An unknown maximum lag-correlation between rainfall and aerosols at 140-160 minutes

2021 ◽  
Author(s):  
Pinhas Alpert ◽  
Haim Shafir ◽  
Emily Elhacham
Keyword(s):  
Time Series ◽  
Time Scales ◽  
Atmospheric Chemistry ◽  
Numerical Study ◽  
Aerosol Concentration ◽  
Hygroscopic Growth ◽  
Plain Language ◽  
Geophysical Research ◽  
Near Surface ◽  
The Earth

<p>Keywords:</p><p>Scavenging process, Rainfall, Aerosols, Lag correlation, Rainfall-aerosol processes</p><p>Abstract</p><p>Rainfall and aerosols play major roles in the Earth climate system and substantially influence our life. Here, the focus is on the local near-surface aerosol/rainfall correlations with time-scales of minutes to days. We investigated 29 experiments including 14 specific rain events, with time resolutions of daily and 60, 30, 10 minutes at ten stations in Israel and California. The highest negative correlations were consistently at a positive lag of about 140-160 minutes where a positive lag means that the aerosol time-series follows that of the rain. The highest negative value is suggested to be the probable outcome of immediate scavenging along with the rise in aerosol concentration after rain depending on aerosol sources, hygroscopic growth and transport. The scavenging dominance is expressed by the mostly negative lag-correlation values in all experiments. In addition, the consistent lack of significant correlation found at negative lags suggest a weak aerosol effect on precipitation (Gryspeerdt et al., 2015).</p><p>Plain Language Summary: Rainfall and atmospheric particles (aerosols) play significant roles in the Earth atmosphere and largely influence our weather and climate. The relations between near-surface aerosol and rainfall on time scales of minutes to days are studied, employing correlations in 10 meteorological stations in Israel and California. The highest negative correlations were consistently at a positive lag of about 140-160 minutes. A positive lag means that the aerosol time-series follows that of the rain. The highest negative correlation value is suggested to be the outcome of scavenging along with the rise in aerosol concentration after rain depending on the sources of aerosols, hygroscopic growth and transport. <strong>Furthermore, our approach provides a more fundamental insight into the local, near-surface rain-aerosol interactions, in contrast to many aerosol-rainfall studies that are climatological and with the tele-connection approach (Alpert et al., 2008), which involves other processes over distances of a few km up to even large synoptic scales.</strong></p><p>            </p><p><strong>Relevant References:</strong></p><p>Alpert, P., Halfon, N., & Levin, Z. (2008). Does Air Pollution Really Suppress Precipitation in Israel? Journal of Applied Meteorology and Climatology. https://doi.org/10.1175/2007jamc1803.1</p><p>Barkan, J., & Alpert, P. (2020). Red Snow occurrence in Eastern Europe - A case study. Weather. https://doi.org/10.1002/wea.3644</p><p>Gryspeerdt, E., Stier, P., White, B. A., & Kipling, Z. (2015). Wet scavenging limits the detection of aerosol effects on precipitation. Atmospheric Chemistry and Physics, 15(13), 7557–7570.</p><p>Tsidulko, M., Krichak, S. O., Alpert, P., Kakaliagou, O., Kallos, G., & Papadopoulos, A. (2002). Numerical study of a very intensive eastern Mediterranean dust storm, 13-16 March 1998. Journal of Geophysical Research: Atmospheres. https://doi.org/10.1029/2001jd001168</p>

scholarly journals Locally and Remotely Forced Subtropical AMOC Variability: A Matter of Time Scales

2020 ◽  
Vol 33 (12) ◽  
pp. 5155-5172
Author(s):  
Quentin Jamet ◽  
William K. Dewar ◽  
Nicolas Wienders ◽  
Bruno Deremble ◽  
Sally Close ◽  
...  
Keyword(s):  
Time Series ◽  
Time Scales ◽  
North Atlantic ◽  
Time Scale ◽  
Low Frequency ◽  
Meridional Overturning Circulation ◽  
Open Boundary ◽  
Overturning Circulation ◽  
Decadal Scale ◽  
Meridional Overturning

