scholarly journals Occurrence and transition probabilities of omega and high-over-low blocking in the Euro-Atlantic region

2021 ◽  
Vol 2 (4) ◽  
pp. 927-952
Author(s):  
Carola Detring ◽  
Annette Müller ◽  
Lisa Schielicke ◽  
Peter Névir ◽  
Henning W. Rust
Keyword(s):  
Northern Hemisphere ◽  
Markov Models ◽  
Transition Probabilities ◽  
Point Vortex ◽  
Department Of Energy ◽  
Occurrence Probability ◽  
Multinomial Regression ◽  
Atlantic Sector ◽  
Blocking High ◽  
Environmental Prediction

Abstract. Stationary, long-lasting blocked weather patterns can lead to extreme conditions such as anomalously high temperatures or heavy rainfall. The exact locations of such extremes depend on the location of the vortices that form the block. There are two main types of blocking: (i) a high-over-low block with a high located poleward of an isolated low and (ii) an omega block with two lows that lie southeast and southwest of the blocking high in the Northern Hemisphere. In this work, we refine a novel method based on the kinematic vorticity number and the point vortex theory that allows us to distinguish between these two blocking types. Based on the National Centers for Environmental Prediction–Department of Energy (NCEP–DOE) Reanalysis 2 data, we study the trends of the occurrence probability and the onset (formation), decay (offset) and transition probabilities of high-over-low and omega blocking in the 30-year period from 1990 to 2019 in the Northern Hemisphere (90∘ W–90∘ E) and in the Euro-Atlantic sector (40∘ W–30∘ E). First, we use logistic regression to investigate long-term changes in blocking probabilities for full years, seasons and months. While trends are small for annual values, changes in occurrence probability are more visible and also more diverse when broken down to seasonal and monthly resolution, showing a prominent increase in February and March and a decrease in December. A three-state multinomial regression describing the occurrence of omega and high-over-low blocking reveals different trends for both types. Particularly the February and December changes are dominated by the omega blocking type. Additionally, we use Markov models to describe transition probabilities for a two-state (unblocked, blocked) and a three-state (unblocked, omega block, high-over-low block) Markov model. We find the largest changes in transition probabilities in the summer season, where the transition probabilities towards omega blocks significantly increase, while the unblocked state becomes less probable. Prominent in winter are decreasing probabilities for transitions from omega to high-over-low and persistence of the latter. Moreover, we show that omega blocking is more likely to occur and to be more persistent than the high-over-low blocking pattern.

scholarly journals Atmospheric blocking types: Frequencies and transitions

2020 ◽  
Author(s):  
Carola Detring ◽  
Annette Müller ◽  
Lisa Schielicke ◽  
Peter Névir ◽  
Henning W. Rust
Keyword(s):  
Markov Model ◽  
Northern Hemisphere ◽  
Transition Probabilities ◽  
Transition Probability ◽  
Time Step ◽  
Pressure System ◽  
Monthly Basis ◽  
Atlantic Sector ◽  
Weather Patterns ◽  
Significant Difference

Abstract. Stationary, long-lasting blocked weather patterns can lead to extreme conditions such as very high temperatures or heavy rainfall. They are defined by a persistent high pressure system in combination with one or two low pressure systems. The mechanisms for the onset of such weather patterns are still not fully understood. Using a novel method based on the kinematic vorticity number we distinguish between two blocking types, namely High-over-Low and Omega block, in previously-identified blocking periods. Our main goal of this work is to study the temporal evolution of the occurrence probability and the onset, offset, and transition probabilities of blocking on the northern hemisphere. We analyze NCEP-DOE Reanalysis 2 data over the30 year period from 1990 to 2019 in two regions: Euro-Atlantic sector (40° W–30° E) and half northern hemisphere (90° W–90° E). First, we use logistic regression to investigate the temporal development of blocking probabilities depending on years, seasons and months. We find no significant difference in blocking numbers over the 30 year period. But we find large differences in the occurrence probabilities on a monthly basis with strongest increases over the 30 year period in February and March that are compensated by a decrease in December and autumn. Second, we use a Markov model to calculate the transition probabilities for two models: One is composed of two states blocking and no blocking, and another Markov model (three states) that additionally distinguishes between the specific blocking types High-over-Low and Omega blocking as well as of the state no blocking. The description with Markov theory reduces the probability to change from one weather regime to another or to stay within the same regime to a dependency only on the previous time step. Over the 30 year period, we found the largest changes in transition probabilities in the summer season, where the transition probability to Omega blocks increase strongly, while the unblocked state becomes less probable. Hence, Omega blocks become more frequent and stable in summer at the expense of the other states. As a main result, we show that Omega blocking is more likely to occur and more persistent than the High-over-Low blocking pattern.

