Role of the Ocean Observing System in an End-to-End Seasonal Forecasting System

Abstract. Vast parts of Africa rely on the rainy season for livestock and agriculture. Droughts can have a severe impact in these areas, which often have a very low resilience and limited capabilities to mitigate drought impacts. This paper assesses the predictive capabilities of an integrated drought monitoring and seasonal forecasting system (up to 5 months lead time) based on the Standardized Precipitation Index (SPI). The system is constructed by extending near-real-time monthly precipitation fields (ECMWF ERA-Interim reanalysis and the Climate Anomaly Monitoring System–Outgoing Longwave Radiation Precipitation Index, CAMS-OPI) with monthly forecasted fields as provided by the ECMWF seasonal forecasting system. The forecasts were then evaluated over four basins in Africa: the Blue Nile, Limpopo, Upper Niger, and Upper Zambezi. There are significant differences in the quality of the precipitation between the datasets depending on the catchments, and a general statement regarding the best product is difficult to make. The generally low number of rain gauges and their decrease in the recent years limits the verification and monitoring of droughts in the different basins, reinforcing the need for a strong investment on climate monitoring. All the datasets show similar spatial and temporal patterns in southern and north-western Africa, while there is a low correlation in the equatorial area, which makes it difficult to define ground truth and choose an adequate product for monitoring. The seasonal forecasts have a higher reliability and skill in the Blue Nile, Limpopo and Upper Niger in comparison with the Zambezi. This skill and reliability depend strongly on the SPI timescale, and longer timescales have more skill. The ECMWF seasonal forecasts have predictive skill which is higher than using climatology for most regions. In regions where no reliable near-real-time data is available, the seasonal forecast can be used for monitoring (first month of forecast). Furthermore, poor-quality precipitation monitoring products can reduce the potential skill of SPI seasonal forecasts in 2 to 4 months lead time.

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A Brief Taxonomy of Hybrid Intelligence

Forecasting ◽

10.3390/forecast3030039 ◽

2021 ◽

Vol 3 (3) ◽

pp. 633-643

Author(s):

Niccolo Pescetelli

Keyword(s):

Prediction Models ◽

Collective Intelligence ◽

Machine Intelligence ◽

Future Research ◽

Human Errors ◽

Wide Range ◽

Hybrid Intelligence ◽

Forecasting System ◽

Problem Solvers

As artificial intelligence becomes ubiquitous in our lives, so do the opportunities to combine machine and human intelligence to obtain more accurate and more resilient prediction models across a wide range of domains. Hybrid intelligence can be designed in many ways, depending on the role of the human and the algorithm in the hybrid system. This paper offers a brief taxonomy of hybrid intelligence, which describes possible relationships between human and machine intelligence for robust forecasting. In this taxonomy, biological intelligence represents one axis of variation, going from individual intelligence (one individual in isolation) to collective intelligence (several connected individuals). The second axis of variation represents increasingly sophisticated algorithms that can take into account more aspects of the forecasting system, from information to task to human problem-solvers. The novelty of the paper lies in the interpretation of recent studies in hybrid intelligence as precursors of a set of algorithms that are expected to be more prominent in the future. These algorithms promise to increase hybrid system’s resilience across a wide range of human errors and biases thanks to greater human-machine understanding. This work ends with a short overview for future research in this field.

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Improving statistical forecasts of seasonal streamflows using hydrological model output

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-9-8701-2012 ◽

2012 ◽

Vol 9 (7) ◽

pp. 8701-8736 ◽

Cited By ~ 3

Author(s):

D. E. Robertson ◽

P. Pokhrel ◽

Q. J. Wang

Keyword(s):

Hydrological Model ◽

Historical Record ◽

Base Flow ◽

Seasonal Forecasting ◽

Streamflow Forecasting ◽

Eastern Australia ◽

True Source ◽

Hybrid Forecasting ◽

Forecasting System ◽

Seasonal Streamflow

Abstract. Statistical methods traditionally applied for seasonal streamflow forecasting use predictors that represent the initial catchment condition and future climate influences on future streamflows. Observations of antecedent streamflows or rainfall commonly used to represent the initial catchment conditions are surrogates for the true source of predictability and can potentially have limitations. This study investigates a hybrid seasonal forecasting system that uses the simulations from a dynamic hydrological model as a predictor to represent the initial catchment condition in a statistical seasonal forecasting method. We compare the skill and reliability of forecasts made using the hybrid forecasting approach to those made using the existing operational practice of the Australian Bureau of Meteorology for 21 catchments in eastern Australia. We investigate the reasons for differences. In general, the hybrid forecasting system produces forecasts that are more skilful than the existing operational practice and as reliable. The greatest increases in forecast skill tend to be (1) when the catchment is wetting up but antecedent streamflows have not responded to antecedent rainfall, (2) when the catchment is drying and the dominant source of antecedent streamflow is in transition between surface runoff and base flow, and (3) when the initial catchment condition is near saturation intermittently throughout the historical record.

