Top PDF Relationship between wind speed and aerosol optical depth over remote ocean

Relationship between wind speed and aerosol optical depth over remote ocean

Relationship between wind speed and aerosol optical depth over remote ocean

with wind speed at the island of Minicoy in the Arabian Sea. However, the region in this study is close to land so that it cannot be considered as a pristine marine environment. Mulcahy et al. (2008) obtained a power-law relationship between wind speed and AOD at four different wavelengths at Mace Head, located on the west coast of Ireland. This site is similarly not representative of the remote ocean. Based on one month of τ data

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An analysis of the collection 5 MODIS over-ocean aerosol optical depth product for its implication in aerosol assimilation

An analysis of the collection 5 MODIS over-ocean aerosol optical depth product for its implication in aerosol assimilation

do not include observations at the 0.55 µm spectral channel, and therefore, the AERONET observations from 0.50 and 0.67 µm were used to estimate aerosol optical depth values at the 0.55 µm based on the method by O’Neill et al., (2001). As satellite over-ocean retrievals were used, only AERONET data from coastal or island sites were selected. Three collocated data sets were included in this study. (1) Terra MODIS C5 aerosol products and AERONET level 2.0 data from 2000 to 2008; (2) Aqua MODIS C5 aerosol prod- ucts and the AERONET level 2.0 data from 2002 to 2008; and (3) MODIS Terra and Aqua C5 aerosol products and AERONET level 1.5 data for 2009 for independent valida- tion efforts. The level 1.5 AERONET data were used for 2009 because the complete set of the quality assured level 2.0 AERONET data were not readily available yet. All three data sets were also collocated with the near surface wind speed data from the Navy Operational Global Analysis and Predic- tion System (NOGAPS) weather forecast model (Hogan and Rosmond, 1991). Because the NOGAPS analyzed wind data are only reported at four fixed times per day (00:00, 06:00, 12:00, and 18:00 UTC), aerosol data were coupled with the wind data by matching the observation time with the closest model output time.
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Global and regional trends in aerosol optical depth based on remote sensing products and pollutant emission estimates between 2000 and 2009

Global and regional trends in aerosol optical depth based on remote sensing products and pollutant emission estimates between 2000 and 2009

over both land and ocean (Martonchik et al., 2009). There is a time difference of 7.5 min between the first and the last camera recording to view the exact geographic position as the satellite passes over. Each path has a swath width of 360 km with a 16-day repeat cycle. In this work global Level 3 CGAS-F15 products are used (Op- tical Depth Average), which are derived from Level 1 and Level 2 products, and are

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Effect of wind speed on aerosol optical depth over remote oceans, based on data from the Maritime Aerosol Network

Effect of wind speed on aerosol optical depth over remote oceans, based on data from the Maritime Aerosol Network

cruises South of 9 ◦ S. An additional restriction imposed on the data set was exclusion of points taken closer than two de- grees from the nearest landmass. Among the selected cruises, we excluded one (presented in Fig. 1), which showed no relationship between AOD and wind speed. For any other individual cruise considered, the slope of the AOD scatter- plot versus wind speed was found to be at least 0.002 s m −1 . This “cherry-picking” is justified by the ultimate goal of find- ing the most robust possible dependence of AOD on wind speed over the oceans. Table 1 presents final dataset used for our analysis. Overall we considered 239 measurement days. Figure 2a shows AOD daily averages as a function of lati- tude, and Fig. 2b presents corresponding daily averages of the ship-based wind speed.
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Radiative forcing and climate response to projected 21st century aerosol decreases

Radiative forcing and climate response to projected 21st century aerosol decreases

Lee, Y. H., Rotstayn, L., Mahowald, N., Milly, G., Faluvegi, G., Balkanski, Y., Collins, W. J., Conley, A. J., Dalsoren, S., Easter, R., Ghan, S., Horowitz, L., Liu, X., Myhre, G., Na- gashima, T., Naik, V., Rumbold, S. T., Skeie, R., Sudo, K., Szopa, S., Takemura, T., Voul- garakis, A., Yoon, J.-H., and Lo, F.: Radiative forcing in the ACCMIP historical and future climate simulations, Atmos. Chem. Phys., 13, 2939–2974, doi:10.5194/acp-13-2939-2013,