AbstractMechanisms driving the North Atlantic meridional overturning circulation (AMOC) variability at low frequency are of central interest for accurate climate predictions. Although the subpolar gyre region has been identified as a preferred place for generating climate time-scale signals, their southward propagation remains under consideration, complicating the interpretation of the observed time series provided by the Rapid Climate Change–Meridional Overturning Circulation and Heatflux Array–Western Boundary Time Series (RAPID–MOCHA–WBTS) program. In this study, we aim at disentangling the respective contribution of the local atmospheric forcing from signals of remote origin for the subtropical low-frequency AMOC variability. We analyze for this a set of four ensembles of a regional (20°S–55°N), eddy-resolving (1/12°) North Atlantic oceanic configuration, where surface forcing and open boundary conditions are alternatively permuted from fully varying (realistic) to yearly repeating signals. Their analysis reveals the predominance of local, atmospherically forced signal at interannual time scales (2–10 years), whereas signals imposed by the boundaries are responsible for the decadal (10–30 years) part of the spectrum. Due to this marked time-scale separation, we show that, although the intergyre region exhibits peculiarities, most of the subtropical AMOC variability can be understood as a linear superposition of these two signals. Finally, we find that the decadal-scale, boundary-forced AMOC variability has both northern and southern origins, although the former dominates over the latter, including at the site of the RAPID array (26.5°N).

scholarly journals A Sequential Autoencoder for Teleconnection Analysis

2020 ◽  
Vol 12 (5) ◽  
pp. 851 ◽  
Author(s):  
Jiena He ◽  
J. Ronald Eastman
Keyword(s):  
Time Series ◽  
Neural Representation ◽  
Strongly Correlated ◽  
Atmospheric Sciences ◽  
Teleconnection Patterns ◽  
The Earth ◽  
Unsupervised Neural Network ◽  
Residual Series ◽  
Components Analysis ◽  
Computationally Intensive

Many aspects of the earth system are known to have preferred patterns of variability, variously known in the atmospheric sciences as modes or teleconnections. Approaches to discovering these patterns have included principal components analysis and empirical orthogonal teleconnection (EOT) analysis. The latter is very effective but is computationally intensive. Here, we present a sequential autoencoder for teleconnection analysis (SATA). Like EOT, it discovers teleconnections sequentially, with subsequent analyses being based on residual series. However, unlike EOT, SATA uses a basic linear autoencoder as the primary tool for analysis. An autoencoder is an unsupervised neural network that learns an efficient neural representation of input data. With SATA, the input is an image time series and the neural representation is a unidimensional time series. SATA then locates the 0.5% of locations with the strongest correlation with the neural representation and averages their temporal vectors to characterize the teleconnection. Evaluation of the procedure showed that it is several orders of magnitude faster than other approaches to EOT, produces teleconnection patterns that are more strongly correlated to well-known teleconnections, and is particularly effective in finding teleconnections with multiple centers of action (such as dipoles).

scholarly journals Onboard Radio Frequency Interference as the Origin of Inter-Satellite Biases for Microwave Humidity Sounders

2019 ◽  
Vol 11 (7) ◽  
pp. 866 ◽  
Author(s):  
Imke Hans ◽  
Martin Burgdorf ◽  
Stefan A. Buehler
Keyword(s):  
Time Series ◽  
Radio Frequency ◽  
Time Dependent ◽  
Radio Frequency Interference ◽  
Climate Variables ◽  
Compelling Evidence ◽  
Climate Data ◽  
The Earth ◽  
Correction Scheme

Understanding the causes of inter-satellite biases in climate data records from observations of the Earth is crucial for constructing a consistent time series of the essential climate variables. In this article, we analyse the strong scan- and time-dependent biases observed for the microwave humidity sounders on board the NOAA-16 and NOAA-19 satellites. We find compelling evidence that radio frequency interference (RFI) is the cause of the biases. We also devise a correction scheme for the raw count signals for the instruments to mitigate the effect of RFI. Our results show that the RFI-corrected, recalibrated data exhibit distinctly reduced biases and provide consistent time series.

