Climatological Problems Research Articles

Turbulent Exchange in Unsteady Air-Sea Interaction at Small and Submesoscales

An adequate description of the interaction between the atmosphere and ocean remains one of the most important problems of modern oceanology and climatology. An extremely wide variety of physical processes occurring in the coupled layers, a large range of scales, a moving boundary, all this factors significantly complicates the creation of models that would allow calculating the physical characteristics in both media with the necessary accuracy. In this paper the temporal variability of dynamic parameters in the driving layer of the atmosphere and in the near-surface layer of the sea on small and sub-mesoscales from one to several tens of hours is considered. The collected experimental data show a very high correlation between the dynamic wind speed and turbulence intensity in the upper sea layer on all scales recorded. An important distinguishing feature of all measured physical quantities in both media is the presence of quasi-periodic oscillations with different periods. For a more accurate description of momentum and energy fluxes from the atmosphere a non-stationary model of turbulent exchange in the near-surface layer of the sea is proposed. The model takes into account quasi-periodicity in the intensity of dynamic interaction between the atmosphere and the sea at these scales. In the model we use the equations of momentum and turbulent energy balance, the system of equations is solved numerically, the calculation results are compared with other models and with experimental data. It is shown that taking into account the non-stationarity of the wind strain improves the correspondence between the calculations and the experimental data. It is noted that in the nonstationary case, the energy and momentum flux from the atmosphere and the turbulence intensity increases compared to the action of a constant average wind of the same duration. Therefore, the strong averaging often used in global models may markedly underestimate the intensity of the dynamic interaction between the atmosphere and ocean.

ИСПОЛЬЗОВАНИЕ РЕЗУЛЬТАТОВ КЛАССИФИКАЦИИ ОБЛАЧНОСТИ ПО СПУТНИКОВЫМ ДАННЫМ ДЛЯ РЕШЕНИЯ ЗАДАЧ КЛИМАТОЛОГИИ И МЕТЕОРОЛОГИИ

Algorithms for classifying cloud images based on MODIS and VIIRS satellite data using artificial neural networks and fuzzy logic methods are considered. A combined classification of recognized cloud types is presented. The results of using the cloud classification for solving some problems of climatology and meteorology are analyzed. A statistical model of the image texture for various cloud types and physical parameters of clouds based on two-parameter distributions is proposed. A description of the algorithm for detecting weather fronts and determining their types from satellite data and the results of its testing for the territory of Western Siberia are presented. An approach to studying internal waves in the atmosphere based on the analysis of their cloud manifestation parameters is considered. The results are presented of studying the long-term variability for some of their parameters over the water area of the Kuril Islands. The methodology and results are discussed of studying the long-term variability of the structure of global cloud fields and their parameters over the natural zones of Western Siberia in summer during 2001 to 2019.

New dendroclimatological research in oak, S (Quercus Frainetto Ten.) forestry in Šumadija-Region- (Central Serbia) as a basis for climate change monitoring

In this work present study to describe the survey of climatic change in the case in Šumadija-Central Serbia. Climate change due to a fragile ecosystem in semi-arid, and arid region such as Serbia is one of the most challenging climatological and hydrological problems. Dendrochronology, wich uses tree rings to their exact year of formation to analyse temporal and spatial patterns of processes in the physical and cultural sciences, can be used to evaluate the effects of climate change. In this study, the effects of climate change werw simulated using dendrochronology (tree rings) and an artificial neural network (ANN) for the period from 1900—2015. The present study was executed using the (Quercus frainetto Ten.). Tree rings width, temperature and precipitation were the input parameters for the study, and climate change parameters were the outputs. After the training process, the model was verified. The verified network and tree rings were used to simulate climatic parameter changes during the past times. The results showed that the integration of dendroclimatology and an ANN renders a high degree of accuracy and efficiency in the simulation of climatic change. The results showed that the climatic of the study area changed from semiarid, to arid, and its annual precipitation decreased significantly.