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Environmental forcing on phytoplankton biomass and primary productivity of the coastal ecosystem in Ubatuba region, southern Brazil

Environmental forcing on phytoplankton biomass and primary productivity of the coastal ecosystem in Ubatuba region, southern Brazil

Although no significant peak was observed in the wind direction and speed, data suggest a relationship, at low frequency, between atmospheric forcing and water column stratification, nit[r]

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Tomografia passiva costiera. Inversion results with active data - phase 2

Tomografia passiva costiera. Inversion results with active data - phase 2

Multiple environmental and geometrical parameter optimization is often a computatio- nally cumbersome task. The optimization hypersurface may be very irregular leading to a severely ill-conditioned problem with a large number of local maxima. When dealing with real data, the inherent model mismatch and the presence of noise create a situation where there is no assurance of existence of an optimum solution in coincidence with (or even close) to the true model parameters. The first approach to the problem is to try to get as much apriori information as possible from the environmental parameters in the baseline model, in order to set them fixed and close to the true parameters so, the search is only done on a few unknown parameters. In practice, it is well known that setting fixed parameters in the model creates severe mismatches with real data that can not be overcomed by the search parameters, leading to poor fit situations and strongly biased estimates. An alternative is a technique proposed by Collins et al. [7], known as focali- zation, where a number of a priori known model parameters, are allowed to be adjusted during the search process in order to compensate for the data-model misfit and possible measurement errors. Generally, a priori known parameters have a smaller degree of vari- ation than the unknwon parameters. This technique provides a high degree of fit and a better conditioned convergence to the true parameter values. Examples of such results are given in recent publications by the authors of this report [8, 9, 10, 11].
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Detailed Aerosol Optical Depth Intercomparison between Brewer and Li-Cor 1800 Spectroradiometers and a Cimel Sun Photometer

Detailed Aerosol Optical Depth Intercomparison between Brewer and Li-Cor 1800 Spectroradiometers and a Cimel Sun Photometer

Aerosol optical depth (AOD) using different instruments during three short and intensive campaigns carried out from 1999 to 2001 at El Arenosillo in Huelva, Spain, are presented and compared. The specific aim of this study is to determine the level of agreement between three different instruments running in operational conditions. This activity, however, is part of a broader objective to recover an extended data series of AOD in the UV range obtained from a Brewer spectroradiometer. This instrument may be used to obtain AOD at the same five UV wavelengths used during normal operation for ozone content determi- nation. As part of the validation of the Brewer AOD data, a Cimel sun photometer and another spectror- adiometer, a Li-Cor 1800, were used. A detailed comparison of these three instruments is carried out by means of near-simultaneous measurements, with particular emphasis on examining diurnal AOD variability. Absolute AOD uncertainties range from 0.02 for the Cimel to 0.08 for the Brewer, with intermediate values for the Li-Cor 1800. All data during the comparison are in reasonable agreement, when taking into account the different performance characteristics of each instrument. The comparison also demonstrates current deficiencies in the Brewer data and thus the difficulty to determine AOD values with low errors.
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Characterization of aerosol pollution events in France using  ground-based and POLDER-2 satellite data

Characterization of aerosol pollution events in France using ground-based and POLDER-2 satellite data

