Months Of Lead Time Research Articles

An analytical approach to prototype vehicle test scheduling

The test planning group within Ford׳s Product Development division develops schedules for building prototype vehicles and assigning them to departments in charge of different vehicle components, systems and aspects (e.g., powertrain, electrical, safety). These departments conduct tests at pre-production phases of each vehicle program (e.g., 2015 Ford Fusion, 2016 Ford Escape) to ensure the vehicles meet all requirements by the time they reach the production phase. Each prototype can cost in excess of $200K because many of the parts and the prototypes themselves are hand-made and highly customized. Parts needed often require months of lead time, which constrains when vehicle builds can start. That, combined with inflexible deadlines for completing tests on those prototypes introduces significant time pressure, an unavoidable and challenging reality. One way to alleviate time pressure is to build more prototype vehicles; however, this would greatly increase the cost of each program. A more efficient way is to develop test plans with tight schedules that combine multiple tests on vehicles to fully utilize all available time. There are many challenges that need to be overcome in implementing this approach, including complex compatibility relationships between the tests and destructive nature of, e.g., crash tests. We introduce analytical approaches for obtaining efficient schedules to replace the tedious manual scheduling process engineers undertake for each program. Our models and algorithms save test planners׳ and engineers׳ time, increases their ability to quickly react to program changes, and save resources by ensuring maximal vehicle utilization.

A Predictive Test of Voters' Economic Benchmarking: The 2013 German Bundestag Election

Do voters judge their national economy relative to economic performance abroad? In 2013 we took advantage of the German Bundestag election to test this hypothesis predictively. Nearly two months prior to the election, we published an election forecast relying on a theory-driven empirical model of election outcomes that draws on previous election outcomes; characteristics of the government and of voters; and, most originally, the relative economic performance of Germany (‘benchmarked' growth) in comparison to the three other most important economies in Europe – France, the UK and Italy. Our forecast put the outgoing coalition government of CDU/CSU and FDP at 47.05 per cent of the popular vote deviating from the actual outcome of 46.3 by 0.75 points. This makes our forecast one of the most accurate in this election cycle. Despite one-and-a-half months of lead time, our forecast performed on par or slightly better than the last poll results issued only two days before the election.

Are General Circulation Models Ready for Operational Streamflow Forecasting for Water Management in the Ganges and Brahmaputra River Basins?

Abstract This study asks the question of whether GCMs are ready to be operationalized for streamflow forecasting in South Asian river basins, and if so, at what temporal scales and for which water management decisions are they likely to be relevant? The authors focused on the Ganges, Brahmaputra, and Meghna basins for which there is a gridded hydrologic model calibrated for the 2002–10 period. The North American Multimodel Ensemble (NMME) suite of eight GCM hindcasts was applied to generate precipitation forecasts for each month of the 1982–2012 (30 year) period at up to 6 months of lead time, which were then downscaled according to the bias-corrected statistical downscaling (BCSD) procedure to daily time steps. A global retrospective forcing dataset was used for this downscaling procedure. The study clearly revealed that a regionally consistent forcing for BCSD, which is currently unavailable for the region, is one of the primary conditions to realize reasonable skill in streamflow forecasting. In terms of relative RMSE (normalized by reference flow obtained from the global retrospective forcings used in downscaling), streamflow forecast uncertainty (RMSE) was found to be 38%–50% at monthly scale and 22%–35% at seasonal (3 monthly) scale. The Ganges River (regulated) experienced higher uncertainty than the Brahmaputra River (unregulated). In terms of anomaly correlation coefficient (ACC), the streamflow forecasting at seasonal (3 monthly) scale was found to have less uncertainty (>0.3) than at monthly scale (<0.25). The forecast skill in the Brahmaputra basin showed more improvement when the time horizon was aggregated from monthly to seasonal than the Ganges basin. Finally, the skill assessment for the individual seasons revealed that the flow forecasting using NMME data had less uncertainty during monsoon season (July–September) in the Brahmaputra basin and in postmonsoon season (October–December) in the Ganges basin. Overall, the study indicated that GCMs can have value for management decisions only at seasonal or annual water balance applications at best if appropriate historical forcings are used in downscaling. The take-home message of this study is that GCMs are not yet ready for prime-time operationalization for a wide variety of multiscale water management decisions for the Ganges and Brahmaputra River basins.

