34 research outputs found
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
Observations of Phytoplankton Community Composition in the Santa Barbara Channel During the Thomas Fire
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<sup>−2</sup> 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