PRELIMINARY RESULTS OF USING GNSS-REFLECTOMETRY METHOD FOR DETERMINING THE HEIGHT TO THE REFLECTING SURFACE

The article deals with a promising method of GNSS-reflectometry which is currently used to solve a range of different problems in oceanology, climatology, hydrography, geophysics, glaciology, hydrometry, and many other related Earth sciences. It is shown that the accuracy of the GNSS-reflectometry method using only one receiver is estimated to be several centimetres and depends on reflecting surfaces. According to preliminary results, the accuracy of the GNSS-reflectometry method using a single receiver lies within a few centimetres and depends not only on reflecting surfaces, but also on the choice of a satellite to analyse. To eliminate any erroneous results in determining the distance to the reflecting surface, it is suggested to analyse a larger number of satellites located at elevation from 5 to 25 degrees.

Лабораторное моделирование в задачах городской климатологии

The paper presents a new approach to the problems of urban climatology, which is based on a laboratory modeling of heat transfer processes in the urban landscape conditions. As an experimental model, we used a three-dimensional layout of the central part of Perm, which takes into account the complex terrain, location and height of buildings on an area of five square kilometers. As an idealized model configuration we considered the nocturnal cooling process of megapolis. The process of cooling of the city model surface after prolonged heating by infrared radiation was studied. The key issue is dependence of the heat transfer on the type of urban structure. The influence of the building density on the cooling process of the layout surface was studied. The comparison was made for two areas with 70 and 90 percent of open area. It was shown that an increase in the number of buildings has influence on the cooling process, which is probably connected with variation of convective circulation and decreasing of the ventilation efficiency. However, stagnant zones with high temperatures in areas of high building density were found for both cases.

Nonstationarity of summer temperature extremes in Texas

AbstractModelling seasonal temperature extremes in weather patterns allows for better forecasting and prediction. Analysis of extreme values over a given time period is usually done by fitting a generalized extreme value (GEV) distribution to the maximum values in the data; however, lack of sufficient weather recordings due to missing data or violation of independence assumptions (block maxima should be over a large time interval) may result in a poor fit for the GEV model. Modelling over larger, clustered regions may overcome some of these problems and can provide insight into macroscopic weather and climate changes. In this article, we analyse temperature measurements in July and August taken from stations across Texas and New Orleans, Louisiana. We introduce clustering techniques which group stations by temperature trends and mutual information before performing extreme value analysis on the clusters of time series. This obviates some of the problems commonly encountered in analysing single station weather data. Extreme analysis of the resulting clusters provides compelling evidence of nonstationarity of the distributional parameters in the GEV model and points to an increased likelihood from the period roughly 1980 to present of observing higher extreme temperatures for the months of July and August. We tabulate the probabilities of extreme temperatures in the clusters according to a nonstationary model. Our techniques can be easily adapted to a wide range of climatological problems.

Impact of productivity on exports: Case of the grape in Peru, 2007-2017 [Impacto de la productividad en las exportaciones: Caso de la uva en Perú, 2007-2017

Peru, in the last 10 years has been experiencing an almost constant growth in the exports of some products of the agricultural sector that have great acceptance in the international market. Among the products most in demand are the grapes, which are exported in significant quantities to the United States, China, the Netherlands, Russia, Spain, among others. Although they have been presenting basically climatological problems, the production has followed an encouraging direction, improving its productivity every year, being the departments Piura, Ica, Lima, Lambayeque and La Libertad, the most representative as far as its production is concerned. In this scenario, where international markets open up thanks to agreements and treaties signed, the demands on producers increase, with productivity being the main indicator of continuous improvement, measuring advances in knowledge, techniques and specialization, generating greater production and better quality. Analyzing the impact of productivity on exports, allows us to have an idea of the cause-effect relationships between one and the other, allowing greater emphasis on strategies to generate improvements. The statistical analysis carried out showed that there is a significant impact of export productivity, so that for each additional tonne increased in production per hectare harvested, exports increase by 36,819 tons. This fact must be considered, since the dedication and the impulse that is granted to improve productivity will have very positive results in exports.