Climate variability modulates western US ozone air quality in spring via deep stratospheric intrusions.

Evidence suggests deep stratospheric intrusions can elevate western US surface ozone to unhealthy levels during spring. These intrusions can be classified as ‘exceptional events', which are not counted towards non-attainment determinations. Understanding the factors driving the year-to-year variability of these intrusions is thus relevant for effective implementation of the US ozone air quality standard. Here we use observations and model simulations to link these events to modes of climate variability. We show more frequent late spring stratospheric intrusions when the polar jet meanders towards the western United States, such as occurs following strong La Niña winters (Niño3.4<−1.0 °C). While El Niño leads to enhancements of upper tropospheric ozone, we find this influence does not reach surface air. Fewer and weaker intrusion events follow in the two springs after the 1991 volcanic eruption of Mt. Pinatubo. The linkage between La Niña and western US stratospheric intrusions can be exploited to provide a few months of lead time during which preparations could be made to deploy targeted measurements aimed at identifying these exceptional events.

Analyses of the Persian Gulf sea surface temperature: prediction and detection of climate change signals

This study was motivated by two main concerns including (a) prediction of the Persian Gulf Sea surface tem- perature (PGSST) anomalies using an autoregressive integrat- ed moving average (ARIMA) model and (b) detection of the climate change signatures in the considered SST data. An ARIMA model was, therefore, developed to predict the SST anomalies having lead times from 1 to 3 months. While the SST time series for the period of 1950-2006 used to fit the model, corresponding records for January 2007 to June 2011 were applied as the test data. The developed model had a minimum value of Akaike information criterion, and its pa- rameters were significantly different from zero. The correla- tion coefficients between the observed and simulated data for the lead times of 1, 2, and 3 months were found to be significant and equal to 0.72, 0.69, and 0.65, respectively. The corresponding hit rates were estimated as 79, 75, and 72 %, indicating a reasonable forecasting capability of the model. The Heidke's forecast scores were 0.59, 0.52, and 0.48 for the prediction schemes having 1, 2, and 3 months of lead time, respectively. It is shown that the Persian Gulf skin temperatures have warmed up about 0.57 °C during the 732 successive months of the period 1950-2010 noted as an upward significant trend. Although a significant trend was not evident for the 1950-1969 and 1970-1989 period, the PGSST has abruptly increased during the recent two decades. Almost all of the observed warming in the PGSST data is related to this period.

Statistical downscaling of climate forecast system seasonal predictions for the Southeastern Mediterranean

Most of the annual rainfall in the Southeastern Mediterranean falls in the wet season from November to March. It is associated with Mediterranean cyclones, and is sensitive to climate variability. Predicting the wet season precipitation with a few months advance is highly valuable for water resource planning and climate-associated risk management in this semi-arid region. The regional water resource managements and climate-sensitive economic activities have relied on seasonal forecasts from global climate prediction centers. However due to their coarse resolutions, global seasonal forecasts lack regional and local scale information required by regional and local water resource managements. In this study, an analog statistical-downscaling algorithm, k-nearest neighbors (KNN), was introduced to bridge the gap between the coarse forecasts from global models and the needed fine-scale information for the Southeastern Mediterranean. The algorithm, driven by the NCEP Climate Forecast System (CFS) operational forecast and the NCEP/DOE reanalysis, provides monthly precipitations at 2–4months of lead-time at 18 stations within the major regional hydrological basins. Large-scale predictors for KNN were objectively determined by the correlations between the station historic daily precipitation and variables in reanalysis and CFS reforecast. Besides a single deterministic forecast, this study constructed sixty ensemble members for probabilistic estimates. The KNN algorithm demonstrated its robustness when validated with NCEP/DOE reanalysis from 1981 to 2009 as hindcasts before applied to downscale CFS forecasts. The downscaled predictions show fine-scale information, such as station-to-station variability. The verification against observations shows improved skills of this downscaling utility relative to the CFS model. The KNN-based downscaling system has been in operation for the Israel Water Authority predicting precipitation and driving hydrologic models estimating river flow and aquifer charge for water supply.

