Identification of Tropopause Height with Atmospheric Refractivity

2021 ◽  
Vol 78 (1) ◽  
pp. 3-16
Author(s):  
Pengfei Xia ◽  
Yingying Shan ◽  
Shirong Ye ◽  
Weiping Jiang
Keyword(s):  
Atmospheric Temperature ◽  
Lapse Rate ◽  
Radiosonde Data ◽  
Tropopause Height ◽  
Multiple Sources ◽  
Data Types ◽  
Transform Method ◽  
Exchange Processes ◽  
Series Of Experiments ◽  
Cold Point

AbstractInvestigation of the structure and variation of the tropopause is crucial for the development of an in-depth understanding of water-vapor exchange processes, the concentrations and nature of chemicals within the tropopause, and their role in climate change and the ecosphere. At present, the common methods used for the estimation of tropopause height are limited by their reliance on area, an overdependence on atmospheric temperature, and the use of many different data types, and thus lack strong generality. Therefore, this study used atmospheric refractivity data from multiple sources to determine the tropopause height. An objective covariance transform method was applied to identify transitions in a refractivity profile. The refractivity tropopause height was compared with the bending angle tropopause (BAT)/cold point tropopause (CPT)/lapse rate tropopause (LRT) height derived from radio occultation (RO) and radiosonde data and revealed a good agreement. An initial analysis of tropopause structure and seasonal changes derived from refractivity method afforded results that were consistent with existing research results, which proved the validity of the method. The refractivity method was also used to analyze various types of data downloaded from the Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) Data Analysis and Archive Center (CDAAC), and show that this method is suitable for the analysis of RO data, radiosonde, reanalysis and analysis/forecast data. A series of experiments were used to verify the generality and utility of the refractivity covariance transform method to determine tropopause height, which will be useful for the analysis of long-term variations in tropopause height.

scholarly journals Comparison of RO tropopause height based on different tropopause determination methods

2019 ◽  
Author(s):  
Ziyan Liu ◽  
Weihua Bai ◽  
Yueqiang Sun ◽  
Junming Xia ◽  
Guangyuan Tan ◽  
...  
Keyword(s):  
Lapse Rate ◽  
Complete Analysis ◽  
Tropopause Height ◽  
Transform Method ◽  
Bending Angle ◽  
Comparison Results ◽  
High Latitude Region ◽  
Latitude Region ◽  
The Mean ◽  
The Difference

Abstract. Tropopause region is a significant layer among the earth's atmosphere, receiving increasing attention from atmosphere and climate researchers. To monitor global tropopause via radio occultation (RO) data, there are mainly two methods, one is the widely used temperature lapse rate method, and the other is bending angle covariance transform method. In this paper, we use FengYun3-C (FY3C) and Meteorological Operational Satellite Program (MetOp) RO data and European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data to analyse the difference of RO tropopause height calculated by the two methods mentioned above. To give an objective and complete analysis, we first take ECMWF lapse rate tropopause (LRT) height (LRTH) as reference to discuss the absolute bias of RO LRTH and RO bending angle tropopause (BAT) height (BATH), and then give the comparison results between RO LRTH and corresponding RO BATH as supplement to analyse the difference between tropopause height derived from the above two methods. The results indicate that BATH show consistent 0.8–1.2 km positive bias over tropics and high latitude region compared with LRTH, and over mid latitude region, results of BATH show less stability. Besides, the mean bias between BATH and LRTH presents different symmetrical characteristic during 2017.12–2018.2 (DJF) and 2018.6–2018.8 (JJA). However, the mean value of both LRTH and BATH show the similar tropopause variation trend, indicating the availability of both two methods.

Construction of a multi-source heterogeneous hybrid platform for big data

2021 ◽  
pp. 1-10
Author(s):  
Ying Wang ◽  
Yiding Liu ◽  
Minna Xia
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Forest Fire ◽  
Original Data ◽  
Big Data Analysis ◽  
Multiple Sources ◽  
Data Types ◽  
Fire Monitoring ◽  
Data Platform

Big data is featured by multiple sources and heterogeneity. Based on the big data platform of Hadoop and spark, a hybrid analysis on forest fire is built in this study. This platform combines the big data analysis and processing technology, and learns from the research results of different technical fields, such as forest fire monitoring. In this system, HDFS of Hadoop is used to store all kinds of data, spark module is used to provide various big data analysis methods, and visualization tools are used to realize the visualization of analysis results, such as Echarts, ArcGIS and unity3d. Finally, an experiment for forest fire point detection is designed so as to corroborate the feasibility and effectiveness, and provide some meaningful guidance for the follow-up research and the establishment of forest fire monitoring and visualized early warning big data platform. However, there are two shortcomings in this experiment: more data types should be selected. At the same time, if the original data can be converted to XML format, the compatibility is better. It is expected that the above problems can be solved in the follow-up research.