scholarly journals Scale-dependent intrinsic entropies of complex time series

2016 ◽  
Vol 374 (2065) ◽  
pp. 20150204 ◽  
Author(s):  
Jia-Rong Yeh ◽  
Chung-Kang Peng ◽  
Norden E. Huang
Keyword(s):  
Time Series ◽  
Time Scales ◽  
Spatial Scales ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Complex Time ◽  
Multi Scale ◽  
Mode Decomposition ◽  
Simulated Time ◽  
Simulated Time Series

Multi-scale entropy (MSE) was developed as a measure of complexity for complex time series, and it has been applied widely in recent years. The MSE algorithm is based on the assumption that biological systems possess the ability to adapt and function in an ever-changing environment, and these systems need to operate across multiple temporal and spatial scales, such that their complexity is also multi-scale and hierarchical. Here, we present a systematic approach to apply the empirical mode decomposition algorithm, which can detrend time series on various time scales, prior to analysing a signal’s complexity by measuring the irregularity of its dynamics on multiple time scales. Simulated time series of fractal Gaussian noise and human heartbeat time series were used to study the performance of this new approach. We show that our method can successfully quantify the fractal properties of the simulated time series and can accurately distinguish modulations in human heartbeat time series in health and disease.

scholarly journals Analysis Methods for Characterizing Salinity Variability from Multivariate Time Series Applied to the Apalachicola Bay Estuary

2012 ◽  
Vol 29 (4) ◽  
pp. 613-628 ◽  
Author(s):  
Steven L. Morey ◽  
Dmitry S. Dukhovskoy
Keyword(s):  
Time Series ◽  
Time Scales ◽  
River Discharge ◽  
River Flow ◽  
High Salinity ◽  
Multivariate Time Series ◽  
Salinity Range ◽  
Apalachicola Bay ◽  
Analysis Methods ◽  
Salinity Variability

Abstract Statistical analysis methods are developed to quantify the impacts of multiple forcing variables on the hydrographic variability within an estuary instrumented with an enduring observational system. The methods are applied to characterize the salinity variability within Apalachicola Bay, a shallow multiple-inlet estuary along the northeastern Gulf of Mexico coast. The 13-yr multivariate time series collected by the National Estuary Research Reserve at three locations within the bay are analyzed to determine how the estuary responds to variations in external forcing mechanisms, such as freshwater discharge, precipitation, tides, and local winds at multiple time scales. The analysis methods are used to characterize the estuarine variability under differing flow regimes of the Apalachicola River, a managed waterway, with particular focus on extreme events and scales of variability that are critical to local ecosystems. Multivariate statistical models are applied that describe the salinity response to winds from multiple directions, river flow, and precipitation at daily, weekly, and monthly time scales to understand the response of the estuary under different climate regimes. Results show that the salinity is particularly sensitive to river discharge and wind magnitude and direction, with local precipitation being largely unimportant. Applying statistical analyses with conditional sampling quantifies how the likelihoods of high-salinity and long-duration high-salinity events, conditions of critical importance to estuarine organisms, change given the state of the river flow. Intraday salinity range is shown to be negatively correlated with the salinity, and correlated with river discharge rate.

scholarly journals Methods of Constructing Time Series for Predicting Local Time Scales by Means of a GMDH-Type Neural Network

2021 ◽  
Vol 11 (12) ◽  
pp. 5615
Author(s):  
Łukasz Sobolewski ◽  
Wiesław Miczulski
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Local Time ◽  
Time Scales ◽  
Time Scale ◽  
Prediction Accuracy ◽  
Prediction Horizon

Ensuring the best possible stability of UTC(k) (local time scale) and its compliance with the UTC scale (Universal Coordinated Time) forces predicting the [UTC-UTC(k)] deviations, the article presents the results of work on two methods of constructing time series (TS) for a neural network (NN), increasing the accuracy of UTC(k) prediction. In the first method, two prepared TSs are based on the deviations determined according to the UTC scale with a 5-day interval. In order to improve the accuracy of predicting the deviations, the PCHIP interpolating function is used in subsequent TSs, obtaining TS elements with a 1-day interval. A limitation in the improvement of prediction accuracy for these TS has been a too large prediction horizon. The introduction in 2012 of the additional UTC Rapid scale by BIPM makes it possible to shorten the prediction horizon, and the building of two TSs has been proposed according to the second method. Each of them consists of two subsets. The first subset is based on deviations determined according to the UTC scale, the second on the UTC Rapid scale. The research of the proposed TS in the field of predicting deviations for the Polish Timescale by means of GMDH-type NN shows that the best accuracy of predicting the deviations has been achieved for TS built according to the second method.

