ARIMA based daily weather forecasting tool: A case study for Varanasi

Abstract. Systems for the early detection of floods over continental and global domains have a key role in providing a quick overview of areas at risk, raise the awareness and prompt higher detail analyses as the events approach. However, the reliability of these systems is prone to spatial inhomogeneity, depending on the quality of the underlying input data and local calibration. This work proposes a simple approach for flood early warning based on ensemble numerical predictions of surface runoff provided by weather forecasting centers. The system is based on a novel indicator, referred to as an extreme runoff index (ERI), which is calculated from the input data through a statistical analysis. It is designed for use in large or poorly gauged domains, as no local knowledge or in situ observations are needed for its setup. Daily runs over 32 months are evaluated against calibrated hydrological simulations for all of Europe. Results show skillful flood early warning capabilities up to a 10-day lead time. A dedicated analysis is performed to investigate the optimal timing of forecasts to maximize the detection of extreme events. A case study for the central European floods of June 2013 is presented and forecasts are compared to the output of a hydro-meteorological ensemble model.

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Envisioning Evolvable Work-Centered Support Systems: Empowering Users to Adapt Their Systems to Changing World Demands

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/154193120504900307 ◽

2005 ◽

Vol 49 (3) ◽

pp. 244-248 ◽

Cited By ~ 1

Author(s):

Ronald Scott ◽

Emilie Roth ◽

Stephen Deutsch ◽

Samuel Kuper ◽

Vincent Schmidt ◽

...

Keyword(s):

Development Process ◽

Weather Forecasting ◽

Support Systems ◽

Collaborative Work ◽

Agent Technology ◽

Timely Manner ◽

Structure Changes ◽

Operations Center ◽

Domain Constraints

Work-Centered Support Systems (WCSS) provide visualizations that reveal domain constraints and affordances based on software agent technology to support cognitive and collaborative work. Here we argue for a need to incorporate facilities that enable users to adapt these systems to the changing requirements of work—– evolvable work-centered support systems. We recently developed a WCSS for weather forecasting and monitoring in an airlift organization that is currently used in their operations center. As part of the development process we conducted field observations both prior and subsequent to system introduction. A striking finding was the constant changes that operations personnel faced (changes in goals and priorities; changes in scale of operations; changes in team roles and structure; changes in information sources and systems). We describe the changes in workplace demands that we observed and the modifications we needed to make to the WCSS in response. Our findings are presented as a case study to illustrate the challenges confronted in designing a WCSS to support a constantly changing environment. For today's fielded systems, making changes that are responsive to users changing requirements in a timely manner is seldom possible.

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Task-specific visualization design: a case study in operational weather forecasting

Proceedings Visualization '98 (Cat. No.98CB36276) ◽

10.1109/visual.1998.745330 ◽

2002 ◽

Cited By ~ 10

Author(s):

L.A. Treinish

Keyword(s):

Weather Forecasting ◽

Visualization Design

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Determining the Effect of Extreme Weather Events on Human Participation in Recreation and Tourism: A Case Study of the Toronto Zoo

Atmosphere ◽

10.3390/atmos11010099 ◽

2020 ◽

Vol 11 (1) ◽

pp. 99

Author(s):

Micah Hewer

Keyword(s):

Extreme Weather ◽

Extreme Weather Events ◽

Maximum Temperature ◽

Distance Method ◽

Daily Weather ◽

Weather Events ◽

Daily Weather Data ◽

Weather Stations ◽

Recreation And Tourism

This study devises a novel approach for defining extreme weather events and assessing their effects on human participation in recreation and tourism, based on a case study of attendance at the Toronto Zoo (Toronto, ON, Canada). Daily zoo attendance data from 1999 to 2018 was obtained and analyzed in connection with daily weather data from local weather stations for the maximum temperature, minimum temperature, total precipitation, and maximum wind speed. The “climatic distance” method, used for evaluating representative weather stations for case studies in applied climatology, was employed to rank and select surrounding weather stations that most accurately captured daily weather observations recorded at the Toronto Zoo from 1990 to 1992. Extreme weather events can be defined as lying in the outermost (most unusual) 10 percent of a place’s history. Using this definition as the foundation, a percentile approach was developed to identify and assess the effects of extreme weather events across the following thresholds: the 99th percentile, the 95th percentile, and the 90th percentile, as well as less than the 1st percentile, less than the 5th percentile, and less than the 10th percentile. Additionally, revealed, theoretical, and binary thresholds were also assessed to verify their merit and determine their effects, and were compared to the extreme weather events defined by the percentiles approach. Overall, extreme daily weather events had statistically significant negative effects on zoo attendance in Toronto, apart from a few cases, such as the positive effect of usually warm daytime temperatures in the winter and usually cool nighttime temperatures in the summer. The most influential weather event across all seasons was extremely hot temperatures, which has important implications for climate change impact assessments.

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Methods of investigating forecast error sensitivity to ensemble size in a limited-area convection-permitting ensemble

10.5194/gmd-2017-260 ◽

2017 ◽

Cited By ~ 2

Author(s):

Ross Noel Bannister ◽

Stefano Migliorini ◽

Alison Clare Rudd ◽

Laura Hart Baker

Keyword(s):