Dependence of Aerosol Particle Concentration in Surface Air over the Moscow Region on Wind Speed and Lapse Rate

2021 ◽  
pp. 103-110
Author(s):  
E. A. Stulov ◽  
◽  
E. V. Sosnikova ◽  
N. A. Monakhova ◽  
◽  
...  
Keyword(s):  
Surface Layer ◽  
Wind Speed ◽  
Particle Concentration ◽  
Meteorological Factors ◽  
Lapse Rate ◽  
Radiosonde Data ◽  
Vertical Temperature Gradient ◽  
Vertical Temperature ◽  
Simple Estimation ◽  
The City

Based on the daily measurements of atmospheric aerosol characteristics in the city of Dolgoprudny (20 km from the center of Moscow) carried out during 2013-2018, the influence of some meteorological factors on the concentration of various aerosol fractions in the surface layer of the atmosphere is analyzed. It is that the aerosol concentration depends most on the wind speed and the vertical temperature gradient. The method of simple estimation of aerosol particles accumulation conditions in the surface layer based on the use of standard radiosonde data is developed.

scholarly journals Scaling behaviour of the global tropopause

2008 ◽  
Vol 8 (5) ◽  
pp. 17891-17905
Author(s):  
C. Varotsos ◽  
M. Efstathiou ◽  
C. Tzanis
Keyword(s):  
Detrended Fluctuation Analysis ◽  
Small Time ◽  
Radiosonde Data ◽  
Fluctuation Analysis ◽  
Tropopause Height ◽  
Time Intervals ◽  
Long Range Correlations ◽  
The Tropics ◽  
Detrended Fluctuation ◽  
Climatic Characteristics

Abstract. Detrended fluctuation analysis is applied to the time series of the global tropopause height derived from the 1980–2004 daily radiosonde data, in order to detect long-range correlations in its time evolution. Global tropopause height fluctuations in small time-intervals are found to be positively correlated to those in larger time intervals in a power-law fashion. The exponent of this dependence is larger in the tropics than in the middle and high latitudes in both hemispheres. Greater persistence is observed in the tropopause of the Northern than in the Southern Hemisphere. This finding for the tropopause height variability should reduce the existing uncertainties in assessing the climatic characteristics.

scholarly journals Interpolation uncertainty of atmospheric temperature radiosoundings

2020 ◽  
Author(s):  
Alessandro Fassò ◽  
Michael Sommer ◽  
Christoph von Rohden
Keyword(s):  
Missing Data ◽  
Standard Error ◽  
Cross Validation ◽  
Interpolation Method ◽  
Average Length ◽  
Linear Interpolation ◽  
Atmospheric Temperature ◽  
Radiosonde Data ◽  
Published Data ◽  
Set Up

Abstract. This paper is motivated by the fact that, although temperature readings made by Vaisala RS41 radiosondes at GRUAN sites (http://www.gruan.org) are given at 1 s resolution, for various reasons, missing data are spread along the atmospheric profile. Such a problem is quite common in radiosonde data and other profile data. Hence, (linear) interpolation is often used to fill the gaps in published data products. In this perspective, the present paper considers interpolation uncertainty. To do this, a statistical approach is introduced giving some understanding of the consequences of substituting missing data by interpolated ones. In particular, a general frame for the computation of interpolation uncertainty based on a Gaussian process (GP) set-up is developed. Using the GP characteristics, a simple formula for computing the linear interpolation standard error is given. Moreover, the GP interpolation is proposed as it provides an alternative interpolation method with its standard error. For the Vaisala RS41, the two approaches are shown to give similar interpolation performances using an extensive cross-validation approach based on the block-bootstrap technique. Statistical results about interpolation uncertainties at various GRUAN sites and for various missing gap lengths are provided. Since both provide an underestimation of the cross-validation interpolation uncertainty, a bootstrap-based correction formula is proposed. Using the root mean square error, it is found that, for short gaps, with an average length of 5 s, the average uncertainty is smaller than 0.10 K. For larger gaps, it increases up to 0.35 K for an average gap length of 30 s, and up to 0.58 K for a gap of 60 s.

