated moving average (ARIMA) model and the nonlinear autoregressive neural network (NARNN) model to forecast the prevalence of schistosomiasis in the future four years. Forecasting performance was compared between the hybrid ARIMA-NARNN model, and the single ARIMA or the single NARNN model. Results The modelling mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of the ARIMA-NARNN model was 0.1869×10−4, 0.0029, 0.0419 with a corresponding testing error of 0.9375×10−4, 0.0081, 0.9064, respectively. These error values generated with the hybrid model were all lower than those obtained from the single ARIMA or NARNN model. The forecasting values were 0.75%, 0.80%, 0.76% and 0.77% in the future four years, which demonstrated a no-downward trend. Conclusion The hybrid model has high quality prediction accuracy in the prevalence of schistosomiasis, which provides a methodological basis for future schistosomiasis monitoring and control strategies in the study area. It is worth attempting to utilize the hybrid detection scheme in other schistosomiasis-endemic areas including other infectious diseases. PMID:25119882
Enviro-HIRLAM online integrated meteorology-chemistry modelling system: strategy, methodology, developments and applications (v7.2)

NASA Astrophysics Data System (ADS)

Baklanov, Alexander; Smith Korsholm, Ulrik; Nuterman, Roman; Mahura, Alexander; Pagh Nielsen, Kristian; Hansen Sass, Bent; Rasmussen, Alix; Zakey, Ashraf; Kaas, Eigil; Kurganskiy, Alexander; Sørensen, Brian; González-Aparicio, Iratxe

2017-08-01

The Environment - High Resolution Limited Area Model (Enviro-HIRLAM) is developed as a fully online integrated numerical weather prediction (NWP) and atmospheric chemical transport (ACT) model for research and forecasting of joint meteorological, chemical and biological weather. The integrated modelling system is developed by the Danish Meteorological Institute (DMI) in collaboration with several European universities. It is the baseline system in the HIRLAM Chemical Branch and used in several countries and different applications. The development was initiated at DMI more than 15 years ago. The model is based on the HIRLAM NWP model with online integrated pollutant transport and dispersion, chemistry, aerosol dynamics, deposition and atmospheric composition feedbacks. To make the model suitable for chemical weather forecasting in urban areas, the meteorological part was improved by implementation of urban parameterisations. The dynamical core was improved by implementing a locally mass-conserving semi-Lagrangian numerical advection scheme, which improves forecast accuracy and model performance. The current version (7.2), in comparison with previous versions, has a more advanced and cost-efficient chemistry, aerosol multi-compound approach, aerosol feedbacks (direct and semi-direct) on radiation and (first and second indirect effects) on cloud microphysics. Since 2004, the Enviro-HIRLAM has been used for different studies, including operational pollen forecasting for Denmark since 2009 and operational forecasting atmospheric composition with downscaling for China since 2017. Following the main research and development strategy, further model developments will be extended towards the new NWP platform - HARMONIE. Different aspects of online coupling methodology, research strategy and possible applications of the modelling system, and fit-for-purpose model configurations for the meteorological and air quality communities are discussed.
Consistency between the global and regional modeling components of CAMS over Europe.

NASA Astrophysics Data System (ADS)

Katragkou, Eleni; Akritidis, Dimitrios; Kontos, Serafim; Zanis, Prodromos; Melas, Dimitrios; Engelen, Richard; Plu, Matthieu; Eskes, Henk

2017-04-01

The Copernicus Atmosphere Monitoring Service (CAMS) is a component of the European Earth Observation programme Copernicus. CAMS consists of two major forecast and analysis systems: i) the CAMS global near-real time service, based on the ECMWF Integrated Forecast System (C-IFS), which provides daily analyses and forecasts of reactive trace gases, greenhouse gases and aerosol concentrations ii) a regional ensemble (ENS) for European air quality, compiled and disseminated by Météo-France, which consists of seven ensemble members. The boundaries from the regional ensemble members are extracted from the global CAMS forecast product. This work reports on the consistency between the global and regional modeling components of CAMS, and the impact of global CAMS boundary conditions on regional forecasts. The current analysis includes ozone (O3) carbon monoxide (CO) and aerosol (PM10/PM2.5) forecasts. The comparison indicates an overall good agreement between the global C-IFS and the regional ENS patterns for O3 and CO, especially above 250m altitude, indicating that the global boundary conditions are efficiently included in the regional ensemble simulations. As expected, differences are found within the PBL, with lower/higher C-IFS O3/CO concentrations over continental Europe with respect to ENS.
Post-processing through linear regression

NASA Astrophysics Data System (ADS)

van Schaeybroeck, B.; Vannitsem, S.

2011-03-01

Various post-processing techniques are compared for both deterministic and ensemble forecasts, all based on linear regression between forecast data and observations. In order to evaluate the quality of the regression methods, three criteria are proposed, related to the effective correction of forecast error, the optimal variability of the corrected forecast and multicollinearity. The regression schemes under consideration include the ordinary least-square (OLS) method, a new time-dependent Tikhonov regularization (TDTR) method, the total least-square method, a new geometric-mean regression (GM), a recently introduced error-in-variables (EVMOS) method and, finally, a "best member" OLS method. The advantages and drawbacks of each method are clarified. These techniques are applied in the context of the 63 Lorenz system, whose model version is affected by both initial condition and model errors. For short forecast lead times, the number and choice of predictors plays an important role. Contrarily to the other techniques, GM degrades when the number of predictors increases. At intermediate lead times, linear regression is unable to provide corrections to the forecast and can sometimes degrade the performance (GM and the best member OLS with noise). At long lead times the regression schemes (EVMOS, TDTR) which yield the correct variability and the largest correlation between ensemble error and spread, should be preferred.
Seasonal Drought Prediction: Advances, Challenges, and Future Prospects

NASA Astrophysics Data System (ADS)

Hao, Zengchao; Singh, Vijay P.; Xia, Youlong

2018-03-01

Drought prediction is of critical importance to early warning for drought managements. This review provides a synthesis of drought prediction based on statistical, dynamical, and hybrid methods. Statistical drought prediction is achieved by modeling the relationship between drought indices of interest and a suite of potential predictors, including large-scale climate indices, local climate variables, and land initial conditions. Dynamical meteorological drought prediction relies on seasonal climate forecast from general circulation models (GCMs), which can be employed to drive hydrological models for agricultural and hydrological drought prediction with the predictability determined by both climate forcings and initial conditions. Challenges still exist in drought prediction at long lead time and under a changing environment resulting from natural and anthropogenic factors. Future research prospects to improve drought prediction include, but are not limited to, high-quality data assimilation, improved model development with key processes related to drought occurrence, optimal ensemble forecast to select or weight ensembles, and hybrid drought prediction to merge statistical and dynamical forecasts.
Innovative Tools for Water Quality/Quantity Management: New York City's Operations Support Tool

NASA Astrophysics Data System (ADS)

Wang, L.; Schaake, J. C.; Day, G. N.; Porter, J.; Sheer, D. P.; Pyke, G.

2011-12-01

The New York City Department of Environmental Protection (DEP) manages New York City's water supply, which is comprised of over 20 reservoirs and supplies more than 1 billion gallons of water per day to over 9 million customers. Recently, DEP has initiated design of an Operations Support Tool (OST), a state-of-the-art decision support system to provide computational and predictive support for water supply operations and planning. This presentation describes the technical structure of OST, including the underlying water supply and water quality models, data sources and database management, reservoir inflow forecasts, and the functionalities required to meet the needs of a diverse group of end users. OST is a major upgrade of DEP's current water supply - water quality model, developed to evaluate alternatives for controlling turbidity in NYC's Catskill reservoirs. While the current model relies on historical hydrologic and meteorological data, OST can be driven by forecasted future conditions. It will receive a variety of near-real-time data from a number of sources. OST will support two major types of simulations: long-term, for evaluating policy or infrastructure changes over an extended period of time; and short-term "position analysis" (PA) simulations, consisting of multiple short simulations, all starting from the same initial conditions. Typically, the starting conditions for a PA run will represent those for the current day and traces of forecasted hydrology will drive the model for the duration of the simulation period. The result of these simulations will be a distribution of future system states based on system operating rules and the range of input ensemble streamflow predictions. DEP managers will analyze the output distributions and make operation decisions using risk-based metrics such as probability of refill. Currently, in the developmental stages of OST, forecasts are based on antecedent hydrologic conditions and are statistical in nature. The statistical algorithm is a relatively simple and versatile, but lacks short-term skill critical for water quality and spill management. To improve short-term skill, OST will ultimately operate with meteorologically driven hydrologic forecasts provided by the National Weather Service (NWS). OST functionalities will support a wide range of DEP uses, including short term operational projections, outage planning and emergency management, operating rule development, and water supply planning. A core use of OST will be to inform reservoir management strategies to control and mitigate turbidity events while ensuring water supply reliability. OST will also allow DEP to manage its complex reservoir system to meet multiple objectives, including ecological flows, tailwater fisheries and recreational releases, and peak flow mitigation for downstream communities.
Application, evaluation and sensitivity analysis of the coupled WRF-CMAQ system from regional to urban scales

EPA Science Inventory

The Community Multiscale Air Quality (CMAQ) model is a state-of-the-science chemical transport model (CTM) capable of simulating the emission, transport and fate of numerous air pollutants. Similarly, the Weather Research and Forecasting (WRF) model is a state-of-the-science mete...
Comment on "Simulation of Surface Ozone Pollution in the Central Gulf Coast Region Using WRF/Chem Model: Sensitivity to PBL and Land Surface Physics"

EPA Science Inventory

A recently published meteorology and air quality modeling study has several serious deficiencies deserving comment. The study uses the weather research and forecasting/chemistry (WRF/Chem) model to compare and evaluate boundary layer and land surface modeling options. The most se...
NWS Marine Forecast Areas

Science.gov Websites

Currents Global Ocean Model Sea Surface Temperatures Gulf Stream ASCII Data Gulf Stream Comparison Gridded ASCAT Scatterometer Winds Lightning Strike Density Satellite Imagery Ocean Global Ocean Model , 2017 19:10:57 UTC Disclaimer Information Quality Help Glossary Privacy Policy Freedom of Information
Why is metal bioaccumulation so variable? Biodynamics as a unifying concept

USGS Publications Warehouse

Luoma, Samuel N.; Rainbow, Philip S.

2005-01-01

Ecological risks from metal contaminants are difficult to document because responses differ among species, threats differ among metals, and environmental influences are complex. Unifying concepts are needed to better tie together such complexities. Here we suggest that a biologically based conceptualization, the biodynamic model, provides the necessary unification for a key aspect in risk: metal bioaccumulation (internal exposure). The model is mechanistically based, but empirically considers geochemical influences, biological differences, and differences among metals. Forecasts from the model agree closely with observations from nature, validating its basic assumptions. The biodynamic metal bioaccumulation model combines targeted, high-quality geochemical analyses from a site of interest with parametrization of key physiological constants for a species from that site. The physiological parameters include metal influx rates from water, influx rates from food, rate constants of loss, and growth rates (when high). We compiled results from 15 publications that forecast species-specific bioaccumulation, and compare the forecasts to bioaccumulation data from the field. These data consider concentrations that cover 7 orders of magnitude. They include 7 metals and 14 species of animals from 3 phyla and 11 marine, estuarine, and freshwater environments. The coefficient of determination (R2) between forecasts and independently observed bioaccumulation from the field was 0.98. Most forecasts agreed with observations within 2-fold. The agreement suggests that the basic assumptions of the biodynamic model are tenable. A unified explanation of metal bioaccumulation sets the stage for a realistic understanding of toxicity and ecological effects of metals in nature.
State estimation and prediction using clustered particle filters.

PubMed

Lee, Yoonsang; Majda, Andrew J

2016-12-20

Particle filtering is an essential tool to improve uncertain model predictions by incorporating noisy observational data from complex systems including non-Gaussian features. A class of particle filters, clustered particle filters, is introduced for high-dimensional nonlinear systems, which uses relatively few particles compared with the standard particle filter. The clustered particle filter captures non-Gaussian features of the true signal, which are typical in complex nonlinear dynamical systems such as geophysical systems. The method is also robust in the difficult regime of high-quality sparse and infrequent observations. The key features of the clustered particle filtering are coarse-grained localization through the clustering of the state variables and particle adjustment to stabilize the method; each observation affects only neighbor state variables through clustering and particles are adjusted to prevent particle collapse due to high-quality observations. The clustered particle filter is tested for the 40-dimensional Lorenz 96 model with several dynamical regimes including strongly non-Gaussian statistics. The clustered particle filter shows robust skill in both achieving accurate filter results and capturing non-Gaussian statistics of the true signal. It is further extended to multiscale data assimilation, which provides the large-scale estimation by combining a cheap reduced-order forecast model and mixed observations of the large- and small-scale variables. This approach enables the use of a larger number of particles due to the computational savings in the forecast model. The multiscale clustered particle filter is tested for one-dimensional dispersive wave turbulence using a forecast model with model errors.
State estimation and prediction using clustered particle filters

PubMed Central

Lee, Yoonsang; Majda, Andrew J.

2016-01-01

Particle filtering is an essential tool to improve uncertain model predictions by incorporating noisy observational data from complex systems including non-Gaussian features. A class of particle filters, clustered particle filters, is introduced for high-dimensional nonlinear systems, which uses relatively few particles compared with the standard particle filter. The clustered particle filter captures non-Gaussian features of the true signal, which are typical in complex nonlinear dynamical systems such as geophysical systems. The method is also robust in the difficult regime of high-quality sparse and infrequent observations. The key features of the clustered particle filtering are coarse-grained localization through the clustering of the state variables and particle adjustment to stabilize the method; each observation affects only neighbor state variables through clustering and particles are adjusted to prevent particle collapse due to high-quality observations. The clustered particle filter is tested for the 40-dimensional Lorenz 96 model with several dynamical regimes including strongly non-Gaussian statistics. The clustered particle filter shows robust skill in both achieving accurate filter results and capturing non-Gaussian statistics of the true signal. It is further extended to multiscale data assimilation, which provides the large-scale estimation by combining a cheap reduced-order forecast model and mixed observations of the large- and small-scale variables. This approach enables the use of a larger number of particles due to the computational savings in the forecast model. The multiscale clustered particle filter is tested for one-dimensional dispersive wave turbulence using a forecast model with model errors. PMID:27930332
Application of time series analysis in modelling and forecasting emergency department visits in a medical centre in Southern Taiwan

PubMed Central

Juang, Wang-Chuan; Huang, Sin-Jhih; Huang, Fong-Dee; Cheng, Pei-Wen; Wann, Shue-Ren

2017-01-01

Objective Emergency department (ED) overcrowding is acknowledged as an increasingly important issue worldwide. Hospital managers are increasingly paying attention to ED crowding in order to provide higher quality medical services to patients. One of the crucial elements for a good management strategy is demand forecasting. Our study sought to construct an adequate model and to forecast monthly ED visits. Methods We retrospectively gathered monthly ED visits from January 2009 to December 2016 to carry out a time series autoregressive integrated moving average (ARIMA) analysis. Initial development of the model was based on past ED visits from 2009 to 2016. A best-fit model was further employed to forecast the monthly data of ED visits for the next year (2016). Finally, we evaluated the predicted accuracy of the identified model with the mean absolute percentage error (MAPE). The software packages SAS/ETS V.9.4 and Office Excel 2016 were used for all statistical analyses. Results A series of statistical tests showed that six models, including ARIMA (0, 0, 1), ARIMA (1, 0, 0), ARIMA (1, 0, 1), ARIMA (2, 0, 1), ARIMA (3, 0, 1) and ARIMA (5, 0, 1), were candidate models. The model that gave the minimum Akaike information criterion and Schwartz Bayesian criterion and followed the assumptions of residual independence was selected as the adequate model. Finally, a suitable ARIMA (0, 0, 1) structure, yielding a MAPE of 8.91%, was identified and obtained as Visitt=7111.161+(at+0.37462 at−1). Conclusion The ARIMA (0, 0, 1) model can be considered adequate for predicting future ED visits, and its forecast results can be used to aid decision-making processes. PMID:29196487

The balance sheet technique. Volume I. The balance sheet analysis technique for preconstruction review of airports and highways

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

LaBelle, S.J.; Smith, A.E.; Seymour, D.A.

1977-02-01

The technique applies equally well to new or existing airports. The importance of accurate accounting of emissions, cannot be overstated. The regional oxidant modelling technique used in conjunction with a balance sheet review must be a proportional reduction technique. This type of emission balancing presumes equality of all sources in the analysis region. The technique can be applied successfully in the highway context, either in planning at the system level or looking only at projects individually. The project-by-project reviews could be used to examine each project in the same way as the airport projects are examined for their impact onmore » regional desired emission levels. The primary limitation of this technique is that it should not be used when simulation models have been used for regional oxidant air quality. In the case of highway projects, the balance sheet technique might appear to be limited; the real limitations are in the transportation planning process. That planning process is not well-suited to the needs of air quality forecasting. If the transportation forecasting techniques are insensitive to change in the variables that affect HC emissions, then no internal emission trade-offs can be identified, and the initial highway emission forecasts are themselves suspect. In general, the balance sheet technique is limited by the quality of the data used in the review. Additionally, the technique does not point out effective trade-off strategies, nor does it indicate when it might be worthwhile to ignore small amounts of excess emissions. Used in the context of regional air quality plans based on proportional reduction models, the balance sheet analysis technique shows promise as a useful method by state or regional reviewing agencies.« less
Hydro-meteorological evaluation of downscaled global ensemble rainfall forecasts

NASA Astrophysics Data System (ADS)

Gaborit, Étienne; Anctil, François; Fortin, Vincent; Pelletier, Geneviève

2013-04-01

Ensemble rainfall forecasts are of high interest for decision making, as they provide an explicit and dynamic assessment of the uncertainty in the forecast (Ruiz et al. 2009). However, for hydrological forecasting, their low resolution currently limits their use to large watersheds (Maraun et al. 2010). In order to bridge this gap, various implementations of the statistic-stochastic multi-fractal downscaling technique presented by Perica and Foufoula-Georgiou (1996) were compared, bringing Environment Canada's global ensemble rainfall forecasts from a 100 by 70-km resolution down to 6 by 4-km, while increasing each pixel's rainfall variance and preserving its original mean. For comparison purposes, simpler methods were also implemented such as the bi-linear interpolation, which disaggregates global forecasts without modifying their variance. The downscaled meteorological products were evaluated using different scores and diagrams, from both a meteorological and a hydrological view points. The meteorological evaluation was conducted comparing the forecasted rainfall depths against nine days of observed values taken from Québec City rain gauge database. These 9 days present strong precipitation events occurring during the summer of 2009. For the hydrologic evaluation, the hydrological models SWMM5 and (a modified version of) GR4J were implemented on a small 6 km2 urban catchment located in the Québec City region. Ensemble hydrologic forecasts with a time step of 3 hours were then performed over a 3-months period of the summer of 2010 using the original and downscaled ensemble rainfall forecasts. The most important conclusions of this work are that the overall quality of the forecasts was preserved during the disaggregation procedure and that the disaggregated products using this variance-enhancing method were of similar quality than bi-linear interpolation products. However, variance and dispersion of the different members were, of course, much improved for the variance-enhanced products, compared to the bi-linear interpolation, which is a decisive advantage. The disaggregation technique of Perica and Foufoula-Georgiou (1996) hence represents an interesting way of bridging the gap between the meteorological models' resolution and the high degree of spatial precision sometimes required by hydrological models in their precipitation representation. References Maraun, D., Wetterhall, F., Ireson, A. M., Chandler, R. E., Kendon, E. J., Widmann, M., Brienen, S., Rust, H. W., Sauter, T., Themeßl, M., Venema, V. K. C., Chun, K. P., Goodess, C. M., Jones, R. G., Onof, C., Vrac, M., and Thiele-Eich, I. 2010. Precipitation downscaling under climate change: recent developments to bridge the gap between dynamical models and the end user. Reviews of Geophysics, 48 (3): RG3003, [np]. Doi: 10.1029/2009RG000314. Perica, S., and Foufoula-Georgiou, E. 1996. Model for multiscale disaggregation of spatial rainfall based on coupling meteorological and scaling descriptions. Journal Of Geophysical Research, 101(D21): 26347-26361. Ruiz, J., Saulo, C. and Kalnay, E. 2009. Comparison of Methods Used to Generate Probabilistic Quantitative Precipitation Forecasts over South America. Weather and forecasting, 24: 319-336. DOI: 10.1175/2008WAF2007098.1 This work is distributed under the Creative Commons Attribution 3.0 Unported License together with an author copyright. This license does not conflict with the regulations of the Crown Copyright.
Integrated Wind Power Planning Tool

NASA Astrophysics Data System (ADS)

Rosgaard, Martin; Giebel, Gregor; Skov Nielsen, Torben; Hahmann, Andrea; Sørensen, Poul; Madsen, Henrik

2013-04-01

This poster presents the current state of the public service obligation (PSO) funded project PSO 10464, with the title "Integrated Wind Power Planning Tool". The goal is to integrate a mesoscale numerical weather prediction (NWP) model with purely statistical tools in order to assess wind power fluctuations, with focus on long term power system planning for future wind farms as well as short term forecasting for existing wind farms. Currently, wind power fluctuation models are either purely statistical or integrated with NWP models of limited resolution. Using the state-of-the-art mesoscale NWP model Weather Research & Forecasting model (WRF) the forecast error is sought quantified in dependence of the time scale involved. This task constitutes a preparative study for later implementation of features accounting for NWP forecast errors in the DTU Wind Energy maintained Corwind code - a long term wind power planning tool. Within the framework of PSO 10464 research related to operational short term wind power prediction will be carried out, including a comparison of forecast quality at different mesoscale NWP model resolutions and development of a statistical wind power prediction tool taking input from WRF. The short term prediction part of the project is carried out in collaboration with ENFOR A/S; a Danish company that specialises in forecasting and optimisation for the energy sector. The integrated prediction model will allow for the description of the expected variability in wind power production in the coming hours to days, accounting for its spatio-temporal dependencies, and depending on the prevailing weather conditions defined by the WRF output. The output from the integrated short term prediction tool constitutes scenario forecasts for the coming period, which can then be fed into any type of system model or decision making problem to be solved. The high resolution of the WRF results loaded into the integrated prediction model will ensure a high accuracy data basis is available for use in the decision making process of the Danish transmission system operator. The need for high accuracy predictions will only increase over the next decade as Denmark approaches the goal of 50% wind power based electricity in 2025 from the current 20%.
WOD - Weather On Demand forecasting system

NASA Astrophysics Data System (ADS)

Rognvaldsson, Olafur; Ragnarsson, Logi; Stanislawska, Karolina

2017-04-01

The backbone of the Belgingur forecasting system (called WOD - Weather On Demand) is the WRF-Chem atmospheric model, with a number of in-house customisations. Initial and boundary data are taken from the Global Forecasting System, operated by the National Oceanic and Atmospheric Administration (NOAA). Operational forecasts use cycling of a number of parameters, mainly deep soil and surface fields. This is done to minimise spin-up effects and to ensure proper book-keeping of hydrological fields such as snow accumulation and runoff, as well as the constituents of various chemical parameters. The WOD system can be used to create conventional short- to medium-range weather forecasts for any location on the globe. The WOD system can also be used for air quality purposes (e.g. dispersion forecasts from volcanic eruptions) and as a tool to provide input to other modelling systems, such as hydrological models. A wide variety of post-processing options are also available, making WOD an ideal tool for creating highly customised output that can be tailored to the specific needs of individual end-users. The most recent addition to the WOD system is an integrated verification system where forecasts can be compared to surface observations from chosen locations. Forecast visualisation, such as weather charts, meteograms, weather icons and tables, is done via number of web components that can be configured to serve the varying needs of different end-users. The WOD system itself can be installed in an automatic way on hardware running a range of Linux based OS. System upgrades can also be done in semi-automatic fashion, i.e. upgrades and/or bug-fixes can be pushed to the end-user hardware without system downtime. Importantly, the WOD system requires only rudimentary knowledge of the WRF modelling, and the Linux operating systems on behalf of the end-user, making it an ideal NWP tool in locations with limited IT infrastructure.
A Study of Subseasonal Predictability of the Atmospheric Circulation Low-frequency Modes based on SL-AV forecasts

NASA Astrophysics Data System (ADS)

Kruglova, Ekaterina; Kulikova, Irina; Khan, Valentina; Tischenko, Vladimir

2017-04-01

The subseasonal predictability of low-frequency modes and the atmospheric circulation regimes is investigated based on the using of outputs from global Semi-Lagrangian (SL-AV) model of the Hydrometcentre of Russia and Institute of Numerical Mathematics of Russian Academy of Science. Teleconnection indices (AO, WA, EA, NAO, EU, WP, PNA) are used as the quantitative characteristics of low-frequency variability to identify zonal and meridional flow regimes with focus on control distribution of high impact weather patterns in the Northern Eurasia. The predictability of weekly and monthly averaged indices is estimated by the methods of diagnostic verification of forecast and reanalysis data covering the hindcast period, and also with the use of the recommended WMO quantitative criteria. Characteristics of the low frequency variability have been discussed. Particularly, it is revealed that the meridional flow regimes are reproduced by SL-AV for summer season better comparing to winter period. It is shown that the model's deterministic forecast (ensemble mean) skill at week 1 (days 1-7) is noticeably better than that of climatic forecasts. The decrease of skill scores at week 2 (days 8-14) and week 3( days 15-21) is explained by deficiencies in the modeling system and inaccurate initial conditions. It was noticed the slightly improvement of the skill of model at week 4 (days 22-28), when the condition of atmosphere is more determined by the flow of energy from the outside. The reliability of forecasts of monthly (days 1-30) averaged indices is comparable to that at week 1 (days 1-7). Numerical experiments demonstrated that the forecast accuracy can be improved (thus the limit of practical predictability can be extended) through the using of probabilistic approach based on ensemble forecasts. It is shown that the quality of forecasts of the regimes of circulation like blocking is higher, than that of zonal flow.
78 FR 37757 - Revisions to the California State Implementation Plan, South Coast Air Quality Management District

Federal Register 2010, 2011, 2012, 2013, 2014

2013-06-24

... the California State Implementation Plan, South Coast Air Quality Management District AGENCY... the South Coast Air Quality Management District (SCAQMD) portion of the California State... Quality Index rather than on 1-hour ozone forecasted values; (b) forecast criteria for allowing a...
Towards an operational high-resolution air quality forecasting system at ECCC

NASA Astrophysics Data System (ADS)

Munoz-Alpizar, Rodrigo; Stroud, Craig; Ren, Shuzhan; Belair, Stephane; Leroyer, Sylvie; Souvanlasy, Vanh; Spacek, Lubos; Pavlovic, Radenko; Davignon, Didier; Moran, Moran

2017-04-01

Urban environments are particularly sensitive to weather, air quality (AQ), and climatic conditions. Despite the efforts made in Canada to reduce pollution in urban areas, AQ continues to be a concern for the population, especially during short-term episodes that could lead to exceedances of daily air quality standards. Furthermore, urban air pollution has long been associated with significant adverse health effects. In Canada, the large percentage of the population living in urban areas ( 81%, according to the Canada's 2011 census) is exposed to elevated air pollution due to local emissions sources. Thus, in order to improve the services offered to the Canadian public, Environment and Climate Change Canada has launched an initiative to develop a high-resolution air quality prediction capacity for urban areas in Canada. This presentation will show observed pollution trends (2010-2016) for Canadian mega-cities along with some preliminary high-resolution air quality modelling results. Short-term and long-term plans for urban AQ forecasting in Canada will also be described.
Tracking and forecasting the Nation’s water quality - Priorities and strategies for 2013-2023

USGS Publications Warehouse

Rowe, Gary L.; Gilliom, Robert J.; Woodside, Michael D.

2013-01-01

Water-quality issues facing the Nation are growing in number and complexity, and solutions are becoming more challenging and costly. Key factors that affect the quality of our drinking water supplies and ecosystem health include contaminants of human and natural origin in streams and groundwater; excess nutrients and sediment; alteration of natural streamflow; eutrophication of lakes, reservoirs, and coastal estuaries; and changes in surface and groundwater quality associated with changes in climate, land and water use, and management practices. Tracking and forecasting the Nation's water quality in the face of these and other pressing water-quality issues are important goals for 2013-2023, the third decade of the U.S. Geological Survey's National Water-Quality Assessment (NAWQA) program. In consultation with stakeholders and the National Research Council, a new strategic Science Plan has been developed that describes a strategy for building upon and enhancing assessment of the Nation's freshwater quality and aquatic ecosystems. The plan continues strategies that have been central to the NAWQA program's long-term success, but it also makes adjustments to the monitoring and modeling approaches NAWQA will use to address critical data and science information needs identified by stakeholders. This fact sheet describes surface-water and groundwater monitoring and modeling activities that will start in fiscal year 2013. It also provides examples of the types of data and information products planned for the next decade, including (1) restored monitoring for reliable and timely status and trend assessments, (2) maps and models that show the distribution of selected contaminants (such as atrazine, nitrate, and arsenic) in streams and aquifers, and (3) Web-based modeling tools that allow managers to evaluate how water quality may change in response to different scenarios of population growth, climate change, or land-use management.
An Empirical Study of Logistics Organization, Electronic Linkage, and Performance

DTIC Science & Technology

1993-01-01

utilization of transportation resources, and improved quality management. Researchers have proposed an information technology (IT) implementation model for...management, more efficient utilization of transportation resources, and improved quality management. Researchers have proposed an information...coordination of (1) facility structure, (2) forecasting and order management, (3) transportation , (4) inventory, and (5) warehousing and packaging. The
An Integrated Ecological Modeling System for Assessing Impacts of Multiple Stressors on Stream and Riverine Ecosystem Services Within River Basins

EPA Science Inventory

We demonstrate a novel, spatially explicit assessment of the current condition of aquatic ecosystem services, with limited sensitivity analysis for the atmospheric contaminant mercury. The Integrated Ecological Modeling System (IEMS) forecasts water quality and quantity, habitat ...
Comparison of Spatial and Temporal Rainfall Characteristics in WRF-Simulated Precipitation to Gauge and Radar Observations

EPA Science Inventory

Weather Research and Forecasting (WRF) meteorological data are used for USEPA multimedia air and water quality modeling applications, within the CMAQ modeling system to estimate wet deposition and to evaluate future climate and land-use scenarios. While it is not expected that hi...
Sub-seasonal predictability of water scarcity at global and local scale

NASA Astrophysics Data System (ADS)

Wanders, N.; Wada, Y.; Wood, E. F.

2016-12-01

Forecasting the water demand and availability for agriculture and energy production has been neglected in previous research, partly due to the fact that most large-scale hydrological models lack the skill to forecast human water demands at sub-seasonal time scale. We study the potential of a sub-seasonal water scarcity forecasting system for improved water management decision making and improved estimates of water demand and availability. We have generated 32 years of global sub-seasonal multi-model water availability, demand and scarcity forecasts. The quality of the forecasts is compared to a reference forecast derived from resampling historic weather observations. The newly developed system has been evaluated for both the global scale and in a real-time local application in the Sacramento valley for the Trinity, Shasta and Oroville reservoirs, where the water demand for agriculture and hydropower is high. On the global scale we find that the reference forecast shows high initial forecast skill (up to 8 months) for water scarcity in the eastern US, Central Asia and Sub-Saharan Africa. Adding dynamical sub-seasonal forecasts results in a clear improvement for most regions in the world, increasing the forecasts' lead time by 2 or more months on average. The strongest improvements are found in the US, Brazil, Central Asia and Australia. For the Sacramento valley we can accurately predict anomalies in the reservoir inflow, hydropower potential and the downstream irrigation water demand 6 months in advance. This allow us to forecast potential water scarcity in the Sacramento valley and adjust the reservoir management to prevent deficits in energy or irrigation water availability. The newly developed forecast system shows that it is possible to reduce the vulnerability to upcoming water scarcity events and allows optimization of the distribution of the available water between the agricultural and energy sector half a year in advance.
The use of a high resolution model in a private environment.

