scholarly journals Stability of the MSU-Derived Atmospheric Temperature Trend

2010 ◽  
Vol 27 (11) ◽  
pp. 1960-1971 ◽  
Author(s):  
Cheng-Zhi Zou ◽  
Wenhui Wang
Keyword(s):  
Time Series ◽  
Correction Term ◽  
Empirical Relationship ◽  
Information Service ◽  
Atmospheric Temperature ◽  
Tropospheric Temperature ◽  
Climate Trends ◽  
Drift Correction ◽  
Target Effect ◽  
Best Fit

Abstract Warm target effect and diurnal drift errors are the main sources of uncertainties in the trend determination from the NOAA Microwave Sounding Unit (MSU) observations. Currently, there are two methods to correct the warm target effect: 1) finding a best root-level (level-1c) calibration nonlinearity using simultaneous nadir overpass (SNO) matchups to minimize this effect for each scene radiance, and 2) finding a best-fit empirical relationship between the correction term of the end-level gridded brightness temperature and warm target temperature and then removing the best fit from the unadjusted time series. The former corrects the warm target effect before the diurnal drift adjustment and provides more accurate, warm target effect–minimized, level-1c scene radiances for reanalysis applications. The latter corrects the warm target effect at the end-level merging step, which depend on the diurnal drift correction that occurred at a previous step. Although minimized, the first method still leaves small residual warm target–related errors due to imperfect calibrations. This study demonstrates that when the diurnal drift effect is negligible, a combination of the two methods completely removes warm target effect and produces an invariant trend that is independent of the level-1c calibration in the SNO framework. The conclusion is directly applicable to the MSU channel-2 oceanic midtropospheric temperature (T2) and global channel-3 upper-tropospheric temperature (T3) and channel-4 lower-stratospheric temperature (T4), which satisfy the condition of negligible diurnal drift effect. On the basis of these results, version 1.2 of the National Environmental Satellite, Data, and Information Service (NESDIS)–Center for Satellite Applications and Research (STAR) multisatellite MSU time series was constructed, including all T2, T3, and T4 products. In addition, a diurnal drift correction based on the Remote Sensing Systems diurnal anomalies was applied to the T2 product, which produces consistent climate trends between land and ocean. The global long-term climate trends for T2 and T4 derived from the STAR V1.2 dataset are, respectively, 0.18 ± 0.05 and −0.39 ± 0.36 K decade−1 during 1979–2006; the T3 trend is 0.11 ± 0.08 K decade−1 for 1981–2006.

scholarly journals Construction of the RSS V3.2 Lower-Tropospheric Temperature Dataset from the MSU and AMSU Microwave Sounders

2009 ◽  
Vol 26 (8) ◽  
pp. 1493-1509 ◽  
Author(s):  
Carl A. Mears ◽  
Frank J. Wentz
Keyword(s):  
Lower Stratosphere ◽  
Weighting Function ◽  
Lower Troposphere ◽  
Weighted Average ◽  
Atmospheric Temperature ◽  
Tropospheric Temperature ◽  
Satellite Measurements ◽  
Microwave Sounding Unit ◽  
Different Types ◽  
Microwave Sounding

Abstract Measurements made by microwave sounding instruments provide a multidecadal record of atmospheric temperature in several thick atmospheric layers. Satellite measurements began in late 1978 with the launch of the first Microwave Sounding Unit (MSU) and have continued to the present via the use of measurements from the follow-on series of instruments, the Advanced Microwave Sounding Unit (AMSU). The weighting function for MSU channel 2 is centered in the middle troposphere but contains significant weight in the lower stratosphere. To obtain an estimate of tropospheric temperature change that is free from stratospheric effects, a weighted average of MSU channel 2 measurements made at different local zenith angles is used to extrapolate the measurements toward the surface, which results in a measurement of changes in the lower troposphere. In this paper, a description is provided of methods that were used to extend the MSU method to the newer AMSU channel 5 measurements and to intercalibrate the results from the different types of satellites. Then, satellite measurements are compared to results from homogenized radiosonde datasets. The results are found to be in excellent agreement with the radiosonde results in the northern extratropics, where the majority of the radiosonde stations are located.

scholarly journals Is the Water Temperature of the Danube River at Bratislava, Slovakia, Rising?