Abstract 3274: Development of a clinically relevant in vivo model of ovarian cancer tumorgrafts in immunodeficient mice

Abstract Despite advances in chemotherapeutic options over the last four decades, ovarian cancer remains the most lethal gynecologic malignancy with a cure rate that is essentially unchanged. A major barrier is lack of treatment-naïve, clinically- relevant in vivo models of epithelial ovarian cancer (EOC). To target this problem, we have developed low-passage in vivo models of EOC derived from patient primary debulking procedures at Mayo Clinic (Rochester, MN). Tumors were obtained from consented patients with suspected ovarian cancer, minced and injected into severe combined immunodeficiency (SCID) mice intraperitoneally for model establishment. To the date, we have heterotransplanted 129 EOC or ovarian malignant mixed mullerian (MMMT) tumors. The most common histologic subtype is serous (70.5%), followed by endometrioid (12.4%), clear cell (6.9%), and others (9.9%). The tumors were injected intraperitoneally into severe combined immunodeficiency (SCID) mice and the average time to moribund (TTM) was 174 +/− 83 days. In general, tumors grew faster with serial passages, TTM for epithelial tumors was 87 +/− 15 days and 19.5 +/− 3 days for MMMT. De novo ascites developed in 17 models and 24 models developed bowel metastasis, two clinically-relevant complications in late-stage ovarian cancer patients. Tumorgrafts were histologically similar to their source patient tumors and were positive for expression epithelial markers (pan-cytokeratin). The engraftment rate was 72.4% (n=87) after ≤ 6 months of lead-time, which is the average time to tumorgraft harvest. Passage of well-established tumorgrafts in SCID mice was successful in 88.6 % (n= 70) of all mice but only 56% (n=16) of athymic nude mice, consistent with prior studies demonstrating improved engraftment rates in more immunodeficient mice. Molecular characterization of tumorgrafts by array comparative genomic hybridization revealed similar gains and losses between the patient, early-passage, and later-passage tumorgraft tissues, indicating the tumorgrafts were genetically similar to their primary tumor. Analysis of tumorgrafts using the Affymetrix Human Plus 2.0 arrays revealed a predominance of previously described molecular subtypes C3, C4, and C5. Current investigations with both standard chemotherapy and novel targeted therapies are ongoing. Taken together, these models are as diverse as the patient population from which they are derived, produce clinically-relevant complications of EOC, maintain genetic fidelity in SCID mice, and are useful preclinical models for testing and development of novel therapeutics for ovarian cancer. High rates of engraftment are feasible. These tumorgrafts may help define predictors of response and eventually, lead to better treatments for patients. This work is funded by the Mayo Ovarian SPORE (CA136393) and the Minnesota Ovarian Cancer Alliance. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3274. doi:1538-7445.AM2012-3274

Application of linear stochastic models for drought forecasting in the Büyük Menderes river basin, western Turkey

