scholarly journals Behavior analysis of convective and stratiform rain using Markovian approach over Mediterranean region from meteorological radar data

2012 ◽  
Vol 9 (5) ◽  
pp. 6225-6250 ◽  
Author(s):  
M. Lazri ◽  
S. Hameg ◽  
S. Ameur ◽  
J. M. Brucker ◽  
F. Ouallouche ◽  
...  
Keyword(s):  
Markov Chain ◽  
Transition Probabilities ◽  
Probabilistic Approach ◽  
Heavy Precipitation ◽  
Radar Data ◽  
Decision Criterion ◽  
Convective Precipitation ◽  
The North ◽  
National Office ◽  
Markovian Approach

Abstract. The aim of this study is to analyze the chronological behavior of precipitation in the north of Algeria using a Markovian approach. The probabilistic approach presented here proposes to study the evolution of the rainfall phenomenon in two distinct study areas, one located in sea and other located in ground. The data that we have used are provided by the National Office of Meteorology in Algiers (ONM). They are a series of images collected by the meterological radar of Setif during the rainy season 2001/2002. A decision criterion is established and based on radar reflectivity in order to classify the precipitation events located in both areas. At each radar observation, a state of precipitations is classified, either convective (heavy precipitation) or stratiform (average precipitation) both for the "sea" and for the "ground". On the whole, a time series of precipitations composed of three states; S0 (no raining), S1 (stratiform precipitation) and S2 (convective precipitation), is obtained for each of the two areas. Thereby, we studied and characterized the behavior of precipitation in time by a Markov chain of order one with three states. Transition probabilities Pij of state Si to state Sj are calculated. The results show that rainfall is well described by a Markov chain of order one with three states. Indeed, the stationary probabilities, which are calculated by using the Markovian model, and the actual probabilities are almost identical.

scholarly journals Contrasting stable water isotope signals from convective and large-scale precipitation phases of a heavy precipitation event in southern Italy during HyMeX IOP 13: a modelling perspective

2019 ◽  
Vol 19 (11) ◽  
pp. 7487-7506
Author(s):  
Keun-Ok Lee ◽  
Franziska Aemisegger ◽  
Stephan Pfahl ◽  
Cyrille Flamant ◽  
Jean-Lionel Lacour ◽  
...  
Keyword(s):  
Large Scale ◽  
Cold Front ◽  
Heavy Precipitation ◽  
Convective Precipitation ◽  
Water Isotopes ◽  
Precipitation Event ◽  
Stable Water Isotopes ◽  
Heavy Precipitation Event ◽  
The North ◽  
Upper Level

Abstract. The dynamical context and moisture transport pathways embedded in large-scale flow and associated with a heavy precipitation event (HPE) in southern Italy (SI) are investigated with the help of stable water isotopes (SWIs) based on a purely numerical framework. The event occurred during the Intensive Observation Period (IOP) 13 of the field campaign of the Hydrological Cycle in the Mediterranean Experiment (HyMeX) on 15 and 16 October 2012, and SI experienced intense rainfall of 62.4 mm over 27 h with two precipitation phases during this event. The first one (P1) was induced by convective precipitation ahead of a cold front, while the second one (P2) was mainly associated with precipitation induced by large-scale uplift. The moisture transport and processes responsible for the HPE are analysed using a simulation with the isotope-enabled regional numerical model COSMOiso. The simulation at a horizontal grid spacing of about 7 km over a large domain (about 4300 km ×3500 km) allows the isotopes signal to be distinguished due to local processes or large-scale advection. Backward trajectory analyses based on this simulation show that the air parcels arriving in SI during P1 originate from the North Atlantic and descend within an upper-level trough over the north-western Mediterranean. The descending air parcels reach elevations below 1 km over the sea and bring dry and isotopically depleted air (median δ18O ≤-25 ‰, water vapour mixing ratio q≤2 g kg−1) close to the surface, which induces strong surface evaporation. These air parcels are rapidly enriched in SWIs (δ18O ≥-14 ‰) and moistened (q≥8 g kg−1) over the Tyrrhenian Sea by taking up moisture from surface evaporation and potentially from evaporation of frontal precipitation. Thereafter, the SWI-enriched low-level air masses arriving upstream of SI are convectively pumped to higher altitudes, and the SWI-depleted moisture from higher levels is transported towards the surface within the downdrafts ahead of the cold front over SI, producing a large amount of convective precipitation in SI. Most of the moisture processes (i.e. evaporation, convective mixing) related to the HPE take place during the 18 h before P1 over SI. A period of 4 h later, during the second precipitation phase P2, the air parcels arriving over SI mainly originate from north Africa. The strong cyclonic flow around the eastward-moving upper-level trough induces the advection of a SWI-enriched African moisture plume towards SI and leads to large-scale uplift of the warm air mass along the cold front. This lifts moist and SWI-enriched air (median δ18O ≥-16 ‰, median q≥6 g kg−1) and leads to gradual rain out of the air parcels over Italy. Large-scale ascent in the warm sector ahead of the cold front takes place during the 72 h preceding P2 in SI. This work demonstrates how stable water isotopes can yield additional insights into the variety of thermodynamic mechanisms occurring at the mesoscale and synoptic scale during the formation of a HPE.