Probability of Transition of Normal Esophageal Mucosa and Precancerous Lesion to the Esophageal Cancer

2021 ◽  
Author(s):  
Zhiyu Chen ◽  
Yong YU ◽  
Xue YANG ◽  
Jing-Xuan WANG ◽  
Wen-Qiang Wei ◽  
...  
Keyword(s):  
Esophageal Cancer ◽  
Carcinoma In Situ ◽  
Markov Models ◽  
Transition Probabilities ◽  
Health State ◽  
Severe Dysplasia ◽  
Esophageal Mucosa ◽  
Mild Dysplasia ◽  
Pathological Stages

Abstract Background: To estimate the transition probabilities of esophageal cancer（EC） and its precancerous lesions by Markov model, which could provide important information for EC screening about choosing reasonable screening and follow-up intervals.Methods: The transition probabilities among pathological stages were estimated by establishing Markov models for the natural history of EC and repeatedly adjusting and calibrating Markov models by comparing the modeled incidence and distributions of pathological stages (alone or combined) with observed data in real-world condition. Results: In one year, the probabilities were 0.024, 0.05, 0.12 for people from health state progressing to mild dysplasia (mD), mild dysplasia (mD) to moderate dysplasia (MD), and moderate dysplasia (MD) to severe dysplasia/carcinoma in situ (SD/CIS), respectively. The age-specific transition probabilities were 0.08~0.18 for severe dysplasia/carcinoma in situ (SD/CIS) progressing to intramucosal carcinoma(IC), 0.4~0.87 for intramucosal carcinoma (IC) to submucosal carcinoma (T1N0M0) (SC), and 0.2~0.85 for submucosal carcinoma (T1N0M0) (SC) to invasive carcinoma (INC). The progression probabilities increased with age and the severity of the disease. Based on the estimated transition probabilities, we predicted the incidence of EC and distributions of its pathological stages. Comparisons between modeled results with observed data confirmed the validation of our transition probabilities.Conclusions: An esophageal cancer transition model in high-risk areas of China has been established with validity. It could be a point of reference for further economic evaluation and policy formulation of esophageal cancer screening.

A semi-markov model for clinical trials

1965 ◽  
Vol 2 (02) ◽  
pp. 269-285 ◽  
Author(s):  
George H. Weiss ◽  
Marvin Zelen
Keyword(s):  
Clinical Trials ◽  
Probability Distribution ◽  
Acute Leukemia ◽  
Markov Model ◽  
Markov Models ◽  
Transition Probabilities ◽  
State Transitions ◽  
Semi Markov Processes ◽  
Absorbing States ◽  
Epidemic Diseases

This paper applies the theory of semi-Markov processes to the construction of a stochastic model for interpreting data obtained from clinical trials. The model characterizes the patient as being in one of a finite number of states at any given time with an arbitrary probability distribution to describe the length of stay in a state. Transitions between states are assumed to be chosen according to a stationary finite Markov chain.Other attempts have been made to develop stochastic models of clinical trials. However, these have all been essentially Markovian with constant transition probabilities which implies that the distribution of time spent during a visit to a state is exponential (or geometric for discrete Markov chains). Markov models need also to assume that the transitions in the state of a patient depend only on absolute time whereas the semi-Markov model assumes that transitions depend on time relative to a patient. Thus the models are applicable to degenerative diseases (cancer, acute leukemia), while Markov models with time dependent transition probabilities are applicable to colds and epidemic diseases. In this paper the Laplace transforms are obtained for (i) probability of being in a state at timet, (ii) probability distribution to reach absorption state and (iii) the probability distribution of the first passage times to go from initial states to transient or absorbing states, transient to transient, and transient to absorbing. The model is applied to a clinical study of acute leukemia in which patients have been treated with methotrexate and 6-mercaptopurine. The agreement between the data and the model is very good.