NASA Astrophysics Data System (ADS)

van Dijke, D.; Malda, D.

2009-09-01

The commercial organisation MeteoGroup uses high resolution modelling for multiple purposes. MeteoGroup uses the Weather Research and Forecasting Model (WRF®1). WRF is used in the operational environment of several MeteoGroup companies across Europe. It is also used in hindcast studies, for example hurricane tracking, wind climate computation and deriving boundary conditions for air quality models. A special operational service was set up for our tornado chasing team that uses high resolution flexible WRF data to chase for super cells and tornados in the USA during spring. Much effort is put into the development and improvement of the pre- and post-processing of the model. At MeteoGroup the static land-use data has been extended and adjusted to improve temperature and wind forecasts. The system has been modified such that sigma level input data from the global ECMWF model can be used for initialisation. By default only pressure level data could be used. During the spin-up of the model synoptical observations are nudged. A program to adjust possible initialisation errors of several surface parameters in coastal areas has been implemented. We developed an algorithm that computes cloud fractions using multiple direct model output variables. Forecasters prefer to use weather codes for their daily forecasts to detect severe weather. For this usage we developed model weather codes using a variety of direct model output and our own derived variables. 1 WRF® is a registered trademark of the University Corporation for Atmospheric Research (UCAR)
Nurse forecasting in Europe (RN4CAST): Rationale, design and methodology

PubMed Central

2011-01-01

Background Current human resources planning models in nursing are unreliable and ineffective as they consider volumes, but ignore effects on quality in patient care. The project RN4CAST aims innovative forecasting methods by addressing not only volumes, but quality of nursing staff as well as quality of patient care. Methods/Design A multi-country, multilevel cross-sectional design is used to obtain important unmeasured factors in forecasting models including how features of hospital work environments impact on nurse recruitment, retention and patient outcomes. In each of the 12 participating European countries, at least 30 general acute hospitals were sampled. Data are gathered via four data sources (nurse, patient and organizational surveys and via routinely collected hospital discharge data). All staff nurses of a random selection of medical and surgical units (at least 2 per hospital) were surveyed. The nurse survey has the purpose to measure the experiences of nurses on their job (e.g. job satisfaction, burnout) as well as to allow the creation of aggregated hospital level measures of staffing and working conditions. The patient survey is organized in a sub-sample of countries and hospitals using a one-day census approach to measure the patient experiences with medical and nursing care. In addition to conducting a patient survey, hospital discharge abstract datasets will be used to calculate additional patient outcomes like in-hospital mortality and failure-to-rescue. Via the organizational survey, information about the organizational profile (e.g. bed size, types of technology available, teaching status) is collected to control the analyses for institutional differences. This information will be linked via common identifiers and the relationships between different aspects of the nursing work environment and patient and nurse outcomes will be studied by using multilevel regression type analyses. These results will be used to simulate the impact of changing different aspects of the nursing work environment on quality of care and satisfaction of the nursing workforce. Discussion RN4CAST is one of the largest nurse workforce studies ever conducted in Europe, will add to accuracy of forecasting models and generate new approaches to more effective management of nursing resources in Europe. PMID:21501487
Nurse forecasting in Europe (RN4CAST): Rationale, design and methodology.

PubMed

Sermeus, Walter; Aiken, Linda H; Van den Heede, Koen; Rafferty, Anne Marie; Griffiths, Peter; Moreno-Casbas, Maria Teresa; Busse, Reinhard; Lindqvist, Rikard; Scott, Anne P; Bruyneel, Luk; Brzostek, Tomasz; Kinnunen, Juha; Schubert, Maria; Schoonhoven, Lisette; Zikos, Dimitrios

2011-04-18

Current human resources planning models in nursing are unreliable and ineffective as they consider volumes, but ignore effects on quality in patient care. The project RN4CAST aims innovative forecasting methods by addressing not only volumes, but quality of nursing staff as well as quality of patient care. A multi-country, multilevel cross-sectional design is used to obtain important unmeasured factors in forecasting models including how features of hospital work environments impact on nurse recruitment, retention and patient outcomes. In each of the 12 participating European countries, at least 30 general acute hospitals were sampled. Data are gathered via four data sources (nurse, patient and organizational surveys and via routinely collected hospital discharge data). All staff nurses of a random selection of medical and surgical units (at least 2 per hospital) were surveyed. The nurse survey has the purpose to measure the experiences of nurses on their job (e.g. job satisfaction, burnout) as well as to allow the creation of aggregated hospital level measures of staffing and working conditions. The patient survey is organized in a sub-sample of countries and hospitals using a one-day census approach to measure the patient experiences with medical and nursing care. In addition to conducting a patient survey, hospital discharge abstract datasets will be used to calculate additional patient outcomes like in-hospital mortality and failure-to-rescue. Via the organizational survey, information about the organizational profile (e.g. bed size, types of technology available, teaching status) is collected to control the analyses for institutional differences.This information will be linked via common identifiers and the relationships between different aspects of the nursing work environment and patient and nurse outcomes will be studied by using multilevel regression type analyses. These results will be used to simulate the impact of changing different aspects of the nursing work environment on quality of care and satisfaction of the nursing workforce. RN4CAST is one of the largest nurse workforce studies ever conducted in Europe, will add to accuracy of forecasting models and generate new approaches to more effective management of nursing resources in Europe.
Comparison of Filtering Methods for the Modeling and Retrospective Forecasting of Influenza Epidemics

PubMed Central

Yang, Wan; Karspeck, Alicia; Shaman, Jeffrey

2014-01-01

A variety of filtering methods enable the recursive estimation of system state variables and inference of model parameters. These methods have found application in a range of disciplines and settings, including engineering design and forecasting, and, over the last two decades, have been applied to infectious disease epidemiology. For any system of interest, the ideal filter depends on the nonlinearity and complexity of the model to which it is applied, the quality and abundance of observations being entrained, and the ultimate application (e.g. forecast, parameter estimation, etc.). Here, we compare the performance of six state-of-the-art filter methods when used to model and forecast influenza activity. Three particle filters—a basic particle filter (PF) with resampling and regularization, maximum likelihood estimation via iterated filtering (MIF), and particle Markov chain Monte Carlo (pMCMC)—and three ensemble filters—the ensemble Kalman filter (EnKF), the ensemble adjustment Kalman filter (EAKF), and the rank histogram filter (RHF)—were used in conjunction with a humidity-forced susceptible-infectious-recovered-susceptible (SIRS) model and weekly estimates of influenza incidence. The modeling frameworks, first validated with synthetic influenza epidemic data, were then applied to fit and retrospectively forecast the historical incidence time series of seven influenza epidemics during 2003–2012, for 115 cities in the United States. Results suggest that when using the SIRS model the ensemble filters and the basic PF are more capable of faithfully recreating historical influenza incidence time series, while the MIF and pMCMC do not perform as well for multimodal outbreaks. For forecast of the week with the highest influenza activity, the accuracies of the six model-filter frameworks are comparable; the three particle filters perform slightly better predicting peaks 1–5 weeks in the future; the ensemble filters are more accurate predicting peaks in the past. PMID:24762780
Modeling of Micro Deval abrasion loss based on some rock properties

NASA Astrophysics Data System (ADS)

Capik, Mehmet; Yilmaz, Ali Osman

2017-10-01

Aggregate is one of the most widely used construction material. The quality of the aggregate is determined using some testing methods. Among these methods, the Micro Deval Abrasion Loss (MDAL) test is commonly used for the determination of the quality and the abrasion resistance of aggregate. The main objective of this study is to develop models for the prediction of MDAL from rock properties, including uniaxial compressive strength, Brazilian tensile strength, point load index, Schmidt rebound hardness, apparent porosity, void ratio Cerchar abrasivity index and Bohme abrasion test are examined. Additionally, the MDAL is modeled using simple regression analysis and multiple linear regression analysis based on the rock properties. The study shows that the MDAL decreases with the increase of uniaxial compressive strength, Brazilian tensile strength, point load index, Schmidt rebound hardness and Cerchar abrasivity index. It is also concluded that the MDAL increases with the increase of apparent porosity, void ratio and Bohme abrasion test. The modeling results show that the models based on Bohme abrasion test and L type Schmidt rebound hardness give the better forecasting performances for the MDAL. More models, including the uniaxial compressive strength, the apparent porosity and Cerchar abrasivity index, are developed for the rapid estimation of the MDAL of the rocks. The developed models were verified by statistical tests. Additionally, it can be stated that the proposed models can be used as a forecasting for aggregate quality.
Impact of Initial Condition Errors and Precipitation Forecast Bias on Drought Simulation and Prediction in the Huaihe River Basin

NASA Astrophysics Data System (ADS)

Xu, H.; Luo, L.; Wu, Z.

2016-12-01

Drought, regarded as one of the major disasters all over the world, is not always easy to detect and forecast. Hydrological models coupled with Numerical Weather Prediction (NWP) has become a relatively effective method for drought monitoring and prediction. The accuracy of hydrological initial condition (IC) and the skill of NWP precipitation forecast can both heavily affect the quality and skill of hydrological forecast. In the study, the Variable Infiltration Capacity (VIC) model and Global Environmental Multi-scale (GEM) model were used to investigate the roles of IC and NWP forecast accuracy on hydrological predictions. A rev-ESP type experiment was conducted for a number of drought events in the Huaihe river basin. The experiment suggests that errors in ICs indeed affect the drought simulations by VIC and thus the drought monitoring. Although errors introduced in the ICs diminish gradually, the influence sometimes can last beyond 12 months. Using the soil moisture anomaly percentage index (SMAPI) as the metric to measure drought severity for the study region, we are able to quantify that time scale of influence from IC ranges. The analysis shows that the time scale is directly related to the magnitude of the introduced IC range and the average precipitation intensity. In order to explore how systematic bias correction in GEM forecasted precipitation can affect precipitation and hydrological forecast, we then both used station and gridded observations to eliminate biases of forecasted data. Meanwhile, different precipitation inputs with corrected data during drought process were conducted by VIC to investigate the changes of drought simulations, thus demonstrated short-term rolling drought prediction using a better performed corrected precipitation forecast. There is a word limit on the length of the abstract. So make sure your abstract fits the requirement. If this version is too long, try to shorten it as much as you can.
The response of numerical weather prediction analysis systems to FGGE 2b data

NASA Technical Reports Server (NTRS)

Hollingsworth, A.; Lorenc, A.; Tracton, S.; Arpe, K.; Cats, G.; Uppala, S.; Kallberg, P.

1985-01-01

An intercomparison of analyses of the main PGGE Level IIb data set is presented with three advanced analysis systems. The aims of the work are to estimate the extent and magnitude of the differences between the analyses, to identify the reasons for the differences, and finally to estimate the significance of the differences. Extratropical analyses only are considered. Objective evaluations of analysis quality, such as fit to observations, statistics of analysis differences, and mean fields are discussed. In addition, substantial emphasis is placed on subjective evaluation of a series of case studies that were selected to illustrate the importance of different aspects of the analysis procedures, such as quality control, data selection, resolution, dynamical balance, and the role of the assimilating forecast model. In some cases, the forecast models are used as selective amplifiers of analysis differences to assist in deciding which analysis was more nearly correct in the treatment of particular data.
Physical and mathematical modeling of pollutant emissions when burning peat

NASA Astrophysics Data System (ADS)

Vasilyev, A.; Lozhkin, V.; Tarkhov, D.; Lozhkina, O.; Timofeev, V.

2017-11-01

The article presents an original neural network model of CO dispersion around the experimentally simulated peat fire. It is a self-learning model considering both the measured CO concentrations in the smoke cloud and the refined coefficients of the main equation. The method is recommended for the development of air quality control and forecasting systems.

Approach for environmental baseline water sampling

USGS Publications Warehouse

Smith, K.S.

2011-01-01

Samples collected during the exploration phase of mining represent baseline conditions at the site. As such, they can be very important in forecasting potential environmental impacts should mining proceed, and can become measurements against which future changes are compared. Constituents in stream water draining mined and mineralized areas tend to be geochemically, spatially, and temporally variable, which presents challenges in collecting both exploration and baseline water-quality samples. Because short-term (daily) variations can complicate long-term trends, it is important to consider recent findings concerning geochemical variability of stream-water constituents at short-term timescales in designing sampling plans. Also, adequate water-quality information is key to forecasting potential ecological impacts from mining. Therefore, it is useful to collect baseline water samples adequate tor geochemical and toxicological modeling. This requires complete chemical analyses of dissolved constituents that include major and minor chemical elements as well as physicochemical properties (including pH, specific conductance, dissolved oxygen) and dissolved organic carbon. Applying chemical-equilibrium and appropriate toxicological models to water-quality information leads to an understanding of the speciation, transport, sequestration, bioavailability, and aquatic toxicity of potential contaminants. Insights gained from geochemical and toxicological modeling of water-quality data can be used to design appropriate mitigation and for economic planning for future mining activities.
Comprehensive Flood Plain Studies Using Spatial Data Management Techniques.

DTIC Science & Technology

1978-06-01

Hydrologic Engineer- ing Center computer programs that forecast urban storm water quality and dynamic in- stream water quality response to waste...determination. Water Quality The water quality analysis planned for the pilot study includes urban storm water quality forecasting and in-streamn...analysis is performed under the direction of Tony Thomas. Chief, Research Branch, by Jess Abbott for storm water quality analysis, R. G. Willey for
A computerized system to measure and predict air quality for emission control

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

Crooks, G.; Ciccone, A.; Frattolillo, P.

1997-12-31

A Supplementary Emission Control (SEC) system has been developed on behalf of the Association Industrielle de l`Est de Montreal (AIEM). The objective of the SEC is to avoid exceedences of the Montreal Urban Community (MUC) 24 hour ambient Air Quality Standard (AQS) for sulphur dioxide in the industrial East Montreal area. The SEC system is comprised of: 3 continuous SO{sub 2} monitoring stations with data loggers and remote communications; a meteorological tower with data logger and modem for acquiring local meteorology; communications with Environment Canada to download meteorological forecast data; a polling PC for data retrieval; and Windows NT basedmore » software running on the AIEM computer server. The SEC software utilizes relational databases to store and maintain measured SO{sub 2} concentration data, emission data, as well as observed and forecast meteorological data. The SEC system automatically executes a numerical dispersion model to forecast SO{sub 2} concentrations up to six hours in the future. Based on measured SO{sub 2} concentrations at the monitoring stations and the six hour forecast concentrations, the system determines if local sources should reduce their emission levels to avoid potential exceedences of the AQS. The SEC system also includes a Graphical User Interface (GUI) for user access to the system. The SEC system and software are described, and the accuracy of the system at forecasting SO{sub 2} concentrations is examined.« less
AIRS Impact on the Analysis and Forecast Track of Tropical Cyclone Nargis in a Global Data Assimilation and Forecasting System

NASA Technical Reports Server (NTRS)

Reale, O.; Lau, W.K.; Susskind, J.; Brin, E.; Liu, E.; Riishojgaard, L. P.; Rosenburg, R.; Fuentes, M.

2009-01-01

Tropical cyclones in the northern Indian Ocean pose serious challenges to operational weather forecasting systems, partly due to their shorter lifespan and more erratic track, compared to those in the Atlantic and the Pacific. Moreover, the automated analyses of cyclones over the northern Indian Ocean, produced by operational global data assimilation systems (DASs), are generally of inferior quality than in other basins. In this work it is shown that the assimilation of Atmospheric Infrared Sounder (AIRS) temperature retrievals under partial cloudy conditions can significantly impact the representation of the cyclone Nargis (which caused devastating loss of life in Myanmar in May 2008) in a global DAS. Forecasts produced from these improved analyses by a global model produce substantially smaller track errors. The impact of the assimilation of clear-sky radiances on the same DAS and forecasting system is positive, but smaller than the one obtained by ingestion of AIRS retrievals, possibly due to poorer coverage.
Forecasting the Human Pathogen Vibrio Parahaemolyticus in Shellfish Tissue within Long Island Sound

NASA Astrophysics Data System (ADS)

Whitney, M. M.; DeRosia-Banick, K.

2016-02-01

Vibrio parahaemolyticus (Vp) is a marine bacterium that occurs naturally in brackish and saltwater environments and may be found in higher concentrations in the warmest months. Vp is a growing threat to producing safe seafood. Consumption of shellfish with high Vp levels can result in gastrointestinal human illnesses. Management response to Vp-related illness outbreaks includes closure of shellfish growing areas. Water quality observations, Vp measurements, and model forecasts are key components to effective management of shellfish growing areas. There is a clear need for observations within the growing area themselves. These areas are offshore of coastal stations and typically inshore of the observing system moorings. New field observations in Long Island Sound (LIS) shellfish growing areas are described and their agreement with high-resolution satellite sea surface temperature data is discussed. A new dataset of Vp concentrations in shellfish tissue is used to determine the LIS-specific Vp vs. temperature relationship following methods in the FDA pre-harvest Vp risk model. This information is combined with output from a high-resolution hydrodynamic model of LIS to make daily forecasts of Vp levels. The influence of river inflows, the role of heat waves, and predictions for future warmer climates are discussed. The key elements of this observational-modeling approach to pathogen forecasting are extendable to other coastal systems.
AQA-PM: Extension of the Air-Quality Model For Austria with Satellite based Particulate Matter Estimates

NASA Astrophysics Data System (ADS)

Hirtl, Marcus; Mantovani, Simone; Krüger, Bernd C.; Triebnig, Gerhard; Flandorfer, Claudia

2013-04-01

Air quality is a key element for the well-being and quality of life of European citizens. Air pollution measurements and modeling tools are essential for assessment of air quality according to EU legislation. The responsibilities of ZAMG as the national weather service of Austria include the support of the federal states and the public in questions connected to the protection of the environment in the frame of advisory and counseling services as well as expert opinions. The Air Quality model for Austria (AQA) is operated at ZAMG in cooperation with the University of Natural Resources and Life Sciences in Vienna (BOKU) by order of the regional governments since 2005. AQA conducts daily forecasts of gaseous and particulate (PM10) air pollutants over Austria. In the frame of the project AQA-PM (funded by FFG), satellite measurements of the Aerosol Optical Thickness (AOT) and ground-based PM10-measurements are combined to highly-resolved initial fields using regression- and assimilation techniques. For the model simulations WRF/Chem is used with a resolution of 3 km over the alpine region. Interfaces have been developed to account for the different measurements as input data. The available local emission inventories provided by the different Austrian regional governments were harmonized and used for the model simulations. An episode in February 2010 is chosen for the model evaluation. During that month exceedances of PM10-thresholds occurred at many measurement stations of the Austrian network. Different model runs (only model/only ground stations assimilated/satellite and ground stations assimilated) are compared to the respective measurements. The goal of this project is to improve the PM10-forecasts for Austria with the integration of satellite based measurements and to provide a comprehensive product-platform.
A regional air quality forecasting system over Europe: the MACC-II daily ensemble production

NASA Astrophysics Data System (ADS)

Marécal, V.; Peuch, V.-H.; Andersson, C.; Andersson, S.; Arteta, J.; Beekmann, M.; Benedictow, A.; Bergström, R.; Bessagnet, B.; Cansado, A.; Chéroux, F.; Colette, A.; Coman, A.; Curier, R. L.; Denier van der Gon, H. A. C.; Drouin, A.; Elbern, H.; Emili, E.; Engelen, R. J.; Eskes, H. J.; Foret, G.; Friese, E.; Gauss, M.; Giannaros, C.; Guth, J.; Joly, M.; Jaumouillé, E.; Josse, B.; Kadygrov, N.; Kaiser, J. W.; Krajsek, K.; Kuenen, J.; Kumar, U.; Liora, N.; Lopez, E.; Malherbe, L.; Martinez, I.; Melas, D.; Meleux, F.; Menut, L.; Moinat, P.; Morales, T.; Parmentier, J.; Piacentini, A.; Plu, M.; Poupkou, A.; Queguiner, S.; Robertson, L.; Rouïl, L.; Schaap, M.; Segers, A.; Sofiev, M.; Thomas, M.; Timmermans, R.; Valdebenito, Á.; van Velthoven, P.; van Versendaal, R.; Vira, J.; Ung, A.

2015-03-01

This paper describes the pre-operational analysis and forecasting system developed during MACC (Monitoring Atmospheric Composition and Climate) and continued in MACC-II (Monitoring Atmospheric Composition and Climate: Interim Implementation) European projects to provide air quality services for the European continent. The paper gives an overall picture of its status at the end of MACC-II (summer 2014). This system is based on seven state-of-the art models developed and run in Europe (CHIMERE, EMEP, EURAD-IM, LOTOS-EUROS, MATCH, MOCAGE and SILAM). These models are used to calculate multi-model ensemble products. The MACC-II system provides daily 96 h forecasts with hourly outputs of 10 chemical species/aerosols (O3, NO2, SO2, CO, PM10, PM2.5, NO, NH3, total NMVOCs and PAN + PAN precursors) over 8 vertical levels from the surface to 5 km height. The hourly analysis at the surface is done a posteriori for the past day using a selection of representative air quality data from European monitoring stations. The performances of the system are assessed daily, weekly and 3 monthly (seasonally) through statistical indicators calculated using the available representative air quality data from European monitoring stations. Results for a case study show the ability of the median ensemble to forecast regional ozone pollution events. The time period of this case study is also used to illustrate that the median ensemble generally outperforms each of the individual models and that it is still robust even if two of the seven models are missing. The seasonal performances of the individual models and of the multi-model ensemble have been monitored since September 2009 for ozone, NO2 and PM10 and show an overall improvement over time. The change of the skills of the ensemble over the past two summers for ozone and the past two winters for PM10 are discussed in the paper. While the evolution of the ozone scores is not significant, there are improvements of PM10 over the past two winters that can be at least partly attributed to new developments on aerosols in the seven individual models. Nevertheless, the year to year changes in the models and ensemble skills are also linked to the variability of the meteorological conditions and of the set of observations used to calculate the statistical indicators. In parallel, a scientific analysis of the results of the seven models and of the ensemble is also done over the Mediterranean area because of the specificity of its meteorology and emissions. The system is robust in terms of the production availability. Major efforts have been done in MACC-II towards the operationalisation of all its components. Foreseen developments and research for improving its performances are discussed in the conclusion.
Strategies in Forecasting Outcomes in Ethical Decision-making: Identifying and Analyzing the Causes of the Problem

PubMed Central

Beeler, Cheryl K.; Antes, Alison L.; Wang, Xiaoqian; Caughron, Jared J.; Thiel, Chase E.; Mumford, Michael D.

2010-01-01

This study examined the role of key causal analysis strategies in forecasting and ethical decision-making. Undergraduate participants took on the role of the key actor in several ethical problems and were asked to identify and analyze the causes, forecast potential outcomes, and make a decision about each problem. Time pressure and analytic mindset were manipulated while participants worked through these problems. The results indicated that forecast quality was associated with decision ethicality, and the identification of the critical causes of the problem was associated with both higher quality forecasts and higher ethicality of decisions. Neither time pressure nor analytic mindset impacted forecasts or ethicality of decisions. Theoretical and practical implications of these findings are discussed. PMID:20352056
The quality and value of seasonal precipitation forecasts for an early warning of large-scale droughts and floods in West Africa

NASA Astrophysics Data System (ADS)

Bliefernicht, Jan; Seidel, Jochen; Salack, Seyni; Waongo, Moussa; Laux, Patrick; Kunstmann, Harald

2017-04-01

Seasonal precipitation forecasts are a crucial source of information for an early warning of hydro-meteorological extremes in West Africa. However, the current seasonal forecasting system used by the West African weather services in the framework of the West African Climate Outlook forum (PRESAO) is limited to probabilistic precipitation forecasts of 1-month lead time. To improve this provision, we use an ensemble-based quantile-quantile transformation for bias correction of precipitation forecasts provided by a global seasonal ensemble prediction system, the Climate Forecast System Version 2 (CFS2). The statistical technique eliminates systematic differences between global forecasts and observations with the potential to preserve the signal from the model. The technique has also the advantage that it can be easily implemented at national weather services with low capacities. The statistical technique is used to generate probabilistic forecasts of monthly and seasonal precipitation amount and other precipitation indices useful for an early warning of large-scale drought and floods in West Africa. The evaluation of the statistical technique is done using CFS hindcasts (1982 to 2009) in a cross-validation mode to determine the performance of the precipitation forecasts for several lead times focusing on drought and flood events depicted over the Volta and Niger basins. In addition, operational forecasts provided by PRESAO are analyzed from 1998 to 2015. The precipitation forecasts are compared to low-skill reference forecasts generated from gridded observations (i.e. GPCC, CHIRPS) and a novel in-situ gauge database from national observation networks (see Poster EGU2017-10271). The forecasts are evaluated using state-of-the-art verification techniques to determine specific quality attributes of probabilistic forecasts such as reliability, accuracy and skill. In addition, cost-loss approaches are used to determine the value of probabilistic forecasts for multiple users in warning situations. The outcomes of the hindcasts experiment for the Volta basin illustrate that the statistical technique can clearly improve the CFS precipitation forecasts with the potential to provide skillful and valuable early precipitation warnings for large-scale drought and flood situations several months in ahead. In this presentation we give a detailed overview about the ensemble-based quantile-quantile-transformation, its validation and verification and the possibilities of this technique to complement PRESAO. We also highlight the performance of this technique for extremes such as the Sahel drought in the 80ties and in comparison to the various reference data sets (e.g. CFS2, PRESAO, observational data sets) used in this study.
Beyond Rating Curves: Time Series Models for in-Stream Turbidity Prediction

NASA Astrophysics Data System (ADS)

Wang, L.; Mukundan, R.; Zion, M.; Pierson, D. C.

2012-12-01

The New York City Department of Environmental Protection (DEP) manages New York City's water supply, which is comprised of over 20 reservoirs and supplies over 1 billion gallons of water per day to more than 9 million customers. DEP's "West of Hudson" reservoirs located in the Catskill Mountains are unfiltered per a renewable filtration avoidance determination granted by the EPA. While water quality is usually pristine, high volume storm events occasionally cause the reservoirs to become highly turbid. A logical strategy for turbidity control is to temporarily remove the turbid reservoirs from service. While effective in limiting delivery of turbid water and reducing the need for in-reservoir alum flocculation, this strategy runs the risk of negatively impacting water supply reliability. Thus, it is advantageous for DEP to understand how long a particular turbidity event will affect their system. In order to understand the duration, intensity and total load of a turbidity event, predictions of future in-stream turbidity values are important. Traditionally, turbidity predictions have been carried out by applying streamflow observations/forecasts to a flow-turbidity rating curve. However, predictions from rating curves are often inaccurate due to inter- and intra-event variability in flow-turbidity relationships. Predictions can be improved by applying an autoregressive moving average (ARMA) time series model in combination with a traditional rating curve. Since 2003, DEP and the Upstate Freshwater Institute have compiled a relatively consistent set of 15-minute turbidity observations at various locations on Esopus Creek above Ashokan Reservoir. Using daily averages of this data and streamflow observations at nearby USGS gauges, flow-turbidity rating curves were developed via linear regression. Time series analysis revealed that the linear regression residuals may be represented using an ARMA(1,2) process. Based on this information, flow-turbidity regressions with ARMA(1,2) errors were fit to the observations. Preliminary model validation exercises at a 30-day forecast horizon show that the ARMA error models generally improve the predictive skill of the linear regression rating curves. Skill seems to vary based on the ambient hydrologic conditions at the onset of the forecast. For example, ARMA error model forecasts issued before a high flow/turbidity event do not show significant improvements over the rating curve approach. However, ARMA error model forecasts issued during the "falling limb" of the hydrograph are significantly more accurate than rating curves for both single day and accumulated event predictions. In order to assist in reservoir operations decisions associated with turbidity events and general water supply reliability, DEP has initiated design of an Operations Support Tool (OST). OST integrates a reservoir operations model with 2D hydrodynamic water quality models and a database compiling near-real-time data sources and hydrologic forecasts. Currently, OST uses conventional flow-turbidity rating curves and hydrologic forecasts for predictive turbidity inputs. Given the improvements in predictive skill over traditional rating curves, the ARMA error models are currently being evaluated as an addition to DEP's Operations Support Tool.
Increasing the temporal resolution of direct normal solar irradiance forecasted series

NASA Astrophysics Data System (ADS)

Fernández-Peruchena, Carlos M.; Gastón, Martin; Schroedter-Homscheidt, Marion; Marco, Isabel Martínez; Casado-Rubio, José L.; García-Moya, José Antonio

2017-06-01

A detailed knowledge of the solar resource is a critical point in the design and control of Concentrating Solar Power (CSP) plants. In particular, accurate forecasting of solar irradiance is essential for the efficient operation of solar thermal power plants, the management of energy markets, and the widespread implementation of this technology. Numerical weather prediction (NWP) models are commonly used for solar radiation forecasting. In the ECMWF deterministic forecasting system, all forecast parameters are commercially available worldwide at 3-hourly intervals. Unfortunately, as Direct Normal solar Irradiance (DNI) exhibits a great variability due to the dynamic effects of passing clouds, 3-h time resolution is insufficient for accurate simulations of CSP plants due to their nonlinear response to DNI, governed by various thermal inertias due to their complex response characteristics. DNI series of hourly or sub-hourly frequency resolution are normally used for an accurate modeling and analysis of transient processes in CSP technologies. In this context, the objective of this study is to propose a methodology for generating synthetic DNI time series at 1-h (or higher) temporal resolution from 3-h DNI series. The methodology is based upon patterns as being defined with help of the clear-sky envelope approach together with a forecast of maximum DNI value, and it has been validated with high quality measured DNI data.
Impact of geostationary satellite water vapor channel data on weather analysis and forecasting

NASA Technical Reports Server (NTRS)

Velden, Christopher S.

1995-01-01

Preliminary results from NWP impact studies are indicating that upper-tropospheric wind information provided by tracking motions in sequences of geostationary satellite water vapor imagery can positively influence forecasts on regional scales, and possibly on global scales as well. The data are complimentary to cloud-tracked winds by providing data in cloud-free regions, as well as comparable in quality. First results from GOES-8 winds are encouraging, and further efforts and model impacts will be directed towards optimizing these data in numerical weather prediction (NWP). Assuming successful launches of GOES-J and GMS-5 satellites in 1995, high quality and resolution water vapor imagers will be available to provide nearly complete global upper-tropospheric wind coverage.
Assessing Applications of GPM and IMERG Passive Microwave Rain Rates in Modeling and Operational Forecasting

NASA Astrophysics Data System (ADS)

Zavodsky, B.; Le Roy, A.; Smith, M. R.; Case, J.