2008 ◽  
Vol 9 (5) ◽  
pp. 1115-1122 ◽  
Author(s):  
Pavla Pekarova ◽  
Dana Halmova ◽  
Pavol Miklanek ◽  
Milan Onderka ◽  
Jan Pekar ◽  
...  
Keyword(s):  
Time Series ◽  
Water Temperature ◽  
Heat Load ◽  
Empirical Relationship ◽  
Danube River ◽  
Average Heat ◽  
The Danube River ◽  
Average Temperature ◽  
Monthly Discharge

Abstract This paper aims to reveal the annual regime, time series, and long-term water temperature trends of the Danube River at Bratislava, Slovakia, between the years 1926 and 2005. First, the main factors affecting the river’s water temperature were identified. Using multiple regression techniques, an empirical relationship is derived between monthly water temperatures and monthly atmospheric temperatures at Vienna (Hohe Warte), Austria, monthly discharge of the Danube, and some other factors as well. In the second part of the study, the long-term trends in the annual time series of water temperature were identified. The following series were evaluated: 1) The average annual water temperature (To) (determined as an arithmetic average of daily temperatures in the Danube at Bratislava), 2) the weighted annual average temperature values (Toυ) (determined from the daily temperatures weighted by the daily discharge rates at Bratislava), and 3) the average heat load (Zt) at the Bratislava station. In the long run, the To series is rising; however, the trend of the weighted long-term average temperature values, Toυ, is near zero. This result indicates that the average heat load of the Danube water did not change during the selected period of 80 yr. What did change is the interannual distribution of the average monthly discharge. Over the past 25 yr, an elevated runoff of “cold” water (increase of the December–April runoff) and a lower runoff of “warm” water (decrease of the river runoff during the summer months of June–August) were observed.

scholarly journals Observing the climate impact of large wildfires on stratospheric temperature

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Matthias Stocker ◽  
Florian Ladstädter ◽  
Andrea K. Steiner
Keyword(s):  
Lower Stratosphere ◽  
Volcanic Eruptions ◽  
Satellite Observation ◽  
Atmospheric Temperature ◽  
Climate Impact ◽  
Atmospheric Composition ◽  
Temperature Structure ◽  
Climate Trends ◽  
Short Term ◽  
Climate Signal

AbstractWildfires are expected to become more frequent and intense in the future. They not only pose a serious threat to humans and ecosystems, but also affect Earth’s atmosphere. Wildfire plumes can reach into the stratosphere, but little is known about their climate impact. Here, we reveal observational evidence that major wildfires can have a severe impact on the atmospheric temperature structure and short-term climate in the stratosphere. Using advanced satellite observation, we find substantial warming of up to 10 K of the lower stratosphere within the wildfire plumes during their early development. The short-term climate signal in the lower stratosphere lasts several months and amounts to 1 K for the Northern American wildfires in 2017, and up to striking 3.5 K for the Australian wildfires in 2020. This is stronger than any signal from recent volcanic eruptions. Such extreme events affect atmospheric composition and climate trends, underpinning their importance for future climate.

