Clustering and forecasting of dissolved oxygen concentration on a river basin

The aim of this contribution is to combine statistical methodologies to geographically classify homogeneous groups of water quality monitoring sites based on similarities in the temporal dynamics of the dissolved oxygen (DO) concentration, in order to obtain accurate forecasts of this quality variable. Our methodology intends to classify the water quality monitoring sites into spatial homogeneous groups, based on the DO concentration, which has been selected and considered relevant to characterize the water quality. We apply clustering techniques based on Kullback Information, measures that are obtained in the state space modelling process. For each homogeneous group of water quality monitoring sites we model the DO concentration using linear and state space models, which incorporate tendency and seasonality components in different ways. Both approaches are compared by the mean squared error (MSE) of forecasts

Graph Regionalization with Clustering and Partitioning: an Application for Daily Commuting Flows in Albania

The paper presents an original application of the recently proposed spatial data mining method named GraphRECAP on daily commuting flows using 2011 Albanian census data. Its aim is to identify several clusters of Albanian municipalities/communes; propose a classification of the Albanian territory based on daily commuting flows among municipalities/communes. Starting from 373 local units, we first applied a spatial clustering technique without imposing any constraining strategy. Based on the input variables, we obtained 16 clusters. In the second step of our analysis, we impose a set of constraining parameters to identify intermediate areas between the local level (municipality/commune) and the national one. We have defined 12 derived regions (same number as the actual Albanian prefectures but with different geographies). These derived regions are quite different from the traditional ones in terms of both geographical dimensions and boundarie

Using random forests to diagnose aviation turbulence

mospheric turbulence poses a significant hazard to aviation, with severe encounters costing airlines millions of dollars per year in compensation, aircraft damage, and delays due to required post-event inspections and repairs. Moreover, attempts to avoid turbulent airspace cause flight delays and en route deviations that increase air traffic controller workload, disrupt schedules of air crews and passengers and use extra fuel. For these reasons, the Federal Aviation Administration and the National Aeronautics and Space Administration have funded the development of automated turbulence detection, diagnosis and forecasting products. This paper describes a methodology for fusing data from diverse sources and producing a real-time diagnosis of turbulence associated with thunderstorms, a significant cause of weather delays and turbulence encounters that is not well-addressed by current turbulence forecasts. The data fusion algorithm is trained using a retrospective dataset that includes objective turbulence reports from commercial aircraft and collocated predictor data. It is evaluated on an independent test set using several performance metrics including receiver operating characteristic curves, which are used for FAA turbulence product evaluations prior to their deployment. A prototype implementation fuses data from Doppler radar, geostationary satellites, a lightning detection network and a numerical weather prediction model to produce deterministic and probabilistic turbulence assessments suitable for use by air traffic managers, dispatchers and pilots. The algorithm is scheduled to be operationally implemented at the National Weather Service's Aviation Weather Center in 2014. Document type: Articl

A WRF simulation of the impact of 3-D radiative transfer on surface hydrology over the Rocky Mountains and Sierra Nevada

We investigate 3-D mountains/snow effects on solar flux distributions and their impact on surface hydrology over the western United States, specifically the Rocky Mountains and Sierra Nevada. The Weather Research and Forecasting (WRF) model, applied at a 30 km grid resolution, is used in conjunction with a 3-D radiative transfer parameterization covering a time period from 1 November 2007 to 31 May 2008, during which abundant snowfall occurred. A comparison of the 3-D WRF simulation with the observed snow water equivalent (SWE) and precipitation from Snowpack Telemetry (SNOTEL) sites shows reasonable agreement in terms of spatial patterns and daily and seasonal variability, although the simulation generally has a positive precipitation bias. We show that 3-D mountain features have a profound impact on the diurnal and monthly variation of surface radiative and heat fluxes, and on the consequent elevation-dependence of snowmelt and precipitation distributions. In particular, during the winter months, large deviations (3-D-PP, in which PP denotes the plane-parallel approach) of the monthly mean surface solar flux are found in the morning and afternoon hours due to shading effects for elevations below 2.5 km. During spring, positive deviations shift to the earlier morning. Over mountaintops higher than 3 km, positive deviations are found throughout the day, with the largest values of 40–60 W m⁻² occurring at noon during the snowmelt season of April to May. The monthly SWE deviations averaged over the entire domain show an increase in lower elevations due to reduced snowmelt, which leads to a reduction in cumulative runoff. Over higher elevation areas, positive SWE deviations are found because of increased solar radiation available at the surface. Overall, this study shows that deviations of SWE due to 3-D radiation effects range from an increase of 18% at the lowest elevation range (1.5–2 km) to a decrease of 8% at the highest elevation range (above 3 km). Since lower elevation areas occupy larger fractions of the land surface, the net effect of 3-D radiative transfer is to extend snowmelt and snowmelt-driven runoff into the warm season. Because 60–90% of water resources originate from mountains worldwide, the aforementioned differences in simulated hydrology due solely to 3-D interactions between solar radiation and mountains/snow merit further investigation in order to understand the implications of modeling mountain water resources, and these resources' vulnerability to climate change and air pollution

Impact of environmental moisture on tropical cyclone intensification

The impacts of environmental moisture on the intensification of a tropical cyclone (TC) are investigated in the Weather Research and Forecasting (WRF) model, with a focus on the azimuthal asymmetry of the moisture impacts relative to the storm path. A series of sensitivity experiments with varying moisture perturbations in the environment are conducted and the Marsupial Paradigm framework is employed to understand the different moisture impacts. We find that modification of environmental moisture has insignificant impacts on the storm in this case unless it leads to convective activity that deforms the quasi-Lagrangian boundary of the storm and changes the moisture transport into the storm. By facilitating convection and precipitation outside the storm, enhanced environmental moisture ahead of the northwestward-moving storm induces a dry air intrusion to the inner core and limits TC intensification. In contrast, increased moisture in the rear quadrants favors intensification by providing more moisture to the inner core and promoting storm symmetry, with primary contributions coming from moisture increase in the boundary layer. The different impacts of environmental moisture on TC intensification are governed by the relative locations of moisture perturbations and their interactions with the storm Lagrangian structure

Impact of environmental moisture on tropical cyclone intensification

The impacts of environmental moisture on the intensification of a tropical cyclone (TC) are investigated in the Weather Research and Forecasting (WRF) model, with a focus on the azimuthal asymmetry of the moisture impacts relative to the storm path. A series of sensitivity experiments with varying moisture perturbations in the environment are conducted and the Marsupial Paradigm framework is employed to understand the different moisture impacts. We find that modification of environmental moisture has insignificant impacts on the storm in this case unless it leads to convective activity that deforms the quasi-Lagrangian boundary of the storm and changes the moisture transport into the storm. By facilitating convection and precipitation outside the storm, enhanced environmental moisture ahead of the northwestward-moving storm induces a dry air intrusion to the inner core and limits TC intensification. In contrast, increased moisture in the rear quadrants favors intensification by providing more moisture to the inner core and promoting storm symmetry, with primary contributions coming from moisture increase in the boundary layer. The different impacts of environmental moisture on TC intensification are governed by the relative locations of moisture perturbations and their interactions with the storm Lagrangian structure