2016-12-01

In support of NASA's recently launched GPM `core' satellite, the NASA-SPoRT project is leveraging experience in research-to-operations transitions and training to provide feedback on the operational utility of GPM products. Thus far, SPoRT has focused on evaluating the Level 2 GPROF passive microwave and IMERG rain rate estimates. Formal evaluations with end-users have occurred, as well as internal evaluations of the datasets. One set of end users for these products is National Weather Service Forecast Offices (WFOs) and National Weather Service River Forecast Centers (RFCs), comprising forecasters and hydrologists. SPoRT has hosted a series of formal assessments to determine uses and utility of these datasets for NWS operations at specific offices. Forecasters primarily have used Level 2 swath rain rates to observe rainfall in otherwise data-void regions and to confirm model QPF for their nowcasting or short-term forecasting. Hydrologists have been evaluating both the Level 2 rain rates and the IMERG rain rates, including rain rate accumulations derived from IMERG; hydrologists have used these data to supplement gauge data for post-event analysis as well as for longer-term forecasting. Results from specific evaluations will be presented. Another evaluation of the GPM passive microwave rain rates has been in using the data within other products that are currently transitioned to end-users, rather than as stand-alone observations. For example, IMERG Early data is being used as a forcing mechanism in the NASA Land Information System (LIS) for real-time soil moisture product over eastern Africa. IMERG is providing valuable precipitation information to LIS in an otherwise data-void region. Results and caveats will briefly be discussed. A third application of GPM data is using the IMERG Late and Final products for model verification in remote regions where high-quality gridded precipitation fields are not readily available. These datasets can now be used to verify NWP model forecasts over Eastern Africa using the SPoRT-MET scripts verification package, a wrapper around the NCAR Model Evaluation Toolkit (MET) verification software.
Benefits of Sharing Information: Supermodel Ensemble and Applications in South America

NASA Astrophysics Data System (ADS)

Dias, P. L.

2006-05-01

A model intercomparison program involving a large number of academic and operational institutions has been implemented in South America since 2003, motivated by the SALLJEX Intercomparison Program in 2003 (a research program focused on the identification of the role of the Andes low level jet moisture transport from the Amazon to the Plata basin) and the WMO/THORPEX (www.wmo.int/thorpex) goals to improve predictability through the proper combination of numerical weather forecasts. This program also explores the potential predictability associated with the combination of a large number of possible scenarios in the time scale of a few days to up to 15 days. Five academic institutions and five operational forecasting centers in several countries in South America, 1 academic institution in the USA, and the main global forecasting centers (NCEP, UKMO, ECMWF) agreed to provide numerical products based on operational and experimental models. The metric for model validation is concentrated on the fit of the forecast to surface observations. Meteorological data from airports, synoptic stations operated by national weather services, automatic data platforms maintained by different institutions, the PIRATA buoys etc are all collected through LDM/NCAR or direct transmission. Approximately 40 models outputs are available on a daily basis, twice a day. A simple procedure based on data assimilation principles was quite successful in combining the available forecasts in order to produce temperature, dew point, wind, pressure and precipitation forecasts at station points in S. America. The procedure is based on removing each model bias at the observational point and a weighted average based on the mean square error of the forecasts. The base period for estimating the bias and mean square error is of the order of 15 to 30 days. Products of the intercomparison model program and the optimal statistical combination of the available forecasts are public and available in real time (www.master.iag.usp.br/). Monitoring of the use of the products reveal a growing trend in the last year (reaching about 10.000 accesses per day in recent months). The intercomparison program provides a rich data set for educational products (real time use in Synoptic Meteorology and Numerical Weather Forecasting lectures), operational weather forecasts in national or regional weather centers and for research purposes. During the first phase of the program it was difficult to convince potential participants to share the information in the public homepage. However, as the system evolved, more and more institutions became associated with the program. The general opinion of the participants is that the system provides an unified metric for evaluation, a forum for discussion of the physical origin of the model forecast differences and therefore improvement of the quality of the numerical guidance.
Using additional external inputs to forecast water quality with an artificial neural network for contamination event detection in source water

NASA Astrophysics Data System (ADS)

Schmidt, F.; Liu, S.

2016-12-01

Source water quality plays an important role for the safety of drinking water and early detection of its contamination is vital to taking appropriate countermeasures. However, compared to drinking water, it is more difficult to detect contamination events because its environment is less controlled and numerous natural causes contribute to a high variability of the background values. In this project, Artificial Neural Networks (ANNs) and a Contamination Event Detection Process (CED Process) were used to identify events in river water. The ANN models the response of basic water quality sensors obtained in laboratory experiments in an off-line learning stage and continuously forecasts future values of the time line in an on-line forecasting step. During this second stage, the CED Process compares the forecast to the measured value and classifies it as regular background or event value, which modifies the ANN's continuous learning and influences its forecasts. In addition to this basic setup, external information is fed to the CED Process: A so-called Operator Input (OI) is provided to inform about unusual water quality levels that are unrelated to the presence of contamination, for example due to cooling water discharge from a nearby power plant. This study's primary goal is to evaluate how well the OI fits into the design of the combined forecasting ANN and CED Process and to understand its effects on the online forecasting stage. To test this, data from laboratory experiments conducted previously at the School of Environment, Tsinghua University, have been used to perform simulations highlighting features and drawbacks of this method. Applying the OI has been shown to have a positive influence on the ANN's ability to handle a sudden change in background values, which is unrelated to contamination. However, it might also mask the presence of an event, an issue that underlines the necessity to have several instances of the algorithm run in parallel. Other difficulties addressed in this study include the source and the format of the OI. This project tries to add to the ongoing research into algorithms for CED. It provides ideas for how results from the binary classification of time series could be evaluated in a more realistic fashion and shows what the advantages and limitations of such a method would be.
Application of data assimilation to solar wind forecasting models

NASA Astrophysics Data System (ADS)

Innocenti, M.; Lapenta, G.; Vrsnak, B.; Temmer, M.; Veronig, A.; Bettarini, L.; Lee, E.; Markidis, S.; Skender, M.; Crespon, F.; Skandrani, C.; Soteria Space-Weather Forecast; Data Assimilation Team

2010-12-01

Data Assimilation through Kalman filtering [1,2] is a powerful statistical tool which allows to combine modeling and observations to increase the degree of knowledge of a given system. We apply this technique to the forecast of solar wind parameters (proton speed, proton temperature, absolute value of the magnetic field and proton density) at 1 AU, using the model described in [3] and ACE data as observations. The model, which relies on GOES 12 observations of the percentage of the meridional slice of the sun covered by coronal holes, grants 1-day and 6-hours in advance forecasts of the aforementioned quantities in quiet times (CMEs are not taken into account) during the declining phase of the solar cycle and is tailored for specific time intervals. We show that the application of data assimilation generally improves the quality of the forecasts during quiet times and, more notably, extends the periods of applicability of the model, which can now provide reliable forecasts also in presence of CMEs and for periods other than the ones it was designed for. Acknowledgement: The research leading to these results has received funding from the European Commission’s Seventh Framework Programme (FP7/2007-2013) under the grant agreement N. 218816 (SOTERIA project: http://www.soteria-space.eu). References: [1] R. Kalman, J. Basic Eng. 82, 35 (1960); [2] G. Welch and G. Bishop, Technical Report TR 95-041, University of North Carolina, Department of Computer Science (2001); [3] B. Vrsnak, M. Temmer, and A. Veronig, Solar Phys. 240, 315 (2007).
AQA-PM: Extension of the Air-Quality model for Austria with satellite based Particulate Matter estimates

NASA Astrophysics Data System (ADS)

Hirtl, M.; Mantovani, S.; Krüger, B. C.; Triebnig, G.

2012-04-01

Air quality is a key element for the well-being and quality of life of European citizens. Air pollution measurements and modeling tools are essential for assessment of air quality according to EU legislation. The responsibilities of ZAMG as the national weather service of Austria include the support of the federal states and the public in questions connected to the protection of the environment in the frame of advisory and counseling services as well as expert opinions. The Air Quality model for Austria (AQA) is operated at ZAMG in cooperation with the University of Natural Resources and Applied Life Sciences in Vienna (BOKU) by order of the regional governments since 2005. AQA conducts daily forecasts of gaseous and particulate (PM10) air pollutants over Austria. In the frame of the project AQA-PM (funded by FFG), satellite measurements of the Aerosol Optical Thickness (AOT) and ground-based PM10-measurements are combined to highly-resolved initial fields using assimilation techniques. It is expected that the assimilation of satellite measurements will significantly improve the quality of AQA. Currently no observations are considered in the modeling system. At the current stage of the project, different datasets have been collected (ground measurements, satellite measurements, fine resolved regional emission inventories) and are analyzed and prepared for further processing. This contribution gives an overview of the project working plan and the upcoming developments. The goal of this project is to improve the PM10-forecasts for Austria with the integration of satellite based measurements and to provide a comprehensive product-platform.
Vertical distribution of Saharan dust over Rome (Italy): Comparison between 3-year model predictions and lidar soundings

NASA Astrophysics Data System (ADS)

Kishcha, P.; Barnaba, F.; Gobbi, G. P.; Alpert, P.; Shtivelman, A.; Krichak, S. O.; Joseph, J. H.

2005-03-01

Mineral dust particles loaded into the atmosphere from the Sahara desert represent one major factor affecting the Earth's radiative budget. Regular model-based forecasts of 3-D dust fields can be used in order to determine the dust radiative effect in climate models, in spite of the large gaps in observations of dust vertical profiles. In this study, dust forecasts by the Tel Aviv University (TAU) dust prediction system were compared to lidar observations to better evaluate the model's capabilities. The TAU dust model was initially developed at the University of Athens and later modified at Tel Aviv University. Dust forecasts are initialized with the aid of the Total Ozone Mapping Spectrometer aerosol index (TOMS AI) measurements. The lidar soundings employed were collected at the outskirts of Rome, Italy (41.84°N, 12.64°E) during the high-dust activity season from March to June of the years 2001, 2002, and 2003. The lidar vertical profiles collected in the presence of dust were used for obtaining statistically significant reference parameters of dust layers over Rome and for model versus lidar comparison. The Barnaba and Gobbi (2001) approach was used in the current study to derive height-resolved dust volumes from lidar measurements of backscatter. Close inspection of the juxtaposed vertical profiles, obtained from lidar and model data near Rome, indicates that the majority (67%) of the cases under investigation can be classified as good or acceptable forecasts of the dust vertical distribution. A more quantitative comparison shows that the model predictions are mainly accurate in the middle part of dust layers. This is supported by high correlation (0.85) between lidar and model data for forecast dust volumes greater than the threshold of 1 × 10-12 cm3/cm3. In general, however, the model tends to underestimate the lidar-derived dust volume profiles. The effect of clouds in the TOMS detection of AI is supposed to be the main factor responsible for this effect. Moreover, some model assumptions on dust sources and particle size and the accuracy of model-simulated meteorological parameters are also likely to affect the dust forecast quality.
Performance and quality assessment of the recent updated CMEMS global ocean monitoring and forecasting real-time system

NASA Astrophysics Data System (ADS)

Le Galloudec, Olivier; Lellouche, Jean-Michel; Greiner, Eric; Garric, Gilles; Régnier, Charly; Drévillon, Marie; Drillet, Yann

2017-04-01

Since May 2015, Mercator Ocean opened the Copernicus Marine Environment and Monitoring Service (CMEMS) and is in charge of the global eddy resolving ocean analyses and forecast. In this context, Mercator Ocean currently delivers in real-time daily services (weekly analyses and daily forecast) with a global 1/12° high resolution system. The model component is the NEMO platform driven at the surface by the IFS ECMWF atmospheric analyses and forecasts. Observations are assimilated by means of a reduced-order Kalman filter with a 3D multivariate modal decomposition of the forecast error. It includes an adaptive-error estimate and a localization algorithm. Along track altimeter data, satellite Sea Surface Temperature and in situ temperature and salinity vertical profiles are jointly assimilated to estimate the initial conditions for numerical ocean forecasting. A 3D-Var scheme provides a correction for the slowly-evolving large-scale biases in temperature and salinity. R&D activities have been conducted at Mercator Ocean these last years to improve the real-time 1/12° global system for recent updated CMEMS version in 2016. The ocean/sea-ice model and the assimilation scheme benefited of the following improvements: large-scale and objective correction of atmospheric quantities with satellite data, new Mean Dynamic Topography taking into account the last version of GOCE geoid, new adaptive tuning of some observational errors, new Quality Control on the assimilated temperature and salinity vertical profiles based on dynamic height criteria, assimilation of satellite sea-ice concentration, new freshwater runoff from ice sheets melting, … This presentation will show the impact of some updates separately, with a particular focus on adaptive tuning experiments of satellite Sea Level Anomaly (SLA) and Sea Surface Temperature (SST) observations errors. For the SLA, the a priori prescribed observation error is globally greatly reduced. The median value of the error changed from 5cm to 2.5cm in a few assimilation cycles. For the SST, we chose to maintain the median value of the error to 0.4°C. The spatial distribution of the SST error follows the model physics and atmospheric variability. Either for SLA or SST, we improve the performances of the system using this adaptive tuning. The overall behavior of the system integrating all updates reporting on the products quality improvements will be also discussed, highlighting the level of performance and the reliability of the new system.
A Watershed-based spatially-explicit demonstration of an Integrated Environmental Modeling Framework for Ecosystem Services in the Coal River Basin (WV, USA)

EPA Science Inventory

We demonstrate a spatially-explicit regional assessment of current condition of aquatic ecoservices in the Coal River Basin (CRB), with limited sensitivity analysis for the atmospheric contaminant mercury. The integrated modeling framework (IMF) forecasts water quality and quant...

Assessment of the contribution of traffic emissions to the mobile vehicle measured PM2.5 concentration by means of WRF-CMAQ simulations.

DOT National Transportation Integrated Search

2012-03-01

The Alaska adapted version of the Weather Research and Forecasting and the Community Modeling and Analysis Quality (WRF-CMAQ) modeling : systems was used to assess the contribution of traffic to the PM2.5-concentration in the Fairbanks nonattainment ...
Can a coupled meteorology–chemistry model reproduce the historical trend in aerosol direct radiative effects over the Northern Hemisphere?

EPA Science Inventory

The ability of a coupled meteorology–chemistry model, i.e., Weather Research and Forecast and Community Multiscale Air Quality (WRF-CMAQ), to reproduce the historical trend in aerosol optical depth (AOD) and clear-sky shortwave radiation (SWR) over the Northern Hemisphere h...
Improving Forecast Skill by Assimilation of AIRS Temperature Soundings

NASA Technical Reports Server (NTRS)

Susskind, Joel; Reale, Oreste

2010-01-01

AIRS was launched on EOS Aqua on May 4, 2002, together with AMSU-A and HSB, to form a next generation polar orbiting infrared and microwave atmospheric sounding system. The primary products of AIRS/AMSU-A are twice daily global fields of atmospheric temperature-humidity profiles, ozone profiles, sea/land surface skin temperature, and cloud related parameters including OLR. The AIRS Version 5 retrieval algorithm, is now being used operationally at the Goddard DISC in the routine generation of geophysical parameters derived from AIRS/AMSU data. A major innovation in Version 5 is the ability to generate case-by-case level-by-level error estimates delta T(p) for retrieved quantities and the use of these error estimates for Quality Control. We conducted a number of data assimilation experiments using the NASA GEOS-5 Data Assimilation System as a step toward finding an optimum balance of spatial coverage and sounding accuracy with regard to improving forecast skill. The model was run at a horizontal resolution of 0.5 deg. latitude X 0.67 deg longitude with 72 vertical levels. These experiments were run during four different seasons, each using a different year. The AIRS temperature profiles were presented to the GEOS-5 analysis as rawinsonde profiles, and the profile error estimates delta (p) were used as the uncertainty for each measurement in the data assimilation process. We compared forecasts analyses generated from the analyses done by assimilation of AIRS temperature profiles with three different sets of thresholds; Standard, Medium, and Tight. Assimilation of Quality Controlled AIRS temperature profiles significantly improve 5-7 day forecast skill compared to that obtained without the benefit of AIRS data in all of the cases studied. In addition, assimilation of Quality Controlled AIRS temperature soundings performs better than assimilation of AIRS observed radiances. Based on the experiments shown, Tight Quality Control of AIRS temperature profile performs best on the average from the perspective of improving Global 7 day forecast skill.
Air pollution forecasting in Ankara, Turkey using air pollution index and its relation to assimilative capacity of the atmosphere.

PubMed

Genc, D Deniz; Yesilyurt, Canan; Tuncel, Gurdal

2010-07-01

Spatial and temporal variations in concentrations of CO, NO, NO(2), SO(2), and PM(10), measured between 1999 and 2000, at traffic-impacted and residential stations in Ankara were investigated. Air quality in residential areas was found to be influenced by traffic activities in the city. Pollutant ratios were proven to be reliable tracers to differentiate between different sources. Air pollution index (API) of the whole city was calculated to evaluate the level of air quality in Ankara. Multiple linear regression model was developed for forecasting API in Ankara. The correlation coefficients were found to be 0.79 and 0.63 for different time periods. The assimilative capacity of Ankara atmosphere was calculated in terms of ventilation coefficient (VC). The relation between API and VC was investigated and found that the air quality in Ankara was determined by meteorology rather than emissions.
Comparison of Groundwater Level Models Based on Artificial Neural Networks and ANFIS

PubMed Central

Domazet, Milka; Stricevic, Ruzica; Pocuca, Vesna; Spalevic, Velibor; Pivic, Radmila; Gregoric, Enika; Domazet, Uros

2015-01-01

Water table forecasting plays an important role in the management of groundwater resources in agricultural regions where there are drainage systems in river valleys. The results presented in this paper pertain to an area along the left bank of the Danube River, in the Province of Vojvodina, which is the northern part of Serbia. Two soft computing techniques were used in this research: an adaptive neurofuzzy inference system (ANFIS) and an artificial neural network (ANN) model for one-month water table forecasts at several wells located at different distances from the river. The results suggest that both these techniques represent useful tools for modeling hydrological processes in agriculture, with similar computing and memory capabilities, such that they constitute an exceptionally good numerical framework for generating high-quality models. PMID:26759830
Comparison of Groundwater Level Models Based on Artificial Neural Networks and ANFIS.

PubMed

Djurovic, Nevenka; Domazet, Milka; Stricevic, Ruzica; Pocuca, Vesna; Spalevic, Velibor; Pivic, Radmila; Gregoric, Enika; Domazet, Uros

2015-01-01

Water table forecasting plays an important role in the management of groundwater resources in agricultural regions where there are drainage systems in river valleys. The results presented in this paper pertain to an area along the left bank of the Danube River, in the Province of Vojvodina, which is the northern part of Serbia. Two soft computing techniques were used in this research: an adaptive neurofuzzy inference system (ANFIS) and an artificial neural network (ANN) model for one-month water table forecasts at several wells located at different distances from the river. The results suggest that both these techniques represent useful tools for modeling hydrological processes in agriculture, with similar computing and memory capabilities, such that they constitute an exceptionally good numerical framework for generating high-quality models.
Assimilation of total lightning data using the three-dimensional variational method at convection-allowing resolution

NASA Astrophysics Data System (ADS)

Zhang, Rong; Zhang, Yijun; Xu, Liangtao; Zheng, Dong; Yao, Wen

2017-08-01

A large number of observational analyses have shown that lightning data can be used to indicate areas of deep convection. It is important to assimilate observed lightning data into numerical models, so that more small-scale information can be incorporated to improve the quality of the initial condition and the subsequent forecasts. In this study, the empirical relationship between flash rate, water vapor mixing ratio, and graupel mixing ratio was used to adjust the model relative humidity, which was then assimilated by using the three-dimensional variational data assimilation system of the Weather Research and Forecasting model in cycling mode at 10-min intervals. To find the appropriate assimilation time-window length that yielded significant improvement in both the initial conditions and subsequent forecasts, four experiments with different assimilation time-window lengths were conducted for a squall line case that occurred on 10 July 2007 in North China. It was found that 60 min was the appropriate assimilation time-window length for this case, and longer assimilation window length was unnecessary since no further improvement was present. Forecasts of 1-h accumulated precipitation during the assimilation period and the subsequent 3-h accumulated precipitation were significantly improved compared with the control experiment without lightning data assimilation. The simulated reflectivity was optimal after 30 min of the forecast, it remained optimal during the following 42 min, and the positive effect from lightning data assimilation began to diminish after 72 min of the forecast. Overall, the improvement from lightning data assimilation can be maintained for about 3 h.
Optimization of Evaporative Demand Models for Seasonal Drought Forecasting

NASA Astrophysics Data System (ADS)

McEvoy, D.; Huntington, J. L.; Hobbins, M.

2015-12-01

Providing reliable seasonal drought forecasts continues to pose a major challenge for scientists, end-users, and the water resources and agricultural communities. Precipitation (Prcp) forecasts beyond weather time scales are largely unreliable, so exploring new avenues to improve seasonal drought prediction is necessary to move towards applications and decision-making based on seasonal forecasts. A recent study has shown that evaporative demand (E0) anomaly forecasts from the Climate Forecast System Version 2 (CFSv2) are consistently more skillful than Prcp anomaly forecasts during drought events over CONUS, and E0 drought forecasts may be particularly useful during the growing season in the farming belts of the central and Midwestern CONUS. For this recent study, we used CFSv2 reforecasts to assess the skill of E0 and of its individual drivers (temperature, humidity, wind speed, and solar radiation), using the American Society for Civil Engineers Standardized Reference Evapotranspiration (ET0) Equation. Moderate skill was found in ET0, temperature, and humidity, with lesser skill in solar radiation, and no skill in wind. Therefore, forecasts of E0 based on models with no wind or solar radiation inputs may prove to be more skillful than the ASCE ET0. For this presentation we evaluate CFSv2 E0 reforecasts (1982-2009) from three different E0 models: (1) ASCE ET0; (2) Hargreaves and Samani (ET-HS), which is estimated from maximum and minimum temperature alone; and (3) Valiantzas (ET-V), which is a modified version of the Penman method for use when wind speed data are not available (or of poor quality) and is driven only by temperature, humidity, and solar radiation. The University of Idaho's gridded meteorological data (METDATA) were used as observations to evaluate CFSv2 and also to determine if ET0, ET-HS, and ET-V identify similar historical drought periods. We focus specifically on CFSv2 lead times of one, two, and three months, and season one forecasts; which are time scales with moderate skill and are more likely to be used in hydro-climatic applications and decision-making.
Joint space-time geostatistical model for air quality surveillance

NASA Astrophysics Data System (ADS)

Russo, A.; Soares, A.; Pereira, M. J.

2009-04-01

Air pollution and peoples' generalized concern about air quality are, nowadays, considered to be a global problem. Although the introduction of rigid air pollution regulations has reduced pollution from industry and power stations, the growing number of cars on the road poses a new pollution problem. Considering the characteristics of the atmospheric circulation and also the residence times of certain pollutants in the atmosphere, a generalized and growing interest on air quality issues led to research intensification and publication of several articles with quite different levels of scientific depth. As most natural phenomena, air quality can be seen as a space-time process, where space-time relationships have usually quite different characteristics and levels of uncertainty. As a result, the simultaneous integration of space and time is not an easy task to perform. This problem is overcome by a variety of methodologies. The use of stochastic models and neural networks to characterize space-time dispersion of air quality is becoming a common practice. The main objective of this work is to produce an air quality model which allows forecasting critical concentration episodes of a certain pollutant by means of a hybrid approach, based on the combined use of neural network models and stochastic simulations. A stochastic simulation of the spatial component with a space-time trend model is proposed to characterize critical situations, taking into account data from the past and a space-time trend from the recent past. To identify near future critical episodes, predicted values from neural networks are used at each monitoring station. In this paper, we describe the design of a hybrid forecasting tool for ambient NO2 concentrations in Lisbon, Portugal.
FORECASTING AIR QUALITY OVER THE UNITED STATES

EPA Science Inventory

Increased awareness of national air quality issues on the part of the media and the general public have recently led to more demand for short-term (1-2 day) air quality forecasts for use in assessing potential health impacts (e.g., on children, the elderly, and asthmatics) and po...
Utility of flood warning systems for emergency management

NASA Astrophysics Data System (ADS)

Molinari, Daniela; Ballio, Francesco; Menoni, Scira

2010-05-01

The presentation is focused on a simple and crucial question for warning systems: are flood and hydrological modelling and forecasting helpful to manage flood events? Indeed, it is well known that a warning process can be invalidated by inadequate forecasts so that the accuracy and robustness of the previsional model is a key issue for any flood warning procedure. However, one problem still arises at this perspective: when forecasts can be considered to be adequate? According to Murphy (1993, Wea. Forecasting 8, 281-293), forecasts hold no intrinsic value but they acquire it through their ability to influence the decisions made by their users. Moreover, we can add that forecasts value depends on the particular problem at stake showing, this way, a multifaceted nature. As a result, forecasts verification should not be seen as a universal process, instead it should be tailored to the particular context in which forecasts are implemented. This presentation focuses on warning problems in mountain regions, whereas the short time which is distinctive of flood events makes the provision of adequate forecasts particularly significant. In this context, the quality of a forecast is linked to its capability to reduce the impact of a flood by improving the correctness of the decision about issuing (or not) a warning as well as of the implementation of a proper set of actions aimed at lowering potential flood damages. The present study evaluates the performance of a real flood forecasting system from this perspective. In detail, a back analysis of past flood events and available verification tools have been implemented. The final objective was to evaluate the system ability to support appropriate decisions with respect not only to the flood characteristics but also to the peculiarities of the area at risk as well as to the uncertainty of forecasts. This meant to consider also flood damages and forecasting uncertainty among the decision variables. Last but not least, the presentation explains how the procedure implemented in the case study could support the definition of a proper warning rule.
Evaluation of data assimilation techniques for a mesoscale meteorological model and their effects on air quality model results

NASA Astrophysics Data System (ADS)

Amicarelli, A.; Gariazzo, C.; Finardi, S.; Pelliccioni, A.; Silibello, C.

2008-05-01

Data assimilation techniques are methods to limit the growth of errors in a dynamical model by allowing observations distributed in space and time to force (nudge) model solutions. They have become common for meteorological model applications in recent years, especially to enhance weather forecast and to support air-quality studies. In order to investigate the influence of different data assimilation techniques on the meteorological fields produced by RAMS model, and to evaluate their effects on the ozone and PM10 concentrations predicted by FARM model, several numeric experiments were conducted over the urban area of Rome, Italy, during a summer episode.
Potential assessment of the "support vector machine" method in forecasting ambient air pollutant trends.

PubMed

Lu, Wei-Zhen; Wang, Wen-Jian

2005-04-01

Monitoring and forecasting of air quality parameters are popular and important topics of atmospheric and environmental research today due to the health impact caused by exposing to air pollutants existing in urban air. The accurate models for air pollutant prediction are needed because such models would allow forecasting and diagnosing potential compliance or non-compliance in both short- and long-term aspects. Artificial neural networks (ANN) are regarded as reliable and cost-effective method to achieve such tasks and have produced some promising results to date. Although ANN has addressed more attentions to environmental researchers, its inherent drawbacks, e.g., local minima, over-fitting training, poor generalization performance, determination of the appropriate network architecture, etc., impede the practical application of ANN. Support vector machine (SVM), a novel type of learning machine based on statistical learning theory, can be used for regression and time series prediction and have been reported to perform well by some promising results. The work presented in this paper aims to examine the feasibility of applying SVM to predict air pollutant levels in advancing time series based on the monitored air pollutant database in Hong Kong downtown area. At the same time, the functional characteristics of SVM are investigated in the study. The experimental comparisons between the SVM model and the classical radial basis function (RBF) network demonstrate that the SVM is superior to the conventional RBF network in predicting air quality parameters with different time series and of better generalization performance than the RBF model.
Forecasting in an integrated surface water-ground water system: The Big Cypress Basin, South Florida

NASA Astrophysics Data System (ADS)

Butts, M. B.; Feng, K.; Klinting, A.; Stewart, K.; Nath, A.; Manning, P.; Hazlett, T.; Jacobsen, T.

2009-04-01

The South Florida Water Management District (SFWMD) manages and protects the state's water resources on behalf of 7.5 million South Floridians and is the lead agency in restoring America's Everglades - the largest environmental restoration project in US history. Many of the projects to restore and protect the Everglades ecosystem are part of the Comprehensive Everglades Restoration Plan (CERP). The region has a unique hydrological regime, with close connection between surface water and groundwater, and a complex managed drainage network with many structures. Added to the physical complexity are the conflicting needs of the ecosystem for protection and restoration, versus the substantial urban development with the accompanying water supply, water quality and flood control issues. In this paper a novel forecasting and real-time modelling system is presented for the Big Cypress Basin. The Big Cypress Basin includes 272 km of primary canals and 46 water control structures throughout the area that provide limited levels of flood protection, as well as water supply and environmental quality management. This system is linked to the South Florida Water Management District's extensive real-time (SCADA) data monitoring and collection system. Novel aspects of this system include the use of a fully distributed and integrated modeling approach and a new filter-based updating approach for accurately forecasting river levels. Because of the interaction between surface- and groundwater a fully integrated forecast modeling approach is required. Indeed, results for the Tropical Storm Fay in 2008, the groundwater levels show an extremely rapid response to heavy rainfall. Analysis of this storm also shows that updating levels in the river system can have a direct impact on groundwater levels.
Representation of transit ITS in network-based travel models

DOT National Transportation Integrated Search

2005-03-01

The increased use of Intelligent Transportation Systems (ITS) technology in public transit has two major impacts on travel forecasting. First, the technology will often result in an improved volume and quality of data that may be used for planning. S...
Assimilation of ASCAT near-surface soil moisture into the French SIM hydrological model

NASA Astrophysics Data System (ADS)

Draper, C.; Mahfouf, J.-F.; Calvet, J.-C.; Martin, E.; Wagner, W.

2011-06-01

The impact of assimilating near-surface soil moisture into the SAFRAN-ISBA-MODCOU (SIM) hydrological model over France is examined. Specifically, the root-zone soil moisture in the ISBA land surface model is constrained over three and a half years, by assimilating the ASCAT-derived surface degree of saturation product, using a Simplified Extended Kalman Filter. In this experiment ISBA is forced with the near-real time SAFRAN analysis, which analyses the variables required to force ISBA from relevant observations available before the real time data cut-off. The assimilation results are tested against ISBA forecasts generated with a higher quality delayed cut-off SAFRAN analysis. Ideally, assimilating the ASCAT data will constrain the ISBA surface state to correct for errors in the near-real time SAFRAN forcing, the most significant of which was a substantial dry bias caused by a dry precipitation bias. The assimilation successfully reduced the mean root-zone soil moisture bias, relative to the delayed cut-off forecasts, by close to 50 % of the open-loop value. The improved soil moisture in the model then led to significant improvements in the forecast hydrological cycle, reducing the drainage, runoff, and evapotranspiration biases (by 17 %, 11 %, and 70 %, respectively). When coupled to the MODCOU hydrogeological model, the ASCAT assimilation also led to improved streamflow forecasts, increasing the mean discharge ratio, relative to the delayed cut off forecasts, from 0.68 to 0.76. These results demonstrate that assimilating near-surface soil moisture observations can effectively constrain the SIM model hydrology, while also confirming the accuracy of the ASCAT surface degree of saturation product. This latter point highlights how assimilation experiments can contribute towards the difficult issue of validating remotely sensed land surface observations over large spatial scales.
Validation of reactive gases and aerosols in the MACC global analysis and forecast system

NASA Astrophysics Data System (ADS)

Eskes, H.; Huijnen, V.; Arola, A.; Benedictow, A.; Blechschmidt, A.-M.; Botek, E.; Boucher, O.; Bouarar, I.; Chabrillat, S.; Cuevas, E.; Engelen, R.; Flentje, H.; Gaudel, A.; Griesfeller, J.; Jones, L.; Kapsomenakis, J.; Katragkou, E.; Kinne, S.; Langerock, B.; Razinger, M.; Richter, A.; Schultz, M.; Schulz, M.; Sudarchikova, N.; Thouret, V.; Vrekoussis, M.; Wagner, A.; Zerefos, C.