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Cross-cutting technologies in education

Revista Amazonia Investiga ◽

10.34069/ai/2021.47.11.3 ◽

2021 ◽

Vol 10 (47) ◽

pp. 27-34

Author(s):

Olga I. Vaganova ◽

Julia M. Tsarapkina ◽

Marina R. Zheltukhina ◽

Elena G. Knyazeva ◽

Julia S. Krasilnikova

Keyword(s):

Vocational Education ◽

Significant Role ◽

Professional Training ◽

Educational Process ◽

Promising Direction ◽

Professional Activities ◽

Article Analysis ◽

End To End ◽

Senior Students

Purpose of the article: analysis of the experience of implementing end-to-end technologies in vocational education. Methodology: the article presents a study aimed at identifying the role of end-to-end technologies in the professional training of students. For this, a survey was conducted among students on the use of end-to-end technologies in the educational process in three age categories (junior, middle, senior). Students had to assess the importance of end-to-end technologies for the implementation of the educational process on a scale from 1 to 4. Results: Students note the significant role of end-to-end technologies in professional activities. This is a promising direction for the development of vocational education.

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Sterilization Modality Selection: Role of Sterility Assurance Subject Matter Expert

Biomedical Instrumentation & Technology ◽

10.2345/0899-8205-55.s3.67 ◽

2021 ◽

Vol 55 (s3) ◽

pp. 67-77

Author(s):

Jami McLaren ◽

Joyce M. Hansen ◽

Vu Le

Keyword(s):

Supply Chain ◽

Subject Matter ◽

Selection Process ◽

Collaborative Partnerships ◽

Supply Chain Logistics ◽

End To End ◽

Negative Impacts ◽

Assurance Process ◽

Selection Of

Abstract Selection of a sterilization modality for a medical device is a critical decision that requires sterility assurance subject matter experts (SME)s to work collaboratively with various company functions. The sterility assurance SME is responsible and accountable for the sterilization modality decision for a product. The modality selection process starts with the sterility assurance SME partnering with research and development to ensure that the sterilization modality allows the device to deliver its intended function in patient care. After the sterilization modality is selected, the sterility assurance SME needs to work with other partners, including quality, supply chain/logistics, operations, and regulatory, to ensure that the selected sterilization modality is appropriately integrated into the end-to-end process. Collaborative partnerships between sterility assurance experts and key partners regarding sterilization modality selection reduce the potential for negative impacts within the end-to-end sterility assurance process, including impacts on product functionality, increased regulatory approval timelines, and inefficiencies and risks throughout the supply chain. This article describes aspects of a comprehensive approach to sterilization modality selection, including critical information necessary to address each of the key considerations.

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Real World Applications

Handbook of Research on Modern Optimization Algorithms and Applications in Engineering and Economics - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-4666-9644-0.ch004 ◽

2016 ◽

pp. 113-132

Author(s):

Ibibia K. Dabipi ◽

Judy A. Perkins ◽

Tierney Moore

Keyword(s):

Supply Chain ◽

Supply Chain Management ◽

Real World ◽

Chain Structure ◽

Total Asset ◽

Chain Management ◽

Real World Applications ◽

End To End ◽

Supply Chain Structure

Over the years the supply chain industry has been transforming to improve the end-to-end (production to delivery) process. Supply chain management (SCM) allows various industries to oversee and better handle how their product is manufactured and delivered. It allows them to track and identify the location of the product and to be more efficient in delivery. Integrating total asset visibility (TAV) technology into the supply chain structure can provide excellent visibility of a product. This kind of visibility complemented with various packaging schemes can assist in accommodating optimization strategies for visualizing the movement of a product throughout the entire supply chain pipeline. The chapter will define SCM, discuss TAV, review how transportation as well as optimization impacts SCM and TAV, and examine the role of packaging in the context of SCM and TAV.

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Exploring the role of ionospheric drivers during the extreme solar minimum of 2008

Annales Geophysicae ◽

10.5194/angeo-31-2147-2013 ◽

2013 ◽

Vol 31 (12) ◽

pp. 2147-2156 ◽

Cited By ~ 10

Author(s):

J. Klenzing ◽

A. G. Burrell ◽

R. A. Heelis ◽

J. D. Huba ◽

R. Pfaff ◽

...