scholarly journals Financial Sustainability Evaluation and Forecasting Using the Markov Chain: The Case of the Wine Business

2020 ◽  
Vol 12 (15) ◽  
pp. 6150 ◽  
Author(s):  
Nataliya Rekova ◽  
Hanna Telnova ◽  
Oleh Kachur ◽  
Iryna Golubkova ◽  
Tomas Baležentis ◽  
...  
Keyword(s):  
Markov Chain ◽  
Transition Probabilities ◽  
Probabilistic Approach ◽  
Financial Sustainability ◽  
Security Level ◽  
Financial Security ◽  
Transition Matrices ◽  
Level Transition ◽  
A Company

This paper proposes a framework for assessing the financial sustainability of a wine producing company. The probabilistic approach is used to model the expected changes in the financial situation of an enterprise based on the historical trends. The case of an enterprise in Ukraine is considered as an illustration. The Markov chain is adopted for the forecasting exercise. Using the Markov chain framework allows one to predict the probability of financial security change for several periods ahead. The forecast relies on the transition probabilities obtained by exploiting the historical data. The proposed framework is implemented by construction of the financial security level transition matrices for three scenarios (optimistic, baseline and pessimistic). The case study of a Ukrainian wine producing company is considered. The possibilities for applying the proposed method in establishing anti-crisis financial strategy are discussed. The research shows how forecasting the financial security level of a company can serve in anti-crisis financial potential buildup.

A Case Study of Radar-Derived Quantitative Precipitation Estimation

2013 ◽  
Vol 726-731 ◽  
pp. 4541-4546 ◽  
Author(s):  
Li Li Yang ◽  
Yi Yang ◽  
You Cun Qi ◽  
Xue Xing Qiu ◽  
Zhong Qiang Gong
Keyword(s):  
Doppler Radar ◽  
Heavy Precipitation ◽  
Radar Data ◽  
Convective Precipitation ◽  
Precipitation Process ◽  
Fuzzy Logic Algorithm ◽  
Rain Gauges ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation

The convective and stratiform precipitations have different precipitation mechanisms. Different reflectivityrainfall rate (ZR) relations should be used for them. A heavy precipitation process on 22nd July, 2009(UTC) in Anhui Province is analyzed with Hefei Doppler radar and 269 rain gauges. First, the type of precipitation is obtained by a fuzzy logic algorithm with radar data. Then the reflectivity values are converted to rainfall rates using an adaptive Z-R relation according to different rain types. It is tested with the case and showed significant improvements over the current operational Z-R QPE when compared with gauges. Results also show that the precipitation process is caused by stratiform and convective precipitation; the rain estimated from radar corresponds well with cloud classification.

An Observational Study of Macroscopic and Microphysical Characteristics of Clouds and Precipitation on Mount Lu, Jiangxi, China

2020 ◽  
Author(s):  
Lijun Guo ◽  
Xueliang Guo ◽  
Xiaofeng Lou ◽  
Guangxian Lu ◽  
Kai Lyu ◽  
...  
Keyword(s):  
Urban Areas ◽  
Heavy Precipitation ◽  
Radar Data ◽  
Early Morning ◽  
Convective Precipitation ◽  
Total Precipitation ◽  
Cloud Droplets ◽  
Coagulation Mechanism ◽  
Observational Station ◽  
Autumn And Winter