2015-11-01

The European MACC (Monitoring Atmospheric Composition and Climate) project is preparing the operational Copernicus Atmosphere Monitoring Service (CAMS), one of the services of the European Copernicus Programme on Earth observation and environmental services. MACC uses data assimilation to combine in situ and remote sensing observations with global and regional models of atmospheric reactive gases, aerosols, and greenhouse gases, and is based on the Integrated Forecasting System of the European Centre for Medium-Range Weather Forecasts (ECMWF). The global component of the MACC service has a dedicated validation activity to document the quality of the atmospheric composition products. In this paper we discuss the approach to validation that has been developed over the past 3 years. Topics discussed are the validation requirements, the operational aspects, the measurement data sets used, the structure of the validation reports, the models and assimilation systems validated, the procedure to introduce new upgrades, and the scoring methods. One specific target of the MACC system concerns forecasting special events with high-pollution concentrations. Such events receive extra attention in the validation process. Finally, a summary is provided of the results from the validation of the latest set of daily global analysis and forecast products from the MACC system reported in November 2014.
Impact assessment of GPS radio occultation data on Antarctic analysis and forecast using WRF 3DVAR

NASA Astrophysics Data System (ADS)

Zhang, H.; Wee, T. K.; Liu, Z.; Lin, H. C.; Kuo, Y. H.

2016-12-01

This study assesses the impact of Global Positioning System (GPS) Radio Occultation (RO) refractivity data on the analysis and forecast in the Antarctic region. The RO data are continuously assimilated into the Weather Research and Forecasting (WRF) Model using the WRF 3DVAR along with other observations that were operationally available to the National Center for Environmental Prediction (NCEP) during a month period, October 2010, including the Advance Microwave Sounding Unit (AMSU) radiance data. For the month-long data assimilation experiments, three RO datasets are used: 1) The actual operational dataset, which was produced by the near real-time RO processing at that time and provided to weather forecasting centers; 2) a post-processed dataset with posterior clock and orbit estimates, and with improved RO processing algorithms; and, 3) another post-processed dataset, produced with a variational RO processing. The data impact is evaluated with comparing the forecasts and analyses to independent driftsonde observations that are made available through the Concordiasi field campaign, in addition to utilizing other traditional means of verification. A denial of RO data (while keeping all other observations) resulted in a remarkable quality degradation of analysis and forecast, indicating the high value of RO data over the Antarctic area. The post-processed RO data showed a significantly larger positive impact compared to the near real-time data, due to extra RO data from the TerraSAR-X satellite (unavailable at the time of the near real-time processing) as well as the supposedly improved data quality as a result of the post-processing. This strongly suggests that the future polar constellation of COSMIC-2 is vital. The variational RO processing further reduced the systematic and random errors in both analysis and forecasts, for instance, leading to a smaller background departure of AMSU radiance. This indicates that the variational RO processing provides an improved reference for the bias correction of satellite radiance, making the bias correction more effective. This study finds that advanced RO data processing algorithms may further enhance the high quality of RO data in high Southern latitudes.
Effect of data quality on a hybrid Coulomb/STEP model for earthquake forecasting

NASA Astrophysics Data System (ADS)

Steacy, Sandy; Jimenez, Abigail; Gerstenberger, Matt; Christophersen, Annemarie

2014-05-01

Operational earthquake forecasting is rapidly becoming a 'hot topic' as civil protection authorities seek quantitative information on likely near future earthquake distributions during seismic crises. At present, most of the models in public domain are statistical and use information about past and present seismicity as well as b-value and Omori's law to forecast future rates. A limited number of researchers, however, are developing hybrid models which add spatial constraints from Coulomb stress modeling to existing statistical approaches. Steacy et al. (2013), for instance, recently tested a model that combines Coulomb stress patterns with the STEP (short-term earthquake probability) approach against seismicity observed during the 2010-2012 Canterbury earthquake sequence. They found that the new model performed at least as well as, and often better than, STEP when tested against retrospective data but that STEP was generally better in pseudo-prospective tests that involved data actually available within the first 10 days of each event of interest. They suggested that the major reason for this discrepancy was uncertainty in the slip models and, in particular, in the geometries of the faults involved in each complex major event. Here we test this hypothesis by developing a number of retrospective forecasts for the Landers earthquake using hypothetical slip distributions developed by Steacy et al. (2004) to investigate the sensitivity of Coulomb stress models to fault geometry and earthquake slip. Specifically, we consider slip models based on the NEIC location, the CMT solution, surface rupture, and published inversions and find significant variation in the relative performance of the models depending upon the input data.
The skill of ECMWF long range Forecasting System to drive impact models for health and hydrology in Africa

NASA Astrophysics Data System (ADS)

Di Giuseppe, F.; Tompkins, A. M.; Lowe, R.; Dutra, E.; Wetterhall, F.

2012-04-01

As the quality of numerical weather prediction over the monthly to seasonal leadtimes steadily improves there is an increasing motivation to apply these fruitfully to the impacts sectors of health, water, energy and agriculture. Despite these improvements, the accuracy of fields such as temperature and precipitation that are required to drive sectoral models can still be poor. This is true globally, but particularly so in Africa, the region of focus in the present study. In the last year ECMWF has been particularly active through EU research founded projects in demonstrating the capability of its longer range forecasting system to drive impact modeling systems in this region. A first assessment on the consequences of the documented errors in ECMWF forecasting system is therefore presented here looking at two different application fields which we found particularly critical for Africa - vector-born diseases prevention and hydrological monitoring. A new malaria community model (VECTRI) has been developed at ICTP and tested for the 3 target regions participating in the QWECI project. The impacts on the mean malaria climate is assessed using the newly realized seasonal forecasting system (Sys4) with the dismissed system 3 (Sys3) which had the same model cycle of the up-to-date ECMWF re-analysis product (ERA-Interim). The predictive skill of Sys4 to be employed for malaria monitoring and forecast are also evaluated by aggregating the fields to country level. As a part of the DEWFORA projects, ECMWF is also developing a system for drought monitoring and forecasting over Africa whose main meteorological input is precipitation. Similarly to what is done for the VECTRI model, the skill of seasonal forecasts of precipitation is, in this application, translated into the capability of predicting drought while ERA-Interim is used in monitoring. On a monitoring level, the near real-time update of ERA-Interim could compensate the lack of observations in the regions. However, ERA-Interim suffers from biases and drifts that limit its application for drought monitoring purposes in some regions.

Climate forecasting services: coming down from the ivory tower

NASA Astrophysics Data System (ADS)

Doblas-Reyes, F. J.; Caron, L. P.; Cortesi, N.; Soret, A.; Torralba, V.; Turco, M.; González Reviriego, N.; Jiménez, I.; Terrado, M.

2016-12-01

Subseasonal-to-seasonal (S2S) climate forecasts are increasingly used across a range of application areas (energy, water management, agriculture, health, insurance) through tailored services using the climate services paradigm. In this contribution we show the value of climate forecasting services through several examples of their application in the energy, reinsurance and agriculture sectors. Climate services aim at making climate information action oriented. In a climate forecasting context the task starts with the identification of climate variables, thresholds and events relevant to the users. These elements are then analysed to determine whether they can be both reliably and skilfully predicted at appropriate time scales. In this contribution we assess climate predictions of precipitation, temperature and wind indices from state-of-the-art operational multi-model forecast systems and if they respond to the expectations and requests from a range of users. This requires going beyond the more traditional assessment of monthly mean values to include assessments of global forecast quality of the frequency of warm, cold, windy and wet extremes (e.g. [1], [2]), as well as of using tools like the Euro-Atlantic weather regimes [3]. The forecast quality of extremes is generally similar to or slightly lower than that of monthly or seasonal averages, but offers a kind of information closer to what some users require. In addition to considering local climate variables, we also explore the use of large-scale climate indices, such as ENSO and NAO, that are associated with large regional synchronous variations of wind or tropical storm frequency. These indices help illustrating the relative merits of climate forecast information to users and are the cornerstone of climate stories that engage them in the co-production of climate information. [1] Doblas-Reyes et al, WIREs, 2013 [2] Pepler et al, Weather and Climate Extremes, 2015 [3] Pavan and Doblas-Reyes, Clim Dyn, 2013
Operational Earthquake Forecasting: Proposed Guidelines for Implementation (Invited)

NASA Astrophysics Data System (ADS)

Jordan, T. H.

2010-12-01

The goal of operational earthquake forecasting (OEF) is to provide the public with authoritative information about how seismic hazards are changing with time. During periods of high seismic activity, short-term earthquake forecasts based on empirical statistical models can attain nominal probability gains in excess of 100 relative to the long-term forecasts used in probabilistic seismic hazard analysis (PSHA). Prospective experiments are underway by the Collaboratory for the Study of Earthquake Predictability (CSEP) to evaluate the reliability and skill of these seismicity-based forecasts in a variety of tectonic environments. How such information should be used for civil protection is by no means clear, because even with hundredfold increases, the probabilities of large earthquakes typically remain small, rarely exceeding a few percent over forecasting intervals of days or weeks. Civil protection agencies have been understandably cautious in implementing formal procedures for OEF in this sort of “low-probability environment.” Nevertheless, the need to move more quickly towards OEF has been underscored by recent experiences, such as the 2009 L’Aquila earthquake sequence and other seismic crises in which an anxious public has been confused by informal, inconsistent earthquake forecasts. Whether scientists like it or not, rising public expectations for real-time information, accelerated by the use of social media, will require civil protection agencies to develop sources of authoritative information about the short-term earthquake probabilities. In this presentation, I will discuss guidelines for the implementation of OEF informed by my experience on the California Earthquake Prediction Evaluation Council, convened by CalEMA, and the International Commission on Earthquake Forecasting, convened by the Italian government following the L’Aquila disaster. (a) Public sources of information on short-term probabilities should be authoritative, scientific, open, and timely, and they need to convey the epistemic uncertainties in the operational forecasts. (b) Earthquake probabilities should be based on operationally qualified, regularly updated forecasting systems. All operational procedures should be rigorously reviewed by experts in the creation, delivery, and utility of earthquake forecasts. (c) The quality of all operational models should be evaluated for reliability and skill by retrospective testing, and the models should be under continuous prospective testing in a CSEP-type environment against established long-term forecasts and a wide variety of alternative, time-dependent models. (d) Short-term models used in operational forecasting should be consistent with the long-term forecasts used in PSHA. (e) Alert procedures should be standardized to facilitate decisions at different levels of government and among the public, based in part on objective analysis of costs and benefits. (f) In establishing alert procedures, consideration should also be made of the less tangible aspects of value-of-information, such as gains in psychological preparedness and resilience. Authoritative statements of increased risk, even when the absolute probability is low, can provide a psychological benefit to the public by filling information vacuums that can lead to informal predictions and misinformation.
Visualization and Nowcasting for Aviation using online verified ensemble weather radar extrapolation.

NASA Astrophysics Data System (ADS)

Kaltenboeck, Rudolf; Kerschbaum, Markus; Hennermann, Karin; Mayer, Stefan

2013-04-01

Nowcasting of precipitation events, especially thunderstorm events or winter storms, has high impact on flight safety and efficiency for air traffic management. Future strategic planning by air traffic control will result in circumnavigation of potential hazardous areas, reduction of load around efficiency hot spots by offering alternatives, increase of handling capacity, anticipation of avoidance manoeuvres and increase of awareness before dangerous areas are entered by aircraft. To facilitate this rapid update forecasts of location, intensity, size, movement and development of local storms are necessary. Weather radar data deliver precipitation analysis of high temporal and spatial resolution close to real time by using clever scanning strategies. These data are the basis to generate rapid update forecasts in a time frame up to 2 hours and more for applications in aviation meteorological service provision, such as optimizing safety and economic impact in the context of sub-scale phenomena. On the basis of tracking radar echoes by correlation the movement vectors of successive weather radar images are calculated. For every new successive radar image a set of ensemble precipitation fields is collected by using different parameter sets like pattern match size, different time steps, filter methods and an implementation of history of tracking vectors and plausibility checks. This method considers the uncertainty in rain field displacement and different scales in time and space. By validating manually a set of case studies, the best verification method and skill score is defined and implemented into an online-verification scheme which calculates the optimized forecasts for different time steps and different areas by using different extrapolation ensemble members. To get information about the quality and reliability of the extrapolation process additional information of data quality (e.g. shielding in Alpine areas) is extrapolated and combined with an extrapolation-quality-index. Subsequently the probability and quality information of the forecast ensemble is available and flexible blending to numerical prediction model for each subarea is possible. Simultaneously with automatic processing the ensemble nowcasting product is visualized in a new innovative way which combines the intensity, probability and quality information for different subareas in one forecast image.
The CMEMS IBI-MFC Forecasting Service in 2017: Evolution and Novelties associated to the CMEMS service release

NASA Astrophysics Data System (ADS)

Lorente, Pablo; Sotillo, Marcos G.; Gutknecht, Elodie; Dabrowski, Tomasz; Aouf, Lotfi; Toledano, Cristina; Amo-Baladron, Arancha; Aznar, Roland; De Pascual, Alvaro; Levier, Bruno; Bowyer, Peter; Rainaud, Romain; Alvarez-Fanjul, Enrique

2017-04-01

The IBI-MFC (Iberia-Biscay-Ireland Monitoring & Forecasting Centre) has been providing daily ocean model estimates and forecasts of diverse physical parameters for the IBI regional seas since 2011, first in the frame of MyOcean projects and later as part of the Copernicus Marine Environment Monitoring Service (CMEMS). By April 2017, coincident with the V3 CMEMS Service Release, the IBI-MFC will extend their near real time (NRT) forecast capabilities. Two new operational IBI forecast systems will be operationally run to generate high resolution biochemical (BIO) and wave (WAV) products on the IBI area. The IBI-NRT-BIO forecast system, based on a 1/36° NEMO-PISCES model application, is run once a week coupled with the IBI physical forecast solution and nested to the CMEMS GLOBAL-BIO solution. On the other hand, the IBI-NRT-WAV system, based on a MeteoFrance-WAM 10km resolution model application, runs twice a day using ECMWF wind forcing. Among other novelties related to the evolution of the IBI physical (PHY) solution, it is worthwhile mentioning the provision, as part of the IBI-NRT-PHY product daily updated, of three-dimensional hourly data on specific areas within the IBI domain. The delivery of these new hourly data along the whole water column has been achieved after the request from IBI users, in order to foster downscaling approaches by providing coherent open boundary conditions to any potential high-resolution coastal model nested to IBI regional solution. An extensive skill assessment of IBI-NRT forecast products has been conducted through the NARVAL (North Atlantic Regional VALidation) web tool, by means of the automatic computation of statistical metrics and quality indicators. By now, this tool has been focused on the validation of the IBI-NRT-PHY system. Nowadays, NARVAL is facing a significant upgrade to validate the aforementioned new biogeochemical and wave IBI products. To this aim, satellite derived observations of chlorophyll and significant wave height will be used, together with in-situ wave parameters measured by mooring buoys. Within this validation framework, special emphasis has been placed on the intercomparison of different forecast model solutions in overlapping areas in order to evaluate models' performances and prognostic capabilities. This common uncertainty estimates of IBI and other model solution is currently performed by NARVAL using both CMEMS forecast model sources (i.e. GLOBAL-MFC, MED-MFC and NWS-MFC) and non-CMEMS operational forecast solutions (mostly downstream application nested to the IBI solution). With respect to the IBI multi-year (MY) products, it is worth mentioning that the actual biogeochemical and physical reanalysis products will be re-run along year 2017, extending its time coverage backwards until 1992. Based on these IBI-MY products, a variety of climatic indicators related to essential oceanographic processes (i.e. western coastal upwelling or the Mediterranean Outflow Water) are currently being computed.
Enhanced road weather forecasting : Clarus regional demonstrations.

DOT National Transportation Integrated Search

2011-01-01

The quality of road weather forecasts has major impacts on users of surface transportation systems and managers of those systems. Improving the quality involves the ability to provide accurate, route-specific road weather information (e.g., timing an...
Road weather forecast quality analysis : project summary

DOT National Transportation Integrated Search

2006-03-01

The purpose of this research is to enhance the use of KDOTs Roadway Weather : Information System by improving the weather forecasts themselves and raising the level of : confidence in these forecasts.
Evaluation of Pollen Apps Forecasts: The Need for Quality Control in an eHealth Service.

PubMed

Bastl, Katharina; Berger, Uwe; Kmenta, Maximilian

2017-05-08

Pollen forecasts are highly valuable for allergen avoidance and thus raising the quality of life of persons concerned by pollen allergies. They are considered as valuable free services for the public. Careful scientific evaluation of pollen forecasts in terms of accurateness and reliability has not been available till date. The aim of this study was to analyze 9 mobile apps, which deliver pollen information and pollen forecasts, with a focus on their accurateness regarding the prediction of the pollen load in the grass pollen season 2016 to assess their usefulness for pollen allergy sufferers. The following number of apps was evaluated for each location: 3 apps for Vienna (Austria), 4 apps for Berlin (Germany), and 1 app each for Basel (Switzerland) and London (United Kingdom). All mobile apps were freely available. Today's grass pollen forecast was compared throughout the defined grass pollen season at each respective location with measured grass pollen concentrations. Hit rates were calculated for the exact performance and for a tolerance in a range of ±2 and ±4 pollen per cubic meter. In general, for most apps, hit rates score around 50% (6 apps). It was found that 1 app showed better results, whereas 3 apps performed less well. Hit rates increased when calculated with tolerances for most apps. In contrast, the forecast for the "readiness to flower" for grasses was performed at a sufficiently accurate level, although only two apps provided such a forecast. The last of those forecasts coincided with the first moderate grass pollen load on the predicted day or 3 days after and performed even from about a month before well within the range of 3 days. Advertisement was present in 3 of the 9 analyzed apps, whereas an imprint mentioning institutions with experience in pollen forecasting was present in only three other apps. The quality of pollen forecasts is in need of improvement, and quality control for pollen forecasts is recommended to avoid potential harm to pollen allergy sufferers due to inadequate forecasts. The inclusion of information on reliability of provided forecasts and a similar handling regarding probabilistic weather forecasts should be considered. ©Katharina Bastl, Uwe Berger, Maximilian Kmenta. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 08.05.2017.
Evaluation of Pollen Apps Forecasts: The Need for Quality Control in an eHealth Service

PubMed Central

Berger, Uwe; Kmenta, Maximilian

2017-01-01

Background Pollen forecasts are highly valuable for allergen avoidance and thus raising the quality of life of persons concerned by pollen allergies. They are considered as valuable free services for the public. Careful scientific evaluation of pollen forecasts in terms of accurateness and reliability has not been available till date. Objective The aim of this study was to analyze 9 mobile apps, which deliver pollen information and pollen forecasts, with a focus on their accurateness regarding the prediction of the pollen load in the grass pollen season 2016 to assess their usefulness for pollen allergy sufferers. Methods The following number of apps was evaluated for each location: 3 apps for Vienna (Austria), 4 apps for Berlin (Germany), and 1 app each for Basel (Switzerland) and London (United Kingdom). All mobile apps were freely available. Today’s grass pollen forecast was compared throughout the defined grass pollen season at each respective location with measured grass pollen concentrations. Hit rates were calculated for the exact performance and for a tolerance in a range of ±2 and ±4 pollen per cubic meter. Results In general, for most apps, hit rates score around 50% (6 apps). It was found that 1 app showed better results, whereas 3 apps performed less well. Hit rates increased when calculated with tolerances for most apps. In contrast, the forecast for the “readiness to flower” for grasses was performed at a sufficiently accurate level, although only two apps provided such a forecast. The last of those forecasts coincided with the first moderate grass pollen load on the predicted day or 3 days after and performed even from about a month before well within the range of 3 days. Advertisement was present in 3 of the 9 analyzed apps, whereas an imprint mentioning institutions with experience in pollen forecasting was present in only three other apps. Conclusions The quality of pollen forecasts is in need of improvement, and quality control for pollen forecasts is recommended to avoid potential harm to pollen allergy sufferers due to inadequate forecasts. The inclusion of information on reliability of provided forecasts and a similar handling regarding probabilistic weather forecasts should be considered. PMID:28483740
State-space based analysis and forecasting of macroscopic road safety trends in Greece.

PubMed

Antoniou, Constantinos; Yannis, George

2013-11-01

In this paper, macroscopic road safety trends in Greece are analyzed using state-space models and data for 52 years (1960-2011). Seemingly unrelated time series equations (SUTSE) models are developed first, followed by richer latent risk time-series (LRT) models. As reliable estimates of vehicle-kilometers are not available for Greece, the number of vehicles in circulation is used as a proxy to the exposure. Alternative considered models are presented and discussed, including diagnostics for the assessment of their model quality and recommendations for further enrichment of this model. Important interventions were incorporated in the models developed (1986 financial crisis, 1991 old-car exchange scheme, 1996 new road fatality definition) and found statistically significant. Furthermore, the forecasting results using data up to 2008 were compared with final actual data (2009-2011) indicating that the models perform properly, even in unusual situations, like the current strong financial crisis in Greece. Forecasting results up to 2020 are also presented and compared with the forecasts of a model that explicitly considers the currently on-going recession. Modeling the recession, and assuming that it will end by 2013, results in more reasonable estimates of risk and vehicle-kilometers for the 2020 horizon. This research demonstrates the benefits of using advanced state-space modeling techniques for modeling macroscopic road safety trends, such as allowing the explicit modeling of interventions. The challenges associated with the application of such state-of-the-art models for macroscopic phenomena, such as traffic fatalities in a region or country, are also highlighted. Furthermore, it is demonstrated that it is possible to apply such complex models using the relatively short time-series that are available in macroscopic road safety analysis. Copyright © 2013 Elsevier Ltd. All rights reserved.
Intercomparison of different operational oceanographic forecast products in the CMEMS IBI area

NASA Astrophysics Data System (ADS)

Lorente, Pablo; Sotillo, Marcos G.; Dabrowski, Tomasz; Amo-Baladrón, Arancha; Aznar, Roland; De Pascual, Alvaro; Levier, Bruno; Bowyer, Peter; Cossarini, Gianpiero; Salon, Stefano; Tonani, Marina; Alvarez-Fanjul, Enrique

2017-04-01

The development of skill assessment software packages and dedicated web applications is a relatively novel theme in operational oceanography. Within the CMEMS IBI-MFC, the quality of IBI (Iberia-Biscay-Ireland) forecast products is assessed by means of NARVAL (North Atlantic Regional VALidation) web-based tool. The validation of IBI against independent in situ and remote-sensing measurements is routinely conducted to evaluate model's veracity and prognostic capabilities. Noticeable efforts are in progress to define meaningful skill scores and statistical metrics to quantitatively assess the quality and reliability of the IBI model solution. Likewise, the IBI-MFC compares the IBI forecast products with other model solutions by setting up specific intercomparison exercises on overlapping areas at diverse timescales. In this context, NARVAL web tool already includes a specific module to evaluate strengths and weaknesses of IBI versus other CMEMS operational ocean forecasting systems (OOFSs). In particular, the IBI physical ocean solution is compared against the CMEMS MED and NWS OOFSs. These CMEMS regional services delivered for the Mediterranean and the North West Shelves include data assimilation schemes in their respective operational chains and generate analogous ocean forecast products to the IBI ones. A number of physical parameters (i.e. sea surface temperature, salinity and current velocities) are evaluated through NARVAL on a daily basis in the overlapping areas existing between these three regional systems. NARVAL is currently being updated in order to extend this intercomparison of ocean model parameters to the biogeochemical solutions provided by the aforementioned OOFSs. More specifically, the simulated chlorophyll concentration is evaluated over several subregions of particular concern by using as benchmark the CMEMS satellite-derived observational products. In addition to this IBI comparison against other regional CMEMS products on overlapping areas, a specific intercomparison between the CMEMS GLOBAL solution and the IBI (regional application dynamically embedded in the former) is conducted in order to check its consistency and ability to outperform the parent model solution. Particular emphasis is placed on the comparison of time-series at specified locations (class-2 metrics). The standardized validation methodology presented here is particularly useful and could encompass the intercomparison of the regional application (IBI) and other nested higher resolution models at coastal/shelf scales to quantify the added value of downscaling in local downstream approaches.
Forecasting air quality time series using deep learning.

PubMed

Freeman, Brian S; Taylor, Graham; Gharabaghi, Bahram; Thé, Jesse

2018-04-13

This paper presents one of the first applications of deep learning (DL) techniques to predict air pollution time series. Air quality management relies extensively on time series data captured at air monitoring stations as the basis of identifying population exposure to airborne pollutants and determining compliance with local ambient air standards. In this paper, 8 hr averaged surface ozone (O 3 ) concentrations were predicted using deep learning consisting of a recurrent neural network (RNN) with long short-term memory (LSTM). Hourly air quality and meteorological data were used to train and forecast values up to 72 hours with low error rates. The LSTM was able to forecast the duration of continuous O 3 exceedances as well. Prior to training the network, the dataset was reviewed for missing data and outliers. Missing data were imputed using a novel technique that averaged gaps less than eight time steps with incremental steps based on first-order differences of neighboring time periods. Data were then used to train decision trees to evaluate input feature importance over different time prediction horizons. The number of features used to train the LSTM model was reduced from 25 features to 5 features, resulting in improved accuracy as measured by Mean Absolute Error (MAE). Parameter sensitivity analysis identified look-back nodes associated with the RNN proved to be a significant source of error if not aligned with the prediction horizon. Overall, MAE's less than 2 were calculated for predictions out to 72 hours. Novel deep learning techniques were used to train an 8-hour averaged ozone forecast model. Missing data and outliers within the captured data set were replaced using a new imputation method that generated calculated values closer to the expected value based on the time and season. Decision trees were used to identify input variables with the greatest importance. The methods presented in this paper allow air managers to forecast long range air pollution concentration while only monitoring key parameters and without transforming the data set in its entirety, thus allowing real time inputs and continuous prediction.
Evaluating the applicability of using daily forecasts from seasonal prediction systems (SPSs) for agriculture: a case study of Nepal's Terai with the NCEP CFSv2

NASA Astrophysics Data System (ADS)

Jha, Prakash K.; Athanasiadis, Panos; Gualdi, Silvio; Trabucco, Antonio; Mereu, Valentina; Shelia, Vakhtang; Hoogenboom, Gerrit

2018-03-01

Ensemble forecasts from dynamic seasonal prediction systems (SPSs) have the potential to improve decision-making for crop management to help cope with interannual weather variability. Because the reliability of crop yield predictions based on seasonal weather forecasts depends on the quality of the forecasts, it is essential to evaluate forecasts prior to agricultural applications. This study analyses the potential of Climate Forecast System version 2 (CFSv2) in predicting the Indian summer monsoon (ISM) for producing meteorological variables relevant to crop modeling. The focus area was Nepal's Terai region, and the local hindcasts were compared with weather station and reanalysis data. The results showed that the CFSv2 model accurately predicts monthly anomalies of daily maximum and minimum air temperature (Tmax and Tmin) as well as incoming total surface solar radiation (Srad). However, the daily climatologies of the respective CFSv2 hindcasts exhibit significant systematic biases compared to weather station data. The CFSv2 is less capable of predicting monthly precipitation anomalies and simulating the respective intra-seasonal variability over the growing season. Nevertheless, the observed daily climatologies of precipitation fall within the ensemble spread of the respective daily climatologies of CFSv2 hindcasts. These limitations in the CFSv2 seasonal forecasts, primarily in precipitation, restrict the potential application for predicting the interannual variability of crop yield associated with weather variability. Despite these limitations, ensemble averaging of the simulated yield using all CFSv2 members after applying bias correction may lead to satisfactory yield predictions.
Forecasting land cover change impacts on drinking water treatment costs in Minneapolis, Minnesota

NASA Astrophysics Data System (ADS)

Woznicki, S. A.; Wickham, J.