The GravIS Portal: User-friendly Global Mass Variations from GRACE and GRACE-FO

2021 ◽  
Author(s):  
Christoph Dahle ◽  
Eva Boergens ◽  
Henryk Dobslaw ◽  
Andreas Groh ◽  
Ingo Sasgen ◽  
...  
Keyword(s):  
Time Series ◽  
Information Service ◽  
Research Centre ◽  
Data Sets ◽  
Ocean Bottom ◽  
Climatic Regions ◽  
Uncertainty Estimates ◽  
Terrestrial Water ◽  
User Friendly ◽  
Global Mean

<p>The German Research Centre for Geosciences (GFZ) maintains the “Gravity Information Service” (GravIS, gravis.gfz-potsdam.de) portal in collaboration with the Alfred-Wegener-Institute (AWI) and Technische Universität Dresden. Main objective of this portal is the dissemination of data describing mass variations in the Earth system based on observations of the satellite gravimetry missions GRACE and GRACE-FO.</p><p>The provided data sets encompass products of terrestrial water storage (TWS) variations over the continents, ocean bottom pressure (OBP) variations from which global mean barystatic sea-level rise can be estimated, and mass changes of the ice sheets in Greenland and Antarctica. All data sets are provided as time series of regular grids for each area, as well as in the form of regional basin averages. Regarding the latter, for the continental TWS data the user can choose between classical river basins and a novel segmentation based on climatic regions. For the oceans, the segmentation into different regions is derived similarly but based on modelled OBP data. All time series are accompanied by realistic uncertainty estimates.</p><p>All data sets can be interactively displayed at the portal and are freely available for download. This contribution aims to show the features and possibilities of the GravIS portal to researchers without a dedicated geodetic background, working in the fields of hydrology, oceanography, and cryosphere.</p>

Forecasting Software Vulnerabilities Using Time-Series Techniques

2019 ◽  
pp. 125-165
Author(s):  
Baidyanath Biswas
Keyword(s):  
Time Series ◽  
Homeland Security ◽  
Moving Average ◽  
Primary Objective ◽  
Data Set ◽  
Software Vulnerabilities ◽  
Systems Security ◽  
Univariate Time Series ◽  
Efficient Prediction ◽  
Best Fit

This chapter discusses the concepts of time-series applications and forecasting in the context of information systems security. The primary objective in such formulation is the training of the models followed by efficient prediction. Although economic and financial forecasting problems extensively use time-series, predicting software vulnerabilities is a novel idea. The chapter also provides appropriate guidelines for the implementation and adaptation of univariate time-series for information security. To achieve this, the authors focus on the following techniques: autoregressive (AR), moving average (MA), autoregressive integrated moving average (ARIMA), and exponential smoothing. The analysis considers a unique data set consisting of the publicly exposed software vulnerabilities, available from the U.S. Dept. of Homeland Security. The problem is presented first, followed by a general framework to identify the problem, estimate the best-fit parameters of that model, and conclude with an illustrative example from the above dataset to familiarize readers with the business problem.

scholarly journals The Nyquist Issue in Linear Inverse Modeling

2019 ◽  
Vol 147 (4) ◽  
pp. 1341-1349 ◽  
Author(s):  
Cécile Penland
Keyword(s):  
Time Series ◽  
Markov Process ◽  
Covariance Matrix ◽  
Inverse Modeling ◽  
Degrees Of Freedom ◽  
Multivariate Time Series ◽  
Statistical Technique ◽  
Natural Mode ◽  
Dimensional System ◽  
Best Fit

Abstract Linear inverse modeling (LIM) is a statistical technique based on covariance statistics that estimates the best-fit linear Markov process to a multivariate time series. An integral, often-ignored part of the technique is a test of whether or not the linear assumptions are valid. One test for linearity is the so-called tau test. While this test can be trusted when it passes, it sometimes fails when it ought to pass. In this article, we discuss one of the reasons for spurious failure, the “Nyquist issue,” which occurs when the lagged covariance matrix used in the analysis is numerically performed at a lag greater than or nearly equal to half the period of a natural mode of variability represented in the time series. As an illustration relevant to a system with many degrees of freedom, but simple enough to solve analytically, we consider a four-dimensional system consisting of two modal pairs. Within this framework, we suggest one solution that can be applied if the time series are long enough. It is hoped that awareness of this issue can prevent misinterpretation of LIM results.