<p>The Mount Lu (Lushan) observational station of cloud and fog in Jiujiang, China was restarted in 2015. The observational experiment of clouds/fog and precipitation was conducted from 2015 to 2018 in Mount Lu station. The observation dataset of clouds/fog on the Mount Lu were collected and established. The observational characteristics of clouds and precipitation were investigated from November 2015 to February 2018, including microphysics properties of clouds/fog and precipitation of 15 months in cold and warm seasons. The statistical results suggested that the heavy precipitation on the Mount Lu was frequent in summer with the maximal daily precipitation exceeding 100 mm. The maximal number of clouds and fogs days reached 25 days per month, with the lowest visibility about 20m. Due to radiative effect of clouds and fog in the (early) morning, the lowest temperature in the diurnal variation of temperature happened at about 9 o’clock, right before the dissipation of clouds and fog. Based on the analysis of radar data, stratiform precipitation, stratocumulus and convective precipitation in the autumn and winter respectively accounted for 29%, 44% and 27% of the total precipitation, and convective and stratocumulus precipitation in the spring and summer respectively accounted for 83% and 17% of the total precipitation. Compared with precipitation in urban areas, the small and medium raindrops were predominant in the precipitation processes on Mount Lu. Compared with fog in urban areas, the clouds and fog were characterized by smaller number concentration, the more significant bimodal and wider spectra. With the increase of precipitation within cloud, the more raindrops in number and larger raindrops in size were easier to initiate the coagulation mechanism, resulting in reduction of cloud droplets smaller than 11μm and larger than 30 μm. As a result, the peak at 11μm became more obvious. During the snowfall periods, the small cloud droplets were abundant, and the solid precipitation growth consumed large freezing cloud droplets through the rimming process.</p>

Data Mining Technique Applied To DNA Sequencing

2015 ◽  
Vol 2 (7) ◽  
pp. 18
Author(s):  
R. Jamuna
Keyword(s):  
Data Mining ◽  
Dna Methylation ◽  
Markov Chain ◽  
Human Genome ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Effective Means ◽  
Cpg Islands ◽  
Chain Model ◽  
The Given

CpG islands (CGIs) play a vital role in genome analysis as genomic markers.  Identification of the CpG pair has contributed not only to the prediction of promoters but also to the understanding of the epigenetic causes of cancer. In the human genome [1] wherever the dinucleotides CG occurs the C nucleotide (cytosine) undergoes chemical modifications. There is a relatively high probability of this modification that mutates C into a T. For biologically important reasons the mutation modification process is suppressed in short stretches of the genome, such as ‘start’ regions. In these regions [2] predominant CpG dinucleotides are found than elsewhere. Such regions are called CpG islands. DNA methylation is an effective means by which gene expression is silenced. In normal cells, DNA methylation functions to prevent the expression of imprinted and inactive X chromosome genes. In cancerous cells, DNA methylation inactivates tumor-suppressor genes, as well as DNA repair genes, can disrupt cell-cycle regulation. The most current methods for identifying CGIs suffered from various limitations and involved a lot of human interventions. This paper gives an easy searching technique with data mining of Markov Chain in genes. Markov chain model has been applied to study the probability of occurrence of C-G pair in the given   gene sequence. Maximum Likelihood estimators for the transition probabilities for each model and analgously for the  model has been developed and log odds ratio that is calculated estimates the presence or absence of CpG is lands in the given gene which brings in many  facts for the cancer detection in human genome.

scholarly journals Assimilating X- and S-Band Radar Data for a Heavy Precipitation Event in Italy

Water ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 1727
Author(s):  
Valerio Capecchi ◽  
Andrea Antonini ◽  
Riccardo Benedetti ◽  
Luca Fibbi ◽  
Samantha Melani ◽  
...  
Keyword(s):  
Extreme Event ◽  
Wrf Model ◽  
Heavy Precipitation ◽  
Radar Data ◽  
Flash Floods ◽  
Precipitation Event ◽  
Weather Research And Forecasting ◽  
Limited Area ◽  
Heavy Precipitation Event ◽  
The Town

During the night between 9 and 10 September 2017, multiple flash floods associated with a heavy-precipitation event affected the town of Livorno, located in Tuscany, Italy. Accumulated precipitation exceeding 200 mm in two hours was recorded. This rainfall intensity is associated with a return period of higher than 200 years. As a consequence, all the largest streams of the Livorno municipality flooded several areas of the town. We used the limited-area weather research and forecasting (WRF) model, in a convection-permitting setup, to reconstruct the extreme event leading to the flash floods. We evaluated possible forecasting improvements emerging from the assimilation of local ground stations and X- and S-band radar data into the WRF, using the configuration operational at the meteorological center of Tuscany region (LaMMA) at the time of the event. Simulations were verified against weather station observations, through an innovative method aimed at disentangling the positioning and intensity errors of precipitation forecasts. A more accurate description of the low-level flows and a better assessment of the atmospheric water vapor field showed how the assimilation of radar data can improve quantitative precipitation forecasts.