In the present study, a seasonal and non-seasonal prediction of the Standardized Precipitation Index (SPI) time series is addressed by means of linear stochastic models. The methodology presented here is to develop adequate linear stochastic models known as autoregressive integrated moving average (ARIMA) and multiplicative seasonal autoregressive integrated moving average (SARIMA) to predict drought in the Buyuk Menderes river basin using SPI as drought index. Temporal characteristics of droughts based on SPI as an indicator of drought severity indicate that the basin is affected by severe and more or less prolonged periods of drought from 1975 to 2006. Therefore, drought prediction plays an important role for water resources management. ARIMA modeling approach involves the following three steps: model identification, parameter estimation, diagnostic checking. In model identification step, considering the autocorrelation function (ACF) and partial autocorrelation function (PACF) results of the SPI series, different ARIMA models are identified. The model gives the minimum Akaike Information Criterion (AIC) and Schwarz Bayesian Criterion (SBC) is selected as the best fit model. Parameter estimation step indicates that the estimated model parameters are significantly different from zero. Diagnostic check step is applied to the residuals of the selected ARIMA models and the results indicated that the residuals are independent, normally distributed and homoscedastic. For the model validation purposes, the predicted results using the best ARIMA models are compared to the observed data. The predicted data show reasonably good agreement with the actual data. The ARIMA models developed to predict drought found to give acceptable results up to 2 months ahead. The stochastic models developed for the Buyuk Menderes river basin can be employed to predict droughts up to 2 months of lead time with reasonably accuracy.

African climate variability in the satellite era

Interannual fluctuations in rainfall and ocean-atmosphere fields over and around Africa were studied in the satellite era of 1979–2007 using singular value decomposition. The leading modes of rainfall variability in GPCP satellite-gauge merged fields include a leading mode over central Africa, two modes of marine origin in the Gulf of Guinea and Eastern Africa, and two sub-tropical modes over the Sahel and Southern Africa. This differs from earlier gauge-based studies that tend to isolate three leading modes over western, eastern, and southern Africa. In the sea-surface temperature, sea-level pressure and upper wind fields, ENSO signals dominate the leading modes. However, for the low-level wind field, a trough circulation over the southeast Atlantic – Kalahari is the leading mode. It demonstrates predictive potential when cross-correlated with rainfall at 6- to 12-month lead time. Based on continuous filtered data, the value of various indices and the predictability of different zones are examined. The Sahel achieves the highest rank followed by the Congo and southern zones in the next tier. The Guinea and East African rains, which peak in the March-to-May season, appear least predictable. The seasonal rainfall is shown to modulate economic growth rate, and multi-variate predictive algorithms are tested at 6-month lead time.

Toward long-lead operational forecasts of drought: An experimental study in the Murray-Darling River Basin

Summary Resiliency and effectiveness in water resources management of drought is strongly depend on advanced knowledge of drought onset, duration and severity. The motivation of this work is to extend the lead time of operational drought forecasts. The research strategy is to explore the predictability of drought severity from space–time varying indices of large-scale climate phenomena relevant to regional hydrometeorology (e.g. ENSO) by integrating linear and non-linear statistical data models, specifically self-organizing maps (SOM) and multivariate linear regression analysis. The methodology is demonstrated through the step-by-step development of a model to forecast monthly spatial patterns of the standard precipitation index (SPI) within the Murray-Darling Basin (MDB) in Australia up to 12 months in advance. First, the rationale for the physical hypothesis and the exploratory data analysis including principal components, wavelet and partial mutual information analysis to identify and select predictor variables are presented. The focus is on spatial datasets of precipitation, sea surface temperature anomaly (SSTA) patterns over the Indian and Pacific Oceans, temporal and spatial gradients of outgoing longwave radiation (OLR) in the Pacific Ocean, and the far western Pacific wind-stress anomaly. Second, the process of model construction, calibration and evaluation is described. The experimental forecasts show that there is ample opportunity to increase the lead time of drought forecasts for decision support using parsimonious data models that capture the governing climate processes at regional scale. OLR gradients proved to be dispensable predictors, whereas SPI-based predictors appear to control predictability when the SSTA in the region [87.5°N–87.5°S; 27.5°E–67.5°W] and eastward wind-stress anomalies in the region [4°N–4°S; 130°E–160°E) are small, respectively, ±1° and ±0.01 dyne/cm 2 , that is when ENSO activity is weak. The areal averaged 12-month lead-time forecasts of SPI in the MDB explain up to 60% of the variance in the observations ( r > 0.7). Based on a threshold SPI of −0.5 for severe drought at the regional scale and for a nominal 12-month lead time, the forecast of the timing of onset is within 0–2 months of the actual threshold being met by the observations, thus effectively a 10-month lead time forecast at a minimum. Spatial analysis suggests that forecast errors can be attributed in part to a mismatch between the spatial heterogeneity of rainfall and raingauge density in the observational network. Forecast uncertainty on the other hand appears associated with the number of redundant predictors used in the forecast model.