Data Assimilation ◽

Weather Forecasting ◽

Sampling Error ◽

Forecast Error ◽

Significant Degree ◽

Limited Area ◽

Ensemble Size ◽

Sampling Errors ◽

Convective Scale

Abstract. Ensemble-based predictions are increasingly used as an aid to weather forecasting and to data assimilation, where the aim is to capture the range of possible outcomes consistent with the underlying uncertainties. Constraints on computing resources mean that ensembles have a relatively small size, which can lead to an incomplete range of possible outcomes, and to inherent sampling errors. This paper discusses how an existing ensemble can be relatively easily increased in size, it develops a range of standard and extended diagnostics to help determine whether a given ensemble is large enough to be useful for forecasting and data assimilation purposes, and it applies the diagnostics to a convective-scale case study for illustration. Diagnostics include the effect of ensemble size on various aspects of rainfall forecasts, kinetic energy spectra, and (co)-variance statistics in the spatial and spectral domains. The work here extends the Met Office's 24 ensemble members to 93. It is found that the extra members do develop a significant degree of linear independence, they increase the ensemble spread (although with caveats to do with non-Gaussianity), they reduce sampling error in many statistical quantities (namely variances, correlations, and length-scales), and improve the effective spatial resolution of the ensemble. The extra members though do not improve the probabilistic rain rate forecasts. It is assumed that the 93-member ensemble approximates the error-free statistics, which is a practical assumption, but the data suggests that this number of members is ultimately not enough to justify this assumption, and therefore more ensembles are likely required for such convective-scale systems to further reduce sampling errors, especially for ensemble data assimilation purposes.

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A Case Study in the Economics of Information and Coordination: The Weather Forecasting System

The Quarterly Journal of Economics ◽

10.2307/1879475 ◽

1964 ◽

Vol 78 (3) ◽

pp. 420 ◽

Cited By ~ 33

Author(s):

Richard R. Nelson ◽

Sidney G. Winter

Keyword(s):

Weather Forecasting ◽

Economics Of Information ◽

Forecasting System

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The Value of Wind Profiler Data in U.S. Weather Forecasting

Bulletin of the American Meteorological Society ◽

10.1175/bams-85-12-1871 ◽

2004 ◽

Vol 85 (12) ◽

pp. 1871-1886 ◽

Cited By ~ 47

Author(s):

Stanley G. Benjamin ◽

Barry E. Schwartz ◽

Edward J. Szoke ◽

Steven E. Koch

Keyword(s):

United States ◽

Case Studies ◽

Short Range ◽

Weather Forecasting ◽

The United States ◽

Test Period ◽

Data Sources ◽

Wind Profiler ◽

Profiler Data

An assessment of the value of data from the NOAA Profiler Network (NPN) on weather forecasting is presented. A series of experiments was conducted using the Rapid Update Cycle (RUC) model/assimilation system in which various data sources were denied in order to assess the relative importance of the profiler data for short-range wind forecasts. Average verification statistics from a 13-day cold-season test period indicate that the profiler data have a positive impact on short-range (3–12 h) forecasts over the RUC domain containing the lower 48 United States, which are strongest at the 3-h projection over a central U.S. subdomain that includes most of the profiler sites, as well as downwind of the profiler observations over the eastern United States. Overall, profiler data reduce wind forecast errors at all levels from 850 to 150 hPa, especially below 300 hPa where there are relatively few automated aircraft observations. At night when fewer commercial aircraft are flying, profiler data also contribute strongly to more accurate 3-h forecasts, including near-tropopause maximum wind levels. For the test period, the profiler data contributed up to 20%–30% (at 700 hPa) of the overall reduction of 3-h wind forecast error by all data sources combined. Inclusion of wind profiler data also reduced 3-h errors for height, relative humidity, and temperature by 5%-15%, averaged over different vertical levels. Time series and statistics from large-error events demonstrate that the impact of profiler data may be much larger in peak error situations. Three data assimilation case studies from cold and warm seasons are presented that illustrate the value of the profiler observations for improving weather forecasts. The first case study indicates that inclusion of profiler data in the RUC model runs for the 3 May 1999 Oklahoma tornado outbreak improved model guidance of convective available potential energy (CAPE), 300-hPa wind, and precipitation in southwestern Oklahoma at the onset of the event. In the second case study, inclusion of profiler data led to better RUC precipitation forecasts associated with a severe snow and ice storm that occurred over the central plains of the United States in February 2001. A third case study describes the effect of profiler data for a tornado event in Oklahoma on 8 May 2003. Summaries of National Weather Service (NWS) forecaster use of profiler data in daily operations, although subjective, support the results from these case studies and the statistical forecast model impact study in the broad sense that profiler data contribute significantly to improved short-range forecasts over the central United States where these observations currently exist.

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Window on the weather: a case study in multi-platform visual communication design, with a relationship to Design Thinking

Visual Communication ◽

10.1177/1470357220948547 ◽

2021 ◽

pp. 147035722094854

Author(s):

Iain Macdonald

Keyword(s):

Design Thinking ◽

Weather Forecasting ◽

Ethnographic Study ◽

Brand Identity ◽

British Broadcasting Corporation ◽

Visual Communication Design ◽

Communication Design ◽

Digital Services ◽

Advanced Computer

In February 2018, after three years of design and development work, the British Broadcasting Corporation (BBC) Weather launched its redesigned service across multiple platforms. The project involved new ways of cross-disciplinary communication design working across broadcast and digital services. This research examines these innovations and considers the transcorporeality of our relationship with weather forecasting. BBC Weather developed an iconography in the mid-1970s that has been integral to its brand identity and which has survived the changes in television graphics technology from magnetised acrylic symbols to digital systems. Satellite imagery, advanced computer weather modelling, mobile interaction, and weather on the move in realtime, have become integral to presenting different layers of visual sophistication and information that require translation and editing to communicate the weather across multiple platforms and formats. An ethnographic study of the leading participants in the design project mapped out the creative process and highlighted reflexive points where design practice was modified and adapted by the interdisciplinarity of the team. Their approach to design anthropology and service design approaches are revealed in the context of Design Thinking, and how the domestication of digital services is linked to a relationship to the weather for a UK and Irish audience.

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