spatial resolution is 6 km×7 km and its wide field of view induces a 2400 km swath that allows a nearly global daily coverage. To increase the signal to noise ratio, the algo- rithm is applied to 3×3 POLDER pixels, leading to a res- olution in the aerosol AOT of 21 km×18 km. AOT retrieval from the POLDER polarized measurements is described by Deuz´e et al. (2001). The AOT is retrieved at the 670 nm and 865 nm channels equipped with polarized filters. Only cloud-free pixels selected according to the cloud-screening algorithm of Br´eon and Colzy (1999) are processed. The surface contribution is modelled as a semi-empirical bidirec- tional polarized distribution function that depends on the nor- malized difference vegetation index (NDVI) and the geo-type of the pixel according to the IGBP classification (Nadal and Br´eon, 1999). Ten mono-modal lognormal size distributions are considered with corresponding effective radius from 0.05 to 0.15 µm and a standard deviation of 0.403 for a complex refractive index of 1.47–0.01 i. In this study, we consider the AOT at 440 nm extrapolated from the measurements at 670 and 865 nm. Physical AOT values are considered to be valid down to 0. Figure 1 shows along with the PM2.5 mea- surements the corresponding POLDER derived AOT over the site of Lille, France between April and October 2003. Gen- erally, the temporal trend in the optical thickness is very sim- ilar to the trend directly recorded in ground-based PM2.5, especially in August. However, some days show a peak of POLDER AOT not recorded at the ground by the PM mea- surements like on 27 May (POLDER AOT = 0.60 and PM2.5 = 15.5 µg/m3). This could be due to the presence of a high altitude aerosol plume. On another hand, days like the 15th of April present a strong local PM value (29 µg/m3) not ob- served by POLDER-2 (AOT=0.08). This phenomenon could be due to the coarse resolution of the POLDER-2 instrument compared to the local station, or the choice of selecting daily mean
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Wind-wave amplification mechanisms: possible models for steep   wave events in finite depth

Wind-wave amplification mechanisms: possible models for steep wave events in finite depth

Abstract. We extend the Miles mechanism of wind-wave generation to finite depth. A β-Miles linear growth rate de- pending on the depth and wind velocity is derived and al- lows the study of linear growth rates of surface waves from weak to moderate winds in finite depth h. The evolution of β is plotted, for several values of the dispersion parameter kh with k the wave number. For constant depths we find that no matter what the values of wind velocities are, at small enough wave age the β-Miles linear growth rates are in the known deep-water limit. However winds of moderate inten- sities prevent the waves from growing beyond a critical wave age, which is also constrained by the water depth and is less than the wave age limit of deep water. Depending on wave age and wind velocity, the Jeffreys and Miles mecha- nisms are compared to determine which of them dominates. A wind-forced nonlinear Schrödinger equation is derived and the Akhmediev, Peregrine and Kuznetsov–Ma breather solu- tions for weak wind inputs in finite depth h are obtained.
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Four-dimensional distribution of the 2010 Eyjafjallajökull volcanic cloud over Europe observed by EARLINET

Four-dimensional distribution of the 2010 Eyjafjallajökull volcanic cloud over Europe observed by EARLINET

over Cabauw often did not permit lidar data inversion. In the successive periods sub- stantial cloud cover and rain limited the possibility to perform measurements, hence data had to be taken more sporadically. The center of mass of the volcanic layer was typically around 3–3.5 km for all stations apart from Maisach where it remained at about 2.5 km. Intrusion into the PBL was a common feature for almost all observations. In the

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Assessment of the MODIS Collections C005 and C004 aerosol optical depth products over the Mediterranean basin

Assessment of the MODIS Collections C005 and C004 aerosol optical depth products over the Mediterranean basin