Data Fusion of Total Solar Irradiance Composite Time Series Using 40 years of Satellite Measurements: First Results 

2021 ◽  
Author(s):  
Jean-Philippe Montillet ◽  
Wolfgang Finsterle ◽  
Werner Schmutz ◽  
Margit Haberreiter ◽  
Rok Sikonja
Keyword(s):  
Time Series ◽  
Data Fusion ◽  
Solar Irradiance ◽  
Total Solar Irradiance ◽  
Maunder Minimum ◽  
The Sun ◽  
Climate Reconstructions ◽  
The Earth ◽  
First Results ◽  
The Difference

<p><span>Since the late 70’s, successive satellite missions have been monitoring the sun’s activity, recording total solar irradiance observations. These measurements are important to estimate the Earth’s energy imbalance, </span><span>i.e. the difference of energy absorbed and emitted by our planet. Climate modelers need the solar forcing time series in their models in order to study the influence of the Sun on the Earth’s climate. With this amount of TSI data, solar irradiance reconstruction models  can be better validated which can also improve studies looking at past climate reconstructions (e.g., Maunder minimum). V</span><span>arious algorithms have been proposed in the last decade to merge the various TSI measurements over the 40 years of recording period. We have developed a new statistical algorithm based on data fusion.  The stochastic noise processes of the measurements are modeled via a dual kernel including white and coloured noise.  We show our first results and compare it with previous releases (PMOD,ACRIM, ... ). </span></p>

scholarly journals Investigation of water adsorption and hygroscopicity of atmospherically relevant particles using a commercial vapor sorption analyzer

2017 ◽  
Vol 10 (10) ◽  
pp. 3821-3832 ◽  
Author(s):  
Wenjun Gu ◽  
Yongjie Li ◽  
Jianxi Zhu ◽  
Xiaohong Jia ◽  
Qinhao Lin ◽  
...  
Keyword(s):  
Atmospheric Chemistry ◽  
Radiative Forcing ◽  
Water Adsorption ◽  
Theoretical Data ◽  
Atmospheric Particles ◽  
Relative Mass ◽  
Nonspherical Particles ◽  
Vapor Sorption ◽  
Hygroscopic Growth ◽  
Dry Conditions

Abstract. Water adsorption and hygroscopicity are among the most important physicochemical properties of aerosol particles, largely determining their impacts on atmospheric chemistry, radiative forcing, and climate. Measurements of water adsorption and hygroscopicity of nonspherical particles under subsaturated conditions are nontrivial because many widely used techniques require the assumption of particle sphericity. In this work we describe a method to directly quantify water adsorption and mass hygroscopic growth of atmospheric particles for temperature in the range of 5–30 °C, using a commercial vapor sorption analyzer. A detailed description of instrumental configuration and experimental procedures, including relative humidity (RH) calibration, is provided first. It is then demonstrated that for (NH4)2SO4 and NaCl, deliquescence relative humidities and mass hygroscopic growth factors measured using this method show good agreements with experimental and/or theoretical data from literature. To illustrate its ability to measure water uptake by particles with low hygroscopicity, we used this instrument to investigate water adsorption by CaSO4 ⋅ 2H2O as a function of RH at 25 °C. The mass hygroscopic growth factor of CaSO4 ⋅ 2H2O at 95 % RH, relative to that under dry conditions (RH  < 1 %), was determined to be (0.450±0.004) % (1σ). In addition, it is shown that this instrument can reliably measure a relative mass change of 0.025 %. Overall, we have demonstrated that this commercial instrument provides a simple, sensitive, and robust method to investigate water adsorption and hygroscopicity of atmospheric particles.

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