A new approach for processing climate missing databases applied to daily rainfall data in Soummam watershed, Algeria

Missing data is a very frequent problem in climatology, it influences on the quality of results that will afford in hydrological studies, as well as water resources management. This paper proposes a new imputation algorithm, based on the optimization of some regression methods, which are hot deck, k-nearest-neighbors imputation, weighted k-nearest-neighbors imputation, multiple imputation, linear regression and simple average method. The choice of these methods was justified by qualitative and quantitative statistical tests analysis. However, the reliability of obtained results depends mainly on percentage of missing data, choice of neighboring stations and data missingness mechanism which should be missing at random. During the study it was found that the most of stations in Soummam watershed don't have a good correlation because the large loss in rainfall data or the geology of watershed which gives a relationship between station position and rainfall variability. For this case, principal component analysis is applied on a set of stations; it showed a positive impact of altitude, latitude and longitude on correlation index between selected stations. The graphical analysis of the normal law on RMSE values, which were obtained by applying the proposed technique in several random cases of missingness, that are 4%, 8%, 12% and 16% respectively, it confirmed the validity and the performance of this approach.

Impact of productivity on exports: Case of the grape in Peru, 2007-2017

Peru, in the last 10 years has been experiencing an almost constant growth in the exports of some products of the agricultural sector that have great acceptance in the international market. Among the products most in demand are the grapes, which are exported in significant quantities to the United States, China, the Netherlands, Russia, Spain, among others. Although they have been presenting basically climatological problems, the production has followed an encouraging direction, improving its productivity every year, being the departments Piura, Ica, Lima, Lambayeque and La Libertad, the most representative as far as its production is concerned. In this scenario, where international markets open up thanks to agreements and treaties signed, the demands on producers increase, with productivity being the main indicator of continuous improvement, measuring advances in knowledge, techniques and specialization, generating greater production and better quality. Analyzing the impact of productivity on exports, allows us to have an idea of the cause-effect relationships between one and the other, allowing greater emphasis on strategies to generate improvements. The statistical analysis carried out showed that there is a significant impact of export productivity, so that for each additional tonne increased in production per hectare harvested, exports increase by 36,819 tons. This fact must be considered, since the dedication and the impulse that is granted to improve productivity will have very positive results in exports.

Development of multi-model ensemble climate estimates using open-source environment

We demonstrate an application of an R open source environment to climatological problems, namely, to the construction of multi-model ensemble estimates. The R ecosystem is used to develop a computation code which automates an ensembling procedure. We apply the code to the CMIP5 runoff data in the Russian territory for a preliminary analysis of the long-term natural variability. It has been found that a multi-model projection demonstrates a non-monotonic behaviour which contradicts the classic “wet getting wetter” pattern, but perfectly corresponds to the long-term natural variability. A detailed analysis of reproducibility of the runoff temporary structure seems to be unavoidable for successful forecasting of the long-term runoff trends.

Quaternary sediment architecture in the Orkhon Valley (central Mongolia) inferred from capacitive coupled resistivity and Georadar measurements