Using Level-3 MODIS AOD data averaged over 1 ◦ ×1 ◦ , (100×100 km) grid areas for comparisons against AERONET data can affect the statistics and the results due to the large spatial and temporal variability of aerosols that might be not adequately represented by Level-3 data. In- stead, Level-2 data with a resolution of 10 km at nadir should be more adequate for such comparisons. To assess the ef- fect of using Level-3 instead of Level-2 MODIS AOD data, we made use of Level-2 MODIS AOD data for compari- son against seven (7) important AERONET sites over the study region (Mediterranean basin). The sites were appro- priately selected in order to be representative for the differ- ent aerosol types observed over the Mediterranean basin (e.g. urban, desert, maritime) also ensuring a homogeneous and complete spatial coverage. The selected AERONET stations are: Nes Ziona, FORTH, Bucarest, Etna, Ispra, Ville Franche and Blida. In our analysis, we applied the spatio-temporal window technique described by Ichoku et al. (2002). We finally used 50×50 km window sizes (Ichoku et al., 2002). For each day of our study period, and for each one sta- tion, we performed the comparisons using the derived Level- 2 AOD data. The results show that the correlation coeffi- cients (R) between MODIS and AERONET did not change drastically using Level-2 instead of Level-3 data. Specifi- cally, the differences in R values did not exceed 0.02 (only for the single case of the Nes Ziona station Level-2 provided a larger increase of R equal to 0.18). It is important that no systematic behavior was found for the examined stations in terms of performance of Level-2 and Level-3 MODIS data against AERONET, i.e. for some (3) stations a better com- parison was found using the Level-2 than Level-3 AOD data, against a worse comparison for some (3) other stations. In addition, the computed correlation coefficients between the Level-2 and Level-3 AOD data are quite high, with values ranging from 0.84 to 0.99. Finally, the differences, 1(AOD), between MODIS and AERONET are similar using either Level-2 or Level-3 data. Thus, the 1(AOD) values range from −0.09 to 0.07 for Level-3 data, and from −0.09 to 0.12 for Level-2. The relative percentage differences, with respect to AERONET AOD values, between using Level-2 and Level-3 AODs are smaller than 5%. Therefore, it ap- pears that the results remain unaffected using either Level-2 or Level-3 MODIS AOD data. The improved performance of MODIS Collection 005 with respect to Collection 004 in terms of comparison against AERONET, as well as the gen- eral decrease of AOD values over the study region (with de- creasing values over land and slightly increasing values over ocean) is valid whether using Level-2 or Level-3 data.
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Global distribution and climate forcing of marine organic aerosol – Part 1: Model improvements and evaluation

Global distribution and climate forcing of marine organic aerosol – Part 1: Model improvements and evaluation

to the CCN budget can be important throughout the whole year. Moreover, according to Eq. (1) the organic mass fraction of sea spray approaches 100 % (pure organics) for particles below 100 nm in dry diameter, reflecting the highest potential enrichment in the organic fraction (Bigg and Leck, 2008). Several studies using laboratory-generated primary marine aerosol proposed that due to their low CCN activity such (sub-200 nm

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Regional and monthly and clear-sky aerosol direct radiative effect (and forcing) derived from the GlobAEROSOL-AATSR satellite aerosol product

Regional and monthly and clear-sky aerosol direct radiative effect (and forcing) derived from the GlobAEROSOL-AATSR satellite aerosol product

T. F., Boucher, O., Chin, M., Collins, W., Dentener, F., Diehl, T., Easter, R., Feichter, J., Fill- more, D., Ghan, S., Ginoux, P., Gong, S., Grini, A., Hendricks, J., Herzog, M., Horowitz, L., Isaksen, I., Iversen, T., Kirkev ˚ag, A., Kloster, S., Koch, D., Kristjansson, J. E., Krol, M., Lauer, A., Lamarque, J. F., Lesins, G., Liu, X., Lohmann, U., Montanaro, V., Myhre, G., Penner, J., Pitari, G., Reddy, S., Seland, O., Stier, P., Takemura, T., and Tie, X.: An AeroCom initial

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Aerosol optical depth over the Arctic: a comparison of ECHAM-HAM and TM5 with ground-based, satellite and reanalysis data

Aerosol optical depth over the Arctic: a comparison of ECHAM-HAM and TM5 with ground-based, satellite and reanalysis data

In this work, we explored the skill of TM5 and ECHAM- HAM in these areas in deeper detail, focusing on the optical parameters AOD and ˚ Angstr¨om, which are directly observed by ground-based and satellite measurements, comparing both their spatial structure and the amplitudes measured at the lo- cation of six Arctic stations. The ˚ Angstr¨om parameter is rea- sonably reproduced at most stations by both models, indi- cating that the distribution of particle sizes is captured cor- rectly, together with its seasonality, characterized by a peak in summer. The main exception is the Summit station where both TM5 and ECHAM-HAM overestimate the parameter; this station is located at high altitude on the Greenland ice sheet, so that orographic effects and model resolution may play a role. The AOD results from in-situ stations show a se- vere underestimation in amplitude of observed values and an absence of the strong seasonality found in the observations, with a peak in summer rather than in late spring. This under- estimation is also confirmed by spatial maps of AOD com- pared with currently available satellite and reanalysis prod- ucts. The latter are highly uncertain in polar areas, but to- gether with the station measurements they confirm an under-
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A permanent Raman lidar station in the Amazon: description, characterization, and first results