Fitting hidden semi-Markov models to breakpoint rainfall data

2001 ◽  
Vol 38 (A) ◽  
pp. 142-157 ◽  
Author(s):  
John Sansom ◽  
Peter Thomson
Keyword(s):  
Dwell Time ◽  
Large Scale ◽  
Markov Models ◽  
Transition Probabilities ◽  
Rainfall Data ◽  
The Em Algorithm ◽  
Semi Markov Process ◽  
The Times ◽  
Log Normal ◽  
Disjoint Domains

The paper proposes a hidden semi-Markov model for breakpoint rainfall data that consist of both the times at which rain-rate changes and the steady rates between such changes. The model builds on and extends the seminal work of Ferguson (1980) on variable duration models for speech. For the rainfall data the observations are modelled as mixtures of log-normal distributions within unobserved states where the states evolve in time according to a semi-Markov process. For the latter, parametric forms need to be specified for the state transition probabilities and dwell-time distributions.Recursions for constructing the likelihood are developed and the EM algorithm used to fit the parameters of the model. The choice of dwell-time distribution is discussed with a mixture of distributions over disjoint domains providing a flexible alternative. The methods are also extended to deal with censored data. An application of the model to a large-scale bivariate dataset of breakpoint rainfall measurements at Wellington, New Zealand, is discussed.

Towards a dynamic description of major depression epidemiology

2004 ◽  
Vol 13 (1) ◽  
pp. 21-28 ◽  
Author(s):  
Scott B. Patten ◽  
Robert C. Lee
Keyword(s):  
Health Policy ◽  
Major Depression ◽  
Population Health ◽  
Markov Models ◽  
Transition Probabilities ◽  
Epidemiological Data ◽  
Policy Decisions ◽  
Future Health ◽  
Substantial Impact ◽  
The Impact

SummaryAims – The substantial impact of major depression on population health is widely acknowledged. To date, health system responses to this condition have been largely shaped by observational findings. In the future, health policy decisions will benefit from an increasingly integrated and dynamic understanding of the epidemiology of this condition. Policy decisions can also be supported by the development of decision-support tools that can simulate the impact of alternative policy decisions on population health. Markov models are useful both in epidemiological modelling and in decision analysis. Methods – In this project, a Markov model describing major depression epidemiology was developed. The model employed a Markov Tunnel in order to depict the dependence of recovery probabilities on episode duration. Transition probabilities, including incidence, recovery and mortality were estimated from Canadian national survey data. Results – Episode incidence was approximately 3% per year. Recovery rates declined exponentially over time. The model predicted point prevalence at slightly less than 1%, agreeing closely with observed prevalence data. Conclusions – Epidemiological models describing the dynamic relationships between major depression incidence, prevalence, recovery and mortality can help to integrate available epidemiological data. Such models offer an attractive option for support of health policy decisions.Declaration of InterestAcknowledgement: Both authors are Research Fellows with the Institute of Health Economics (www.ihe.ab.ca). This study was supported by an operating grant from the Canadian Institutes of Health Research (www.cihr.ca).

scholarly journals A Global Atmospheric Analysis Dataset Downscaled from the NCEP–DOE Reanalysis

2012 ◽  
Vol 25 (7) ◽  
pp. 2527-2534 ◽  
Author(s):  
Jung-Eun Kim ◽  
Song-You Hong
Keyword(s):  
Large Scale ◽  
Hydrological Cycle ◽  
Analysis Data ◽  
Reanalysis Data ◽  
Horizontal Resolution ◽  
Department Of Energy ◽  
Quality Analysis ◽  
Resolution Model ◽  
Decadal Trend ◽  
Environmental Prediction

Abstract A global atmospheric analysis dataset is constructed via a spectral nudging technique. The 6-hourly National Centers for Environmental Prediction (NCEP)–Department of Energy (DOE) reanalysis from January 1979 to February 2011 is utilized to force large-scale information, whereas a higher-resolution structure is resolved by a global model with improved physics. The horizontal resolution of the downscaled data is about 100 km, twice that of the NCEP–DOE reanalysis. A comparison of the 31-yr downscaled data with reanalysis data and observations reveals that the downscaled precipitation climatology is improved by correcting inherent biases in the lower-resolution reanalysis, and large-scale patterns are preserved. In addition, it is found that global downscaling is an efficient way to generate high-quality analysis data due to the use of a higher-resolution model with improved physics. The uniqueness of the obtained data lies in the fact that an undesirable decadal trend in the analysis due to a change in the amount of observations used in reanalysis is avoided. As such, a downscaled dataset may be used to investigate changes in the hydrological cycle and related mechanisms.