scholarly journals Connecting the Time Series of Microwave Sounding Observations from AMSU to ATMS for Long-Term Monitoring of Climate

2014 ◽  
Vol 31 (10) ◽  
pp. 2206-2222 ◽  
Author(s):  
Xiaolei Zou ◽  
Fuzhong Weng ◽  
H. Yang
Keyword(s):  
Time Series ◽  
Atmospheric Temperature ◽  
Advanced Technology ◽  
Central Frequency ◽  
Brightness Temperatures ◽  
Microwave Sounding Unit ◽  
Microwave Sounding ◽  
Cross Calibration ◽  
Term Monitoring

Abstract The measurements from the Microwave Sounding Unit (MSU) and the Advanced Microwave Sounding Unit-A (AMSU-A) on board NOAA polar-orbiting satellites have been extensively utilized for detecting atmospheric temperature trend during the last several decades. After the launch of the Suomi National Polar-orbiting Partnership (Suomi-NPP) satellite on 28 October 2011, MSU and AMSU-A time series will be overlapping with the Advanced Technology Microwave Sounder (ATMS) measurements. While ATMS inherited the central frequency and bandpass from most of AMSU-A sounding channels, its spatial resolution and noise features are, however, distinctly different from those of AMSU. In this study, the Backus–Gilbert method is used to optimally resample the ATMS data to AMSU-A fields of view (FOVs). The differences between the original and resampled ATMS data are demonstrated. By using the simultaneous nadir overpass (SNO) method, ATMS-resampled observations are collocated in space and time with AMSU-A data. The intersensor biases are then derived for each pair of ATMS–AMSU-A channels. It is shown that the brightness temperatures from ATMS now fall well within the AMSU data family after resampling and SNO cross calibration. Thus, the MSU–AMSU time series can be extended into future decades for more climate applications.

scholarly journals Monthly-Term Associations Between Air Pollutants and Respiratory Morbidity in South Brazil 2013–2016: A Multi-City, Time-Series Analysis

2019 ◽  
Vol 16 (20) ◽  
pp. 3787 ◽  
Author(s):  
Dayana Milena Agudelo-Castañeda ◽  
Elba Calesso Teixeira ◽  
Larissa Alves ◽  
Julián Alfredo Fernández-Niño ◽  
Laura Andrea Rodríguez-Villamizar
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Air Pollutants ◽  
Hospital Admissions ◽  
Age Groups ◽  
Information Service ◽  
Average Concentration ◽  
Respiratory Morbidity ◽  
Maximum Effect ◽  
South Brazil

Most air pollution research conducted in Brazil has focused on assessing the daily-term effects of pollutants, but little is known about the health effects of air pollutants at an intermediate time term. The objective of this study was to determine the monthly-term association between air pollution and respiratory morbidity in five cities in South Brazil. An ecological time-series study was performed using the municipality as the unit of observation in five cities in South Brazil (Gravataí, Triunfo, Esteio, Canoas, and Charqueadas) between 2013 and 2016. Data for hospital admissions was obtained from the records of the Hospital Information Service. Air pollution data, including PM10, SO2, CO, NO2, and O3 (µg/m3) were obtained from the environmental government agency in Rio Grande do Sul State. Panel multivariable Poisson regression models were adjusted for monthly counts of respiratory hospitalizations. An increase of 10 μg/m3 in the monthly average concentration of PM10 was associated with an increase of respiratory hospitalizations in all age groups, with the maximum effect on the population aged between 16 and 59 years (IRR: Incidence rate ratio 2.04 (95% CI: Confidence interval = 1.97–2.12)). For NO2 and SO2, stronger intermediate-term effects were found in children aged between 6 and 15 years, while for O3 higher effects were found in children under 1 year. This is the first multi-city study conducted in South Brazil to account for intermediate-term effects of air pollutants on respiratory health.