A permanent Raman lidar station in the Amazon: description, characterization, and first results

For identifying the source of aerosol particles observed dur- ing this week, backward trajectories from the Hysplit model (Draxler and Hess, 1998) and fire spots identified by the In- stituto Nacional de Pesquisas Espaciais (INPE) using a com- bination of satellites 1 were used. As the largest AOD values ( ∼ 0.7) were measured on 3 September (Fig. 16), back trajec- tories were started at 12:00 UTC of that day from the height of maximum extinction ( ∼ 1.5 km, see Fig. 11). Hysplit was run in ensemble mode, by shifting the starting point by one model grid box up/down, east/west and north/south. These 27 different trajectories are shown in Fig. 17 with all fire spots observed between 30 August to 1 September. Some trajecto- ries could carry biomass burning aerosol as they cross nearby fire spots in West Para and more distant ones in East Para and Maranhão. Other trajectories, however, come straight from the ocean and should bring clean air. Local sources of fires could also contribute. The dilution of the polluted air masses could explain the large variations observed in AOD during this week, from below 0.05 up to 0.75, with a rather constant particle lidar ratio (Fig. 11).
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Uncertainties in satellite remote sensing of aerosols and impact on monitoring its long-term trend: a review and perspective

Uncertainties in satellite remote sensing of aerosols and impact on monitoring its long-term trend: a review and perspective

The largest systematic discrepancy is found between Dataset 1 and 5, due simply to the use of different calibra- tions. This is not surprising, as the AOD signal is extracted from a reflectance signal that is so faint that any change in the calibration may substantially alter the retrieval of AOD. From this finding, one may thus make a conjecture that the differences in the calibration as given in Table 1 might ac- count for a great deal to the AOD differences shown here. As such, to make the AOD compatible, we must assure ra- diances measured by different sensors agree first. The SNO calibration method applied to the PATMOS-X is therefore a sound and valid approach to bring the historical AVHRR data into agreement with the MODIS. For trend detection, however, absolute calibration is less important than other fac- tors. The same input data, however, do not necessarily lead to the same AOD retrieval. The AOD from dataset 2 dif- fers considerably from the MODIS AOD, even though their calibrated radiances are similar. This is because of large dif- ferences in the aerosol model and treatment of surface re- flectance. In narrowing the discrepancy, Zhao et al. (2004) adopted a relatively more realistic aerosol model based on the validation with the AERONET observation. After re- tuning based on AERONET observations, the revised algo- rithm (dataset 4) leads to the AOD in close proximity with the MODIS products. This is supported by the finding of Jeong et al. (2005) showing very large differences brought by applying two distinct models of aerosol size distribution, the power law and bi-lognormal functions to the same radi- ance data. Ironically, not only does the dataset 4 AOD agree broadly with the MODIS AOD, it also agrees with the GACP AOD, while the latter employed the power-law size distri- bution, as is shown more clearly in Fig. 4. Such a level of consistency in AOD is unprecedented, even though regional discrepancies may still be much larger. Yet there is still dis- agreement in the tendency over the short overlapping period (see Fig. 1) from weak positive (MODIS) to weak negative (GACP, PATMOX-x, SeaWiFS, and MISR), which we think is due to the differences in the cloud screening schemes and warrants a more detailed investigation.
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The comparison of MODIS-Aqua (C5) and CALIOP (V2 & V3) aerosol optical depth