Ensemble Probabilistic Prediction of a Mesoscale Convective System and Associated Polarimetric Radar Variables Using Single-Moment and Double-Moment Microphysics Schemes and EnKF Radar Data Assimilation

2017 ◽  
Vol 145 (6) ◽  
pp. 2257-2279 ◽  
Author(s):  
Bryan J. Putnam ◽  
Ming Xue ◽  
Youngsun Jung ◽  
Nathan A. Snook ◽  
Guifu Zhang
Keyword(s):  
Mesoscale Convective System ◽  
Radar Data ◽  
Convective Precipitation ◽  
Convective System ◽  
Ensemble Forecasts ◽  
Verification Methods ◽  
Probabilistic Forecasts ◽  
Mesoscale Convective ◽  
Single Moment ◽  
Double Moment

Abstract Ensemble-based probabilistic forecasts are performed for a mesoscale convective system (MCS) that occurred over Oklahoma on 8–9 May 2007, initialized from ensemble Kalman filter analyses using multinetwork radar data and different microphysics schemes. Two experiments are conducted, using either a single-moment or double-moment microphysics scheme during the 1-h-long assimilation period and in subsequent 3-h ensemble forecasts. Qualitative and quantitative verifications are performed on the ensemble forecasts, including probabilistic skill scores. The predicted dual-polarization (dual-pol) radar variables and their probabilistic forecasts are also evaluated against available dual-pol radar observations, and discussed in relation to predicted microphysical states and structures. Evaluation of predicted reflectivity (Z) fields shows that the double-moment ensemble predicts the precipitation coverage of the leading convective line and stratiform precipitation regions of the MCS with higher probabilities throughout the forecast period compared to the single-moment ensemble. In terms of the simulated differential reflectivity (ZDR) and specific differential phase (KDP) fields, the double-moment ensemble compares more realistically to the observations and better distinguishes the stratiform and convective precipitation regions. The ZDR from individual ensemble members indicates better raindrop size sorting along the leading convective line in the double-moment ensemble. Various commonly used ensemble forecast verification methods are examined for the prediction of dual-pol variables. The results demonstrate the challenges associated with verifying predicted dual-pol fields that can vary significantly in value over small distances. Several microphysics biases are noted with the help of simulated dual-pol variables, such as substantial overprediction of KDP values in the single-moment ensemble.

scholarly journals Calibration of Transition Intensities for a Multistate Model: Application to Long-Term Care

Risks ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 37
Author(s):  
Manuel L. Esquível ◽  
Gracinda R. Guerreiro ◽  
Matilde C. Oliveira ◽  
Pedro Corte Real
Keyword(s):  
Markov Chain ◽  
Continuous Time ◽  
Long Term Care ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Chain Model ◽  
Chain Process ◽  
Term Care ◽  
National Network

We consider a non-homogeneous continuous time Markov chain model for Long-Term Care with five states: the autonomous state, three dependent states of light, moderate and severe dependence levels and the death state. For a general approach, we allow for non null intensities for all the returns from higher dependence levels to all lesser dependencies in the multi-state model. Using data from the 2015 Portuguese National Network of Continuous Care database, as the main research contribution of this paper, we propose a method to calibrate transition intensities with the one step transition probabilities estimated from data. This allows us to use non-homogeneous continuous time Markov chains for modeling Long-Term Care. We solve numerically the Kolmogorov forward differential equations in order to obtain continuous time transition probabilities. We assess the quality of the calibration using the Portuguese life expectancies. Based on reasonable monthly costs for each dependence state we compute, by Monte Carlo simulation, trajectories of the Markov chain process and derive relevant information for model validation and premium calculation.

scholarly journals Exploring Antarctic subglacial lakes with scientific probes: a formal probabilistic approach for operational risk management

2012 ◽  
Vol 58 (212) ◽  
pp. 1085-1097 ◽  
Author(s):  
M.P. Brito ◽  
G. Griffiths ◽  
M. Mowlem
Keyword(s):  
Markov Chain ◽  
Probabilistic Approach ◽  
Operational Risk ◽  
Formal Approach ◽  
Fault Trees ◽  
Subglacial Lake ◽  
Complex Processes ◽  
Formal Framework ◽  
Past Climate Change ◽  
Subglacial Lakes

AbstractSince their discovery, Antarctic subglacial lakes have become of great interest to the science community. It is hypothesized that they may hold unique forms of biological life and that they hold detailed sedimentary records of past climate change. According to the latest inventory, a total of 387 subglacial lakes have been identified in Antarctica (Wright and Siegert, 2011). However, exploration using scientific probes has yet to be performed. We propose a generic, formal approach to manage the operational risk of deploying probes during clean access to subglacial lake exploration. A representation of the entire probe deployment process is captured in a Markov chain. The transition from one state to the next depends on several factors, including reliability of components and processes. We use fault trees to quantify the probability of failure of the complex processes that must take place to facilitate the transition from one state to another. Therefore, the formal framework consists of integrating a Markov chain, fault trees, component and subsystem reliability data and expert judgment. To illustrate its application we describe how the approach can be used to address a series of what-if scenarios, using the intended Ellsworth Subglacial Lake probe deployment as a case study.

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