Seasonal Climate Predictability in a Coupled OAGCM Using a Different Approach for Ensemble Forecasts

Abstract Predictabilities of tropical climate signals are investigated using a relatively high resolution Scale Interaction Experiment–Frontier Research Center for Global Change (FRCGC) coupled GCM (SINTEX-F). Five ensemble forecast members are generated by perturbing the model’s coupling physics, which accounts for the uncertainties of both initial conditions and model physics. Because of the model’s good performance in simulating the climatology and ENSO in the tropical Pacific, a simple coupled SST-nudging scheme generates realistic thermocline and surface wind variations in the equatorial Pacific. Several westerly and easterly wind bursts in the western Pacific are also captured. Hindcast results for the period 1982–2001 show a high predictability of ENSO. All past El Niño and La Niña events, including the strongest 1997/98 warm episode, are successfully predicted with the anomaly correlation coefficient (ACC) skill scores above 0.7 at the 12-month lead time. The predicted signals of some particular events, however, become weak with a delay in the phase at mid and long lead times. This is found to be related to the intraseasonal wind bursts that are unpredicted beyond a few months of lead time. The model forecasts also show a “spring prediction barrier” similar to that in observations. Spatial SST anomalies, teleconnection, and global drought/flood during three different phases of ENSO are successfully predicted at 9–12-month lead times. In the tropical North Atlantic and southwestern Indian Ocean, where ENSO has predominant influences, the model shows skillful predictions at the 7–12-month lead times. The distinct signal of the Indian Ocean dipole (IOD) event in 1994 is predicted at the 6-month lead time. SST anomalies near the western coast of Australia are also predicted beyond the 12-month lead time because of pronounced decadal signals there.

Drought forecasting using stochastic models

Drought is a global phenomenon that occurs virtually in all landscapes causing significant damage both in natural environment and in human lives. Due to the random nature of contributing factors, occurrence and severity of droughts can be treated as stochastic in nature. Early indication of possible drought can help to set out drought mitigation strategies and measures in advance. Therefore drought forecasting plays an important role in the planning and management of water resource systems. In this study, linear stochastic models known as ARIMA and multiplicative Seasonal Autoregressive Integrated Moving Average (SARIMA) models were used to forecast droughts based on the procedure of model development. The models were applied to forecast droughts using standardized precipitation index (SPI) series in the Kansabati river basin in India, which lies in the Purulia district of West Bengal state in eastern India. The predicted results using the best models were compared with the observed data. The predicted results show reasonably good agreement with the actual data, 1–2 months ahead. The predicted value decreases with increase in lead-time. So the models can be used to forecast droughts up to 2 months of lead-time with reasonably accuracy.