2017-12-01

Source protection is a critical aspect of drinking water treatment. The benefits of protecting source water quality in reducing drinking water treatment costs are clear. However, forecasting the impacts of environmental change on source water quality and its potential to influence future treatment processes is lacking. The drinking water treatment plant in Minneapolis, MN has recognized that land cover change threatens water quality in their source watershed, the Upper Mississippi River Basin (UMRB). Over 1,000 km2 of forests, wetlands, and grasslands in the UMRB were lost to agriculture from 2008-2013. This trend, coupled with a projected population increase of one million people in Minnesota by 2030, concerns drinking water treatment plant operators in Minneapolis with respect to meeting future demand for clean water in the UMRB. The objective of this study is to relate land cover change (forest and wetland loss, agricultural expansion, urbanization) to changes in treatment costs for the Minneapolis, MN drinking water utility. To do this, we first developed a framework to determine the relationship between land cover change and water quality in the context of recent historical changes and projected future changes in land cover. Next we coupled a watershed model, the Soil and Water Assessment Tool (SWAT) to projections of land cover change from the FOREcasting SCEnarios of Land-use Change (FORE-SCE) model for the mid-21st century. Using historical Minneapolis drinking water treatment data (chemical usage and costs), source water quality in the UMRB was linked to changes in treatment requirements as a function of projected future land cover change. These analyses will quantify the value of natural landscapes in protecting drinking water quality and future treatment processes requirements. In addition, our study provides the Minneapolis drinking water utility with information critical to their planning and capital improvement process.
On using scatterometer and altimeter data to improve storm surge forecasting in the Adriatic Sea

NASA Astrophysics Data System (ADS)

Bajo, Marco; Umgiesser, Georg; De Biasio, Francesco; Vignudelli, Stefano; Zecchetto, Stefano

2017-04-01

Satellite data are seldom used in storm surge forecasting. Among the most important issues related to the storm surge forecasting are the quality of the model wind forcing and the initial condition of the sea surface elevation. In this work, focused on storm surge forecasting in the Adriatic Sea, satellite scatterometer wind data are used to correct the wind speed and direction biases of the ECMWF global atmospheric model by tuning the spatial fields, as an alternative to data assimilation. The capability of such an unbiased wind is tested against that of a high resolution wind, produced by a regional non-hydrostatic model. On the other hand, altimeter Total Water Level Envelope (TWLE) data, which provide the sea level elevation, are used to improve the accuracy of the initial state of the model simulations. This is done by assimilating into a storm surge model the TWLE obtained by the altimeter observations along ground tracks, after subtraction of the tidal components. In order to test the methodology, eleven storm surge events recorded in Venice, from 2008 to 2012, have been simulated using different configurations of forcing wind and altimeter data assimilation. Results show that the relative error on the estimation of the maximum surge peak, averaged over the cases considered, decreases from 13% to 7% using both the unbiased wind and the altimeter data assimilation, while forcing the hydrodynamic model with the high resolution wind (no tuning), the altimeter data assimilation reduces the error from 9% to 6%.
Operational air quality forecast guidance for the United States

NASA Astrophysics Data System (ADS)

Stajner, Ivanka; Lee, Pius; Tong, Daniel; Pan, Li; McQueen, Jeff; Huang, Jinaping; Djalalova, Irina; Wilczak, James; Huang, Ho-Chun; Wang, Jun; Stein, Ariel; Upadhayay, Sikchya

2016-04-01

NOAA provides operational air quality predictions for ozone and wildfire smoke over the United States (U.S.) and predictions of airborne dust over the contiguous 48 states at http://airquality.weather.gov. These predictions are produced using U.S. Environmental Protection Agency (EPA) Community Model for Air Quality (CMAQ) and NOAA's HYSPLIT model (Stein et al., 2015) with meteorological inputs from the North American Mesoscale Forecast System (NAM). The current efforts focus on improving test predictions of fine particulate matter (PM2.5) from CMAQ. Emission inputs for ozone and PM2.5 predictions include inventory information from the U.S. EPA and recently added contributions of particulate matter from intermittent wildfires and windblown dust that rely on near real-time information. Current testing includes refinement of the vertical grid structure in CMAQ and inclusion of contributions of dust transport from global sources into the U.S. domain using the NEMS Global Aerosol Capability (NGAC). The addition of wildfire smoke and dust contributions in CMAQ reduced model underestimation of PM2.5 in summertime. Wintertime overestimation of PM2.5 was reduced by suppressing emissions of soil particles when the terrain is covered by snow or ice. Nevertheless, seasonal biases and biases in the diurnal cycle of PM2.5 are still substantial. Therefore, a new bias correction procedure based on an analog ensemble approach was introduced (Djalalova et al., 2015). It virtually eliminates biases in monthly means or in the diurnal cycle, but it also reduces day-to-day variability in PM2.5 predictions. Refinements to the bias correction procedure are being developed. Upgrades for the representation of wildfire smoke emissions within the domain and from global sources are in testing. Another area of active development includes approaches to scale emission inventories for nitrogen oxides in order to reproduce recent changes observed by the AirNow surface monitoring network and by satellite instruments (Tong et al., 2015) and to use these updated emissions to improve ozone predictions (Pan et al., 2015). An overview of the impacts of these recent and ongoing efforts to improve predictions of ozone, smoke and PM2.5 will be presented. Djalalova, I. et al., 2015: PM2.5 analog forecast and Kalman filter post-processing for the Community Multiscale Air Quality (CMAQ) model. Atmospheric Environment, doi:10.1016/j.atmosenv.2015.05.057. Pan L. et al., 2014: Assessment of NOx and O3 forecasting performances in the U.S. National Air Quality Forecasting Capability before and after the 2012 major emissions updates. Atmospheric Environment, doi: 10.1016/j.atmosenv.2014.06.020. Stein, A. et al., 2015: NOAA's HYSPLIT atmospheric transport and dispersion modeling system. Bull. Amer. Meteor. Soc., doi:10.1175/BAMS-D-14-00110.1. Tong, D.Q. et al., 2015: Long-term NOx trends over large cities in the United States during the great recession: Comparison of satellite retrievals, ground observations, and emission inventories. Atmospheric Environment, doi:10.1016/j.atmosenv.2015.01.035.
Application of time series analysis in modelling and forecasting emergency department visits in a medical centre in Southern Taiwan.

PubMed

Juang, Wang-Chuan; Huang, Sin-Jhih; Huang, Fong-Dee; Cheng, Pei-Wen; Wann, Shue-Ren

2017-12-01

Emergency department (ED) overcrowding is acknowledged as an increasingly important issue worldwide. Hospital managers are increasingly paying attention to ED crowding in order to provide higher quality medical services to patients. One of the crucial elements for a good management strategy is demand forecasting. Our study sought to construct an adequate model and to forecast monthly ED visits. We retrospectively gathered monthly ED visits from January 2009 to December 2016 to carry out a time series autoregressive integrated moving average (ARIMA) analysis. Initial development of the model was based on past ED visits from 2009 to 2016. A best-fit model was further employed to forecast the monthly data of ED visits for the next year (2016). Finally, we evaluated the predicted accuracy of the identified model with the mean absolute percentage error (MAPE). The software packages SAS/ETS V.9.4 and Office Excel 2016 were used for all statistical analyses. A series of statistical tests showed that six models, including ARIMA (0, 0, 1), ARIMA (1, 0, 0), ARIMA (1, 0, 1), ARIMA (2, 0, 1), ARIMA (3, 0, 1) and ARIMA (5, 0, 1), were candidate models. The model that gave the minimum Akaike information criterion and Schwartz Bayesian criterion and followed the assumptions of residual independence was selected as the adequate model. Finally, a suitable ARIMA (0, 0, 1) structure, yielding a MAPE of 8.91%, was identified and obtained as Visit t =7111.161+(a t +0.37462 a t -1). The ARIMA (0, 0, 1) model can be considered adequate for predicting future ED visits, and its forecast results can be used to aid decision-making processes. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.
An Integrated Modeling Framework Forecasting Ecosystem Exposure-- A Systems Approach to the Cumulative Impacts of Multiple Stressors

NASA Astrophysics Data System (ADS)

Johnston, J. M.

2013-12-01

Freshwater habitats provide fishable, swimmable and drinkable resources and are a nexus of geophysical and biological processes. These processes in turn influence the persistence and sustainability of populations, communities and ecosystems. Climate change and landuse change encompass numerous stressors of potential exposure, including the introduction of toxic contaminants, invasive species, and disease in addition to physical drivers such as temperature and hydrologic regime. A systems approach that includes the scientific and technologic basis of assessing the health of ecosystems is needed to effectively protect human health and the environment. The Integrated Environmental Modeling Framework 'iemWatersheds' has been developed as a consistent and coherent means of forecasting the cumulative impact of co-occurring stressors. The Framework consists of three facilitating technologies: Data for Environmental Modeling (D4EM) that automates the collection and standardization of input data; the Framework for Risk Assessment of Multimedia Environmental Systems (FRAMES) that manages the flow of information between linked models; and the Supercomputer for Model Uncertainty and Sensitivity Evaluation (SuperMUSE) that provides post-processing and analysis of model outputs, including uncertainty and sensitivity analysis. Five models are linked within the Framework to provide multimedia simulation capabilities for hydrology and water quality processes: the Soil Water Assessment Tool (SWAT) predicts surface water and sediment runoff and associated contaminants; the Watershed Mercury Model (WMM) predicts mercury runoff and loading to streams; the Water quality Analysis and Simulation Program (WASP) predicts water quality within the stream channel; the Habitat Suitability Index (HSI) model scores physicochemical habitat quality for individual fish species; and the Bioaccumulation and Aquatic System Simulator (BASS) predicts fish growth, population dynamics and bioaccumulation of toxic substances. The capability of the Framework to address cumulative impacts will be demonstrated for freshwater ecosystem services and mountaintop mining.
Refinement of pollutant gas emissions in the state of Rio de Janeiro for applications in modeling air quality on a local scale

NASA Astrophysics Data System (ADS)

Domínguez Chovert, Angel; Félix Alonso, Marcelo; Frassoni, Ariane; José Ferreira, Valter; Eiras, Denis; Longo, Karla; Freitas, Saulo

2017-04-01

Numerical modeling is a fundamental tool for studying the earth system components along with weather and climate forecast. In fact, the development of on-line models allows to simulate conditions of the atmosphere, for example, to evaluate certain chemicals in weather events with the purpose of improving a region's quality of air. For this determined purpose, the on-line models employ information from a broad range of sources in order to generate its variables forecasts. But beyond vast information sources, for a region's quality of air study, the data concerning the amount and distribution of emissions of polluting gases must be representative, as well as, it's required complete georeferenced emissions for simulations made with high resolution. Consequently, the modifications made in this work to the PREP-CHEM-SRC (Preprocessor of trace gas and aerosol emission fields for regional and a global atmospheric chemistry models) tool are presented to meliorate the initialization files for BRAMS models, 5.2 version (Brazilian Developments on the Regional Atmospheric Modeling System) and WRF (Weather Research and Forecasting Model) with vehicle emissions in the state of Rio de Janeiro, Brazil. It was determined the annual vehicle emission, until the year 2030, of the nitrogen oxides species (NOx) and carbon monoxide (CO) for each city and using different scenarios. For Rio de Janeiro city, a process of distribution by emissions of the main pollutant gases was implemented. In total, five different types of routes were used and the emission percentage for each one was calculated using the most current traffic information in them. For to check the industrial contributions to the emissions were used the global datasets RETRO (REanalysis of TROpospheric chemical composition) and EDGAR-HTAP (Emission Database for Global Atmospheric Research). On the other hand, for the biogenic contributions was used information from the MEGAN model (Model of Emissions of Gases and Aerosols from Nature). For all the analyzed species it was possible to observe the strong influence of the vehicular activity on the emission distribution.
Forecasting human exposure to atmospheric pollutants in Portugal - A modelling approach

NASA Astrophysics Data System (ADS)

Borrego, C.; Sá, E.; Monteiro, A.; Ferreira, J.; Miranda, A. I.

2009-12-01

Air pollution has become one main environmental concern because of its known impact on human health. Aiming to inform the population about the air they are breathing, several air quality modelling systems have been developed and tested allowing the assessment and forecast of air pollution ambient levels in many countries. However, every day, an individual is exposed to different concentrations of atmospheric pollutants as he/she moves from and to different outdoor and indoor places (the so-called microenvironments). Therefore, a more efficient way to prevent the population from the health risks caused by air pollution should be based on exposure rather than air concentrations estimations. The objective of the present study is to develop a methodology to forecast the human exposure of the Portuguese population based on the air quality forecasting system available and validated for Portugal since 2005. Besides that, a long-term evaluation of human exposure estimates aims to be obtained using one-year of this forecasting system application. Additionally, a hypothetical 50% emission reduction scenario has been designed and studied as a contribution to study emission reduction strategies impact on human exposure. To estimate the population exposure the forecasting results of the air quality modelling system MM5-CHIMERE have been combined with the population spatial distribution over Portugal and their time-activity patterns, i.e. the fraction of the day time spent in specific indoor and outdoor places. The population characterization concerning age, work, type of occupation and related time spent was obtained from national census and available enquiries performed by the National Institute of Statistics. A daily exposure estimation module has been developed gathering all these data and considering empirical indoor/outdoor relations from literature to calculate the indoor concentrations in each one of the microenvironments considered, namely home, office/school, and other indoors (leisure activities like shopping areas, gym, theatre/cinema and restaurants). The results show how this developed modelling system can be useful to anticipate air pollution episodes and to estimate their effects on human health on a long-term basis. The two metropolitan areas of Porto and Lisbon are identified as the most critical ones in terms of air pollution effects on human health over Portugal in a long-term as well as in a short-term perspective. The coexistence of high concentration values and high population density is the key factor for these stressed areas. Regarding the 50% emission reduction scenario, the model results are significantly different for both pollutants: there is a small overall reduction in the individual exposure values of PM 10 (<10 μg m -3 h), but for O 3, in contrast, there is an extended area where exposure values increase with emission reduction. This detailed knowledge is a prerequisite for the development of effective policies to reduce the foreseen adverse impact of air pollution on human health and to act on time.
Performance assessment of retrospective meteorological inputs for use in air quality modeling during TexAQS 2006

NASA Astrophysics Data System (ADS)

Ngan, Fong; Byun, Daewon; Kim, Hyuncheol; Lee, Daegyun; Rappenglück, Bernhard; Pour-Biazar, Arastoo

2012-07-01

To achieve more accurate meteorological inputs than was used in the daily forecast for studying the TexAQS 2006 air quality, retrospective simulations were conducted using objective analysis and 3D/surface analysis nudging with surface and upper observations. Model ozone using the assimilated meteorological fields with improved wind fields shows better agreement with the observation compared to the forecasting results. In the post-frontal conditions, important factors for ozone modeling in terms of wind patterns are the weak easterlies in the morning for bringing in industrial emissions to the city and the subsequent clockwise turning of the wind direction induced by the Coriolis force superimposing the sea breeze, which keeps pollutants in the urban area. Objective analysis and nudging employed in the retrospective simulation minimize the wind bias but are not able to compensate for the general flow pattern biases inherited from large scale inputs. By using an alternative analyses data for initializing the meteorological simulation, the model can re-produce the flow pattern and generate the ozone peak location closer to the reality. The inaccurate simulation of precipitation and cloudiness cause over-prediction of ozone occasionally. Since there are limitations in the meteorological model to simulate precipitation and cloudiness in the fine scale domain (less than 4-km grid), the satellite-based cloud is an alternative way to provide necessary inputs for the retrospective study of air quality.

Municipal water consumption forecast accuracy

NASA Astrophysics Data System (ADS)

Fullerton, Thomas M.; Molina, Angel L.

2010-06-01

Municipal water consumption planning is an active area of research because of infrastructure construction and maintenance costs, supply constraints, and water quality assurance. In spite of that, relatively few water forecast accuracy assessments have been completed to date, although some internal documentation may exist as part of the proprietary "grey literature." This study utilizes a data set of previously published municipal consumption forecasts to partially fill that gap in the empirical water economics literature. Previously published municipal water econometric forecasts for three public utilities are examined for predictive accuracy against two random walk benchmarks commonly used in regional analyses. Descriptive metrics used to quantify forecast accuracy include root-mean-square error and Theil inequality statistics. Formal statistical assessments are completed using four-pronged error differential regression F tests. Similar to studies for other metropolitan econometric forecasts in areas with similar demographic and labor market characteristics, model predictive performances for the municipal water aggregates in this effort are mixed for each of the municipalities included in the sample. Given the competitiveness of the benchmarks, analysts should employ care when utilizing econometric forecasts of municipal water consumption for planning purposes, comparing them to recent historical observations and trends to insure reliability. Comparative results using data from other markets, including regions facing differing labor and demographic conditions, would also be helpful.
Impact of Interactive Aerosol on the African Easterly Jet in the NASA GEOS-5 Global Forecasting System

NASA Technical Reports Server (NTRS)

Reale, O.; Lau, K. M.; da Silva, A.

2010-01-01

The real-time treatment of interactive realistically varying aerosol in a global operational forecasting system, as opposed to prescribed (fixed or climatologically varying) aerosols, is a very difficult challenge that only recently begins to be addressed. Experiment results from a recent version of the NASA GEOS-5 forecasting system, inclusive of interactive aerosol treatment, are presented in this work. Four sets of 30 5-day forecasts are initialized from a high quality set of analyses previously produced and documented to cover the period from 15 August to 16 September 2006, which corresponds to the NASA African Monsoon Multidisciplinary Analysis (NAMMA) observing campaign. The four forecast sets are at two different horizontal resolutions and with and without interactive aerosol treatment. The net impact of aerosol, at times in which there is a strong dust outbreak, is a temperature increase at the dust level and decrease in the near-surface levels, in complete agreement with previous observational and modeling studies. Moreover, forecasts in which interactive aerosols are included depict an African Easterly (AEJ) at slightly higher elevation, and slightly displace northward, with respect to the forecasts in which aerosols are not include. The shift in the AEJ position goes in the direction of observations and agrees with previous results.
Improving Assimilated Global Data Sets using TMI Rainfall and Columnar Moisture Observations

NASA Technical Reports Server (NTRS)

Hou, Arthur Y.; Zhang, Sara Q.; daSilva, Arlindo M.; Olson, William S.

1999-01-01

A global analysis that optimally combine observations from diverse sources with physical models of atmospheric and land processes can provide a comprehensive description of the climate systems. Currently, such data products contain significant errors in primary hydrological fields such as precipitation and evaporation, especially in the tropics. In this study, we show that assimilating precipitation and total precipitable water (TPW) retrievals derived from the TRMM Microwave Imager (TMI) improves not only the hydrological cycle but also key climate parameters such as clouds, radiation, and the large-scale circulation produced by the Goddard Earth Observing System (GEOS) data assimilation system (DAS). In particular, assimilating TMI rain improves clouds and radiation in areas of active convection, as well as the latent heating distribution and the large-scale motion field in the tropics, while assimilating TMI TPW heating distribution and the large-scale motion field in the tropics, while assimilating TMI TPW retrievals leads to reduced moisture biases and improved radiative fluxes in clear-sky regions. The improved analysis also improves short-range forecasts in the tropics. Ensemble forecasts initialized with the GEOS analysis incorporating TMI rain rates and TPW yield smaller biases in tropical precipitation forecasts beyond 1 day and better 500 hPa geopotential height forecasts up to 5 days. Results of this study demonstrate the potential of using high-quality space-borne rainfall and moisture observations to improve the quality of assimilated global data for climate analysis and weather forecasting applications
An Assessment of the Subseasonal Forecast Performance in the Extended Global Ensemble Forecast System (GEFS)

NASA Astrophysics Data System (ADS)

Sinsky, E.; Zhu, Y.; Li, W.; Guan, H.; Melhauser, C.

2017-12-01

Optimal forecast quality is crucial for the preservation of life and property. Improving monthly forecast performance over both the tropics and extra-tropics requires attention to various physical aspects such as the representation of the underlying SST, model physics and the representation of the model physics uncertainty for an ensemble forecast system. This work focuses on the impact of stochastic physics, SST and the convection scheme on forecast performance for the sub-seasonal scale over the tropics and extra-tropics with emphasis on the Madden-Julian Oscillation (MJO). A 2-year period is evaluated using the National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS). Three experiments with different configurations than the operational GEFS were performed to illustrate the impact of the stochastic physics, SST and convection scheme. These experiments are compared against a control experiment (CTL) which consists of the operational GEFS but its integration is extended from 16 to 35 days. The three configurations are: 1) SPs, which uses a Stochastically Perturbed Physics Tendencies (SPPT), Stochastic Perturbed Humidity (SHUM) and Stochastic Kinetic Energy Backscatter (SKEB); 2) SPs+SST_bc, which uses a combination of SPs and a bias-corrected forecast SST from the NCEP Climate Forecast System Version 2 (CFSv2); and 3) SPs+SST_bc+SA_CV, which combines SPs, a bias-corrected forecast SST and a scale aware convection scheme. When comparing to the CTL experiment, SPs shows substantial improvement. The MJO skill has improved by about 4 lead days during the 2-year period. Improvement is also seen over the extra-tropics due to the updated stochastic physics, where there is a 3.1% and a 4.2% improvement during weeks 3 and 4 over the northern hemisphere and southern hemisphere, respectively. Improvement is also seen when the bias-corrected CFSv2 SST is combined with SPs. Additionally, forecast performance enhances when the scale aware convection scheme (SPs+SST_bc+SA_CV) is added, especially over the tropics. Among the three experiments, the SPs+SST_bc+SA_CV is the best configuration in MJO forecast skill.
The Wind Forecast Improvement Project (WFIP). A Public/Private Partnership for Improving Short Term Wind Energy Forecasts and Quantifying the Benefits of Utility Operations -- the Northern Study Area

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

Finley, Cathy

2014-04-30

This report contains the results from research aimed at improving short-range (0-6 hour) hub-height wind forecasts in the NOAA weather forecast models through additional data assimilation and model physics improvements for use in wind energy forecasting. Additional meteorological observing platforms including wind profilers, sodars, and surface stations were deployed for this study by NOAA and DOE, and additional meteorological data at or near wind turbine hub height were provided by South Dakota State University and WindLogics/NextEra Energy Resources over a large geographical area in the U.S. Northern Plains for assimilation into NOAA research weather forecast models. The resulting improvements inmore » wind energy forecasts based on the research weather forecast models (with the additional data assimilation and model physics improvements) were examined in many different ways and compared with wind energy forecasts based on the current operational weather forecast models to quantify the forecast improvements important to power grid system operators and wind plant owners/operators participating in energy markets. Two operational weather forecast models (OP_RUC, OP_RAP) and two research weather forecast models (ESRL_RAP, HRRR) were used as the base wind forecasts for generating several different wind power forecasts for the NextEra Energy wind plants in the study area. Power forecasts were generated from the wind forecasts in a variety of ways, from very simple to quite sophisticated, as they might be used by a wide range of both general users and commercial wind energy forecast vendors. The error characteristics of each of these types of forecasts were examined and quantified using bulk error statistics for both the local wind plant and the system aggregate forecasts. The wind power forecast accuracy was also evaluated separately for high-impact wind energy ramp events. The overall bulk error statistics calculated over the first six hours of the forecasts at both the individual wind plant and at the system-wide aggregate level over the one year study period showed that the research weather model-based power forecasts (all types) had lower overall error rates than the current operational weather model-based power forecasts, both at the individual wind plant level and at the system aggregate level. The bulk error statistics of the various model-based power forecasts were also calculated by season and model runtime/forecast hour as power system operations are more sensitive to wind energy forecast errors during certain times of year and certain times of day. The results showed that there were significant differences in seasonal forecast errors between the various model-based power forecasts. The results from the analysis of the various wind power forecast errors by model runtime and forecast hour showed that the forecast errors were largest during the times of day that have increased significance to power system operators (the overnight hours and the morning/evening boundary layer transition periods), but the research weather model-based power forecasts showed improvement over the operational weather model-based power forecasts at these times.« less
Evaluation of statistical models for forecast errors from the HBV model

NASA Astrophysics Data System (ADS)

Engeland, Kolbjørn; Renard, Benjamin; Steinsland, Ingelin; Kolberg, Sjur

2010-04-01

SummaryThree statistical models for the forecast errors for inflow into the Langvatn reservoir in Northern Norway have been constructed and tested according to the agreement between (i) the forecast distribution and the observations and (ii) median values of the forecast distribution and the observations. For the first model observed and forecasted inflows were transformed by the Box-Cox transformation before a first order auto-regressive model was constructed for the forecast errors. The parameters were conditioned on weather classes. In the second model the Normal Quantile Transformation (NQT) was applied on observed and forecasted inflows before a similar first order auto-regressive model was constructed for the forecast errors. For the third model positive and negative errors were modeled separately. The errors were first NQT-transformed before conditioning the mean error values on climate, forecasted inflow and yesterday's error. To test the three models we applied three criterions: we wanted (a) the forecast distribution to be reliable; (b) the forecast intervals to be narrow; (c) the median values of the forecast distribution to be close to the observed values. Models 1 and 2 gave almost identical results. The median values improved the forecast with Nash-Sutcliffe R eff increasing from 0.77 for the original forecast to 0.87 for the corrected forecasts. Models 1 and 2 over-estimated the forecast intervals but gave the narrowest intervals. Their main drawback was that the distributions are less reliable than Model 3. For Model 3 the median values did not fit well since the auto-correlation was not accounted for. Since Model 3 did not benefit from the potential variance reduction that lies in bias estimation and removal it gave on average wider forecasts intervals than the two other models. At the same time Model 3 on average slightly under-estimated the forecast intervals, probably explained by the use of average measures to evaluate the fit.
Modeling crop residue burning experiments to evaluate smoke emissions and plume transport

Treesearch

Luxi Zhou; Kirk R. Baker; Sergey L. Napelenok; George Pouliot; Robert Elleman; Susan M. O' Neill; Shawn P. Urbanski; David C. Wong

2018-01-01

Crop residue burning is a common land management practice that results in emissions of a variety of pollutants with negative health impacts. Modeling systems are used to estimate air quality impacts of crop residue burning to support retrospective regulatory assessments and also for forecasting purposes. Ground and airborne measurements from a recent field experiment...
Online coupled regional meteorology-chemistry models in Europe: current status and prospects

NASA Astrophysics Data System (ADS)

Baklanov, A.; Schluenzen, K. H.; Suppan, P.; Baldasano, J.; Brunner, D.; Aksoyoglu, S.; Carmichael, G.; Douros, J.; Flemming, J.; Forkel, R.; Galmarini, S.; Gauss, M.; Grell, G.; Hirtl, M.; Joffre, S.; Jorba, O.; Kaas, E.; Kaasik, M.; Kallos, G.; Kong, X.; Korsholm, U.; Kurganskiy, A.; Kushta, J.; Lohmann, U.; Mahura, A.; Manders-Groot, A.; Maurizi, A.; Moussiopoulos, N.; Rao, S. T.; Savage, N.; Seigneur, C.; Sokhi, R.; Solazzo, E.; Solomos, S.; Sørensen, B.; Tsegas, G.; Vignati, E.; Vogel, B.; Zhang, Y.

2013-05-01

The simulation of the coupled evolution of atmospheric dynamics, pollutant transport, chemical reactions and atmospheric composition is one of the most challenging tasks in environmental modelling, climate change studies, and weather forecasting for the next decades as they all involve strongly integrated processes. Weather strongly influences air quality (AQ) and atmospheric transport of hazardous materials, while atmospheric composition can influence both weather and climate by directly modifying the atmospheric radiation budget or indirectly affecting cloud formation. Until recently, however, due to the scientific complexities and lack of computational power, atmospheric chemistry and weather forecasting have developed as separate disciplines, leading to the development of separate modelling systems that are only loosely coupled. The continuous increase in computer power has now reached a stage that enables us to perform online coupling of regional meteorological models with atmospheric chemical transport models. The focus on integrated systems is timely, since recent research has shown that meteorology and chemistry feedbacks are important in the context of many research areas and applications, including numerical weather prediction (NWP), AQ forecasting as well as climate and Earth system modelling. However, the relative importance of online integration and its priorities, requirements and levels of detail necessary for representing different processes and feedbacks can greatly vary for these related communities: (i) NWP, (ii) AQ forecasting and assessments, (iii) climate and earth system modelling. Additional applications are likely to benefit from online modelling, e.g.: simulation of volcanic ash or forest fire plumes, pollen warnings, dust storms, oil/gas fires, geo-engineering tests involving changes in the radiation balance. The COST Action ES1004 - European framework for online integrated air quality and meteorology modelling (EuMetChem) - aims at paving the way towards a new generation of online integrated atmospheric chemical transport and meteorology modelling with two-way interactions between different atmospheric processes including dynamics, chemistry, clouds, radiation, boundary layer and emissions. As its first task, we summarise the current status of European modelling practices and experience with online coupled modelling of meteorology with atmospheric chemistry including feedback mechanisms and attempt reviewing the various issues connected to the different modules of such online coupled models but also providing recommendations for coping with them for the benefit of the modelling community at large.
Evaluating the improvements of the BOLAM meteorological model operational at ISPRA: A case study approach - preliminary results

NASA Astrophysics Data System (ADS)

Mariani, S.; Casaioli, M.; Lastoria, B.; Accadia, C.; Flavoni, S.

2009-04-01

The Institute for Environmental Protection and Research - ISPRA (former Agency for Environmental Protection and Technical Services - APAT) runs operationally since 2000 an integrated meteo-marine forecasting chain, named the Hydro-Meteo-Marine Forecasting System (Sistema Idro-Meteo-Mare - SIMM), formed by a cascade of four numerical models, telescoping from the Mediterranean basin to the Venice Lagoon, and initialized by means of analyses and forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF). The operational integrated system consists of a meteorological model, the parallel verision of BOlogna Limited Area Model (BOLAM), coupled over the Mediterranean sea with a WAve Model (WAM), a high-resolution shallow-water model of the Adriatic and Ionian Sea, namely the Princeton Ocean Model (POM), and a finite-element version of the same model (VL-FEM) on the Venice Lagoon, aimed to forecast the acqua alta events. Recently, the physically based, fully distributed, rainfall-runoff TOPographic Kinematic APproximation and Integration (TOPKAPI) model has been integrated into the system, coupled to BOLAM, over two river basins, located in the central and northeastern part of Italy, respectively. However, at the present time, this latter part of the forecasting chain is not operational and it is used in a research configuration. BOLAM was originally implemented in 2000 onto the Quadrics parallel supercomputer (and for this reason referred to as QBOLAM, as well) and only at the end of 2006 it was ported (together with the other operational marine models of the forecasting chain) onto the Silicon Graphics Inc. (SGI) Altix 8-processor machine. In particular, due to the Quadrics implementation, the Kuo scheme was formerly implemented into QBOLAM for the cumulus convection parameterization. On the contrary, when porting SIMM onto the Altix Linux cluster, it was achievable to implement into QBOLAM the more advanced convection parameterization by Kain and Fritsch. A fully updated serial version of the BOLAM code has been recently acquired. Code improvements include a more precise advection scheme (Weighted Average Flux); explicit advection of five hydrometeors, and state-of-the-art parameterization schemes for radiation, convection, boundary layer turbulence and soil processes (also with possible choice among different available schemes). The operational implementation of the new code into the SIMM model chain, which requires the development of a parallel version, will be achieved during 2009. In view of this goal, the comparative verification of the different model versions' skill represents a fundamental task. On this purpose, it has been decided to evaluate the performance improvement of the new BOLAM code (in the available serial version, hereinafter BOLAM 2007) with respect to the version with the Kain-Fritsch scheme (hereinafter KF version) and to the older one employing the Kuo scheme (hereinafter Kuo version). In the present work, verification of precipitation forecasts from the three BOLAM versions is carried on in a case study approach. The intense rainfall episode occurred on 10th - 17th December 2008 over Italy has been considered. This event produced indeed severe damages in Rome and its surrounding areas. Objective and subjective verification methods have been employed in order to evaluate model performance against an observational dataset including rain gauge observations and satellite imagery. Subjective comparison of observed and forecast precipitation fields is suitable to give an overall description of the forecast quality. Spatial errors (e.g., shifting and pattern errors) and rainfall volume error can be assessed quantitatively by means of object-oriented methods. By comparing satellite images with model forecast fields, it is possible to investigate the differences between the evolution of the observed weather system and the predicted ones, and its sensitivity to the improvements in the model code. Finally, the error in forecasting the cyclone evolution can be tentatively related with the precipitation forecast error.
Multi-model seasonal forecast of Arctic sea-ice: forecast uncertainty at pan-Arctic and regional scales

NASA Astrophysics Data System (ADS)

Blanchard-Wrigglesworth, E.; Barthélemy, A.; Chevallier, M.; Cullather, R.; Fučkar, N.; Massonnet, F.; Posey, P.; Wang, W.; Zhang, J.; Ardilouze, C.; Bitz, C. M.; Vernieres, G.; Wallcraft, A.; Wang, M.

2017-08-01

Dynamical model forecasts in the Sea Ice Outlook (SIO) of September Arctic sea-ice extent over the last decade have shown lower skill than that found in both idealized model experiments and hindcasts of previous decades. Additionally, it is unclear how different model physics, initial conditions or forecast post-processing (bias correction) techniques contribute to SIO forecast uncertainty. In this work, we have produced a seasonal forecast of 2015 Arctic summer sea ice using SIO dynamical models initialized with identical sea-ice thickness in the central Arctic. Our goals are to calculate the relative contribution of model uncertainty and irreducible error growth to forecast uncertainty and assess the importance of post-processing, and to contrast pan-Arctic forecast uncertainty with regional forecast uncertainty. We find that prior to forecast post-processing, model uncertainty is the main contributor to forecast uncertainty, whereas after forecast post-processing forecast uncertainty is reduced overall, model uncertainty is reduced by an order of magnitude, and irreducible error growth becomes the main contributor to forecast uncertainty. While all models generally agree in their post-processed forecasts of September sea-ice volume and extent, this is not the case for sea-ice concentration. Additionally, forecast uncertainty of sea-ice thickness grows at a much higher rate along Arctic coastlines relative to the central Arctic ocean. Potential ways of offering spatial forecast information based on the timescale over which the forecast signal beats the noise are also explored.
A seasonal agricultural drought forecast system for food-insecure regions of East Africa

USGS Publications Warehouse

Shukla, Shraddhanand; McNally, Amy; Husak, Gregory; Funk, Christopher C.