scholarly journals Effect of tropical cyclones on the tropical tropopause parameters observed using COSMIC GPS RO data

2015 ◽  
Vol 15 (18) ◽  
pp. 10239-10249 ◽  
Author(s):  
S. Ravindra Babu ◽  
M. Venkat Ratnam ◽  
G. Basha ◽  
B. V. Krishnamurthy ◽  
B. Venkateswararao
Keyword(s):  
Tropical Cyclones ◽  
Lower Stratosphere ◽  
Thermal Structure ◽  
Radial Distance ◽  
Lapse Rate ◽  
Tropical Tropopause Layer ◽  
Water Vapour Transport ◽  
Tropical Tropopause ◽  
High Vertical Resolution ◽  
Cold Point

Abstract. Tropical cyclones (TCs) are deep convective synoptic-scale systems that play an important role in modifying the thermal structure, tropical tropopause parameters and hence also modify stratosphere–troposphere exchange (STE) processes. In the present study, high vertical resolution and high accuracy measurements from COSMIC Global Positioning System (GPS) radio occultation (RO) measurements are used to investigate and quantify the effect of tropical cyclones that occurred over Bay of Bengal and Arabian Sea in the last decade on the tropical tropopause parameters. The tropopause parameters include cold-point tropopause altitude (CPH) and temperature (CPT), lapse-rate tropopause altitude (LRH) and temperature (LRT) and the thickness of the tropical tropopause layer (TTL), that is defined as the layer between convective outflow level (COH) and CPH, obtained from GPS RO data. From all the TC events, we generate the mean cyclone-centred composite structure for the tropopause parameters and removed it from the climatological mean obtained from averaging the GPS RO data from 2002 to 2013. Since the TCs include eye, eye walls and deep convective bands, we obtained the tropopause parameters based on radial distance from the cyclone eye. In general, decrease in the CPH in the eye is noticed as expected. However, as the distance from the cyclone eye increases by 300, 400, and 500 km, an enhancement in CPH (CPT) and LRH (LRT) is observed. Lowering of CPH (0.6 km) and LRH (0.4 km) values with coldest CPT and LRT (2–3 K) within a 500 km radius of the TC centre is noticed. Higher (2 km) COH leading to the lowering of TTL thickness (2–3 km) is clearly observed. There are multiple tropopause structures in the profiles of temperature obtained within 100 km from the centre of the TC. These changes in the tropopause parameters are expected to influence the water vapour transport from the troposphere to the lower stratosphere, and ozone from the lower stratosphere to the upper troposphere, hence influencing STE processes.

The Digital Earth Viewer: A new visualization approach for geospatial time series data

2021 ◽  
Author(s):  
Valentin Buck ◽  
Flemming Stäbler ◽  
Everardo Gonzalez ◽  
Jens Greinert
Keyword(s):  
Time Series Data ◽  
Environmental Data ◽  
Series Data ◽  
Multiple Sources ◽  
Data Types ◽  
New Approach ◽  
Digital Earth ◽  
Spatio Temporal ◽  
Diverse Data

<p>The study of the earth’s systems depends on a large amount of observations from homogeneous sources, which are usually scattered around time and space and are tightly intercorrelated to each other. The understanding of said systems depends on the ability to access diverse data types and contextualize them in a global setting suitable for their exploration. While the collection of environmental data has seen an enormous increase over the last couple of decades, the development of software solutions necessary to integrate observations across disciplines seems to be lagging behind. To deal with this issue, we developed the Digital Earth Viewer: a new program to access, combine, and display geospatial data from multiple sources over time.</p><p>Choosing a new approach, the software displays space in true 3D and treats time and time ranges as true dimensions. This allows users to navigate observations across spatio-temporal scales and combine data sources with each other as well as with meta-properties such as quality flags. In this way, the Digital Earth Viewer supports the generation of insight from data and the identification of observational gaps across compartments.</p><p>Developed as a hybrid application, it may be used both in-situ as a local installation to explore and contextualize new data, as well as in a hosted context to present curated data to a wider audience.</p><p>In this work, we present this software to the community, show its strengths and weaknesses, give insight into the development process and talk about extending and adapting the software to custom usecases.</p>

XML Data Integration

2010 ◽  
pp. 333-360 ◽  
Author(s):  
Yan Qi ◽  
Huiping Cao ◽  
K. Selçuk Candan ◽  
Maria Luisa Sapino
Keyword(s):  
Data Integration ◽  
Query Processing ◽  
Multiple Sources ◽  
Data Types ◽  
Xml Data ◽  
Data Schema ◽  
Integration Data ◽  
Feedback Techniques ◽  
Different Sources ◽  
Xml Data Integration

