Lagrangian Integro-Difference Equation Model for Precipitation Nowcasting

2021 ◽  
Author(s):  
Seppo Pulkkinen ◽  
V. Chandrasekar ◽  
Tero Niemi
Keyword(s):  
Difference Equation ◽  
Urban Areas ◽  
Short Range ◽  
Autoregressive Process ◽  
Skill Score ◽  
Equation Model ◽  
Validation Dataset ◽  
Time Range ◽  
Stochastic Perturbations ◽  
Second Best

AbstractDelivering reliable nowcasts (short-range forecasts) of severe rainfall and the resulting flash floods is important in densely populated urban areas. The conventional method is advection-based extrapolation of radar echoes. However, during rapidly evolving convective rainfall this so-called Lagrangian persistence (LP) approach is limited to deterministic and very short-range nowcasts. To address these limitations in the one-hour time range, a novel extension of LP, called Lagrangian INtegro-Difference equation model withAutoregression (LINDA), is proposed. The model consists of five components: 1) identification of rain cells, 2) advection, 3) autoregressive process describing growth and decay of the cells, 4) convolution describing loss of predictability at small scales and 5) stochastic perturbations to simulate forecast uncertainty. Advection is separated from the other components that are applied in the Lagrangian coordinates. The reliability of LINDA is evaluated using the NEXRAD WSR-88D radar that covers the Dallas-Fort Worth metropolitan area, as well as the NEXRAD mosaic covering the continental United States. This is done with two different configurations: LINDA-D for deterministic and LINDA-P for probabilistic nowcasts. The validation dataset consists of 11 rainfall events during 2018-2020. For predicting moderate to heavy rainfall (5-20 mm/h), LINDA outperforms the previously proposed LP-based approaches. The most significant improvement is seen for the ETS and POD statistics with the 5 mm/h threshold. For 30-minute nowcasts, they show 15% and 16% increase, respectively, to the second-best method and 48% and 34% increase compared to LP. For the 5 mm/h threshold, the increase in the ROC skill score of 30-minute nowcasts from the second-best method is 10%.

scholarly journals Prediction of Hourly Effect of Land Use on Crime

2018 ◽  
Vol 8 (1) ◽  
pp. 16 ◽  
Author(s):  
Irina Matijosaitiene ◽  
Peng Zhao ◽  
Sylvain Jaume ◽  
Joseph Gilkey Jr
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Logistic Regression ◽  
Hot Spots ◽  
Urban Areas ◽  
Prediction Models ◽  
Hot Spot ◽  
Time Range ◽  
Land Uses ◽  
Police Departments

Predicting the exact urban places where crime is most likely to occur is one of the greatest interests for Police Departments. Therefore, the goal of the research presented in this paper is to identify specific urban areas where a crime could happen in Manhattan, NY for every hour of a day. The outputs from this research are the following: (i) predicted land uses that generates the top three most committed crimes in Manhattan, by using machine learning (random forest and logistic regression), (ii) identifying the exact hours when most of the assaults are committed, together with hot spots during these hours, by applying time series and hot spot analysis, (iii) built hourly prediction models for assaults based on the land use, by deploying logistic regression. Assault, as a physical attack on someone, according to criminal law, is identified as the third most committed crime in Manhattan. Land use (residential, commercial, recreational, mixed use etc.) is assigned to every area or lot in Manhattan, determining the actual use or activities within each particular lot. While plotting assaults on the map for every hour, this investigation has identified that the hot spots where assaults occur were ‘moving’ and not confined to specific lots within Manhattan. This raises a number of questions: Why are hot spots of assaults not static in an urban environment? What makes them ‘move’—is it a particular urban pattern? Is the ‘movement’ of hot spots related to human activities during the day and night? Answering these questions helps to build the initial frame for assault prediction within every hour of a day. Knowing a specific land use vulnerability to assault during each exact hour can assist the police departments to allocate forces during those hours in risky areas. For the analysis, the study is using two datasets: a crime dataset with geographical locations of crime, date and time, and a geographic dataset about land uses with land use codes for every lot, each obtained from open databases. The study joins two datasets based on the spatial location and classifies data into 24 classes, based on the time range when the assault occurred. Machine learning methods reveal the effect of land uses on larceny, harassment and assault, the three most committed crimes in Manhattan. Finally, logistic regression provides hourly prediction models and unveils the type of land use where assaults could occur during each hour for both day and night.