Long-Range Forecasting of the Nile River Flows Using Climatic Forcing

Correlation analysis is used to determine the linear relationship between the Nile River flows and leading climatic indicators, such as SST and precipitation, in an effort to establish a basis for quantitative long-term streamflow prediction. The analysis of the lead–lag correlations between the Blue Nile River flows during the “flood season” [July–August–September–October (JASO)] and SSTs led to the identification of a number of regions in the oceans that are significantly correlated and suggests that the SSTs may be useful for predicting the Blue Nile flows. The significant correlation regions between SST in the Pacific and Blue Nile JASO flows evolve through time in a manner that is consistent with the ENSO development; that is, the evolution of the ENSO signal in the Pacific Ocean is reflected in the evolution of the referred cross-correlation field. In addition, the Blue Nile River JASO flow is significantly correlated with the previous year August–November Guinea precipitation, which suggests that the Guinea precipitation is another potential predictor of the Blue Nile River flows with 11 months of lead time. Furthermore, models based on multiple linear regression (MLR) and principal component analysis (PCA) are used to forecast the Blue Nile flows based on SST in the three oceans and the previous year of Guinea precipitation. The models based on PCA showed significant improvement in forecast accuracy over MLR models that were developed in terms of the original variables. The predictability is shown to be the highest for forecasts made in the preceding season and decreases as the lead time increases. The coefficients of multiple determination R2 for validation based on PCA models vary in the range 84%–59% for forecast lead times of 4–16 months. Further analysis using only SST predictors for the period 1913–89 indicates that the predictability of the Blue Nile River JASO flows is more affected by the variability of SSTs in the Pacific Ocean than by those of the other oceans. The conclusion is that long-range forecasting of the Blue Nile River flows with lead times over 1 yr is possible with a high degree of explained variance by using SST in a few regions in the Pacific Ocean and the previous year of Guinea precipitation.

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AFP-L3: a new generation of tumor marker for hepatocellular carcinoma

Background: Alpha-fetoprotein (AFP) from hepatocellular carcinoma (HCC) displays differential affinity to lectin Lens culinaris agglutinin (LCA) compared to that from chronic hepatitis/liver cirrhosis. According to their binding capability to LCA, total AFP can be separated into three different glycoforms, AFP-L1, AFP-L2, and AFP-L3. AFP-L1 is the non-LCA-bound fraction, which constitutes the major glycoform of AFP in serum of chronic hepatitis and liver cirrhosis. AFP-L3 is the LCA-bound fraction of AFP. It has been reported that malignant liver cells produce AFP-L3, even when HCC is at its early stages, and especially when the tumor mass is supplied by the hepatic artery. Clinical research has determined that AFP-L3 is a highly specific marker for HCC. The AFP-L3 can be detected in the serum of approximately 35% of the patients with small HCC (<2 cm). The AFP-L3-positive HCC has potential for rapid growth and early metastasis. Compared to imaging techniques, it has been shown to have 9–12 months of lead-time in early HCC recognition. Combined sensitivity of AFP-L3 for HCC is 56%, with a specificity of >95%. Methods: Automated assay for measuring AFP-L3 has been developed and introduced in clinical use. The new automated method for measurement of ALP-L3 is based on liquid phase binding of the AFP-L3 glycoform with LCA and two specific monoclonal antibodies labeled with peroxidase and polysulfated tyrosine peptide, respectively. Conclusion: AFP-L3 is a new generation of tumor marker for HCC and yields useful information on HCC for clinical decision making.

NCEP Forecasts of the El Niño of 1997—98 and Its U.S. Impacts

Abstract The strong El Nino of 1997-98 provided a unique opportunity for National Weather Service, National Centers for Environmental Prediction, Climate Prediction Center (CPC) forecasters to apply several years of accumulated new knowledge of the U.S. impacts of El Nino to their long-lead seasonal forecasts with more clarity and confidence than ever previously. This paper examines the performance of CPC's official forecasts, and its individual component forecast tools, during this event. Heavy winter precipitation across California and the southern plains-Gulf coast region was accurately forecast with at least six months of lead time. Dryness was also correctly forecast in Montana and in the southwestern Ohio Valley. The warmth across the northern half of the country was correctly forecast, but extended farther south and east than predicted. As the winter approached, forecaster confidence in the forecast pattern increased, and the probability anomalies that were assigned reached unprecedented levels in ...

Preface

Although this is the March issue preface, it was composed in early January, a time which triggered two peripheral thoughts., One was simply that the shorter lead times envisioned by electronic publishing are very appealing. This preface had almost two months of lead time, but the rest of the issue's content required twice that. According to a book reviewed in this issue (page 41), electronic publishing not only shortens production time but also speeds dissemination. However, the “catch” in this “paperless information system” is that authors and editors/publishers and readers need to be members of an information network. While this is feasible, it is not yet practical. The emerging possibilities would be a good subject for discussion in our new Forum feature (page 37). Write to me at the address below.