2014-01-01

The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. More accurate seasonal agricultural drought forecasts for this region can inform better water and agricultural management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts and floods. Here we describe the development and implementation of a seasonal agricultural drought forecast system for East Africa (EA) that provides decision support for the Famine Early Warning Systems Network's science team. We evaluate this forecast system for a region of equatorial EA (2° S to 8° N, and 36° to 46° E) for the March-April-May growing season. This domain encompasses one of the most food insecure, climatically variable and socio-economically vulnerable regions in EA, and potentially the world: this region has experienced famine as recently as 2011. To assess the agricultural outlook for the upcoming season our forecast system simulates soil moisture (SM) scenarios using the Variable Infiltration Capacity (VIC) hydrologic model forced with climate scenarios for the upcoming season. First, to show that the VIC model is appropriate for this application we forced the model with high quality atmospheric observations and found that the resulting SM values were consistent with the Food and Agriculture Organization's (FAO's) Water Requirement Satisfaction Index (WRSI), an index used by FEWS NET to estimate crop yields. Next we tested our forecasting system with hindcast runs (1993–2012). We found that initializing SM forecasts with start-of-season (5 March) SM conditions resulted in useful SM forecast skill (> 0.5 correlation) at 1-month, and in some cases at 3 month lead times. Similarly, when the forecast was initialized with mid-season (i.e. 5 April) SM conditions the skill until the end-of-season improved. This shows that early-season rainfall is critical for end-of-season outcomes. Finally we show that, in terms of forecasting spatial patterns of SM anomalies, the skill of this agricultural drought forecast system is generally greater (> 0.8 correlation) during drought years. This means that this system might be particularity useful for identifying the events that present the greatest risk to the region.
The effect of long-range air mass transport pathways on PM10 and NO2 concentrations at urban and rural background sites in Ireland: Quantification using clustering techniques.

PubMed

Donnelly, Aoife A; Broderick, Brian M; Misstear, Bruce D

2015-01-01

The specific aims of this paper are to: (i) quantify the effects of various long range transport pathways nitrogen dioxide (NO2) and particulate matter with diameter less than 10μm (PM10) concentrations in Ireland and identify air mass movement corridors which may lead to incidences poor air quality for application in forecasting; (ii) compare the effects of such pathways at various sites; (iii) assess pathways associated with a period of decreased air quality in Ireland. The origin of and the regions traversed by an air mass 96h prior to reaching a receptor is modelled and k-means clustering is applied to create air-mass groups. Significant differences in air pollution levels were found between air mass cluster types at urban and rural sites. It was found that easterly or recirculated air masses lead to higher NO2 and PM10 levels with average NO2 levels varying between 124% and 239% of the seasonal mean and average PM10 levels varying between 103% and 199% of the seasonal mean at urban and rural sites. Easterly air masses are more frequent during winter months leading to higher overall concentrations. The span in relative concentrations between air mass clusters is highest at the rural site indicating that regional factors are controlling concentration levels. The methods used in this paper could be applied to assist in modelling and forecasting air quality based on long range transport pathways and forecast meteorology without the requirement for detailed emissions data over a large regional domain or the use of computationally demanding modelling techniques.
Aerosol Optical Depth over Europe: Evaluation of the CALIOPE air quality modelling system with direct-sun AERONET observations

NASA Astrophysics Data System (ADS)

Basart, Sara; Pay, María. Teresa; Pérez, Carlos; Cuevas, Emilio; Jorba, Oriol; Piot, Matthias; María Baldasano, Jose

2010-05-01

In the frame of the CALIOPE project (Baldasano et al., 2008), the Barcelona Supercomputing Center (BSC-CNS) currently operates a high-resolution air quality forecasting system based on daily photochemical forecasts in Europe (12km x 12km resolution) with the WRF-ARW/HERMES/CMAQ modelling system (http://www.bsc.es/caliope) and desert dust forecasts over Southern Europe with BSC-DREAM8b (Pérez et al., 2006; http://www.bsc.es/projects/earthscience/DREAM). High resolution simulations and forecasts are possible through their implementation on MareNostrum supercomputer at BSC-CNS. As shown in previous air quality studies (e.g. Rodríguez et al., 2001; Jiménez-Guerrero et al., 2008), the contribution of desert dust on particulate matter levels in Southern Europe is remarkable due to its proximity to African desert dust sources. When considering only anthropogenic emissions (Baldasano et al., 2008) and the current knowledge about aerosol physics and chemistry, chemistry-transport model simulations underestimate the PM10 concentrations by 30-50%. As a first approach, the natural dust contribution from BSC-DREAM8b is on-line added to the anthropogenic aerosol output of CMAQ. The aim of the present work is the quantitative evaluation of the WRF-ARW/HERMES/ CMAQ/BSC-DREAM8b forecast system to simulate the Aerosol Optical Depth (AOD) over Europe. The performance of the modelled AOD has been quantitatively evaluated with discrete and categorical (skill scores) statistics by a comparison to direct-sun AERONET observations for 2004. The contribution of different types of aerosols will be analyzed by means of the O'Neill fine mode AOD products (O'Neill et al., 2001). A previous aerosol characterization of AERONET data was performed (Basart et al., 2009) in order to discriminate the different aerosol source contributions within the study region. The results indicate a remarkable improvement in the discrete and skill-scores evaluation (accuracy, critical success index and probability of detection) of AOD when using CMAQ+DREAM8b compared to CMAQ-alone simulations. An accurate analysis of the relative contributions of anthropogenic aerosols and desert dust to AOD over Europe and their seasonality will be also presented. References: Baldasano J.M, P. Jiménez-Guerrero, O. Jorba, C. Pérez, E. López, P. Güereca, F. Martin, M. García-Vivanco, I. Palomino, X. Querol, M. Pandolfi, M.J. Sanz and J.J. Diéguez: "CALIOPE: An operational air quality forecasting system for the Iberian Peninsula, Balearic Islands and Canary Islands- First annual evaluation and ongoing developments", Adv. Sci. and Res., 2: 89-98, 2008. Baldasano J.M., L. P. Güereca, E. López, S. Gassó, P. Jimenez-Guerrero. "Development of a high resolution (1 km x 1 km, 1 h) emission model for Spain: the High-Elective Resolution Modelling Emission System (HERMES)". Atmospheric Environment, 42: 7215-7233 doi: 10.1016/j.atmosenv.2008.07.026, 2008. Basart, S., Pérez, C., Cuevas, E., Baldasano, J. M. and Gobbi, G. P. "Aerosol characterization in Northern Africa, Northeastern Atlantic, Mediterranean Basin and Middle East from direct-sun AERONET observations", Atmos. Chem. Phys.., 9, 7707-7745, 2009. Jiménez-Guerrero, P., Pérez, C., Jorba, O. and Baldasano, J. M. "Contribution of Saharan dust in an integrated air quality system and its on-line assessment", Geophys. Res. Lett., 35(3), 2008. O'Neill, N. T., Dubovik, O., and Eck, T. F.: A modified Angstrom coefficient for the characterization of sub-micron aerosols, App. Opt., 40(14), 2368-2375, 2001. Pérez, C., Nickovic, S., Pejanovic, G., Baldasano, J. M. and Ozsoy, E. "Interactive dust-radiation modeling: A step to improve weather forecasts", Geophys. Res., 11(D16206),doi:10.1029/2005JD006717, 2006. Rodríguez, S., Querol, X., Alastuey, A., Kallos, G. and Kakaliagou, O. "Saharan dust contributions to PM10 and TSP levels in Southern and Eastern Spain", Atmos. Environ., 35, 2433-2447, 2001.
Recent Progress of Solar Weather Forecasting at Naoc

NASA Astrophysics Data System (ADS)

He, Han; Wang, Huaning; Du, Zhanle; Zhang, Liyun; Huang, Xin; Yan, Yan; Fan, Yuliang; Zhu, Xiaoshuai; Guo, Xiaobo; Dai, Xinghua

The history of solar weather forecasting services at National Astronomical Observatories, Chinese Academy of Sciences (NAOC) can be traced back to 1960s. Nowadays, NAOC is the headquarters of the Regional Warning Center of China (RWC-China), which is one of the members of the International Space Environment Service (ISES). NAOC is responsible for exchanging data, information and space weather forecasts of RWC-China with other RWCs. The solar weather forecasting services at NAOC cover short-term prediction (within two or three days), medium-term prediction (within several weeks), and long-term prediction (in time scale of solar cycle) of solar activities. Most efforts of the short-term prediction research are concentrated on the solar eruptive phenomena, such as flares, coronal mass ejections (CMEs) and solar proton events, which are the key driving sources of strong space weather disturbances. Based on the high quality observation data of the latest space-based and ground-based solar telescopes and with the help of artificial intelligence techniques, new numerical models with quantitative analyses and physical consideration are being developed for the predictions of solar eruptive events. The 3-D computer simulation technology is being introduced for the operational solar weather service platform to visualize the monitoring of solar activities, the running of the prediction models, as well as the presenting of the forecasting results. A new generation operational solar weather monitoring and forecasting system is expected to be constructed in the near future at NAOC.
Evaluation of the Impact of AIRS Radiance and Profile Data Assimilation in Partly Cloudy Regions

NASA Technical Reports Server (NTRS)

Zavodsky, Bradley; Srikishen, Jayanthi; Jedlovec, Gary

2013-01-01

Improvements to global and regional numerical weather prediction have been demonstrated through assimilation of data from NASA s Atmospheric Infrared Sounder (AIRS). Current operational data assimilation systems use AIRS radiances, but impact on regional forecasts has been much smaller than for global forecasts. Retrieved profiles from AIRS contain much of the information that is contained in the radiances and may be able to reveal reasons for this reduced impact. Assimilating AIRS retrieved profiles in an identical analysis configuration to the radiances, tracking the quantity and quality of the assimilated data in each technique, and examining analysis increments and forecast impact from each data type can yield clues as to the reasons for the reduced impact. By doing this with regional scale models individual synoptic features (and the impact of AIRS on these features) can be more easily tracked. This project examines the assimilation of hyperspectral sounder data used in operational numerical weather prediction by comparing operational techniques used for AIRS radiances and research techniques used for AIRS retrieved profiles. Parallel versions of a configuration of the Weather Research and Forecasting (WRF) model with Gridpoint Statistical Interpolation (GSI) are run to examine the impact AIRS radiances and retrieved profiles. Statistical evaluation of a long-term series of forecast runs will be compared along with preliminary results of in-depth investigations for select case comparing the analysis increments in partly cloudy regions and short-term forecast impacts.
Evaluation of the Impact of Atmospheric Infrared Sounder (AIRS) Radiance and Profile Data Assimilation in Partly Cloudy Regions

NASA Technical Reports Server (NTRS)

Zavodsky, Bradley; Srikishen, Jayanthi; Jedlovec, Gary

2013-01-01

Improvements to global and regional numerical weather prediction have been demonstrated through assimilation of data from NASA s Atmospheric Infrared Sounder (AIRS). Current operational data assimilation systems use AIRS radiances, but impact on regional forecasts has been much smaller than for global forecasts. Retrieved profiles from AIRS contain much of the information that is contained in the radiances and may be able to reveal reasons for this reduced impact. Assimilating AIRS retrieved profiles in an identical analysis configuration to the radiances, tracking the quantity and quality of the assimilated data in each technique, and examining analysis increments and forecast impact from each data type can yield clues as to the reasons for the reduced impact. By doing this with regional scale models individual synoptic features (and the impact of AIRS on these features) can be more easily tracked. This project examines the assimilation of hyperspectral sounder data used in operational numerical weather prediction by comparing operational techniques used for AIRS radiances and research techniques used for AIRS retrieved profiles. Parallel versions of a configuration of the Weather Research and Forecasting (WRF) model with Gridpoint Statistical Interpolation (GSI) are run to examine the impact AIRS radiances and retrieved profiles. Statistical evaluation of 6 weeks of forecast runs will be compared along with preliminary results of in-depth investigations for select case comparing the analysis increments in partly cloudy regions and short-term forecast impacts.
Forecasting daily source air quality using multivariate statistical analysis and radial basis function networks.

PubMed

Sun, Gang; Hoff, Steven J; Zelle, Brian C; Nelson, Minda A

2008-12-01

It is vital to forecast gas and particle matter concentrations and emission rates (GPCER) from livestock production facilities to assess the impact of airborne pollutants on human health, ecological environment, and global warming. Modeling source air quality is a complex process because of abundant nonlinear interactions between GPCER and other factors. The objective of this study was to introduce statistical methods and radial basis function (RBF) neural network to predict daily source air quality in Iowa swine deep-pit finishing buildings. The results show that four variables (outdoor and indoor temperature, animal units, and ventilation rates) were identified as relative important model inputs using statistical methods. It can be further demonstrated that only two factors, the environment factor and the animal factor, were capable of explaining more than 94% of the total variability after performing principal component analysis. The introduction of fewer uncorrelated variables to the neural network would result in the reduction of the model structure complexity, minimize computation cost, and eliminate model overfitting problems. The obtained results of RBF network prediction were in good agreement with the actual measurements, with values of the correlation coefficient between 0.741 and 0.995 and very low values of systemic performance indexes for all the models. The good results indicated the RBF network could be trained to model these highly nonlinear relationships. Thus, the RBF neural network technology combined with multivariate statistical methods is a promising tool for air pollutant emissions modeling.
Evaluation of atmospheric dust prediction models using ground-based observations

NASA Astrophysics Data System (ADS)

Terradellas, Enric; María Baldasano, José; Cuevas, Emilio; Basart, Sara; Huneeus, Nicolás; Camino, Carlos; Dundar, Cinhan; Benincasa, Francesco

2013-04-01

An important step in numerical prediction of mineral dust is the model evaluation aimed to assess its performance to forecast the atmospheric dust content and to lead to new directions in model development and improvement. The first problem to address the evaluation is the scarcity of ground-based routine observations intended for dust monitoring. An alternative option would be the use of satellite products. They have the advantage of a large spatial coverage and a regular availability. However, they do have numerous drawbacks that make the quantitative retrievals of aerosol-related variables difficult and imprecise. This work presents the use of different ground-based observing systems for the evaluation of dust models in the Regional Center for Northern Africa, Middle East and Europe of the World Meteorological Organization (WMO) Sand and Dust Storm Warning Advisory and Assessment System (SDS-WAS). The dust optical depth at 550 nm forecast by different models is regularly compared with the AERONET measurements of Aerosol Optical Depth (AOD) for 40 selected stations. Photometric measurements are a powerful tool for remote sensing of the atmosphere allowing retrieval of aerosol properties, such as AOD. This variable integrates the contribution of different aerosol types, but may be complemented with spectral information that enables hypotheses about the nature of the particles. Comparison is restricted to cases with low Ångström exponent values in order to ensure that coarse mineral dust is the dominant aerosol type. Additionally to column dust load, it is important to evaluate dust surface concentration and dust vertical profiles. Air quality monitoring stations are the main source of data for the evaluation of surface concentration. However they are concentrated in populated and industrialized areas around the Mediterranean. In the present contribution, results of different models are compared with observations of PM10 from the Turkish air quality network for April 2011, when several dust episodes where recorded. In regions devoid of air quality stations (as Saharan and Arabian deserts), model forecasts are regularly evaluated for 38 dust-prone sites through the use of an empirical relationship between visibility data (obtained from meteorological reports) and dust surface concentration. Finally, active remote sensing with lidar or ceilometers is the only way to inquire about the dust vertical distribution. Analysis of selected cases comparing model forecasts and lidar observations at Santa Cruz de Tenerife (Canary Islands) yields promising results regarding the identification of the dust plume thickness. From the results of this pilot trial, the convenience of a regular evaluation will be assessed.
Ensemble Streamflow Forecast Improvements in NYC's Operations Support Tool

NASA Astrophysics Data System (ADS)

Wang, L.; Weiss, W. J.; Porter, J.; Schaake, J. C.; Day, G. N.; Sheer, D. P.

2013-12-01

Like most other water supply utilities, New York City's Department of Environmental Protection (DEP) has operational challenges associated with drought and wet weather events. During drought conditions, DEP must maintain water supply reliability to 9 million customers as well as meet environmental release requirements downstream of its reservoirs. During and after wet weather events, DEP must maintain turbidity compliance in its unfiltered Catskill and Delaware reservoir systems and minimize spills to mitigate downstream flooding. Proactive reservoir management - such as release restrictions to prepare for a drought or preventative drawdown in advance of a large storm - can alleviate negative impacts associated with extreme events. It is important for water managers to understand the risks associated with proactive operations so unintended consequences such as endangering water supply reliability with excessive drawdown prior to a storm event are minimized. Probabilistic hydrologic forecasts are a critical tool in quantifying these risks and allow water managers to make more informed operational decisions. DEP has recently completed development of an Operations Support Tool (OST) that integrates ensemble streamflow forecasts, real-time observations, and a reservoir system operations model into a user-friendly graphical interface that allows its water managers to take robust and defensible proactive measures in the face of challenging system conditions. Since initial development of OST was first presented at the 2011 AGU Fall Meeting, significant improvements have been made to the forecast system. First, the monthly AR1 forecasts ('Hirsch method') were upgraded with a generalized linear model (GLM) utilizing historical daily correlations ('Extended Hirsch method' or 'eHirsch'). The development of eHirsch forecasts improved predictive skill over the Hirsch method in the first week to a month from the forecast date and produced more realistic hydrographs on the tail end of high flow periods. These improvements allowed DEP to more effectively manage water quality control and spill mitigation operations immediately after storm events. Later on, post-processed hydrologic forecasts from the National Weather Service (NWS) including the Advanced Hydrologic Prediction Service (AHPS) and the Hydrologic Ensemble Forecast Service (HEFS) were implemented into OST. These forecasts further increased the predictive skill over the initial statistical models as current basin conditions (e.g. soil moisture, snowpack) and meteorological forecasts (with HEFS) are now explicitly represented. With the post-processed HEFS forecasts, DEP may now truly quantify impacts associated with wet weather events on the horizon, rather than relying on statistical representations of current hydrologic trends. This presentation will highlight the benefits of the improved forecasts using examples from actual system operations.
Empowering Geoscience with Improved Data Assimilation Using the Data Assimilation Research Testbed "Manhattan" Release.

NASA Astrophysics Data System (ADS)

Raeder, K.; Hoar, T. J.; Anderson, J. L.; Collins, N.; Hendricks, J.; Kershaw, H.; Ha, S.; Snyder, C.; Skamarock, W. C.; Mizzi, A. P.; Liu, H.; Liu, J.; Pedatella, N. M.; Karspeck, A. R.; Karol, S. I.; Bitz, C. M.; Zhang, Y.

2017-12-01

The capabilities of the Data Assimilation Research Testbed (DART) at NCAR have been significantly expanded with the recent "Manhattan" release. DART is an ensemble Kalman filter based suite of tools, which enables researchers to use data assimilation (DA) without first becoming DA experts. Highlights: significant improvement in efficient ensemble DA for very large models on thousands of processors, direct read and write of model state files in parallel, more control of the DA output for finer-grained analysis, new model interfaces which are useful to a variety of geophysical researchers, new observation forward operators and the ability to use precomputed forward operators from the forecast model. The new model interfaces and example applications include the following: MPAS-A; Model for Prediction Across Scales - Atmosphere is a global, nonhydrostatic, variable-resolution mesh atmospheric model, which facilitates multi-scale analysis and forecasting. The absence of distinct subdomains eliminates problems associated with subdomain boundaries. It demonstrates the ability to consistently produce higher-quality analyses than coarse, uniform meshes do. WRF-Chem; Weather Research and Forecasting + (MOZART) Chemistry model assimilates observations from FRAPPÉ (Front Range Air Pollution and Photochemistry Experiment). WACCM-X; Whole Atmosphere Community Climate Model with thermosphere and ionosphere eXtension assimilates observations of electron density to investigate sudden stratospheric warming. CESM (weakly) coupled assimilation; NCAR's Community Earth System Model is used for assimilation of atmospheric and oceanic observations into their respective components using coupled atmosphere+land+ocean+sea+ice forecasts. CESM2.0; Assimilation in the atmospheric component (CAM, WACCM) of the newly released version is supported. This version contains new and extensively updated components and software environment. CICE; Los Alamos sea ice model (in CESM) is used to assimilate multivariate sea ice concentration observations to constrain the model's ice thickness, concentration, and parameters.

Predicting Software Suitability Using a Bayesian Belief Network

NASA Technical Reports Server (NTRS)

Beaver, Justin M.; Schiavone, Guy A.; Berrios, Joseph S.

2005-01-01

The ability to reliably predict the end quality of software under development presents a significant advantage for a development team. It provides an opportunity to address high risk components earlier in the development life cycle, when their impact is minimized. This research proposes a model that captures the evolution of the quality of a software product, and provides reliable forecasts of the end quality of the software being developed in terms of product suitability. Development team skill, software process maturity, and software problem complexity are hypothesized as driving factors of software product quality. The cause-effect relationships between these factors and the elements of software suitability are modeled using Bayesian Belief Networks, a machine learning method. This research presents a Bayesian Network for software quality, and the techniques used to quantify the factors that influence and represent software quality. The developed model is found to be effective in predicting the end product quality of small-scale software development efforts.
Quantifying model uncertainty in seasonal Arctic sea-ice forecasts

NASA Astrophysics Data System (ADS)

Blanchard-Wrigglesworth, Edward; Barthélemy, Antoine; Chevallier, Matthieu; Cullather, Richard; Fučkar, Neven; Massonnet, François; Posey, Pamela; Wang, Wanqiu; Zhang, Jinlun; Ardilouze, Constantin; Bitz, Cecilia; Vernieres, Guillaume; Wallcraft, Alan; Wang, Muyin

2017-04-01

Dynamical model forecasts in the Sea Ice Outlook (SIO) of September Arctic sea-ice extent over the last decade have shown lower skill than that found in both idealized model experiments and hindcasts of previous decades. Additionally, it is unclear how different model physics, initial conditions or post-processing techniques contribute to SIO forecast uncertainty. In this work, we have produced a seasonal forecast of 2015 Arctic summer sea ice using SIO dynamical models initialized with identical sea-ice thickness in the central Arctic. Our goals are to calculate the relative contribution of model uncertainty and irreducible error growth to forecast uncertainty and assess the importance of post-processing, and to contrast pan-Arctic forecast uncertainty with regional forecast uncertainty. We find that prior to forecast post-processing, model uncertainty is the main contributor to forecast uncertainty, whereas after forecast post-processing forecast uncertainty is reduced overall, model uncertainty is reduced by an order of magnitude, and irreducible error growth becomes the main contributor to forecast uncertainty. While all models generally agree in their post-processed forecasts of September sea-ice volume and extent, this is not the case for sea-ice concentration. Additionally, forecast uncertainty of sea-ice thickness grows at a much higher rate along Arctic coastlines relative to the central Arctic ocean. Potential ways of offering spatial forecast information based on the timescale over which the forecast signal beats the noise are also explored.
Solar Resource Assessment with Sky Imagery and a Virtual Testbed for Sky Imager Solar Forecasting

NASA Astrophysics Data System (ADS)

Kurtz, Benjamin Bernard

In recent years, ground-based sky imagers have emerged as a promising tool for forecasting solar energy on short time scales (0 to 30 minutes ahead). Following the development of sky imager hardware and algorithms at UC San Diego, we present three new or improved algorithms for sky imager forecasting and forecast evaluation. First, we present an algorithm for measuring irradiance with a sky imager. Sky imager forecasts are often used in conjunction with other instruments for measuring irradiance, so this has the potential to decrease instrumentation costs and logistical complexity. In particular, the forecast algorithm itself often relies on knowledge of the current irradiance which can now be provided directly from the sky images. Irradiance measurements are accurate to within about 10%. Second, we demonstrate a virtual sky imager testbed that can be used for validating and enhancing the forecast algorithm. The testbed uses high-quality (but slow) simulations to produce virtual clouds and sky images. Because virtual cloud locations are known, much more advanced validation procedures are possible with the virtual testbed than with measured data. In this way, we are able to determine that camera geometry and non-uniform evolution of the cloud field are the two largest sources of forecast error. Finally, with the assistance of the virtual sky imager testbed, we develop improvements to the cloud advection model used for forecasting. The new advection schemes are 10-20% better at short time horizons.
Optimizing Tsunami Forecast Model Accuracy

NASA Astrophysics Data System (ADS)

Whitmore, P.; Nyland, D. L.; Huang, P. Y.

2015-12-01

Recent tsunamis provide a means to determine the accuracy that can be expected of real-time tsunami forecast models. Forecast accuracy using two different tsunami forecast models are compared for seven events since 2006 based on both real-time application and optimized, after-the-fact "forecasts". Lessons learned by comparing the forecast accuracy determined during an event to modified applications of the models after-the-fact provide improved methods for real-time forecasting for future events. Variables such as source definition, data assimilation, and model scaling factors are examined to optimize forecast accuracy. Forecast accuracy is also compared for direct forward modeling based on earthquake source parameters versus accuracy obtained by assimilating sea level data into the forecast model. Results show that including assimilated sea level data into the models increases accuracy by approximately 15% for the events examined.
A versatile data-visualization application for the Norwegian flood forecasting service

NASA Astrophysics Data System (ADS)

Kobierska, Florian; Langsholt, Elin G.; Hamududu, Byman H.; Engeland, Kolbjørn

2017-04-01

- General motivation A graphical user interface has been developed to visualize multi-model hydrological forecasts at the flood forecasting service of the Norwegian water and energy directorate. It is based on the R 'shiny' package, with which interactive web applications can quickly be prototyped. The app queries multiple data sources, building a comprehensive infographics dashboard for the decision maker. - Main features of the app The visualization application comprises several tabs, each built with different functionality and focus. A map of forecast stations gives a rapid insight of the flood situation and serves, concurrently, as a map station selection (based on the 'leaflet' package). The map selection is linked to multi-panel forecast plots which can present input, state or runoff parameters. Another tab focuses on past model performance and calibration runs. - Software design choices The application was programmed with a focus on flexibility regarding data-sources. The parsing of text-based model results was explicitly separated from the app (in the separate R package 'NVEDATA'), so that it only loads standardized RData binary files. We focused on allowing re-usability in other contexts by structuring the app into specific 'shiny' modules. The code was bundled into an R package, which is available on GitHub. - Documentation efforts A documentation website is under development. For easier collaboration, we chose to host it on the 'GitHub Pages' branch of the repository and build it automatically with a continuous integration service. The aim is to gather all information about the flood forecasting methodology at NVE in one location. This encompasses details on each hydrological model used as well as the documentation of the data-visualization application. - Outlook for further development The ability to select a group of stations by filtering a table (i.e. past performance, past major flood events, catchment parameters) and exporting it to the forecast tab could be of interest for detailed model analysis. The design choices for this app were motivated by a need for extensibility and modularity and those qualities will be tested and improved as new datasets need integrating into this tool.
A short-term ensemble wind speed forecasting system for wind power applications

NASA Astrophysics Data System (ADS)

Baidya Roy, S.; Traiteur, J. J.; Callicutt, D.; Smith, M.

2011-12-01

This study develops an adaptive, blended forecasting system to provide accurate wind speed forecasts 1 hour ahead of time for wind power applications. The system consists of an ensemble of 21 forecasts with different configurations of the Weather Research and Forecasting Single Column Model (WRFSCM) and a persistence model. The ensemble is calibrated against observations for a 2 month period (June-July, 2008) at a potential wind farm site in Illinois using the Bayesian Model Averaging (BMA) technique. The forecasting system is evaluated against observations for August 2008 at the same site. The calibrated ensemble forecasts significantly outperform the forecasts from the uncalibrated ensemble while significantly reducing forecast uncertainty under all environmental stability conditions. The system also generates significantly better forecasts than persistence, autoregressive (AR) and autoregressive moving average (ARMA) models during the morning transition and the diurnal convective regimes. This forecasting system is computationally more efficient than traditional numerical weather prediction models and can generate a calibrated forecast, including model runs and calibration, in approximately 1 minute. Currently, hour-ahead wind speed forecasts are almost exclusively produced using statistical models. However, numerical models have several distinct advantages over statistical models including the potential to provide turbulence forecasts. Hence, there is an urgent need to explore the role of numerical models in short-term wind speed forecasting. This work is a step in that direction and is likely to trigger a debate within the wind speed forecasting community.
Short-term forecasting tools for agricultural nutrient management

USDA-ARS?s Scientific Manuscript database

The advent of real time/short term farm management tools is motivated by the need to protect water quality above and beyond the general guidance offered by existing nutrient management plans. Advances in high performance computing and hydrologic/climate modeling have enabled rapid dissemination of ...
NOWCASTING AND FORECASTING BEACH BACTERIA CONCENTRATION USING THE EPA VIRTUAL BEACH SOFTWARE

EPA Science Inventory

Beaches are subject to closure when bacterial counts exceed water quality criteria. Many authorities base these decisions on sample counts, which typically require a day or more to analyze. Sometimes called the persistence model, because conditions are assumed to persist, experie...
Tsunami forecast by joint inversion of real-time tsunami waveforms and seismic of GPS data: application to the Tohoku 2011 tsunami

USGS Publications Warehouse

Yong, Wei; Newman, Andrew V.; Hayes, Gavin P.; Titov, Vasily V.; Tang, Liujuan

2014-01-01

Correctly characterizing tsunami source generation is the most critical component of modern tsunami forecasting. Although difficult to quantify directly, a tsunami source can be modeled via different methods using a variety of measurements from deep-ocean tsunameters, seismometers, GPS, and other advanced instruments, some of which in or near real time. Here we assess the performance of different source models for the destructive 11 March 2011 Japan tsunami using model–data comparison for the generation, propagation, and inundation in the near field of Japan. This comparative study of tsunami source models addresses the advantages and limitations of different real-time measurements with potential use in early tsunami warning in the near and far field. The study highlights the critical role of deep-ocean tsunami measurements and rapid validation of the approximate tsunami source for high-quality forecasting. We show that these tsunami measurements are compatible with other real-time geodetic data, and may provide more insightful understanding of tsunami generation from earthquakes, as well as from nonseismic processes such as submarine landslide failures.
Validation of reactive gases and aerosols in the MACC global analysis and forecast system

NASA Astrophysics Data System (ADS)

Eskes, H.; Huijnen, V.; Arola, A.; Benedictow, A.; Blechschmidt, A.-M.; Botek, E.; Boucher, O.; Bouarar, I.; Chabrillat, S.; Cuevas, E.; Engelen, R.; Flentje, H.; Gaudel, A.; Griesfeller, J.; Jones, L.; Kapsomenakis, J.; Katragkou, E.; Kinne, S.; Langerock, B.; Razinger, M.; Richter, A.; Schultz, M.; Schulz, M.; Sudarchikova, N.; Thouret, V.; Vrekoussis, M.; Wagner, A.; Zerefos, C.