A difference equation model of partial series

1994 ◽  
Vol 61 (2-3) ◽  
pp. 301-305
Author(s):  
K.J. Bunch ◽  
R.W. Grow
Keyword(s):  
Difference Equation ◽  
Equation Model

scholarly journals How Does China’s New Consumption Era Reshape Residents’ Shopping Behaviors from the Perspective of Community in Hohhot, China

2021 ◽  
Vol 13 (14) ◽  
pp. 7599
Author(s):  
Fangqu Niu ◽  
Fang Wang
Keyword(s):  
Urban Areas ◽  
Structural Equation ◽  
Field Survey ◽  
Penetration Rate ◽  
Equation Model ◽  
New Era ◽  
Different Types ◽  
Variable Influence ◽  
The City

In the new consumption era, the popularization and application of information technology has continuously enriched residents’ consumption channels, gradually reshaping their consumption concepts and shopping behaviors. In this paper, Hohhot is taken as a case study, using open-source big data and field survey data to theorize the characteristics and mechanism of residents’ shopping behaviors in different segments of consumers based on geography. First, communities were divided into five types according to their location and properties: main communities in urban areas (MCs), historical communities in urban areas (HCs), high-grade communities in the outskirts of the city (HGCs), mid-grade communities in urban peripheries (MGCs), and urban villages (UVs). On this basis, a structural equation model is used to explore the characteristics of residents’ shopping behaviors and their influencing mechanisms in the new consumption era. The results showed that: (1) The online shopping penetration rate of residents in UVs and HCs is lowest, and that of residents in HGC is highest. (2) The types of products purchased in online and offline shopping by different types of community show certain differences. (3) From the perspective of influencing mechanisms, residents’ characteristics directly affect their shopping behaviors and, indirectly (through the choice of community where they live and their consumption attitudes), their differences in shopping behaviors. Different properties of communities cannot directly affect residents’ shopping behaviors, but they can affect them indirectly by influencing consumption attitudes and then affect such behaviors. Typical consumption attitudes of the new era, such as shopping for luxuries and emerging consumption, have the most significant and direct influence on shopping behaviors, as well as an intermediate and variable influence.

Generalized Model of Prediction of Natural Gas Consumption

2004 ◽  
Vol 126 (2) ◽  
pp. 90-98 ◽  
Author(s):  
S. Gil ◽  
J. Deferrari
Keyword(s):  
Natural Gas ◽  
Urban Areas ◽  
Short Range ◽  
Region Of Interest ◽  
Daily Consumption ◽  
Annual Peak ◽  
Load Factors ◽  
Transportation Capacity ◽  
Gas Consumption ◽  
Natural Gas Consumption

We present a model intended to predict mainly the residential and commercial natural gas consumption in urban areas, for the short and intermediate ranges of time. In the short range, the model has been successfully used to forecast the daily gas consumption of major cities of Argentina. It is able to predict the consumption 1 to 5 days in advance with 10% of uncertainty. In the intermediate range (1 to 5 years), the model allows us to estimate the annual peak consumption, load factors and the optimal transportation capacity for a given region of interest. We also present a novel procedure to obtain the distribution of daily consumption from the monthly consumption obtained from the monthly billing.

scholarly journals Mapping Informal Settlements in the Middle East Environment using an Object-Based Machine-Learning Approach

2018 ◽  
Author(s):  
Ahmad Fallatah ◽  
Simon Jones ◽  
David Mitchell
Keyword(s):  
Machine Learning ◽  
Urban Areas ◽  
Hybrid Approach ◽  
Informal Settlements ◽  
Validation Dataset ◽  
Relative Importance ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Machine Learning Approach ◽  
Very High