The comparison of MODIS-Aqua (C5) and CALIOP (V2 & V3) aerosol optical depth

Abstract. We assess the consistency between instanta- neously collocated level-2 aerosol optical depth (AOD) re- trievals from MODIS-Aqua (C5) and CALIOP (Version 2 & 3), comparing the standard MODIS AOD (MYD04 L2) data to the AOD calculated from CALIOP aerosol extinction profiles for both the previous release (V2) and the latest re- lease (V3) of CALIOP data. Based on data collected in Jan- uary 2007, we investigate the most useful criteria for screen- ing the MODIS and CALIOP retrievals to achieve the best agreement between the two data sets. Applying these criteria to eight months of data (Jan, Apr, Jul, Oct 2007 and 2009), we find an order of magnitude increase for the CALIOP V3 data density (by comparison to V2), that is generally accom- panied by equal or better agreement with MODIS AOD. Dif- ferences in global, monthly mean, over-ocean AOD (532 nm) between CALIOP and MODIS range between 0.03 and 0.04 for CALIOP V3, with CALIOP generally biased low, when all available data from both sensors are considered. Root- mean-squares (RMS) differences in instantaneously collo- cated AOD retrievals by the two instruments are reduced from values ranging between 0.14 and 0.19 using the un- screened V3 data to values ranging from 0.09 to 0.1 for the screened data. A restriction to scenes with cloud fractions less than 1 % (as defined in the MODIS aerosol retrievals) generally results in improved correlation (R 2 > 0.5), except
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A decadal regional and global trend analysis of the aerosol optical depth using a data-assimilation grade over-water MODIS and Level 2 MISR aerosol products

A decadal regional and global trend analysis of the aerosol optical depth using a data-assimilation grade over-water MODIS and Level 2 MISR aerosol products

In this paper we begin to explore these issues using the simplest case possible: the 10 year time series of global MODIS and MISR over ocean AOD data. Because many biases are removed, we also employ the adapted data assim- ilation (DA) grade level-three product of Zhang and Reid (2006) with updated coefficients for data collect 5 by Shi et al. (2010). Limited comparisons to AERONET, Aqua MODIS and MISR are provided to display the range of possi- ble outcomes. Our study period is from March 2000 through December 2009; nearly 10 years of data. The period includes three El Nino and two La Nina events, and major swings in global biomass burning activity in tropical, mid-latitude and boreal regions (e.g. Giglio et al., 2006). We begin with a de- scription of the level 3 product generation process used for this study. This is followed by a discussion of the statistical techniques used to gauge the data. Data are subsequently de- biased and a global time series maps of AOD trends and vari- ability is then presented. Further exploration is made for re- gions with statistically significant trends. We conclude with a discussion of how our findings compare to other recent trend analyses presented in the literature.
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Underwater noise propagation models and its application in renewable energy parks: WaveRoller Case Study

Underwater noise propagation models and its application in renewable energy parks: WaveRoller Case Study

On the other hand, Attenuation loss varies linearly with range and it’s expressed by a certain number of decibels per unit of distance (Urick, 1983). An important property is the fact that it increases with signal frequency due to the transfer of acoustic energy into heat (Absortion). The effects of sound reflection at the surface, bottom and any objects, and sound refraction in the water leads to a Multipath Propagation phenomenon. When a source launches a beam or rays, each one will follow a different path, and a receiver placed at some distances will observe multiple signal arrivals. Propagation paths and their strengths and delays are determined by the geometry of the channels and its reflection and refraction properties, so a ray travelling over a longer path may do so at a higher speed, thus reaching the receiver before a direct stronger ray (Stojanovic and Preisig, 2009). These phenomena cause fluctuations in phase and amplitude at a signal receiver, signal distortion, decorrelation of signal between separated receivers, and frequency broadening (Urick, 1983). Time variability is also an important factor. Channel’s time variability can be caused by inherent changes in the propagation medium or changes that occur because of the transmitter/receiver motion. The first case can occur in very long timescales such as monthly changes on water’s temperature and does not affect the instantaneous communication level, or in short timescales and affect the signal. An example of this happens when surface waves cause the displacement of the reflection point and, as a result, the signal suffers scattering and there’s a spread of the Doppler Effect (Stojanovic and Preisig, 2009).
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