scholarly journals Quantifying Interagency Differences in Tropical Cyclone Best-Track Wind Speed Estimates

2010 ◽  
Vol 138 (4) ◽  
pp. 1459-1473 ◽  
Author(s):  
Kenneth R. Knapp ◽  
Michael C. Kruk
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Tropical Cyclone ◽  
Temporal Changes ◽  
Data Availability ◽  
Spatially Inhomogeneous ◽  
The World ◽  
Using Data ◽  
Over Time

Abstract Numerous agencies around the world perform postseason analysis of tropical cyclone position and intensity, a process described as “best tracking.” However, this process is temporally and spatially inhomogeneous because data availability, operational techniques, and knowledge have changed over time and differ among agencies. The net result is that positions and intensities often vary for any given storm for different agencies. In light of these differences, it is imperative to analyze and document the interagency differences in tropical cyclone intensities. To that end, maximum sustained winds from different agencies were compared using data from the International Best Track Archive for Climate Stewardship (IBTrACS) global tropical cyclone dataset. Comparisons were made for a recent 5-yr period to investigate the current differences, where linear systematic differences were evident. Time series of the comparisons also showed temporal changes in the systematic differences, which suggest changes in operational procedures. Initial attempts were made to normalize maximum sustained winds by correcting for known changes in operational procedures. The result was mixed, in that the adjustments removed some but not all of the systematic differences. This suggests that more details on operational procedures are needed and that a complete reanalysis of tropical cyclone intensities should be performed.

scholarly journals Public support for empowering police during the COVID-19 crisis: Evidence from London

2021 ◽  
Author(s):  
Julia Yesberg ◽  
Zoe Hobson ◽  
Krisztián Pósch ◽  
Ben Bradford ◽  
Jonathan Jackson ◽  
...  
Keyword(s):  
Public Support ◽  
Specific Factor ◽  
Representative Survey ◽  
Social Distancing ◽  
Police Services ◽  
The World ◽  
The Face ◽  
Using Data ◽  
Over Time

In the face of the COVID-19 pandemic, police services around the world were granted unprecedented new powers to enforce social distancing restrictions to help to get the virus under control. Using data from a representative survey of Londoners fielded during the height of the first wave of the pandemic (April – June 2020), we explore the scale of public support for giving police additional powers to enforce the regulations, how support for different powers changed over time, and what factors predicted support. Aside from one lockdown-specific factor, we find that even in the midst of a pandemic, trust, legitimacy and affect were the most important predictors of support for police empowerment.

scholarly journals Effort toward the Sustainable Agricultural Development within the Territory the ASEAN

2019 ◽  
Vol 8 (6) ◽  
pp. 3328-3331
Keyword(s):  
Time Series ◽  
Agricultural Development ◽  
Rice Yield ◽  
Organic Manure ◽  
Research Results ◽  
Rice Grains ◽  
Pollution Research ◽  
The World ◽  
Using Data

ASEAN Indonesia and the such as Thailand and Vietnam knew the world largest rice-producing, one of its inputs for the is manure. Manure used in addition to also organic manure, fertilizer impact on pollution. Research objective analyze whether the use of fertilizer, affect the contents of rice grains, production, and productivity of the three countries, rice ASEAN this study using data time series and analyzed use software SPSS, the research results show that Indonesia users more fertilizer compared to countries Thailand and manure vietnam.Use in Indonesia influential production by, but not on productivity and contents of rice grains, in Thailand the use of fertilizer had real impact production by rice, yield, contents of rice grains, but not on in Vietnam influential manure production by, productivity, but not on contents of rice grains

E-Government and Related Indices

2020 ◽  
pp. 1810-1824
Author(s):  
Kathleen M. Boyer-Wright ◽  
Jeffrey E. Kottemann
Keyword(s):  
Future Research ◽  
Government Services ◽  
Telecommunications Infrastructure ◽  
The World Bank ◽  
The World ◽  
Efficacy Index ◽  
Using Data ◽  
Changes Over Time ◽  
Over Time ◽  
Qualitative Analyses

The primary United Nations E-Government Index is a composite of three component indices: telecommunications infrastructure, human capital, and online e-government services, where the first two can be seen as enablers of the third. This study investigates the addition of a complementary component index for institutional efficacy, which is hypothesized to be another enabling factor. The institutional efficacy index is operationalized using existing measures gathered and made available by the World Bank. Statistical analysis shows that the institutional efficacy index is indeed a significant, additional predictor of online e-government services across nations. Following the presentation of basic results, qualitative analyses are undertaken to develop an assortment of generic national profiles. Preliminary analyses of changes over time are also presented using data from prior years, and directions for future research are outlined.

