Ionosphere Models and Their Correction Using Operational Data from the Ionospheric Observation Network

2021 ◽  
Vol 3 ◽  
pp. 77-85
Author(s):  
N. G. Kotonaeva ◽  
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Measuring Instrument ◽  
Observation Station ◽  
Single Observation ◽  
Model Correction ◽  
The Earth ◽  
Observation Network ◽  
Operational Data

A possibility of correcting ionosphere climate models based on data from a single observation station equipped with the vertical radio sounding ionosonde is studied to monitor the Earth plasma shell in the vicinity of this measuring instrument. The SIMP-STANDARD and IRI models are used for the study. The probabilities of the fact that relative deviations of corrected models are below 10% are taken as criteria of the efficiency of ionosphere climate model correction. The size of the areas around each ionosonde of the state ionospheric network is determined, where an increase in the quality of ionospheric monitoring is possible by correcting the ionosphere climate model based on the data of this particular ionosonde.

Simulation of the observed climate extremes trends during 1901–2010 with INMCM5

2020 ◽  
Author(s):  
Maria Tarasevich ◽  
Evgeny Volodin
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Great Influence ◽  
Coupled Model ◽  
Climate Extremes ◽  
Reanalysis Data ◽  
Natural Systems ◽  
Weather Events ◽  
Intercomparison Project

<p>Extreme climate and weather events have a great influence on society and natural systems. That’s why it is important to be able to precisely simulate these events with the climate models. To asses the quality of such simulations 27 climate extremes indices were defined by ETCCDI. In the present work these indices are calculated for the 1901–2010 in order to estimate their trends.<br>Climate extremes trends are studied on the basis of ten historical runs with the up-to-date INM RAS climate model (INMCM5) under the scenario proposed for the Coupled Model Intercomparison Project Phase 6 (CMIP6). Developed by ECMWF ERA-20C and CERA-20C reanalyses are taken as observational data.<br>Trends obtained from the reanalysis data are compared with the simulation results of the INMCM5. The comparison shows that the simulated land-averaged climate extremes trends are in good agreement with the reanalysis data, but their spatial distributions differ significantly even between the reanalyses themselves.</p>

scholarly journals New Tendencies in Global Climate Change and Their Effects on the Climate of Hungary

Hadmérnök ◽  
2019 ◽  
Vol 14 (1) ◽  
pp. 99-107
Author(s):  
László Földi ◽  
László Halász
Keyword(s):  
Climate Change ◽  
Carbon Dioxide ◽  
Greenhouse Gases ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Regional Climate ◽  
Global Climate Models ◽  
Lower Atmosphere ◽  
The Earth

Defining the term of climate, we investigate the role of natural causes and effects of human activities in climate change. The temperature of the Earth is determined by the balance between the amount of radiation energy received from the Sun and that emitted from the surface of the Earth towards the outer space. Greenhouse gases in the atmosphere, including water vapor, carbon dioxide, methane and nitrous oxides, act to make the surface much warmer, because they absorb and emit heat energy in all directions (including downwards), keeping Earth’s surface and lower atmosphere warm. The primary cause of climate change is the burning of fossil fuels, such as oil and coal, which emits greenhouse gases into the atmosphere – primarily carbon dioxide. We give a review about the activity of the Intergovernmental Panel on Climate Change and the United Nations Climate Change Conferences. Shortly investigate the different global climate models and some regional climate models. Finally discuss the results of regional climate model simulations for the Carpathian Basin.

scholarly journals A community diagnostic tool for chemistry climate model validation

2012 ◽  
Vol 5 (5) ◽  
pp. 1061-1073 ◽  
Author(s):  
A. Gettelman ◽  
V. Eyring ◽  
C. Fischer ◽  
H. Shiona ◽  
I. Cionni ◽  
...  
Keyword(s):  
Model Validation ◽  
Climate Models ◽  
Climate Model ◽  
Model Development ◽  
Coupled Model ◽  
Technical Note ◽  
The Earth ◽  
Complex Evaluation ◽  
System User ◽  
Intercomparison Project