Extending hidden Markov models to allow conditioning on previous observations

2018 ◽  
Vol 16 (05) ◽  
pp. 1850019 ◽  
Author(s):  
Ioannis A. Tamposis ◽  
Margarita C. Theodoropoulou ◽  
Konstantinos D. Tsirigos ◽  
Pantelis G. Bagos
Keyword(s):  
Hidden Markov Models ◽  
Probabilistic Models ◽  
Markov Models ◽  
Transition Probabilities ◽  
Hidden Markov ◽  
Simple Extension ◽  
Computational Molecular Biology ◽  
Current State ◽  
Observation Sequence ◽  
Hidden States

Hidden Markov Models (HMMs) are probabilistic models widely used in computational molecular biology. However, the Markovian assumption regarding transition probabilities which dictates that the observed symbol depends only on the current state may not be sufficient for some biological problems. In order to overcome the limitations of the first order HMM, a number of extensions have been proposed in the literature to incorporate past information in HMMs conditioning either on the hidden states, or on the observations, or both. Here, we implement a simple extension of the standard HMM in which the current observed symbol (amino acid residue) depends both on the current state and on a series of observed previous symbols. The major advantage of the method is the simplicity in the implementation, which is achieved by properly transforming the observation sequence, using an extended alphabet. Thus, it can utilize all the available algorithms for the training and decoding of HMMs. We investigated the use of several encoding schemes and performed tests in a number of important biological problems previously studied by our team (prediction of transmembrane proteins and prediction of signal peptides). The evaluation shows that, when enough data are available, the performance increased by 1.8%–8.2% and the existing prediction methods may improve using this approach. The methods, for which the improvement was significant (PRED-TMBB2, PRED-TAT and HMM-TM), are available as web-servers freely accessible to academic users at www.compgen.org/tools/ .

Two Markov Models of Neighborhood Housing Turnover

1972 ◽  
Vol 4 (2) ◽  
pp. 133-146 ◽  
Author(s):  
G Gilbert
Keyword(s):  
Mathematical Models ◽  
Markov Processes ◽  
Renewal Process ◽  
Markov Models ◽  
Transition Probabilities ◽  
Markov Renewal Process ◽  
Turnover Process

This paper develops two mathematical models of housing turnover in a neighborhood. The first of these draws upon the theory of non-homogeneous Markov processes and includes the effects of present neighborhood composition upon future turnover probabilities. The second model considers the turnover process as a Markov renewal process and therefore allows the inclusion of length of occupancy as a determinant of transition probabilities. Example calculations for both models are included, and procedures for using the models are outlined.

Northern Hemisphere Atmospheric Blocking Representation in Global Climate Models: Twenty Years of Improvements?

2016 ◽  
Vol 29 (24) ◽  
pp. 8823-8840 ◽  
Author(s):  
Paolo Davini ◽  
Fabio D’Andrea
Keyword(s):  
Northern Hemisphere ◽  
General Circulation ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Model ◽  
Weather Prediction ◽  
Global Climate Models ◽  
Main Concern ◽  
Atmospheric Blocking ◽  
Atlantic Sector

Abstract The correct simulation of midlatitude atmospheric blocking has always been a main concern since the earliest days of numerical modeling of Earth’s atmosphere. To this day blocking represents a considerable source of error for general circulation models from both a numerical weather prediction and a climate perspective. In the present work, 20 years of global climate model (GCM) developments are analyzed from the special point of view of Northern Hemisphere atmospheric blocking simulation. Making use of a series of equivalent metrics, three generations of GCMs are compared. This encompasses a total of 95 climate models, many of which are different—successive—versions of the same model. Results from model intercomparison projects AMIP1 (1992), CMIP3 (2007), and CMIP5 (2012) are taken into consideration. Although large improvements are seen over the Pacific Ocean, only minor advancements have been achieved over the Euro-Atlantic sector. Some of the most recent GCMs still exhibit the same negative bias as 20 years ago in this region, associated with large geopotential height systematic errors. Some individual models, nevertheless, have improved and do show good performances in both sectors. Negligible differences emerge among ocean-coupled or atmosphere-only simulations, suggesting weak relevance of sea surface temperature biases. Conversely, increased horizontal resolution seems to be able to alleviate the Euro-Atlantic blocking bias.

Sign in / Sign up

Export Citation Format

Share Document