2015-02-01

The European MACC (Monitoring Atmospheric Composition and Climate) project is preparing the operational Copernicus Atmosphere Monitoring Service (CAMS), one of the services of the European Copernicus Programme on Earth observation and environmental services. MACC uses data assimilation to combine in-situ and remote sensing observations with global and regional models of atmospheric reactive gases, aerosols and greenhouse gases, and is based on the Integrated Forecast System of the ECMWF. The global component of the MACC service has a dedicated validation activity to document the quality of the atmospheric composition products. In this paper we discuss the approach to validation that has been developed over the past three years. Topics discussed are the validation requirements, the operational aspects, the measurement data sets used, the structure of the validation reports, the models and assimilation systems validated, the procedure to introduce new upgrades, and the scoring methods. One specific target of the MACC system concerns forecasting special events with high pollution concentrations. Such events receive extra attention in the validation process. Finally, a summary is provided of the results from the validation of the latest set of daily global analysis and forecast products from the MACC system reported in November 2014.
Road safety forecasts in five European countries using structural time series models.

PubMed

Antoniou, Constantinos; Papadimitriou, Eleonora; Yannis, George

2014-01-01

Modeling road safety development is a complex task and needs to consider both the quantifiable impact of specific parameters as well as the underlying trends that cannot always be measured or observed. The objective of this research is to apply structural time series models for obtaining reliable medium- to long-term forecasts of road traffic fatality risk using data from 5 countries with different characteristics from all over Europe (Cyprus, Greece, Hungary, Norway, and Switzerland). Two structural time series models are considered: (1) the local linear trend model and the (2) latent risk time series model. Furthermore, a structured decision tree for the selection of the applicable model for each situation (developed within the Road Safety Data, Collection, Transfer and Analysis [DaCoTA] research project, cofunded by the European Commission) is outlined. First, the fatality and exposure data that are used for the development of the models are presented and explored. Then, the modeling process is presented, including the model selection process, introduction of intervention variables, and development of mobility scenarios. The forecasts using the developed models appear to be realistic and within acceptable confidence intervals. The proposed methodology is proved to be very efficient for handling different cases of data availability and quality, providing an appropriate alternative from the family of structural time series models in each country. A concluding section providing perspectives and directions for future research is presented.
Artificial neural network modeling of the water quality index using land use areas as predictors.

PubMed

Gazzaz, Nabeel M; Yusoff, Mohd Kamil; Ramli, Mohammad Firuz; Juahir, Hafizan; Aris, Ahmad Zaharin

2015-02-01

This paper describes the design of an artificial neural network (ANN) model to predict the water quality index (WQI) using land use areas as predictors. Ten-year records of land use statistics and water quality data for Kinta River (Malaysia) were employed in the modeling process. The most accurate WQI predictions were obtained with the network architecture 7-23-1; the back propagation training algorithm; and a learning rate of 0.02. The WQI forecasts of this model had significant (p < 0.01), positive, very high correlation (ρs = 0.882) with the measured WQI values. Sensitivity analysis revealed that the relative importance of the land use classes to WQI predictions followed the order: mining > rubber > forest > logging > urban areas > agriculture > oil palm. These findings show that the ANNs are highly reliable means of relating water quality to land use, thus integrating land use development with river water quality management.
Assimilation of Atmospheric InfraRed Sounder (AIRS) Profiles using WRF-Var

NASA Technical Reports Server (NTRS)

Zavodsky, Brad; Jedlovec, Gary J.; Lapenta, William

2008-01-01

The Weather Research and Forecasting (WRF) model contains a three-dimensional variational (3DVAR) assimilation system (WRF-Var), which allows a user to join data from multiple sources into one coherent analysis. WRF-Var combines observations with a background field traditionally generated using a previous model forecast through minimization of a cost function. In data sparse regions, remotely-sensed observations may be able to improve analyses and produce improved forecasts. One such source comes from the Atmospheric Infrared Sounder (AIRS), which together with the Advanced Microwave Sounding Unit (AMSU), represents one of the most advanced space-based atmospheric sounding systems. The combined AIRS/AMSU system provides radiance measurements used as input to a sophisticated retrieval scheme which has been shown to produce temperature profiles with an accuracy of 1 K over 1 km layers and humidity profiles with accuracy of 15% in 2 km layers in both clear and partly cloudy conditions. The retrieval algorithm also provides estimates of the accuracy of the retrieved values at each pressure level, allowing the user to select profiles based on the required error tolerances of the application. The purpose of this paper is to describe a procedure to optimally assimilate high-resolution AIRS profile data into a regional configuration of the Advanced Research WRF (ARW) version 2.2 using WRF-Var. The paper focuses on development of background error covariances for the regional domain and background field type using gen_be and an optimal methodology for ingesting AIRS temperature and moisture profiles as separate overland and overwater retrievals with different error characteristics in the WRF-Var. The AIRS thermodynamic profiles are obtained from the version 5.0 Earth Observing System (EOS) science team retrieval algorithm and contain information about the quality of each temperature layer. The quality indicators are used to select the highest quality temperature and moisture data for each profile location and pressure level. Analyses are run to produce quasi-real-time regional weather forecasts over the continental U.S. The preliminary assessment of the impact of the AIRS profiles will focus on intelligent use of the quality indicators, optimized tuning of the WRF-Var, and comparison of analysis soundings to radiosondes.
Data Quality Assessment of FY-3C MWRI Microwave Imager from CMA, ECMWF and the Met Office

NASA Astrophysics Data System (ADS)

Lu, Q.; WU, S.; Dou, F.; Sun, F.; Lawrence, H.; Geer, A.; English, S.; Newman, S.; Bell, W.; Bormann, N.; Carminati, F.

2017-12-01

MWRI is a conical-scanning microwave imager following on from the heritage of similar instruments such as SSMI/S and AMSR-2, with ten channels at frequencies between 10.65 GHz and 89 GHz. MWRI is flown on the China Meteorological Administration's (CMA's) Feng-Yun-3 (FY-3) satellite series, including on FY-3C and the upcoming FY-3D, scheduled for launch in September 2017. Here we present an evaluation of the data from MWRI on the FY-3C satellite launched in 2013. At CMA, the MWRI instrumental parameters and statistics between observation and simulation from RTTOV and CRTM radiative transfer modeling were monitored to characterise instrumental uncertainty from calibration and assess the data quality. The data were also assessed using model-equivalent brightness temperatures from the ECMWF and Met Office short-range forecasts. The forecasts were first transformed into brightness temperature space using the RTTOV radiative transfer code. By analysing observed minus model background ("O-B") brightness temperature departures we were able to investigate the instrument and geophysical state dependence of biases. We show examples of how biases can impact the data quality, related to ascending/descending node differences and radio frequency interference. We discuss the prospects of assimilation of MWRI data at NWP centres.
Near-field hazard assessment of March 11, 2011 Japan Tsunami sources inferred from different methods

USGS Publications Warehouse

Wei, Y.; Titov, V.V.; Newman, A.; Hayes, G.; Tang, L.; Chamberlin, C.

2011-01-01

Tsunami source is the origin of the subsequent transoceanic water waves, and thus the most critical component in modern tsunami forecast methodology. Although impractical to be quantified directly, a tsunami source can be estimated by different methods based on a variety of measurements provided by deep-ocean tsunameters, seismometers, GPS, and other advanced instruments, some in real time, some in post real-time. Here we assess these different sources of the devastating March 11, 2011 Japan tsunami by model-data comparison for generation, propagation and inundation in the near field of Japan. This study provides a comparative study to further understand the advantages and shortcomings of different methods that may be potentially used in real-time warning and forecast of tsunami hazards, especially in the near field. The model study also highlights the critical role of deep-ocean tsunami measurements for high-quality tsunami forecast, and its combination with land GPS measurements may lead to better understanding of both the earthquake mechanisms and tsunami generation process. ?? 2011 MTS.
ManUniCast: A Community Weather and Air-Quality Forecasting Teaching Portal

NASA Astrophysics Data System (ADS)

Schultz, David M.; Anderson, Stuart; Fairman, Jonathan G.; Lowe, Douglas; McFiggans, Gordon; Lee, Elsa; Seo-Zindy, Ryo

2014-05-01

Manunicast was borne out of the needs of our teaching program: students were entering a world where environmental prediction via numerical model was an essential skill, but were not exposed to the production or output of such models. Our site is an educational testbed to explain to students and the public how weather, air-quality, and air-chemistry forecasts are made using real-time predictions as examples. As far as we know, this site provides the first freely available real-time predictions for the UK. We perform two simulations a day over three domains using the most popular, freely available, community atmospheric mesoscale and chemistry models WRF-ARW and WRF-Chem: 1. a WRF-ARW domain over the North Atlantic and western Europe (20-km horizontal grid spacing) 2. a WRF-ARW domain over the UK and Ireland (4-km grid spacing, nested within the 20-km domain) 3. a WRF-Chem domain over the UK and Ireland (12-km grid spacing) Called ManUniCast (Manchester University Forecast), we offer a suite of products from horizontal maps, time series at stations (meteograms), skew-T-logp charts, and cross sections to help students better visualize the weather and the relationships between the various fields more effectively, specifically through the ability to overlay and fade between different plotted products. This presentation discusses how we funded and built ManUniCast, the struggles we faced, and its use in our classes.
Water quality observations of ice-covered, stagnant, eutrophic water bodies and analysis of influence of ice-covered period on water quality

NASA Astrophysics Data System (ADS)

sugihara, K.; Nakatsugawa, M.

2013-12-01

The water quality characteristics of ice-covered, stagnant, eutrophic water bodies have not been clarified because of insufficient observations. It has been pointed out that climate change has been shortening the duration of ice-cover; however, the influence of climate change on water quality has not been clarified. This study clarifies the water quality characteristics of stagnant, eutrophic water bodies that freeze in winter, based on our surveys and simulations, and examines how climate change may influence those characteristics. We made fixed-point observation using self-registering equipment and vertical water sampling. Self-registering equipment measured water temperature and dissolved oxygen(DO).vertical water sampling analyzed biological oxygen demand(BOD), total nitrogen(T-N), nitrate nitrogen(NO3-N), nitrite nitrogen(NO2-N), ammonium nitrogen(NH4-N), total phosphorus(TP), orthophosphoric phosphorus(PO4-P) and chlorophyll-a(Chl-a). The survey found that climate-change-related increases in water temperature were suppressed by ice covering the water area, which also blocked oxygen supply. It was also clarified that the bottom sediment consumed oxygen and turned the water layers anaerobic beginning from the bottom layer, and that nutrient salts eluted from the bottom sediment. The eluted nutrient salts were stored in the water body until the ice melted. The ice-covered period of water bodies has been shortening, a finding based on the analysis of weather and water quality data from 1998 to 2008. Climate change was surveyed as having caused decreases in nutrient salts concentration because of the shortened ice-covered period. However, BOD in spring showed a tendency to increase because of the proliferation of phytoplankton that was promoted by the climate-change-related increase in water temperature. To forecast the water quality by using these findings, particularly the influence of climate change, we constructed a water quality simulation model that incorporates the freezing-over of water bodies. The constructed model shows good temporal and spatial reproducibility and enables water quality to be forecast throughout the year, including during the ice-covered period. The forecasts using the model agree well with the survey results of shortened ice period and climate-change-related increase in the BOD in spring. From the result of calculations and observations, it is suggested that water quality of spring has been deteriorate because of freezing period to be shortened due to temperature rising.
An RSM Study of the Effects of Simulation Work and Metamodel Specification on the Statistical Quality of Metamodel Estimates

DTIC Science & Technology

1994-03-01

optimize, and perform "what-if" analysis on a complicated simulation model of the greenhouse effect . Regression metamodels were applied to several modules of...the large integrated assessment model of the greenhouse effect . In this study, the metamodels gave "acceptable forecast errors" and were shown to
Validating the southern variant forest vegetation simulator height predictions on southeastern hardwoods in Kentucky and Tennessee

Treesearch

Bernard R. Parresol; Steven C. Stedman

2004-01-01

The accuracy of forest growth and yield forecasts affects the quality of forest management decisions (Rauscher et al. 2000). Users of growth and yield models want assurance that model outputs are reasonable and mimic local/regional forest structure and composition and accurately reflect the influences of stand dynamics such as competition and disturbance. As such,...
Comparative Evaluation of the Impact of WRF-NMM and WRF-ARW Meteorology on CMAQ Simulations for O3 and Related Species During the 2006 TexAQS/GoMACCS Campaign

EPA Science Inventory

In this paper, impact of meteorology derived from the Weather, Research and Forecasting (WRF)– Non–hydrostatic Mesoscale Model (NMM) and WRF–Advanced Research WRF (ARW) meteorological models on the Community Multiscale Air Quality (CMAQ) simulations for ozone and its related prec...

Improving Air Quality Forecasts with AURA Observations

NASA Technical Reports Server (NTRS)

Newchurch, M. J.; Biazer, A.; Khan, M.; Koshak, W. J.; Nair, U.; Fuller, K.; Wang, L.; Parker, Y.; Williams, R.; Liu, X.

2008-01-01

Past studies have identified model initial and boundary conditions as sources of reducible errors in air-quality simulations. In particular, improving the initial condition improves the accuracy of short-term forecasts as it allows for the impact of local emissions to be realized by the model and improving boundary conditions improves long range transport through the model domain, especially in recirculating anticyclones. During the August 2006 period, we use AURA/OMI ozone measurements along with MODIS and CALIPSO aerosol observations to improve the initial and boundary conditions of ozone and Particulate Matter. Assessment of the model by comparison of the control run and satellite assimilation run to the IONS06 network of ozonesonde observations, which comprise the densest ozone sounding campaign ever conducted in North America, to AURA/TES ozone profile measurements, and to the EPA ground network of ozone and PM measurements will show significant improvement in the CMAQ calculations that use AURA initial and boundary conditions. Further analyses of lightning occurrences from ground and satellite observations and AURA/OMI NO2 column abundances will identify the lightning NOx signal evident in OMI measurements and suggest pathways for incorporating the lightning and NO2 data into the CMAQ simulations.
WRF Test on IBM BG/L:Toward High Performance Application to Regional Climate Research

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

Chin, H S

The effects of climate change will mostly be felt on local to regional scales (Solomon et al., 2007). To develop better forecast skill in regional climate change, an integrated multi-scale modeling capability (i.e., a pair of global and regional climate models) becomes crucially important in understanding and preparing for the impacts of climate change on the temporal and spatial scales that are critical to California's and nation's future environmental quality and economical prosperity. Accurate knowledge of detailed local impact on the water management system from climate change requires a resolution of 1km or so. To this end, a high performancemore » computing platform at the petascale appears to be an essential tool in providing such local scale information to formulate high quality adaptation strategies for local and regional climate change. As a key component of this modeling system at LLNL, the Weather Research and Forecast (WRF) model is implemented and tested on the IBM BG/L machine. The objective of this study is to examine the scaling feature of WRF on BG/L for the optimal performance, and to assess the numerical accuracy of WRF solution on BG/L.« less
A Performance Evaluation of the National Air Quality Forecast Capability for the Summer of 2007

EPA Science Inventory

This paper provides a performance evaluation of the real-time, CONUS-scale National Air Quality Forecast Capability (NAQFC), developed collaboratively by the National Oceanic and Atmospheric Administration (NOAA) and Environmental Protection Agency (EPA), that supported, in part,...
NCEP Air Quality Forecast(AQF) Graphics

Science.gov Websites

NAM-CMAQ Experimental Run predictions 00 03 06 09 12 15 18 21 24 27 30 33 36 39 42 45 48 Select experimental bias correction predictions NAM vs Nest forecasts Change Variable Type: Hourly CMAQ Forecasts
Nowcasting and Forecasting Concentrations of Biological Contaminants at Beaches: A Feasibility and Case Study

EPA Science Inventory

Public concern over microbial contamination of recreational waters has increased in recent years. A common approach to evaluating beach water quality has been to use the persistence model which assumes that day-old monitoring results provide accurate estimates of current concentr...
Mixed Single/Double Precision in OpenIFS: A Detailed Study of Energy Savings, Scaling Effects, Architectural Effects, and Compilation Effects

NASA Astrophysics Data System (ADS)

Fagan, Mike; Dueben, Peter; Palem, Krishna; Carver, Glenn; Chantry, Matthew; Palmer, Tim; Schlacter, Jeremy

2017-04-01

It has been shown that a mixed precision approach that judiciously replaces double precision with single precision calculations can speed-up global simulations. In particular, a mixed precision variation of the Integrated Forecast System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF) showed virtually the same quality model results as the standard double precision version (Vana et al., Single precision in weather forecasting models: An evaluation with the IFS, Monthly Weather Review, in print). In this study, we perform detailed measurements of savings in computing time and energy using a mixed precision variation of the -OpenIFS- model. The mixed precision variation of OpenIFS is analogous to the IFS variation used in Vana et al. We (1) present results for energy measurements for simulations in single and double precision using Intel's RAPL technology, (2) conduct a -scaling- study to quantify the effects that increasing model resolution has on both energy dissipation and computing cycles, (3) analyze the differences between single core and multicore processing, and (4) compare the effects of different compiler technologies on the mixed precision OpenIFS code. In particular, we compare intel icc/ifort with gnu gcc/gfortran.
Predicting Global Fund grant disbursements for procurement of artemisinin-based combination therapies

PubMed Central

Cohen, Justin M; Singh, Inder; O'Brien, Megan E

2008-01-01

Background An accurate forecast of global demand is essential to stabilize the market for artemisinin-based combination therapy (ACT) and to ensure access to high-quality, life-saving medications at the lowest sustainable prices by avoiding underproduction and excessive overproduction, each of which can have negative consequences for the availability of affordable drugs. A robust forecast requires an understanding of the resources available to support procurement of these relatively expensive antimalarials, in particular from the Global Fund, at present the single largest source of ACT funding. Methods Predictive regression models estimating the timing and rate of disbursements from the Global Fund to recipient countries for each malaria grant were derived using a repeated split-sample procedure intended to avoid over-fitting. Predictions were compared against actual disbursements in a group of validation grants, and forecasts of ACT procurement extrapolated from disbursement predictions were evaluated against actual procurement in two sub-Saharan countries. Results Quarterly forecasts were correlated highly with actual smoothed disbursement rates (r = 0.987, p < 0.0001). Additionally, predicted ACT procurement, extrapolated from forecasted disbursements, was correlated strongly with actual ACT procurement supported by two grants from the Global Fund's first (r = 0.945, p < 0.0001) and fourth (r = 0.938, p < 0.0001) funding rounds. Conclusion This analysis derived predictive regression models that successfully forecasted disbursement patterning for individual Global Fund malaria grants. These results indicate the utility of this approach for demand forecasting of ACT and, potentially, for other commodities procured using funding from the Global Fund. Further validation using data from other countries in different regions and environments will be necessary to confirm its generalizability. PMID:18831742
Air Quality Modeling Using the NASA GEOS-5 Multispecies Data Assimilation System

NASA Technical Reports Server (NTRS)

Keller, Christoph A.; Pawson, Steven; Wargan, Krzysztof; Weir, Brad

2018-01-01

The NASA Goddard Earth Observing System (GEOS) data assimilation system (DAS) has been expanded to include chemically reactive tropospheric trace gases including ozone (O3), nitrogen dioxide (NO2), and carbon monoxide (CO). This system combines model analyses from the GEOS-5 model with detailed atmospheric chemistry and observations from MLS (O3), OMI (O3 and NO2), and MOPITT (CO). We show results from a variety of assimilation test experiments, highlighting the improvements in the representation of model species concentrations by up to 50% compared to an assimilation-free control experiment. Taking into account the rapid chemical cycling of NO2 when applying the assimilation increments greatly improves assimilation skills for NO2 and provides large benefits for model concentrations near the surface. Analysis of the geospatial distribution of the assimilation increments suggest that the free-running model overestimates biomass burning emissions but underestimates lightning NOx emissions by 5-20%. We discuss the capability of the chemical data assimilation system to improve atmospheric composition forecasts through improved initial value and boundary condition inputs, particularly during air pollution events. We find that the current assimilation system meaningfully improves short-term forecasts (1-3 day). For longer-term forecasts more emphasis on updating the emissions instead of initial concentration fields is needed.
Puget Sound Operational Forecast System - A Real-time Predictive Tool for Marine Resource Management and Emergency Responses

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

Yang, Zhaoqing; Khangaonkar, Tarang; Chase, Jared M.

2009-12-01

To support marine ecological resource management and emergency response and to enhance scientific understanding of physical and biogeochemical processes in Puget Sound, a real-time Puget Sound Operational Forecast System (PS-OFS) was developed by the Coastal Ocean Dynamics & Ecosystem Modeling group (CODEM) of Pacific Northwest National Laboratory (PNNL). PS-OFS employs the state-of-the-art three-dimensional coastal ocean model and closely follows the standards and procedures established by National Oceanic and Atmospheric Administration (NOAA) National Ocean Service (NOS). PS-OFS consists of four key components supporting the Puget Sound Circulation and Transport Model (PS-CTM): data acquisition, model execution and product archive, model skill assessment,more » and model results dissemination. This paper provides an overview of PS-OFS and its ability to provide vital real-time oceanographic information to the Puget Sound community. PS-OFS supports pacific northwest region’s growing need for a predictive tool to assist water quality management, fish stock recovery efforts, maritime emergency response, nearshore land-use planning, and the challenge of climate change and sea level rise impacts. The structure of PS-OFS and examples of the system inputs and outputs, forecast results are presented in details.« less
Application of Time-series Model to Predict Groundwater Quality Parameters for Agriculture: (Plain Mehran Case Study)

NASA Astrophysics Data System (ADS)

Mehrdad Mirsanjari, Mir; Mohammadyari, Fatemeh

2018-03-01

Underground water is regarded as considerable water source which is mainly available in arid and semi arid with deficient surface water source. Forecasting of hydrological variables are suitable tools in water resources management. On the other hand, time series concepts is considered efficient means in forecasting process of water management. In this study the data including qualitative parameters (electrical conductivity and sodium adsorption ratio) of 17 underground water wells in Mehran Plain has been used to model the trend of parameters change over time. Using determined model, the qualitative parameters of groundwater is predicted for the next seven years. Data from 2003 to 2016 has been collected and were fitted by AR, MA, ARMA, ARIMA and SARIMA models. Afterward, the best model is determined using information criterion or Akaike (AIC) and correlation coefficient. After modeling parameters, the map of agricultural land use in 2016 and 2023 were generated and the changes between these years were studied. Based on the results, the average of predicted SAR (Sodium Adsorption Rate) in all wells in the year 2023 will increase compared to 2016. EC (Electrical Conductivity) average in the ninth and fifteenth holes and decreases in other wells will be increased. The results indicate that the quality of groundwater for Agriculture Plain Mehran will decline in seven years.
HEPS4Power - Extended-range Hydrometeorological Ensemble Predictions for Improved Hydropower Operations and Revenues

NASA Astrophysics Data System (ADS)

Bogner, Konrad; Monhart, Samuel; Liniger, Mark; Spririg, Christoph; Jordan, Fred; Zappa, Massimiliano

2015-04-01

In recent years large progresses have been achieved in the operational prediction of floods and hydrological drought with up to ten days lead time. Both the public and the private sectors are currently using probabilistic runoff forecast in order to monitoring water resources and take actions when critical conditions are to be expected. The use of extended-range predictions with lead times exceeding 10 days is not yet established. The hydropower sector in particular might have large benefits from using hydro meteorological forecasts for the next 15 to 60 days in order to optimize the operations and the revenues from their watersheds, dams, captions, turbines and pumps. The new Swiss Competence Centers in Energy Research (SCCER) targets at boosting research related to energy issues in Switzerland. The objective of HEPS4POWER is to demonstrate that operational extended-range hydro meteorological forecasts have the potential to become very valuable tools for fine tuning the production of energy from hydropower systems. The project team covers a specific system-oriented value chain starting from the collection and forecast of meteorological data (MeteoSwiss), leading to the operational application of state-of-the-art hydrological models (WSL) and terminating with the experience in data presentation and power production forecasts for end-users (e-dric.ch). The first task of the HEPS4POWER will be the downscaling and post-processing of ensemble extended-range meteorological forecasts (EPS). The goal is to provide well-tailored forecasts of probabilistic nature that should be reliable in statistical and localized at catchment or even station level. The hydrology related task will consist in feeding the post-processed meteorological forecasts into a HEPS using a multi-model approach by implementing models with different complexity. Also in the case of the hydrological ensemble predictions, post-processing techniques need to be tested in order to improve the quality of the forecasts against observed discharge. Analysis should be specifically oriented to the maximisation of hydroelectricity production. Thus, verification metrics should include economic measures like cost loss approaches. The final step will include the transfer of the HEPS system to several hydropower systems, the connection with the energy market prices and the development of probabilistic multi-reservoir production and management optimizations guidelines. The baseline model chain yielding three-days forecasts established for a hydropower system in southern-Switzerland will be presented alongside with the work-plan to achieve seasonal ensemble predictions.
Potential assessment of a neural network model with PCA/RBF approach for forecasting pollutant trends in Mong Kok urban air, Hong Kong.

PubMed

Lu, Wei-Zhen; Wang, Wen-Jian; Wang, Xie-Kang; Yan, Sui-Hang; Lam, Joseph C

2004-09-01

The forecasting of air pollutant trends has received much attention in recent years. It is an important and popular topic in environmental science, as concerns have been raised about the health impacts caused by unacceptable ambient air pollutant levels. Of greatest concern are metropolitan cities like Hong Kong. In Hong Kong, respirable suspended particulates (RSP), nitrogen oxides (NOx), and nitrogen dioxide (NO2) are major air pollutants due to the dominant usage of diesel fuel by commercial vehicles and buses. Hence, the study of the influence and the trends relating to these pollutants is extremely significant to the public health and the image of the city. The use of neural network techniques to predict trends relating to air pollutants is regarded as a reliable and cost-effective method for the task of prediction. The works reported here involve developing an improved neural network model that combines both the principal component analysis technique and the radial basis function network and forecasts pollutant tendencies based on a recorded database. Compared with general neural network models, the proposed model features a more simple network architecture, a faster training speed, and a more satisfactory prediction performance. The improved model was evaluated with hourly time series of RSP, NOx and NO2 concentrations monitored at the Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000 and proved to be effective. The model developed is a potential tool for forecasting air quality parameters and is superior to traditional neural network methods.
Medium-range reference evapotranspiration forecasts for the contiguous United States based on multi-model numerical weather predictions

NASA Astrophysics Data System (ADS)

Medina, Hanoi; Tian, Di; Srivastava, Puneet; Pelosi, Anna; Chirico, Giovanni B.

2018-07-01

Reference evapotranspiration (ET0) plays a fundamental role in agronomic, forestry, and water resources management. Estimating and forecasting ET0 have long been recognized as a major challenge for researchers and practitioners in these communities. This work explored the potential of multiple leading numerical weather predictions (NWPs) for estimating and forecasting summer ET0 at 101 U.S. Regional Climate Reference Network stations over nine climate regions across the contiguous United States (CONUS). Three leading global NWP model forecasts from THORPEX Interactive Grand Global Ensemble (TIGGE) dataset were used in this study, including the single model ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (EC), the National Centers for Environmental Prediction Global Forecast System (NCEP), and the United Kingdom Meteorological Office forecasts (MO), as well as multi-model ensemble forecasts from the combinations of these NWP models. A regression calibration was employed to bias correct the ET0 forecasts. Impact of individual forecast variables on ET0 forecasts were also evaluated. The results showed that the EC forecasts provided the least error and highest skill and reliability, followed by the MO and NCEP forecasts. The multi-model ensembles constructed from the combination of EC and MO forecasts provided slightly better performance than the single model EC forecasts. The regression process greatly improved ET0 forecast performances, particularly for the regions involving stations near the coast, or with a complex orography. The performance of EC forecasts was only slightly influenced by the size of the ensemble members, particularly at short lead times. Even with less ensemble members, EC still performed better than the other two NWPs. Errors in the radiation forecasts, followed by those in the wind, had the most detrimental effects on the ET0 forecast performances.
Evaluation of a regional assimilation system coupled with the WRF-chem model

NASA Astrophysics Data System (ADS)

Liu, Yan-an; Gao, Wei; Huang, Hung-lung; Strabala, Kathleen; Liu, Chaoshun; Shi, Runhe

2013-09-01

Air quality has become a social issue that is causing great concern to humankind across the globe, but particularly in developing countries. Even though the Weather Research and Forecasting with Chemistry (WRF-Chem) model has been applied in many regions, the resolution for inputting meteorology field analysis still impacts the accuracy of forecast. This article describes the application of the CIMSS Regional Assimilation System (CRAS) in East China, and its capability to assimilate the direct broadcast (DB) satellite data for obtaining more detailed meteorological information, including cloud top pressure (CTP) and total precipitation water (TPW) from MODIS. Performance evaluation of CRAS is based on qualitative and quantitative analyses. Compared with data collected from ERA-Interim, Radiosonde, and the Tropical Rainfall Measuring Mission (TRMM) precipitation measurements using bias and Root Mean Square Error (RMSE), CRAS has a systematic error due to the impact of topography and other factors; however, the forecast accuracy of all elements in the model center area is higher at various levels. The bias computed with Radiosonde reveals that the temperature and geopotential height of CRAS are better than ERA-Interim at first guess. Moreover, the location of the 24 h accumulated precipitation forecast are highly consistent with the TRMM retrieval precipitation, which means that the performance of CRAS is excellent. In summation, the newly built Vtable can realize the function of inputting the meteorology field from CRAS output into WRF, which couples the CRAS with WRF-Chem. Therefore, this study not only provides for forecast accuracy of CRAS, but also increases the capability of running the WRF-Chem model at higher resolutions in the future.
The circadian profile of epilepsy improves seizure forecasting.

PubMed

Karoly, Philippa J; Ung, Hoameng; Grayden, David B; Kuhlmann, Levin; Leyde, Kent; Cook, Mark J; Freestone, Dean R

2017-08-01

It is now established that epilepsy is characterized by periodic dynamics that increase seizure likelihood at certain times of day, and which are highly patient-specific. However, these dynamics are not typically incorporated into seizure prediction algorithms due to the difficulty of estimating patient-specific rhythms from relatively short-term or unreliable data sources. This work outlines a novel framework to develop and assess seizure forecasts, and demonstrates that the predictive power of forecasting models is improved by circadian information. The analyses used long-term, continuous electrocorticography from nine subjects, recorded for an average of 320 days each. We used a large amount of out-of-sample data (a total of 900 days for algorithm training, and 2879 days for testing), enabling the most extensive post hoc investigation into seizure forecasting. We compared the results of an electrocorticography-based logistic regression model, a circadian probability, and a combined electrocorticography and circadian model. For all subjects, clinically relevant seizure prediction results were significant, and the addition of circadian information (combined model) maximized performance across a range of outcome measures. These results represent a proof-of-concept for implementing a circadian forecasting framework, and provide insight into new approaches for improving seizure prediction algorithms. The circadian framework adds very little computational complexity to existing prediction algorithms, and can be implemented using current-generation implant devices, or even non-invasively via surface electrodes using a wearable application. The ability to improve seizure prediction algorithms through straightforward, patient-specific modifications provides promise for increased quality of life and improved safety for patients with epilepsy. © The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
Subseasonal to Seasonal Forecasting at NASA in Support of the National Earth System Prediction Capability

NASA Astrophysics Data System (ADS)

Considine, D. B.; Pawson, S.; Koster, R. D.; Kovach, R. M.; Vernieres, G.; Schubert, S. D.