Detailed information on shallow sediment distribution in basins is required to achieve solutions for problems in Quaternary geology, geomorphology, neotectonics, (geo)archaeology, and climatology. Usually, detailed information is obtained by studying outcrops and shallow drillings. Unfortunately, such data are often sparsely distributed and thus cannot characterise entire basins in detail. Therefore, they are frequently combined with remote sensing methods to overcome this limitation. Remote sensing can cover entire basins but provides information of the land surface only. Geophysical methods can close the gap between detailed sequences of the shallow sediment inventory from drillings at a few spots and continuous surface information from remote sensing. However, their interpretation in terms of sediment types is often challenging, especially if permafrost conditions complicate their interpretation. Here we present an approach for the joint interpretation of the geophysical methods ground penetrating radar (GPR) and capacitive coupled resistivity (CCR), drill core, and remote sensing data. The methods GPR and CCR were chosen because they allow relatively fast surveying and provide complementary information. We apply the approach to the middle Orkhon Valley in central Mongolia where fluvial, alluvial, and aeolian processes led to complex sediment architecture.The GPR and CCR data, measured on profiles with a total length of about 60km, indicate the presence of two distinct layers over the complete surveying area: (i) a thawed layer at the surface, and (ii) a frozen layer below. In a first interpretation step, we establish a geophysical classification by considering the geophysical signatures of both layers. We use sedimentological information from core logs to relate the geophysical classes to sediment types. This analysis reveals internal structures of Orkhon River sediments, such as channels and floodplain sediments. We also distinguish alluvial fan deposits and aeolian sediments by their distinct geophysical signature. With this procedure we map aeolian sediments, debris flow sediments, floodplains, and channel sediments along the measured profiles in the entire basin.We show that the joint interpretation of drillings and geophysical profile measurements matches the information from remote sensing data, i.e., the sediment architecture of vast areas can be characterised by combining these techniques. The method presented here proves powerful for characterising large areas with minimal effort and can be applied to similar settings.

From Atmospheric Gas Spectroscopy to Climatological Problems

Readers are offered a brief review of sample results obtained from spectroscopic investigations begun in Laboratory of Spectroscopy of V. D. Kuznetsov Siberian Physical-Technical Institute at Tomsk State University as early as the 1930s. Specifically, we present findings of theoretical studies of relevance to a description of the light absorption in atmospheric windows and hence to solutions of global climate problems associated with the processes involving frequencies lying far from line centers. In the course of years, a large amount of spectroscopic data has been accumulated, which led to rapid development of information-computational systems to be discussed in the present work. Not only are the considered systems capable of making computations according to preset algorithms, but they can also provide answers relating to the completeness and comparison of the data collected in the system, taking into account the structure of the subject domain in question. A prototype of a computational-information infrastructure (virtual research environment) supporting climate studies is proposed.

Multiscale recurrence analysis of spatio-temporal data.

The description and analysis of spatio-temporal dynamics is a crucial task in many scientific disciplines. In this work, we propose a method which uses the mapogram as a similarity measure between spatially distributed data instances at different time points. The resulting similarity values of the pairwise comparison are used to construct a recurrence plot in order to benefit from established tools of recurrence quantification analysis and recurrence network analysis. In contrast to other recurrence tools for this purpose, the mapogram approach allows the specific focus on different spatial scales that can be used in a multi-scale analysis of spatio-temporal dynamics. We illustrate this approach by application on mixed dynamics, such as traveling parallel wave fronts with additive noise, as well as more complicate examples, pseudo-random numbers and coupled map lattices with a semi-logistic mapping rule. Especially the complicate examples show the usefulness of the multi-scale consideration in order to take spatial pattern of different scales and with different rhythms into account. So, this mapogram approach promises new insights in problems of climatology, ecology, or medicine.

The MeteoMet project – metrology for meteorology: challenges and results

ABSTRACTThe study describes significant outcomes of the ‘Metrology for Meteorology’ project, MeteoMet, which is an attempt to bridge the meteorological and metrological communities. The concept of traceability, an idea used in both fields but with a subtle difference in meaning, is at the heart of the project. For meteorology, a traceable measurement is the one that can be traced back to a particular instrument, time and location. From a metrological perspective, traceability further implies that the measurement can be traced back to a primary realization of the quantity being measured in terms of the base units of the International System of Units, the SI. These two perspectives reflect long‐standing differences in culture and practice and this project – and this study – represents only the first step towards better communication between the two communities.The 3 year MeteoMet project was funded by the European Metrology Research Program (EMRP) and involved 18 European National Metrological Institutes, 3 universities and 35 collaborating stakeholders including national meteorology organizations, research institutes, universities, associations and instrument companies.The project brought a metrological perspective to several long‐standing measurement problems in meteorology and climatology, varying from conventional ground‐based measurements to those made in the upper atmosphere. It included development and testing of novel instrumentation as well as improved calibration procedures and facilities, instrument intercomparison under realistic conditions and best practice dissemination. Additionally, the validation of historical temperature data series with respect to measurement uncertainties and a methodology for recalculation of the values were included.