Abstract. This technical note presents an overview of the Chemistry-Climate Model Validation Diagnostic (CCMVal-Diag) tool for model evaluation. The CCMVal-Diag tool is a flexible and extensible open source package that facilitates the complex evaluation of global models. Models can be compared to other models, ensemble members (simulations with the same model), and/or many types of observations. The initial construction and application is to coupled chemistry-climate models (CCMs) participating in CCMVal, but the evaluation of climate models that submitted output to the Coupled Model Intercomparison Project (CMIP) is also possible. The package has been used to assist with analysis of simulations for the 2010 WMO/UNEP Scientific Ozone Assessment and the SPARC Report on the Evaluation of CCMs. The CCMVal-Diag tool is described and examples of how it functions are presented, along with links to detailed descriptions, instructions and source code. The CCMVal-Diag tool supports model development as well as quantifies model changes, both for different versions of individual models and for different generations of community-wide collections of models used in international assessments. The code allows further extensions by different users for different applications and types, e.g. to other components of the Earth system. User modifications are encouraged and easy to perform with minimum coding.

scholarly journals Assessment of soil moisture fields from imperfect climate models with uncertain satellite observations

2009 ◽  
Vol 6 (2) ◽  
pp. 2733-2750 ◽  
Author(s):  
G. Schumann ◽  
D. J. Lunt ◽  
P. J. Valdes ◽  
R. A. M. de Jeu ◽  
K. Scipal ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
General Circulation ◽  
Climate Models ◽  
Climate Model ◽  
Hydrological Cycle ◽  
Circulation Model ◽  
Data Uncertainty ◽  
Limited Area

Abstract. We demonstrate that global satellite products can be used to evaluate climate model soil moisture predictions but conclusions should be drawn with care. The quality of a limited area climate model (LAM) was compared to a general circulation model (GCM) using soil moisture data from two different Earth observing satellites within a model validation scheme that copes with the presence of uncertain data. Results showed that in the face of imperfect models and data, it is difficult to investigate the quality of current land surface schemes in simulating hydrology accurately. Nevertheless, a LAM provides, in general, a better representation of spatial patterns and dynamics of soil moisture. However, in months when data uncertainty is higher, particularly in colder months and in periods when vegetation cover and soil moisture are out of phase (e.g. August in the case of Western Europe), it is not possible to draw firm conclusions about model acceptability. Our work indicates that a higher resolution LAM has more benefits to soil moisture prediction than are due to the resolution alone and can be attributed to an overall intensification of the hydrological cycle relative to the GCM.

scholarly journals Observation Impact Assessment on the Prediction of the Earth System Dynamics Using the Adjoint-Based Method

2018 ◽  
Vol 6 (61) ◽  
pp. 5-28
Author(s):  
Peter Steinle ◽  
Chris Tingwell ◽  
Sergei Soldatenko
Keyword(s):  
Earth System ◽  
Initial State ◽  
Future State ◽  
The Earth ◽  
Observation Network ◽  
Australian Community ◽  
Accuracy Of Prediction ◽  
The Impact

Mathematical models of the Earth system and its components represent one of the most powerful and effective instruments applied to explore the Earth system's behaviour in the past and present, and to predict its future state considering external influence. These models are critically reliant on a large number of various observations (in situ and remotely sensed) since the prediction accuracy is determined by, amongst other things, the accuracy of the initial state of the system in question, which, in turn, is defined by observational data provided by many different instrument types. The development of an observing network is very costly, hence the estimation of the effectiveness of existing observation network and the design of a prospective one, is very important. The objectives of this paper are (1) to present the adjoint-based approach that allows us to estimate the impact of various observations on the accuracy of prediction of the Earth system and its components, and (2) to illustrate the application of this approach to two coupled low-order chaotic dynamical systems and to the ACCESS (Australian Community Climate and Earth System Simulator) global model used operationally in the Australian Bureau of Meteorology. The results of numerical experiments show that by using the adjoint-based method it is possible to rank the observations by the degree of their importance and also to estimate the influence of target observations on the quality of predictions.

scholarly journals Downscaling Extremes: An Intercomparison of Multiple Methods for Future Climate