The identification of informal settlements in urban areas is an important step in developing and implementing pro-poor urban policies. Understanding when, where and who lives inside informal settlements is critical to efforts to improve their resilience. This study aims to analyse the capability of machine-learning (ML) methods to map informal areas in Jeddah, Saudi Arabia, using very-high-resolution (VHR) imagery and terrain data. Fourteen indicators of settlement characteristics were derived and mapped using an object-based ML approach and VHR imagery. These indicators were categorised according to three different spatial levels: environ, settlement and object. The most useful indicators for prediction were found to be density and texture measures, (with random forest (RF) relative importance measures of over 25% and 23% respectively). The success of this approach was evaluated using a small, fully independent validation dataset. Informal areas were mapped with an overall accuracy of 91%. Object-based ML as a hybrid approach performed better (8%) than object-based image analysis alone due to its ability to encompass all available geospatial levels.

scholarly journals Strategic Mapping and Optimised Allocation of Automated External Defibrillators in Urban Areas

2020 ◽  
pp. 138-145
Author(s):  
Kanteler Despoina ◽  
Katsaros Evangelos ◽  
Bakouros Yiannis
Keyword(s):  
Cardiac Arrest ◽  
Urban Areas ◽  
Hot Spot ◽  
Public Access ◽  
Capital City ◽  
Public Health Issue ◽  
Time Range ◽  
Public Access Defibrillation ◽  
Full Coverage ◽  
The City

<p><strong>Background</strong>: Out-of-hospital cardiac arrest (OHCA) is a leading cause of death and is regarded as a significant public health issue. Immediate treatment with an automated external defibrillator (AED) increases OHCA patient survival potential. For AEDs to be used and fulfil their lifesaving potential, they need to be in close proximity to the victim and accessible at the time of a cardiac arrest. The current paper sheds light upon an optimized location-allocation method achieving full coverage with immediate accessibility in an urban context given a limited number of available AEDs for deployment using GIS. The case study is the Region of Western Macedonia (RWM) in Greece for a pilot AED placement program for the Governance of RWM. The focus of the current study is the capital city of RWM, Kozani. The initial number of the defibrillators (120) that are needed to be distributed is very small and cannot cover the needs for every major city or rural area in the region. Out of the 120 AEDs, the challenge is to find the minimum required number of AEDs to allocate in the city providing full coverage and accessibility. This paper focuses only on one city, however, the same methodology was applied to allocate AEDs in the other selected cities of the region. The rural dimension and methodology are not in the scope of this paper. <br> <strong>Methods</strong>: Road network data, spatio-temporal analysis of accessibility network, digital elevation model, land uses, population density, seasonal fluctuations and socio-demographic variables were used. GIS algorithms such as spatial analysis, kernel density, hot spot analysis, maximal covering location problem (MCLP) tests, proximity algorithms, buffer zoning, were a few of the tests made in order to find the most efficient positions and maximize coverage keeping in mind that access to an AED until defibrillation time must not exceed the time range of five minutes. <br> <strong>Results</strong>: optimised sites and allocated AEDs in urban areas we managed to achieve full city coverage with 17 AEDs. In every part of the city, people can have access to a nearby AED with its critical radius of less than or equal to 250m achieving defibrillation in the critical period of 5 minutes. The results are promising for the establishment and expansion of optimised AED deployment in cities. <br> <strong>Conclusions</strong>: The progress of the project must be monitored and there are still unresolved problems that need to be tackled to provide a robust allocation of future defibrillators. Further research to enhance our understanding on public access defibrillation and optimize the accessibility and functionality of the medical health care services is needed. A network of engaged and informed citizens ready to act is required for a successful public access defibrillation program.</p>

scholarly journals Australian tidal currents – assessment of a barotropic model with an unstructured grid

2020 ◽  
Author(s):  
David A. Griffin ◽  
Mike Herzfeld ◽  
Mark Hemer
Keyword(s):  
Unstructured Grid ◽  
Tide Gauge ◽  
Major Axis ◽  
Equation Model ◽  
Tidal Currents ◽  
Tidal Range ◽  
Validation Dataset ◽  
Velocity Amplitude ◽  
Shelf Seas ◽  
Relative Errors