Stability of the zero solution of nonlinear differential equations under the influence of white noise

The paper deals with a system of nonlinear differential equations under the influence of white noise. This system can be used as a mathematical model of various real problems in finance, mathematical biology, climatology, signal theory and others. Necessary and sufficient conditions for the asymptotic mean square stability of the zero solution of this system are derived in the paper. The paper introduces a new approach to studying such problems - construction of a suitable deterministic system with the use of Lyapunov function.

Towards analysis and predicting maps of ultraviolet index from experimental astronomical parameters (solar elevation, total ozone level, aerosol index, reflectivity). Artificial neural networks global scale approach

UV radiation is an important problem in climatology, ecology but also has direct effect on human health. A novel method for analysis and prediction of global erythemal UV for clear-sky at noon at any localisation, expressed as the UV index, has been proposed. The supervised artificial neural networks (ANN) were trained using purely experimental astronomical parameters (input: solar elevation, total ozone level, aerosol index, reflectivity and required output: erythemal local noon UV irradiance expressed as the UV index) for all dates from a 3-year representative period (2001–2003) collected by Total Ozone Mapping Spectrometer (TOMS). The input data from the 3-year period provide three sets of 1095 grids, each consisting of 288×180 i.e. 56,764,800 training vectors for total ozone level, aerosol index, reflectivity, while the output data provide only one set of data of the same resolution. The trained network delivers a good long-term representation of the physical problem as it is able to predict clear-sky global UV index maps (UVI for any location and date) with an excellent accuracy close to the detection error (3%, 0.5 unit of UVI). The omission of data on aerosol index (slightly) and reflectivity (highly) deteriorates the quality of UVI prediction (MSPE error 6.4%, 1 unit of UVI) but also confirms the importance of total ozone level for the UVI prediction. The neglect of data on aerosol index and reflectivity evidently removes the inhomogeneities and results in smoother and less reliable UVI maps. Reflectivity plays a very important role in variation of UV radiation level. The results are presented in the form of 2D rectangular maps (WGS-84 projection) of UV index. The neural network approach to UVI forecasting and analysis yields reasonable results and can be considered as an alternative to traditional approaches mainly based on radiative-transfer or regression models.

Exact analytical expansion of an off-axis Gaussian laser beam using the translation theorems for the vector spherical harmonics

The interaction of a Gaussian laser beam with a particle that is located off axis is a fundamental problem encountered across many scientific fields, including biological physics, chemistry, and medicine. For spherical geometries, generalized Lorenz-Mie theory affords a solution of Maxwell's equations for the scattering from such a particle. The solution can be obtained by expanding the laser fields in terms of vector spherical harmonics (VSHs). However, the computation of the VSH expansion coefficients for off-axis beams has proven challenging. In the present study, we provide a very viable, theoretical framework to efficiently compute the sought-after expansion coefficients with high numerical accuracy. We use the existing theory for the expansion of an on-axis laser beam and employ Cruzan's translation theorems [Q. Appl. Math.20, 33 (1962)] for the VSHs to obtain a description for more general off-axis beams. The expansion coefficients for the off-axis laser beam are presented in an analytical form in terms of an infinite series over the underlying translation coefficients. A direct comparison of the electromagnetic fields of such a beam expansion with the original laser fields and with results obtained using numerical quadratures shows excellent agreement (relative errors are on the order of ≲10(-3). In practice, the analytical approach presented in this study has numerous applications, reaching from multiparticle scattering problems in atmospheric physics and climatology to optical trapping, sorting, and sizing techniques.