2013 ◽  
Vol 26 (10) ◽  
pp. 3429-3449 ◽  
Author(s):  
G. Bürger ◽  
S. R. Sobie ◽  
A. J. Cannon ◽  
A. T. Werner ◽  
T. Q. Murdock
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Models ◽  
Minor Influence ◽  
Cold Events ◽  
The Mean ◽  
Extreme Cold ◽  
A Minor

Abstract This study follows up on a previous downscaling intercomparison for present climate. Using a larger set of eight methods the authors downscale atmospheric fields representing present (1981–2000) and future (2046–65) conditions, as simulated by six global climate models following three emission scenarios. Local extremes were studied at 20 locations in British Columbia as measured by the same set of 27 indices, ClimDEX, as in the precursor study. Present and future simulations give 2 × 3 × 6 × 8 × 20 × 27 = 155 520 index climatologies whose analysis in terms of mean change and variation is the purpose of this study. The mean change generally reinforces what is to be expected in a warmer climate: that extreme cold events become less frequent and extreme warm events become more frequent, and that there are signs of more frequent precipitation extremes. There is considerable variation, however, about this tendency, caused by the influence of scenario, climate model, downscaling method, and location. This is analyzed using standard statistical techniques such as analysis of variance and multidimensional scaling, along with an assessment of the influence of each modeling component on the overall variation of the simulated change. It is found that downscaling generally has the strongest influence, followed by climate model; location and scenario have only a minor influence. The influence of downscaling could be traced back in part to various issues related to the methods, such as the quality of simulated variability or the dependence on predictors. Using only methods validated in the precursor study considerably reduced the influence of downscaling, underpinning the general need for method verification.

scholarly journals USE OF A REGIONAL CLIMATE MODEL (REMO) FOR WATER FLOW TRENDS EVALUATION IN THE DNIESTER RIVER BASIN

2018 ◽  
pp. 22-28
Author(s):  
V. Greben ◽  
K. Mudra
Keyword(s):  
River Basin ◽  
Water Flow ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Pair Correlation ◽  
Observation Network ◽  
Flow Formation ◽  
Annual Water ◽  
Predicted Values

In order to confirm the possibility of predictive climate models using for the flow modelling in the Dniester river basin, the REMO climate model was verified. The verification was carried out on the basis of comparison of the simulated values and data from the hydrological observation network. The data of 28 hydrological stations on the Dniester and its tributaries were used. The reference period for testing the model was from 1971 to 2000. In total, 11 136 values of the average monthly and 917 values of the average annual water flow were used. According to the results of the conducted research, it was found that in most cases, the average annual flow value, taken from the model REMO, is lower, compared with the data from the hydrological observation network. The mean annual flow in the Dniester basin according to the hydrological observation network is 9.25 l/s∙km2, based on the model REMO – 8.27 l/s∙km2. In order to reduce the deviations of the predicted values, it was proposed to use a correction factor, it can reduce the percentage of deviations from the measured values by half. The assessment of the relationship between the data from the hydrological observation network and model values was carried out on the basis of determining the coefficient of pair correlation with the subsequent calculation of the regression equation. It was found that the correlation coefficient for a pair of data of the average long-term value – measured and model – is close to 1, which confirms the possibility of modelling not only for certain designated areas but also for individual hydrological stations. This research shows that the model REMO reliably predicts water flow changes in the Dniester river basin, taking into account the peculiarities of flow formation in different parts of the studied basin.

Software suite for determining the tilts of tower-type structures according to terrestrial laser scanning

2020 ◽  
Vol 961 (7) ◽  
pp. 2-7
Author(s):  
A.V. Zubov ◽  
N.N. Eliseeva
Keyword(s):  
Laser Scanning ◽  
Optimization Problems ◽  
Optimization Methods ◽  
User Participation ◽  
Software Suite ◽  
Current State ◽  
Visual Basic For Applications ◽  
Operational Data ◽  
Theoretical Developments