Abstract. While the variations of tidal range are large and fairly well known across Australia (less than 1 m near Perth but more than 14 m in King Sound), the properties of the tidal currents are not. We describe a new regional model of Australian tides and assess it against a validation dataset comprising tidal height and velocity constituents at 615 tide gauge sites and 95 current meter sites. The model is a barotropic implementation of COMPAS, an unstructured-grid primitive-equation model that is forced at the open boundaries by TPXO9v1. The Mean Absolute value of the Error (MAE) of the modelled M2 height amplitude is 9.3 cm, or 13 % of the 73 cm mean observed amplitude. The MAE of phase (11°), however, is significant, so the M2 Mean Magnitude of Vector Error (MMVE, 20 cm) is significantly greater. Results for 5 other major constituents are similar. We conclude that while the model has skill at height in all regions, there is definitely room for improvement (especially at some specific locations) before harmonic predictions based on observations are rendered obsolete. For the M2 major-axis velocity amplitude, the MAE across the 95 current meter sites, where the observed amplitude ranges from 0.1 cm s−1 to 144 cm s−1, is 6.5 cm s−1, or 20 % of the 31.7 cm s−1 observed mean. This nationwide average result is not much greater than the equivalent for height, but it conceals a larger regional variation. Relative errors on the narrow shelves of NSW and Western Australia exceed 100 %, but tidal currents are weak and negligible there compared to non-tidal currents. We show that the model has predictive value for much of the 79 % of Australia's shelf seas where tides are a major component of the total velocity variability. In descending order this includes the Bass Strait, Kimberley to Arnhem Land and Southern Great Barrier Reef regions. There is limited evidence the model is also valuable for currents in other regions across northern Australia. We plan to commence publishing unofficial tidal current predictions for chosen regions in the near future, based on both the limited number of observations, and the COMPAS model.

Very Short Time Range Forecasting Using CReSS-3DVAR for a Meso-γ-Scale, Localized, Extremely Heavy Rainfall Event: Comparison with an Extrapolation-Based Nowcast

2017 ◽  
Vol 12 (5) ◽  
pp. 967-979 ◽  
Author(s):  
Ryohei Kato ◽  
◽  
Shingo Shimizu ◽  
Ken-ichi Shimose ◽  
Koyuru Iwanami
Keyword(s):  
Heavy Rainfall ◽  
Spatial Scales ◽  
Weather Prediction ◽  
Forecast Accuracy ◽  
Skill Score ◽  
Radar Reflectivity ◽  
Heavy Rainfall Event ◽  
Time Range ◽  
Nwp Model ◽  
Short Time

The forecast accuracy of a numerical weather prediction (NWP) model for a very short time range (≤1 h) for a meso-γ-scale (2–20 km) extremely heavy rainfall (MγExHR) event that caused flooding at the Shibuya railway station in Tokyo, Japan on 24 July 2015 was compared with that of an extrapolation-based nowcast (EXT). The NWP model used CReSS with 0.7 km horizontal grid spacing, and storm-scale data from dense observation networks (radars, lidars, and microwave radiometers) were assimilated using CReSS-3DVAR. The forecast accuracy of the heavy rainfall area (≥20 mm h-1), as a function of forecast time (FT), was investigated for the NWP model and EXT predictions using the fractions skill score (FSS) for various spatial scales of displacement error (L). These predictions were started 30 minutes before the onset of extremely heavy rainfall at Shibuya station. The FSS for L=1 km, i.e., grid-scale verification, showed NWP accuracy was lower than that of EXT before FT=40 min; however, NWP accuracy surpassed that of EXT from FT=45 to 60 min. This suggests the possibility of seamless, high-accuracy forecasts of heavy rainfall (≥20 mm h-1) associated with MγExHR events within a very short time range (≤1 h) by blending EXT and NWP outputs. The factors behind the fact that the NWP model predicted heavy rainfall area within the very short time range of ≤1 h more correctly than did EXT are also discussed. To enable this discussion of the factors, additional sensitivity experiments with a different assimilation method of radar reflectivity were performed. It was found that a moisture adjustment above the lifting condensation level using radar reflectivity was critical to the forecasting of heavy rainfall near Shibuya station after 25 min.

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