Temporal and Spatial Consistency

2010 ◽  
pp. 89-110 ◽  
Author(s):  
Oliver Duke-Williams ◽  
John Stillwell
Keyword(s):  
Time Series ◽  
Age Groups ◽  
Register Data ◽  
Data Sets ◽  
Interaction Data ◽  
Patient Register ◽  
Health Authorities ◽  
Spatial Consistency ◽  
Using Data ◽  
Over Time

One of the major problems challenging time series research based on stock and flow data is the inconsistency that occurs over time due to changes in variable definition, data classification and spatial boundary configuration. The census of population is a prime example of a source whose data are fraught with these problems, resulting in even the simplest comparison between the 2001 Census and its predecessor in 1991 being difficult. The first part of this chapter introduces the subject of inconsistencies between related data sets, with general reference to census interaction data. Various types of inconsistency are described. A number of approaches to dealing with inconsistency are then outlined, with examples of how these have been used in practice. The handling of journey to work data of persons who work from home is then used as an illustrative example of the problems posed by inconsistencies in base populations. Home-workers have been treated in different ways in successive UK censuses, a factor which can cause difficulties not only for researchers interested in such working practices, but also for those interested in other aspects of commuting. The latter set of problems are perhaps more pernicious, as users are less likely to be aware of the biases introduced into data sets that are being compared. In the second half of this chapter, we make use of a time series data set of migration interaction data that does have temporal consistency to explore how migration propensities and patterns in England and Wales have changed since 1999 and in particular since the year prior to the 2001 Census. The data used are those that are produced by the Office of National Statistics based on comparisons of NHS patient records from one year to the next and adjusted using data on NHS patients re-registering in different health authorities. The analysis of these data suggests that the massive exodus of individuals from major metropolitan across the country that has been identified in previous studies is continuing apace, particularly from London whose net losses doubled in absolute terms between 1999 and 2004 before reducing marginally in 2005 and 2006. Whilst this pattern of counterurbanisation is evident for all-age flows, it conceals significant variations for certain age groups, not least those aged between 16 and 24, whose migration propensities are high and whose net redistribution is closely connected with the location of universities. The time series analyses are preceded by a comparison of patient register data with corresponding data from the 2001 Census. This suggests strong correlation between the indicators selected and strengthens the argument that patient register data in more recent years provide reliable evidence for researchers and policy makers on how propensities and patterns change over time.

The Kitchen Sink

2018 ◽  
Author(s):  
Gary Smith
Keyword(s):  
Multiple Regression ◽  
Historical Period ◽  
Scatter Plot ◽  
Consumer Spending ◽  
Explanatory Variables ◽  
The World ◽  
Scatter Plots ◽  
Using Data ◽  
The One ◽  
Over Time

Back in the 1980s, I talked to an economics professor who made forecasts for a large bank based on simple correlations like the one in Figure 1. If he wanted to forecast consumer spending, he made a scatter plot of income and spending and used a transparent ruler to draw a line that seemed to fit the data. If the scatter looked like Figure 1, then when income went up, he predicted that spending would go up. The problem with his simple scatter plots is that the world is not simple. Income affects spending, but so does wealth. What if this professor happened to draw his scatter plot using data from a historical period in which income rose (increasing spending) but the stock market crashed (reducing spending) and the wealth effect was more powerful than the income effect, so that spending declined, as in Figure 2? The professor’s scatter plot of spending and income will indicate that an increase in income reduces spending. Then, when he tries to forecast spending for a period when income and wealth both increase, his prediction of a decline in spending will be disastrously wrong. Multiple regression to the rescue. Multiple regression models have multiple explanatory variables. For example, a model of consumer spending might be: C = a + bY + cW where C is consumer spending, Y is household income, and W is wealth. The order in which the explanatory variables are listed does not matter. What does matter is which variables are included in the model and which are left out. A large part of the art of regression analysis is choosing explanatory variables that are important and ignoring those that are unimportant. The coefficient b measures the effect on spending of an increase in income, holding wealth constant, and c measures the effect on spending of an increase in wealth, holding income constant. The math for estimating these coefficients is complicated but the principle is simple: choose the estimates that give the best predictions of consumer spending for the data used to estimate the model. In Chapter 4, we saw that spurious correlations can appear when we compare variables like spending, income, and wealth that all tend to increase over time.

Temporal and Spatial Consistency

2013 ◽  
pp. 1675-1696
Author(s):  
Oliver Duke-Williams ◽  
John Stillwell
Keyword(s):  
Time Series ◽  
Age Groups ◽  
Register Data ◽  
Data Sets ◽  
Interaction Data ◽  
Patient Register ◽  
Health Authorities ◽  
Spatial Consistency ◽  
Using Data ◽  
Over Time