Break function regression

The break is a continuous function consisting of two linear parts. It serves as a regression model for trend changes in time series. A typical application field of such a model is climatology. We introduce break-model fitting by combining a weighted least-squares criterion with a brute-force search. We explain how to determine error bars and confidence intervals for the break model parameters by means of autoregressive bootstrap resampling. Our approach takes into account the statistical properties of real-world climatological problems: non-Gaussian distributional shape, serial dependence, uneven time spacing and timescale uncertainties. A Monte Carlo experiment shows the excellent coverage performance of bootstrap bias-corrected and accelerated confidence intervals for data sizes above 100 or 200. An application quantifies trend changes in modelled Arctic river runoff during the interval from 1936 to 2001.

Toward Elimination of the Warm Bias in Historic Radiosonde Temperature Records—Some New Results from a Comprehensive Intercomparison of Upper-Air Data

Abstract The apparent cooling trend in observed global mean temperature series from radiosonde records relative to Microwave Sounding Unit (MSU) radiances has been a long-standing problem in upper-air climatology. It is very likely caused by a warm bias of radiosonde temperatures in the 1980s, which has been reduced over time with better instrumentation and correction software. The warm bias in the MSU-equivalent lower stratospheric (LS) layer is estimated as 0.6 ± 0.3 K in the global mean and as 1.0 ± 0.3 K in the tropical (20°S–20°N) mean. These estimates are based on comparisons of unadjusted radiosonde data, not only with MSU data but also with background forecast (BG) temperature time series from the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) and with two new homogenized radiosonde datasets. One of the radiosonde datasets [Radiosonde Observation Correction using Reanalyses (RAOBCORE) version 1.4] employs the BG as reference for homogenization, which is not strictly independent of MSU data. The second radiosonde dataset uses the dates of the breakpoints detected by RAOBCORE as metadata for homogenization. However, it relies only on homogeneous segments of neighboring radiosonde data for break-size estimation. Therefore, adjustments are independent of satellite data. Both of the new adjusted radiosonde time series are in better agreement with satellite data than comparable published radiosonde datasets, not only for zonal means but also at most single stations. A robust warming maximum of 0.2–0.3K (10 yr)−1 for the 1979–2006 period in the tropical upper troposphere could be found in both homogenized radiosonde datasets. The maximum is consistent with mean temperatures of a thick layer in the upper troposphere and upper stratosphere (TS), derived from M3U3 radiances. Inferred from these results is that it is possible to detect and remove most of the mean warm bias from the radiosonde records, and thus most of the trend discrepancy compared to MSU LS and TS temperature products. The comprehensive intercomparison also suggests that the BG is temporally quite homogeneous after 1986. Only in the early 1980s could some inhomogeneities in the BG be detected and quantified.

Lagrangian Approach to the Study of Level Sets: Application to a Free Boundary Problem in Climatology

We study the dynamics and regularity of level sets in solutions of the semilinear parabolic equation $$u_{t} - \Delta\,u \in a\,\mathbb {H}(u - \mu) \quad {\rm in} \quad Q = {\it \Omega} \times (0, T],$$ where \({\Omega \subset \mathbb{R}^{n}}\) is a ring-shaped domain, a and μ are given positive constants, \({\mathbb{H}(\cdot)}\) is the Heaviside maximal monotone graph: \({\mathbb{H}(s) = 1}\) if s > 0, \({\mathbb{H}(0) = [0, 1],\, \mathbb{H}(s) = 0}\) if s < 0. Such equations arise in climatology (the so-called Budyko energy balance model), as well as in other contexts such as combustion. We show that under certain conditions on the initial data the level sets \({\it \Gamma}_{\mu }=\{(\mathbf{x}, t) :u(\mathbf{x}, t)=\mu\}\) are n-dimensional hypersurfaces in the (x, t)-space and show that the dynamics of Γμ is governed by a differential equation which generalizes the classical Darcy law in filtration theory. This differential equation expresses the velocity of advancement of the level surface Γμ through spatial derivatives of the solution u. Our approach is based on the introduction of a local set of Lagrangian coordinates: the equation is formally considered as the mass balance law in the motion of a fluid and the passage to Lagrangian coordinates allows us to watch the trajectory of each of the fluid particles.