The authors describe a software suite for determining tilt degrees of tower-type structures according to ground laser scanning indication. Defining the tilt of the pipe is carried out with a set of measured data through approximating the sections by circumferences. They are constructed using one of the simplest search engine optimization methods (evolutionary algorithm). Automatic filtering the scan of the current section from distorting data is performed by the method of assessing the quality of models constructed with that of least squares. The software was designed using Visual Basic for Applications. It contains several blocks (subprograms), with each of them performing a specific task. The developed complex enables obtaining operational data on the current state of the object with minimal user participation in the calculation process. The software suite is the result of practical implementing theoretical developments on the possibilities of using search methods at solving optimization problems in geodetic practice.

scholarly journals Projecting Health Impacts of Future Temperature: A Comparison of Quantile-Mapping Bias-Correction Methods

2021 ◽  
Vol 18 (4) ◽  
pp. 1992
Author(s):  
Weijia Qian ◽  
Howard H. Chang
Keyword(s):  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Warm Season ◽  
Climate Science ◽  
Regional Climate Models ◽  
Health Impact ◽  
Quantile Mapping ◽  
Ed Visits

Health impact assessments of future environmental exposures are routinely conducted to quantify population burdens associated with the changing climate. It is well-recognized that simulations from climate models need to be bias-corrected against observations to estimate future exposures. Quantile mapping (QM) is a technique that has gained popularity in climate science because of its focus on bias-correcting the entire exposure distribution. Even though improved bias-correction at the extreme tails of exposure may be particularly important for estimating health burdens, the application of QM in health impact projection has been limited. In this paper we describe and apply five QM methods to estimate excess emergency department (ED) visits due to projected changes in warm-season minimum temperature in Atlanta, USA. We utilized temperature projections from an ensemble of regional climate models in the North American-Coordinated Regional Climate Downscaling Experiment (NA-CORDEX). Across QM methods, we estimated consistent increase in ED visits across climate model ensemble under RCP 8.5 during the period 2050 to 2099. We found that QM methods can significantly reduce between-model variation in health impact projections (50–70% decreases in between-model standard deviation). Particularly, the quantile delta mapping approach had the largest reduction and is recommended also because of its ability to preserve model-projected absolute temporal changes in quantiles.

scholarly journals Sensitivity of projected climate impacts to climate model weighting: multi-sector analysis in eastern Africa

2021 ◽  
Vol 164 (3-4) ◽  
Author(s):  
Seshagiri Rao Kolusu ◽  
Christian Siderius ◽  
Martin C. Todd ◽  
Ajay Bhave ◽  
Declan Conway ◽  
...  
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Investment Decisions ◽  
Climate Impacts ◽  
Future Climate ◽  
Eastern Africa ◽  
Climate Projections ◽  
Systematic Effect ◽  
Risk Profiles ◽  
Model Weighting

AbstractUncertainty in long-term projections of future climate can be substantial and presents a major challenge to climate change adaptation planning. This is especially so for projections of future precipitation in most tropical regions, at the spatial scale of many adaptation decisions in water-related sectors. Attempts have been made to constrain the uncertainty in climate projections, based on the recognised premise that not all of the climate models openly available perform equally well. However, there is no agreed ‘good practice’ on how to weight climate models. Nor is it clear to what extent model weighting can constrain uncertainty in decision-relevant climate quantities. We address this challenge, for climate projection information relevant to ‘high stakes’ investment decisions across the ‘water-energy-food’ sectors, using two case-study river basins in Tanzania and Malawi. We compare future climate risk profiles of simple decision-relevant indicators for water-related sectors, derived using hydrological and water resources models, which are driven by an ensemble of future climate model projections. In generating these ensembles, we implement a range of climate model weighting approaches, based on context-relevant climate model performance metrics and assessment. Our case-specific results show the various model weighting approaches have limited systematic effect on the spread of risk profiles. Sensitivity to climate model weighting is lower than overall uncertainty and is considerably less than the uncertainty resulting from bias correction methodologies. However, some of the more subtle effects on sectoral risk profiles from the more ‘aggressive’ model weighting approaches could be important to investment decisions depending on the decision context. For application, model weighting is justified in principle, but a credible approach should be very carefully designed and rooted in robust understanding of relevant physical processes to formulate appropriate metrics.

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