A New Look at Snowpack Trends in the Cascade Mountains

2010 ◽  
Vol 23 (10) ◽  
pp. 2473-2491 ◽  
Author(s):  
Mark T. Stoelinga ◽  
Mark D. Albright ◽  
Clifford F. Mass
Keyword(s):  
Time Series ◽  
Global Warming ◽  
Pacific Northwest ◽  
North Pacific ◽  
Climate Model ◽  
North Pacific Ocean ◽  
Tropospheric Temperature ◽  
Anthropogenic Global Warming ◽  
The North ◽  
The North Pacific

Abstract This study examines the changes in Cascade Mountain spring snowpack since 1930. Three new time series facilitate this analysis: a water-balance estimate of Cascade snowpack from 1930 to 2007 that extends the observational record 20 years earlier than standard snowpack measurements; a radiosonde-based time series of lower-tropospheric temperature during onshore flow, to which Cascade snowpack is well correlated; and a new index of the North Pacific sea level pressure pattern that encapsulates modes of variability to which Cascade spring snowpack is particularly sensitive. Cascade spring snowpack declined 23% during 1930–2007. This loss is nearly statistically significant at the 5% level. The snowpack increased 19% during the recent period of most rapid global warming (1976–2007), though this change is not statistically significant because of large annual variability. From 1950 to 1997, a large and statistically significant decline of 48% occurred. However, 80% of this decline is connected to changes in the circulation patterns over the North Pacific Ocean that vary naturally on annual to interdecadal time scales. The residual time series of Cascade snowpack after Pacific variability is removed displays a relatively steady loss rate of 2.0% decade−1, yielding a loss of 16% from 1930 to 2007. This loss is very nearly statistically significant and includes the possible impacts of anthropogenic global warming. The dates of maximum snowpack and 90% melt out have shifted 5 days earlier since 1930. Both shifts are statistically insignificant. A new estimate of the sensitivity of Cascade spring snowpack to temperature of −11% per °C, when combined with climate model projections of 850-hPa temperatures offshore of the Pacific Northwest, yields a projected 9% loss of Cascade spring snowpack due to anthropogenic global warming between 1985 and 2025.

scholarly journals Discriminating low frequency components from long range persistent fluctuations in daily atmospheric temperature variability

2009 ◽  
Vol 9 (14) ◽  
pp. 4537-4544 ◽  
Author(s):  
M. Lanfredi ◽  
T. Simoniello ◽  
V. Cuomo ◽  
M. Macchiato
Keyword(s):  
Time Series ◽  
Long Range ◽  
Power Law ◽  
Low Frequency ◽  
Atmospheric Temperature ◽  
Fluctuation Analysis ◽  
Climate Modelling ◽  
Scale Free ◽  
Statistical Confidence ◽  
Frequency Components

Abstract. This study originated from recent results reported in literature, which support the existence of long-range (power-law) persistence in atmospheric temperature fluctuations on monthly and inter-annual scales. We investigated the results of Detrended Fluctuation Analysis (DFA) carried out on twenty-two historical daily time series recorded in Europe in order to evaluate the reliability of such findings in depth. More detailed inspections emphasized systematic deviations from power-law and high statistical confidence for functional form misspecification. Rigorous analyses did not support scale-free correlation as an operative concept for Climate modelling, as instead suggested in literature. In order to understand the physical implications of our results better, we designed a bivariate Markov process, parameterised on the basis of the atmospheric observational data by introducing a slow dummy variable. The time series generated by this model, analysed both in time and frequency domains, tallied with the real ones very well. They accounted for both the deceptive scaling found in literature and the correlation details enhanced by our analysis. Our results seem to evidence the presence of slow fluctuations from another climatic sub-system such as ocean, which inflates temperature variance up to several months. They advise more precise re-analyses of temperature time series before suggesting dynamical paradigms useful for Climate modelling and for the assessment of Climate Change.

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