In XML Data Integration, data/metadata merging and query processing are indispensable. Specifically, merging integrates multiple disparate (heterogeneous and autonomous) input data sources together for further usage, while query processing is one main reason why the data need to be integrated in the first place. Besides, when supported with appropriate user feedback techniques, queries can also provide contexts in which conflicts among the input sources can be interpreted and resolved. The flexibility of XML structure provides opportunities for alleviating some of the difficulties that other less flexible data types face in the presence of uncertainty; yet, this flexibility also introduces new challenges in merging multiple sources and query processing over integrated data. In this chapter, the authors discuss two alternative ways XML data/schema can be integrated: conflict-eliminating (where the result is cleaned from any conflicts that the different sources might have with each other) and conflict-preserving (where the resulting XML data or XML schema captures the alternative interpretations of the data). They also present techniques for query processing over integrated, possibly imprecise, XML data, and cover strategies that can be used for resolving underlying conflicts.

scholarly journals An Alternative Bias Correction Scheme for CrIS Data Assimilation in a Regional Model

2019 ◽  
Vol 147 (3) ◽  
pp. 809-839 ◽  
Author(s):  
Xin Li ◽  
Xiaolei Zou ◽  
Mingjian Zeng
Keyword(s):  
Data Assimilation ◽  
Bias Correction ◽  
Moving Average ◽  
Atmospheric Temperature ◽  
Lapse Rate ◽  
Specific Humidity ◽  
Convective Precipitation ◽  
Temperature Structure ◽  
The Impact ◽  
Mean Square Errors

Bias correction (BC) is a crucial step for satellite radiance data assimilation (DA). In this study, the traditional airmass BC scheme in the National Centers for Environmental Prediction (NCEP) Gridpoint Statistical Interpolation (GSI) is investigated for Cross-track Infrared Sounder (CrIS) DA. The ability of the airmass predictors to model CrIS biases is diagnosed. Correlations between CrIS observation-minus-background ( O − B) samples and the two lapse rate–related airmass predictors employed by GSI are found to be very weak, indicating that the bias correction contributed by the airmass BC scheme is small. A modified BC scheme, which directly calculates the moving average of O − B departures from data of the previous 2 weeks with respect to scan position and latitudinal band, is proposed and tested. The impact of the modified BC scheme on CrIS radiance DA is compared with the variational airmass BC scheme. Results from 1-month analysis/forecast experiments show that the modified BC scheme removes nearly all scan-dependent and latitude-dependent biases, while residual biases are still found in some channels when the airmass BC scheme is applied. Smaller predicted root-mean-square errors of temperature and specific humidity and higher equivalent threat scores are obtained by the DA experiment using the modified BC scheme. If O − B samples are replaced by observation-minus-analysis ( O − A) samples for bias estimates in the modified BC scheme, the forecast impacts are reduced but remain positive. A convective precipitation case that occurred on 21 August 2016 is investigated. Using the modified BC scheme, the atmospheric temperature structure and the geopotential height structures near trough/ridge areas are better resolved, resulting in better precipitation forecasts.

Geophysics introduces new section on multiphysics and joint inversion

2020 ◽  
Vol 39 (10) ◽  
pp. 753-754
Author(s):  
Jiajia Sun ◽  
Daniele Colombo ◽  
Yaoguo Li ◽  
Jeffrey Shragge
Keyword(s):  
Big Data ◽  
Joint Inversion ◽  
Effective Means ◽  
Added Value ◽  
Data Sets ◽  
Computational Power ◽  
Sources Of Information ◽  
Multiple Sources ◽  
Data Types ◽  
Geoscientific Data

Geophysicists seek to extract useful and potentially actionable information about the subsurface by interpreting various types of geophysical data together with prior geologic information. It is well recognized that reliable imaging, characterization, and monitoring of subsurface systems require integration of multiple sources of information from a multitude of geoscientific data sets. With increasing data volumes and computational power, new data types, constant development of inversion algorithms, and the advent of the big data era, Geophysics editors see multiphysics integration as an effective means of meeting some of the challenges arising from imaging subsurface systems with higher resolution and reliability as well as exploring geologically more complicated areas. To advance the field of multiphysics integration and to showcase its added value, Geophysics will introduce a new section “Multiphysics and Joint Inversion” in 2021. Submissions are accepted now.

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