2016-12-01

NASA has developed and maintains, within the Goddard Modeling and Assimilation Office (GMAO), a seasonal-to-interannual prediction activity in support of the National ESPC, based on the GEOS-5 Atmosphere-Ocean General Circulation Model (AOGCM). This system generates atmospheric, land, and ocean/ice analyses that are used to produce global forecasts. Each month, a 17-member ensemble of forecasts is made, from which various oceanic indices (e.g., El Niño, East Indian Dipole, Atlantic SST anomalies), are computed. Additionally, monthly and seasonal anomalies are computed for several variables from the atmosphere (e.g., 2-meter temperatures, precipitation, geopotential heights), land (drought indices), ocean (subsurface temperature anomalies), and sea ice. These forecasts are provided to the National Multi Model Ensemble (NMME) and the Study of Environmental Arctic Change (SEARCH) sea ice outlook. The quasi-operational nature of this system, with constant generation of products that are shared with the broader community, allows for continual assessment of the impacts of NASA observations on seasonal forecasts - a current example is the altimetry data from the JASON series of satellites. The GMAO's seasonal prediction system is currently being upgraded. Alongside typical enhancements, such as increased spatial resolution and use of more recent model versions with improved representation of physical processes, these developments are designed to enhance the use of NASA observations. One example is the use of aerosol information from NASA's EOS instruments (MODIS). A major motivation is also to include NASA's novel data types, such as soil-moisture from SMAP and other sources of oceanic information (such as salinity). This approach enables NASA to continue contributing to national seasonal forecasting efforts, while simultaneously introducing its novel observing capabilities into the seasonal system in a manner that can demonstrate their systematic impacts on the quality of the products.
Forecasting land cover change impacts on drinking water treatment costs in Minneapolis, Minnesota

EPA Science Inventory

Source protection is a critical aspect of drinking water treatment. The benefits of protecting source water quality in reducing drinking water treatment costs are clear. However, forecasting the impacts of environmental change on source water quality and its potential to influenc...
Multi-Year Revenue and Expenditure Forecasting for Small Municipal Governments.

DTIC Science & Technology

1981-03-01

Management Audit Econometric Revenue Forecast Gap and Impact Analysis Deterministic Expenditure Forecast Municipal Forecasting Municipal Budget Formlto...together with a multi-year revenue and expenditure forecasting model for the City of Monterey, California. The Monterey model includes an econometric ...65 5 D. FORECAST BASED ON THE ECONOMETRIC MODEL ------- 67 E. FORECAST BASED ON EXPERT JUDGMENT AND TREND ANALYSIS
A New Hybrid Model FPA-SVM Considering Cointegration for Particular Matter Concentration Forecasting: A Case Study of Kunming and Yuxi, China.

PubMed

Li, Weide; Kong, Demeng; Wu, Jinran

2017-01-01

Air pollution in China is becoming more serious especially for the particular matter (PM) because of rapid economic growth and fast expansion of urbanization. To solve the growing environment problems, daily PM2.5 and PM10 concentration data form January 1, 2015, to August 23, 2016, in Kunming and Yuxi (two important cities in Yunnan Province, China) are used to present a new hybrid model CI-FPA-SVM to forecast air PM2.5 and PM10 concentration in this paper. The proposed model involves two parts. Firstly, due to its deficiency to assess the possible correlation between different variables, the cointegration theory is introduced to get the input-output relationship and then obtain the nonlinear dynamical system with support vector machine (SVM), in which the parameters c and g are optimized by flower pollination algorithm (FPA). Six benchmark models, including FPA-SVM, CI-SVM, CI-GA-SVM, CI-PSO-SVM, CI-FPA-NN, and multiple linear regression model, are considered to verify the superiority of the proposed hybrid model. The empirical study results demonstrate that the proposed model CI-FPA-SVM is remarkably superior to all considered benchmark models for its high prediction accuracy, and the application of the model for forecasting can give effective monitoring and management of further air quality.
A New Hybrid Model FPA-SVM Considering Cointegration for Particular Matter Concentration Forecasting: A Case Study of Kunming and Yuxi, China

PubMed Central

Wu, Jinran

2017-01-01

Air pollution in China is becoming more serious especially for the particular matter (PM) because of rapid economic growth and fast expansion of urbanization. To solve the growing environment problems, daily PM2.5 and PM10 concentration data form January 1, 2015, to August 23, 2016, in Kunming and Yuxi (two important cities in Yunnan Province, China) are used to present a new hybrid model CI-FPA-SVM to forecast air PM2.5 and PM10 concentration in this paper. The proposed model involves two parts. Firstly, due to its deficiency to assess the possible correlation between different variables, the cointegration theory is introduced to get the input-output relationship and then obtain the nonlinear dynamical system with support vector machine (SVM), in which the parameters c and g are optimized by flower pollination algorithm (FPA). Six benchmark models, including FPA-SVM, CI-SVM, CI-GA-SVM, CI-PSO-SVM, CI-FPA-NN, and multiple linear regression model, are considered to verify the superiority of the proposed hybrid model. The empirical study results demonstrate that the proposed model CI-FPA-SVM is remarkably superior to all considered benchmark models for its high prediction accuracy, and the application of the model for forecasting can give effective monitoring and management of further air quality. PMID:28932237

Forecasting daily attendances at an emergency department to aid resource planning

PubMed Central

Sun, Yan; Heng, Bee Hoon; Seow, Yian Tay; Seow, Eillyne

2009-01-01

Background Accurate forecasting of emergency department (ED) attendances can be a valuable tool for micro and macro level planning. Methods Data for analysis was the counts of daily patient attendances at the ED of an acute care regional general hospital from July 2005 to Mar 2008. Patients were stratified into three acuity categories; i.e. P1, P2 and P3, with P1 being the most acute and P3 being the least acute. The autoregressive integrated moving average (ARIMA) method was separately applied to each of the three acuity categories and total patient attendances. Independent variables included in the model were public holiday (yes or no), ambient air quality measured by pollution standard index (PSI), daily ambient average temperature and daily relative humidity. The seasonal components of weekly and yearly periodicities in the time series of daily attendances were also studied. Univariate analysis by t-tests and multivariate time series analysis were carried out in SPSS version 15. Results By time series analyses, P1 attendances did not show any weekly or yearly periodicity and was only predicted by ambient air quality of PSI > 50. P2 and total attendances showed weekly periodicities, and were also significantly predicted by public holiday. P3 attendances were significantly correlated with day of the week, month of the year, public holiday, and ambient air quality of PSI > 50. After applying the developed models to validate the forecast, the MAPE of prediction by the models were 16.8%, 6.7%, 8.6% and 4.8% for P1, P2, P3 and total attendances, respectively. The models were able to account for most of the significant autocorrelations present in the data. Conclusion Time series analysis has been shown to provide a useful, readily available tool for predicting emergency department workload that can be used to plan staff roster and resource planning. PMID:19178716
Air quality impact assessment of multiple open pit coal mines in northern Colombia.

PubMed

Huertas, José I; Huertas, María E; Izquierdo, Sebastián; González, Enrique D

2012-01-01

The coal mining region in northern Colombia is one of the largest open pit mining regions of the world. In 2009, there were 8 mining companies in operation with an approximate coal production of ∼70 Mtons/year. Since 2007, the Colombian air quality monitoring network has reported readings that exceed the daily and annual air quality standards for total suspended particulate (TSP) matter and particles with an equivalent aerodynamic diameter smaller than 10 μm (PM₁₀) in nearby villages. This paper describes work carried out in order to establish an appropriate clean air program for this region, based on the Colombian national environmental authority requirement for modeling of TSP and PM(10) dispersion. A TSP and PM₁₀ emission inventory was initially developed, and topographic and meteorological information for the region was collected and analyzed. Using this information, the dispersion of TSP was modeled in ISC3 and AERMOD using meteorological data collected by 3 local stations during 2008 and 2009. The results obtained were compared to actual values measured by the air quality monitoring network. High correlation coefficients (>0.73) were obtained, indicating that the models accurately described the main factors affecting particle dispersion in the region. The model was then used to forecast concentrations of particulate matter for 2010. Based on results from the model, areas within the modeling region were identified as highly, fairly, moderately and marginally polluted according to local regulations. Additionally, the contribution particulate matter to the pollution at each village was estimated. Using these predicted values, the Colombian environmental authority imposed new decontamination measures on the mining companies operating in the region. These measures included the relocation of three villages financed by the mine companies based on forecasted pollution levels. Copyright Â© 2011. Published by Elsevier Ltd.
Selecting Single Model in Combination Forecasting Based on Cointegration Test and Encompassing Test

PubMed Central

Jiang, Chuanjin; Zhang, Jing; Song, Fugen

2014-01-01

Combination forecasting takes all characters of each single forecasting method into consideration, and combines them to form a composite, which increases forecasting accuracy. The existing researches on combination forecasting select single model randomly, neglecting the internal characters of the forecasting object. After discussing the function of cointegration test and encompassing test in the selection of single model, supplemented by empirical analysis, the paper gives the single model selection guidance: no more than five suitable single models can be selected from many alternative single models for a certain forecasting target, which increases accuracy and stability. PMID:24892061
Selecting single model in combination forecasting based on cointegration test and encompassing test.

PubMed

Jiang, Chuanjin; Zhang, Jing; Song, Fugen

2014-01-01

Combination forecasting takes all characters of each single forecasting method into consideration, and combines them to form a composite, which increases forecasting accuracy. The existing researches on combination forecasting select single model randomly, neglecting the internal characters of the forecasting object. After discussing the function of cointegration test and encompassing test in the selection of single model, supplemented by empirical analysis, the paper gives the single model selection guidance: no more than five suitable single models can be selected from many alternative single models for a certain forecasting target, which increases accuracy and stability.
Coupling Radar Rainfall to Hydrological Models for Water Abstraction Management

NASA Astrophysics Data System (ADS)

Asfaw, Alemayehu; Shucksmith, James; Smith, Andrea; MacDonald, Ken

2015-04-01

The impacts of climate change and growing water use are likely to put considerable pressure on water resources and the environment. In the UK, a reform to surface water abstraction policy has recently been proposed which aims to increase the efficiency of using available water resources whilst minimising impacts on the aquatic environment. Key aspects to this reform include the consideration of dynamic rather than static abstraction licensing as well as introducing water trading concepts. Dynamic licensing will permit varying levels of abstraction dependent on environmental conditions (i.e. river flow and quality). The practical implementation of an effective dynamic abstraction strategy requires suitable flow forecasting techniques to inform abstraction asset management. Potentially the predicted availability of water resources within a catchment can be coupled to predicted demand and current storage to inform a cost effective water resource management strategy which minimises environmental impacts. The aim of this work is to use a historical analysis of UK case study catchment to compare potential water resource availability using modelled dynamic abstraction scenario informed by a flow forecasting model, against observed abstraction under a conventional abstraction regime. The work also demonstrates the impacts of modelling uncertainties on the accuracy of predicted water availability over range of forecast lead times. The study utilised a conceptual rainfall-runoff model PDM - Probability-Distributed Model developed by Centre for Ecology & Hydrology - set up in the Dove River catchment (UK) using 1km2 resolution radar rainfall as inputs and 15 min resolution gauged flow data for calibration and validation. Data assimilation procedures are implemented to improve flow predictions using observed flow data. Uncertainties in the radar rainfall data used in the model are quantified using artificial statistical error model described by Gaussian distribution and propagated through the model to assess its influence on the forecasted flow uncertainty. Furthermore, the effects of uncertainties at different forecast lead times on potential abstraction strategies are assessed. The results show that over a 10 year period, an average of approximately 70 ML/d of potential water is missed in the study catchment under a convention abstraction regime. This indicates a considerable potential for the use of flow forecasting models to effectively implement advanced abstraction management and more efficiently utilize available water resources in the study catchment.
Water balance models in one-month-ahead streamflow forecasting

USGS Publications Warehouse

Alley, William M.

1985-01-01

Techniques are tested that incorporate information from water balance models in making 1-month-ahead streamflow forecasts in New Jersey. The results are compared to those based on simple autoregressive time series models. The relative performance of the models is dependent on the month of the year in question. The water balance models are most useful for forecasts of April and May flows. For the stations in northern New Jersey, the April and May forecasts were made in order of decreasing reliability using the water-balance-based approaches, using the historical monthly means, and using simple autoregressive models. The water balance models were useful to a lesser extent for forecasts during the fall months. For the rest of the year the improvements in forecasts over those obtained using the simpler autoregressive models were either very small or the simpler models provided better forecasts. When using the water balance models, monthly corrections for bias are found to improve minimum mean-square-error forecasts as well as to improve estimates of the forecast conditional distributions.
Evaluation Of Statistical Models For Forecast Errors From The HBV-Model

NASA Astrophysics Data System (ADS)

Engeland, K.; Kolberg, S.; Renard, B.; Stensland, I.

2009-04-01

Three statistical models for the forecast errors for inflow to the Langvatn reservoir in Northern Norway have been constructed and tested according to how well the distribution and median values of the forecasts errors fit to the observations. For the first model observed and forecasted inflows were transformed by the Box-Cox transformation before a first order autoregressive model was constructed for the forecast errors. The parameters were conditioned on climatic conditions. In the second model the Normal Quantile Transformation (NQT) was applied on observed and forecasted inflows before a similar first order autoregressive model was constructed for the forecast errors. For the last model positive and negative errors were modeled separately. The errors were first NQT-transformed before a model where the mean values were conditioned on climate, forecasted inflow and yesterday's error. To test the three models we applied three criterions: We wanted a) the median values to be close to the observed values; b) the forecast intervals to be narrow; c) the distribution to be correct. The results showed that it is difficult to obtain a correct model for the forecast errors, and that the main challenge is to account for the auto-correlation in the errors. Model 1 and 2 gave similar results, and the main drawback is that the distributions are not correct. The 95% forecast intervals were well identified, but smaller forecast intervals were over-estimated, and larger intervals were under-estimated. Model 3 gave a distribution that fits better, but the median values do not fit well since the auto-correlation is not properly accounted for. If the 95% forecast interval is of interest, Model 2 is recommended. If the whole distribution is of interest, Model 3 is recommended.
Meteorology, Emissions, and Grid Resolution: Effects on Discrete and Probabilistic Model Performance

EPA Science Inventory

In this study, we analyze the impacts of perturbations in meteorology and emissions and variations in grid resolution on air quality forecast simulations. The meteorological perturbations con-sidered in this study introduce a typical variability of ~1°C, 250 - 500 m, 1 m/s, and 1...
MEETING IN MEXICO: NOWCASTING AND FORECASTING BEACH BACTERIA CONCENTRATION USING EPA'S VIRTUAL BEACH SOFTWARE

EPA Science Inventory

Beaches in the United States of (North) America are subject to closure when bacterial counts exceed water quality criteria. Many authorities base these decisions on water samples that typically require at least 18 hours to analyze. This persistence approach, or model, often leads...
Dynamic evaluation of two decades of WRF-CMAQ ozone simulations over the contiguous United States

EPA Science Inventory

Dynamic evaluation of the fully coupled Weather Research and Forecasting (WRF)– Community Multi-scale Air Quality (CMAQ) model ozone simulations over the contiguous United States (CONUS) using two decades of simulations covering the period from 1990 to 2010 is conducted to ...
Dynamic evaluation of two decades of WRF-CMAQ ozone simulations over the contiguous United States

EPA Science Inventory

Dynamic evaluation of the fully coupled Weather Research and Forecasting (WRF)– Community Multi-scale Air Quality (CMAQ) model ozone simulations over the contiguous United States (CONUS) using two decades of simulations covering the period from 1990 to 2010 is conducted to assess...
A SIMPLE MODEL FOR FORECASTING THE EFFECTS OF NITROGEN LOADS ON CHESAPEAKE BAY HYPOXIA

EPA Science Inventory

The causes and consequences of oxygen depletion in Chesapeake Bay have been the focus of research, assessment, and policy action over the past several decades. An ongoing scientific re-evaluation of what nutrients load reductions are necessary to meet the water quality goals is ...
Comprehensive evaluation of multi-year real-time air quality forecasting using an online-coupled meteorology-chemistry model over southeastern United States

NASA Astrophysics Data System (ADS)

Zhang, Yang; Hong, Chaopeng; Yahya, Khairunnisa; Li, Qi; Zhang, Qiang; He, Kebin

2016-08-01

An online-coupled meteorology-chemistry model, WRF/Chem-MADRID, has been deployed for real time air quality forecast (RT-AQF) in southeastern U.S. since 2009. A comprehensive evaluation of multi-year RT-AQF shows overall good performance for temperature and relative humidity at 2-m (T2, RH2), downward surface shortwave radiation (SWDOWN) and longwave radiation (LWDOWN), and cloud fraction (CF), ozone (O3) and fine particles (PM2.5) at surface, tropospheric ozone residuals (TOR) in O3 seasons (May-September), and column NO2 in winters (December-February). Moderate-to-large biases exist in wind speed at 10-m (WS10), precipitation (Precip), cloud optical depth (COT), ammonium (NH4+), sulfate (SO42-), and nitrate (NO3-) from the IMPROVE and SEARCH networks, organic carbon (OC) at IMPROVE, and elemental carbon (EC) and OC at SEARCH, aerosol optical depth (AOD) and column carbon monoxide (CO), sulfur dioxide (SO2), and formaldehyde (HCHO) in both O3 and winter seasons, column nitrogen dioxide (NO2) in O3 seasons, and TOR in winters. These biases indicate uncertainties in the boundary layer and cloud process treatments (e.g., surface roughness, microphysics cumulus parameterization), emissions (e.g., O3 and PM precursors, biogenic, mobile, and wildfire emissions), upper boundary conditions for all major gases and PM2.5 species, and chemistry and aerosol treatments (e.g., winter photochemistry, aerosol thermodynamics). The model shows overall good skills in reproducing the observed multi-year trends and inter-seasonal variability in meteorological and radiative variables such as T2, WS10, Precip, SWDOWN, and LWDOWN, and relatively well in reproducing the observed trends in surface O3 and PM2.5, but relatively poor in reproducing the observed column abundances of CO, NO2, SO2, HCHO, TOR, and AOD. The sensitivity simulations using satellite-constrained boundary conditions for O3 and CO show substantial improvement for both spatial distribution and domain-mean performance statistics. The model's forecasting skills for air quality can be further enhanced through improving model inputs (e.g., anthropogenic emissions for urban areas and upper boundary conditions of chemical species), meteorological forecasts (e.g., WS10, Precip) and meteorologically-dependent emissions (e.g., biogenic and wildfire emissions), and model physics and chemical treatments (e.g., gas-phase chemistry in winter conditions, cloud processes and their interactions with radiation and aerosol).
Multivariate postprocessing techniques for probabilistic hydrological forecasting

NASA Astrophysics Data System (ADS)

Hemri, Stephan; Lisniak, Dmytro; Klein, Bastian

2016-04-01

Hydrologic ensemble forecasts driven by atmospheric ensemble prediction systems need statistical postprocessing in order to account for systematic errors in terms of both mean and spread. Runoff is an inherently multivariate process with typical events lasting from hours in case of floods to weeks or even months in case of droughts. This calls for multivariate postprocessing techniques that yield well calibrated forecasts in univariate terms and ensure a realistic temporal dependence structure at the same time. To this end, the univariate ensemble model output statistics (EMOS; Gneiting et al., 2005) postprocessing method is combined with two different copula approaches that ensure multivariate calibration throughout the entire forecast horizon. These approaches comprise ensemble copula coupling (ECC; Schefzik et al., 2013), which preserves the dependence structure of the raw ensemble, and a Gaussian copula approach (GCA; Pinson and Girard, 2012), which estimates the temporal correlations from training observations. Both methods are tested in a case study covering three subcatchments of the river Rhine that represent different sizes and hydrological regimes: the Upper Rhine up to the gauge Maxau, the river Moselle up to the gauge Trier, and the river Lahn up to the gauge Kalkofen. The results indicate that both ECC and GCA are suitable for modelling the temporal dependences of probabilistic hydrologic forecasts (Hemri et al., 2015). References Gneiting, T., A. E. Raftery, A. H. Westveld, and T. Goldman (2005), Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation, Monthly Weather Review, 133(5), 1098-1118, DOI: 10.1175/MWR2904.1. Hemri, S., D. Lisniak, and B. Klein, Multivariate postprocessing techniques for probabilistic hydrological forecasting, Water Resources Research, 51(9), 7436-7451, DOI: 10.1002/2014WR016473. Pinson, P., and R. Girard (2012), Evaluating the quality of scenarios of short-term wind power generation, Applied Energy, 96, 12-20, DOI: 10.1016/j.apenergy.2011.11.004. Schefzik, R., T. L. Thorarinsdottir, and T. Gneiting (2013), Uncertainty quantification in complex simulation models using ensemble copula coupling, Statistical Science, 28, 616-640, DOI: 10.1214/13-STS443.
Accuracy of short‐term sea ice drift forecasts using a coupled ice‐ocean model

PubMed Central

Zhang, Jinlun

2015-01-01

Abstract Arctic sea ice drift forecasts of 6 h–9 days for the summer of 2014 are generated using the Marginal Ice Zone Modeling and Assimilation System (MIZMAS); the model is driven by 6 h atmospheric forecasts from the Climate Forecast System (CFSv2). Forecast ice drift speed is compared to drifting buoys and other observational platforms. Forecast positions are compared with actual positions 24 h–8 days since forecast. Forecast results are further compared to those from the forecasts generated using an ice velocity climatology driven by multiyear integrations of the same model. The results are presented in the context of scheduling the acquisition of high‐resolution images that need to follow buoys or scientific research platforms. RMS errors for ice speed are on the order of 5 km/d for 24–48 h since forecast using the sea ice model compared with 9 km/d using climatology. Predicted buoy position RMS errors are 6.3 km for 24 h and 14 km for 72 h since forecast. Model biases in ice speed and direction can be reduced by adjusting the air drag coefficient and water turning angle, but the adjustments do not affect verification statistics. This suggests that improved atmospheric forecast forcing may further reduce the forecast errors. The model remains skillful for 8 days. Using the forecast model increases the probability of tracking a target drifting in sea ice with a 10 km × 10 km image from 60 to 95% for a 24 h forecast and from 27 to 73% for a 48 h forecast. PMID:27818852
HOKF: High Order Kalman Filter for Epilepsy Forecasting Modeling.

PubMed

Nguyen, Ngoc Anh Thi; Yang, Hyung-Jeong; Kim, Sunhee

2017-08-01

Epilepsy forecasting has been extensively studied using high-order time series obtained from scalp-recorded electroencephalography (EEG). An accurate seizure prediction system would not only help significantly improve patients' quality of life, but would also facilitate new therapeutic strategies to manage epilepsy. This paper thus proposes an improved Kalman Filter (KF) algorithm to mine seizure forecasts from neural activity by modeling three properties in the high-order EEG time series: noise, temporal smoothness, and tensor structure. The proposed High-Order Kalman Filter (HOKF) is an extension of the standard Kalman filter, for which higher-order modeling is limited. The efficient dynamic of HOKF system preserves the tensor structure of the observations and latent states. As such, the proposed method offers two main advantages: (i) effectiveness with HOKF results in hidden variables that capture major evolving trends suitable to predict neural activity, even in the presence of missing values; and (ii) scalability in that the wall clock time of the HOKF is linear with respect to the number of time-slices of the sequence. The HOKF algorithm is examined in terms of its effectiveness and scalability by conducting forecasting and scalability experiments with a real epilepsy EEG dataset. The results of the simulation demonstrate the superiority of the proposed method over the original Kalman Filter and other existing methods. Copyright © 2017 Elsevier B.V. All rights reserved.
Operational 0-3 h probabilistic quantitative precipitation forecasts: Recent performance and potential enhancements

NASA Astrophysics Data System (ADS)

Sokol, Z.; Kitzmiller, D.; Pešice, P.; Guan, S.

2009-05-01

The NOAA National Weather Service has maintained an automated, centralized 0-3 h prediction system for probabilistic quantitative precipitation forecasts since 2001. This advective-statistical system (ADSTAT) produces probabilities that rainfall will exceed multiple threshold values up to 50 mm at some location within a 40-km grid box. Operational characteristics and development methods for the system are described. Although development data were stratified by season and time of day, ADSTAT utilizes only a single set of nation-wide equations that relate predictor variables derived from radar reflectivity, lightning, satellite infrared temperatures, and numerical prediction model output to rainfall occurrence. A verification study documented herein showed that the operational ADSTAT reliably models regional variations in the relative frequency of heavy rain events. This was true even in the western United States, where no regional-scale, gridded hourly precipitation data were available during the development period in the 1990s. An effort was recently launched to improve the quality of ADSTAT forecasts by regionalizing the prediction equations and to adapt the model for application in the Czech Republic. We have experimented with incorporating various levels of regional specificity in the probability equations. The geographic localization study showed that in the warm season, regional climate differences and variations in the diurnal temperature cycle have a marked effect on the predictor-predictand relationships, and thus regionalization would lead to better statistical reliability in the forecasts.
New watershed-based climate forecast products for hydrologists and water managers

NASA Astrophysics Data System (ADS)

Baker, S. A.; Wood, A.; Rajagopalan, B.; Lehner, F.; Peng, P.; Ray, A. J.; Barsugli, J. J.; Werner, K.

2017-12-01

Operational sub-seasonal to seasonal (S2S) climate predictions have advanced in skill in recent years but are yet to be broadly utilized by stakeholders in the water management sector. While some of the challenges that relate to fundamental predictability are difficult or impossible to surmount, other hurdles related to forecast product formulation, translation, relevance, and accessibility can be directly addressed. These include products being misaligned with users' space-time needs, products disseminated in formats users cannot easily process, and products based on raw model outputs that are biased relative to user climatologies. In each of these areas, more can be done to bridge the gap by enhancing the usability, quality, and relevance of water-oriented predictions. In addition, water stakeholder impacts can benefit from short-range extremes predictions (such as 2-3 day storms or 1-week heat waves) at S2S time-scales, for which few products exist. We present interim results of a Research to Operations (R2O) effort sponsored by the NOAA MAPP Climate Testbed to (1) formulate climate prediction products so as to reduce hurdles to in water stakeholder adoption, and to (2) explore opportunities for extremes prediction at S2S time scales. The project is currently using CFSv2 and National Multi-Model Ensemble (NMME) reforecasts and forecasts to develop real-time watershed-based climate forecast products, and to train post-processing approaches to enhance the skill and reliability of raw real-time S2S forecasts. Prototype S2S climate data products (forecasts and associated skill analyses) are now being operationally staged at NCAR on a public website to facilitate further product development through interactions with water managers. Initial demonstration products include CFSv2-based bi-weekly climate forecasts (weeks 1-2, 2-3, and 3-4) for sub-regional scale hydrologic units, and NMME-based monthly and seasonal prediction products. Raw model mean skill at these time-space resolutions for some periods (e.g., weeks 3-4) is unusably low, but for other periods, and for multi-month leads with NMME, precipitation and particularly temperature forecasts exhibit useful skill. Website: http://hydro.rap.ucar.edu/s2s/
Effect of Streamflow Forecast Uncertainty on Real-Time Reservoir Operation

NASA Astrophysics Data System (ADS)

Zhao, T.; Cai, X.; Yang, D.

2010-12-01

Various hydrological forecast products have been applied to real-time reservoir operation, including deterministic streamflow forecast (DSF), DSF-based probabilistic streamflow forecast (DPSF), and ensemble streamflow forecast (ESF), which represent forecast uncertainty in the form of deterministic forecast error, deterministic forecast error-based uncertainty distribution, and ensemble forecast errors, respectively. Compared to previous studies that treat these forecast products as ad hoc inputs for reservoir operation models, this paper attempts to model the uncertainties involved in the various forecast products and explores their effect on real-time reservoir operation decisions. In hydrology, there are various indices reflecting the magnitude of streamflow forecast uncertainty; meanwhile, few models illustrate the forecast uncertainty evolution process. This research introduces Martingale Model of Forecast Evolution (MMFE) from supply chain management and justifies its assumptions for quantifying the evolution of uncertainty in streamflow forecast as time progresses. Based on MMFE, this research simulates the evolution of forecast uncertainty in DSF, DPSF, and ESF, and applies the reservoir operation models (dynamic programming, DP; stochastic dynamic programming, SDP; and standard operation policy, SOP) to assess the effect of different forms of forecast uncertainty on real-time reservoir operation. Through a hypothetical single-objective real-time reservoir operation model, the results illustrate that forecast uncertainty exerts significant effects. Reservoir operation efficiency, as measured by a utility function, decreases as the forecast uncertainty increases. Meanwhile, these effects also depend on the type of forecast product being used. In general, the utility of reservoir operation with ESF is nearly as high as the utility obtained with a perfect forecast; the utilities of DSF and DPSF are similar to each other but not as efficient as ESF. Moreover, streamflow variability and reservoir capacity can change the magnitude of the effects of forecast uncertainty, but not the relative merit of DSF, DPSF, and ESF. Schematic diagram of the increase in forecast uncertainty with forecast lead-time and the dynamic updating property of real-time streamflow forecast
The DACCIWA model evaluation project: representation of the meteorology of southern West Africa in state-of-the-art weather, seasonal and climate prediction models

NASA Astrophysics Data System (ADS)

Kniffka, Anke; Benedetti, Angela; Knippertz, Peter; Stanelle, Tanja; Brooks, Malcolm; Deetz, Konrad; Maranan, Marlon; Rosenberg, Philip; Pante, Gregor; Allan, Richard; Hill, Peter; Adler, Bianca; Fink, Andreas; Kalthoff, Norbert; Chiu, Christine; Vogel, Bernhard; Field, Paul; Marsham, John

2017-04-01

DACCIWA (Dynamics-Aerosol-Chemistry-Cloud Interactions in West Africa) is an EU-funded project that aims to determine the influence of anthropogenic and natural emissions on the atmospheric composition, air quality, weather and climate over southern West Africa. DACCIWA organised a major international field campaign in June-July 2016 and involves a wide range of modelling activities. Here we report about the coordinated model evaluation performed in the framework of DACCIWA focusing on meteorological fields. This activity consists of two elements: (a) the quality of numerical weather prediction during the field campaign, (b) the ability of seasonal and climate models to represent the mean state and its variability. For the first element, the extensive observations from the main field campaign in West Africa in June-July 2016 (ground supersites, radiosondes, aircraft measurements) will be combined with conventional data (synoptic stations, satellites data from various sensors) to evaluate models against. The forecasts include operational products from centres such as the ECMWF, UK MetOffice and the German Weather Service and runs specifically conducted for the planning and the post-analysis of the field campaign using higher resolutions (e.g., WRF, COSMO). The forecast and the observations are analysed in a concerted way to assess the ability of the models to represent the southern West African weather systems and secondly to provide a comprehensive synoptic overview of the state of the atmosphere. In a second step the process will be extended to long-term modelling periods. This includes both seasonal and climate models, respectively. In this case, the observational dataset contains long-term satellite observations and station data, some of which were digitised from written records in the framework of DACCIWA. Parameter choice and spatial averaging will build directly on the weather forecasting evaluation to allow an assessment of the impact of short-term errors on long-term simulations.

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