Keyword(s):

Solar Activity ◽

Solar Minimum ◽

Wind Model ◽

Ion Density ◽

Density Data ◽

Early Afternoon ◽

Minimum Solar Activity ◽

Forecasting System ◽

Electric Field Instrument

Abstract. During the recent solar minimum, solar activity reached the lowest levels observed during the space age, resulting in a contracted atmosphere. This extremely low solar activity provides an unprecedented opportunity to understand the variability of the Earth's ambient ionosphere. The average E × B drifts measured by the Vector Electric Field Instrument (VEFI) on the Communications/Navigation Outage Forecasting System (C/NOFS) satellite during this period are found to have several differences from the expected climatology based on previous solar minima, including downward drifts in the early afternoon and a weak to non-existent pre-reversal enhancement. Using SAMI2 (Sami2 is Another Model of the Ionosphere) as a computational engine, we investigate the effects of these electrodynamical changes as well as the contraction of the thermosphere and reduced EUV ionization on the ionosphere. The sensitivity of the simulations to wind models is also discussed. These modeled ionospheres are compared to the C/NOFS average topside ion density and composition and Formosa Satellite-3/Constellation Observing System for Meteorology, Ionosphere, and Climate average NmF2 and hmF2. In all cases, incorporating the VEFI drift data significantly improves the model results when compared to both the C/NOFS density data and the F3/C GOX data. Changing the MSIS and EUVAC models produced changes in magnitude, but not morphology with respect to local time. The choice of wind model modulates the resulting topside density and composition, but only the use of the VEFI E × B drifts produces the observed post-sunset drop in the F peak.

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An Adaptive End-to-End Classification Approach for Mobile Laser Scanning Point Clouds Based on Knowledge in Urban Scenes

Remote Sensing ◽

10.3390/rs11020186 ◽

2019 ◽

Vol 11 (2) ◽

pp. 186 ◽

Cited By ~ 2

Author(s):

Mingxue Zheng ◽

Huayi Wu ◽

Yong Li

Keyword(s):

Laser Scanning ◽

Point Clouds ◽

Basic Knowledge ◽

Multiple Objects ◽

Urban Scenes ◽

Knowledge Based ◽

Training Samples ◽

End To End ◽

Heavy Training

It is fundamental for 3D city maps to efficiently classify objects of point clouds in urban scenes. However, it is still a large challenge to obtain massive training samples for point clouds and to sustain the huge training burden. To overcome it, a knowledge-based approach is proposed. The knowledge-based approach can explore discriminating features of objects based on people’s understanding of the surrounding environment, which exactly replaces the role of training samples. To implement the approach, a two-step segmentation procedure is carried out in this paper. In particular, Fourier Fitting is applied for second adaptive segmentation to separate points of multiple objects lying within a single group of the first segmentation. Then height difference and three geometrical eigen-features are extracted. In comparison to common classification methods, which need massive training samples, only basic knowledge of objects in urban scenes is needed to build an end-to-end match between objects and extracted features in the proposed approach. In addition, the proposed approach has high computational efficiency because of no heavy training process. Qualitative and quantificational experimental results show the proposed approach has promising performance for object classification in various urban scenes.

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Recommendations for Improving Integration in National End-to-End Flood Forecasting Systems: An Overview of the FFIR (Flooding From Intense Rainfall) Programme

Water ◽

10.3390/w11040725 ◽

2019 ◽

Vol 11 (4) ◽

pp. 725 ◽

Cited By ~ 6

Author(s):

David Flack ◽

Christopher Skinner ◽

Lee Hawkness-Smith ◽

Greg O’Donnell ◽

Robert Thompson ◽

...

Keyword(s):

Surface Water ◽

Lead Time ◽

End Users ◽

Intense Rainfall ◽

Weather Forecasts ◽

Propagation Of Uncertainty ◽

Effective Dissemination ◽

Forecasting System ◽

End To End ◽

The Uk

Recent surface-water and flash floods have caused millions of pounds worth of damage in the UK. These events form rapidly and are difficult to predict due to their short-lived and localised nature. The interdisciplinary Flooding From Intense Rainfall (FFIR) programme investigated the feasibility of enhancing the integration of an end-to-end forecasting system for flash and surface-water floods to help increase the lead time for warnings for these events. Here we propose developments to the integration of an operational end-to-end forecasting system based on the findings of the FFIR programme. The suggested developments include methods to improve radar-derived rainfall rates and understanding of the uncertainty in the position of intense rainfall in weather forecasts; the addition of hydraulic modelling components; and novel education techniques to help lead to effective dissemination of flood warnings. We make recommendations for future advances such as research into the propagation of uncertainty throughout the forecast chain. We further propose the creation of closer bonds to the end users to allow for an improved, integrated, end-to-end forecasting system that is easily accessible for users and end users alike, and will ultimately help mitigate the impacts of flooding from intense rainfall by informed and timely action.

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