One of the major problems challenging time series research based on stock and flow data is the inconsistency that occurs over time due to changes in variable definition, data classification and spatial boundary configuration. The census of population is a prime example of a source whose data are fraught with these problems, resulting in even the simplest comparison between the 2001 Census and its predecessor in 1991 being difficult. The first part of this chapter introduces the subject of inconsistencies between related data sets, with general reference to census interaction data. Various types of inconsistency are described. A number of approaches to dealing with inconsistency are then outlined, with examples of how these have been used in practice. The handling of journey to work data of persons who work from home is then used as an illustrative example of the problems posed by inconsistencies in base populations. Home-workers have been treated in different ways in successive UK censuses, a factor which can cause difficulties not only for researchers interested in such working practices, but also for those interested in other aspects of commuting. The latter set of problems are perhaps more pernicious, as users are less likely to be aware of the biases introduced into data sets that are being compared. In the second half of this chapter, we make use of a time series data set of migration interaction data that does have temporal consistency to explore how migration propensities and patterns in England and Wales have changed since 1999 and in particular since the year prior to the 2001 Census. The data used are those that are produced by the Office of National Statistics based on comparisons of NHS patient records from one year to the next and adjusted using data on NHS patients re-registering in different health authorities. The analysis of these data suggests that the massive exodus of individuals from major metropolitan across the country that has been identified in previous studies is continuing apace, particularly from London whose net losses doubled in absolute terms between 1999 and 2004 before reducing marginally in 2005 and 2006. Whilst this pattern of counterurbanisation is evident for all-age flows, it conceals significant variations for certain age groups, not least those aged between 16 and 24, whose migration propensities are high and whose net redistribution is closely connected with the location of universities. The time series analyses are preceded by a comparison of patient register data with corresponding data from the 2001 Census. This suggests strong correlation between the indicators selected and strengthens the argument that patient register data in more recent years provide reliable evidence for researchers and policy makers on how propensities and patterns change over time.

scholarly journals Prediction of Wind Speed Using Hybrid Techniques

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6284
Author(s):  
Luis Lopez ◽  
Ingrid Oliveros ◽  
Luis Torres ◽  
Lacides Ripoll ◽  
Jose Soto ◽  
...  
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Least Squares ◽  
Support Vector ◽  
Single Time ◽  
Hybrid Techniques ◽  
Mode Decomposition ◽  
Vector Machines ◽  
Auto Regressive ◽  
Using Data

This paper presents a methodology to calculate day-ahead wind speed predictions based on historical measurements done by weather stations. The methodology was tested for three locations: Colombia, Ecuador, and Spain. The data is input into the process in two ways: (1) As a single time series containing all measurements, and (2) as twenty-four separate parallel sequences, corresponding to the values of wind speed at each of the 24 h in the day over several months. The methodology relies on the use of three non-parametric techniques: Least-squares support vector machines, empirical mode decomposition, and the wavelet transform. Moreover, the traditional and simple auto-regressive model is applied. The combination of the aforementioned techniques results in nine methods for performing wind prediction. Experiments using a matlab implementation showed that the least-squares support vector machine using data as a single time series outperformed the other combinations, obtaining the least root mean square error (RMSE).

scholarly journals A new tropical cyclone surge index incorporating the effects of coastal geometry, bathymetry and storm information

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Md. Rezuanul Islam ◽  
Chia-Ying Lee ◽  
Kyle T. Mandli ◽  
Hiroshi Takagi
Keyword(s):  
Wind Speed ◽  
Tropical Cyclone ◽  
Retrospective Analysis ◽  
Real Time ◽  
Numerical Computation ◽  
Storm Surge ◽  
Public Awareness ◽  
Translation Speed ◽  
Potential Index ◽  
Using Data

AbstractThis study presents a new storm surge hazard potential index (SSHPI) for estimating tropical cyclone (TC) induced peak surge levels at a coast. The SSHPI incorporates parameters that are often readily available at real-time: intensity in 10-min maximum wind speed, radius of 50-kt wind, translation speed, coastal geometry, and bathymetry information. The inclusion of translation speed and coastal geometry information lead to improvements of the SSHPI to other existing surge indices. A retrospective analysis of SSHPI using data from 1978–2019 in Japan suggests that this index captures historical events reasonably well. In particular, it explains ~ 66% of the observed variance and ~ 74% for those induced by TCs whose landfall intensity was larger than 79-kt. The performance of SSHPI is not sensitive to the type of coastal geometry (open coasts or semi-enclosed bays). Such a prediction methodology can decrease numerical computation requirements, improve public awareness of surge hazards, and may also be useful for communicating surge risk.

scholarly journals Prediction of Wind Speed Using Hybrid Techniques. Three locations: Colombia, Ecuador and Spain

2020 ◽  
Author(s):  
Luis Lopez ◽  
Ingrid Oliveros ◽  
Luis Torres ◽  
Lacides Ripoll ◽  
Jose Soto ◽  
...  
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Least Squares ◽  
Support Vector ◽  
Least Mean Square ◽  
Single Time ◽  
Hybrid Techniques ◽  
Mode Decomposition ◽  
Auto Regressive ◽  
Using Data

This paper presents a methodology to calculate day-ahead wind speed predictions based on historical measurements done by weather stations. The methodology was tested for three locations: Colombia, Ecuador, and Spain. The data is input into the process in two ways: 1) as a single time series containing all measurements, and 2) as twenty-four separate parallel sequences, corresponding to the values of wind speed at each of the 24 hours in the day over several months. The methodology relies on the use of three non-parametric techniques: Least-Squares Support Vector Machines, Empirical Mode Decomposition, and the Wavelet Transform. Also, the traditional and simple Auto-Regressive model is applied. The combination of the aforementioned techniques results in nine methods for performing wind prediction. Experiments using a MATLAB implementation showed that the Least-squares Support Vector Machine using data as a single time series outperformed the other combinations, obtaining the least mean square error.

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