Interannual variability in global wave climate from satellite data

1994 ◽  
Author(s):  
David P. Cotton ◽  
David J. Carter
2006 ◽  
Vol 6 (2) ◽  
pp. 3175-3226 ◽  
Author(s):  
G. R. van der Werf ◽  
J. T. Randerson ◽  
L. Giglio ◽  
G. J. Collatz ◽  
P. S. Kasibhatla ◽  
...  

Abstract. Biomass burning represents an important source of atmospheric aerosols and greenhouse gases, yet little is known about its interannual variability or the underlying mechanisms regulating this variability at continental to global scales. Here we investigated fire emissions during the 8 year period from 1997 to 2004 using satellite data and the CASA biogeochemical model. Burned area from 2001–2004 was derived using newly available active fire and 500 m burned area datasets from MODIS following the approach described by Giglio et al. (2005). ATSR and VIRS satellite data were used to extend the burned area time series back in time through 1997. In our analysis we estimated fuel loads, including peatland fuels, and the net flux from terrestrial ecosystems as the balance between net primary production (NPP), heterotrophic respiration (Rh), and biomass burning, using time varying inputs of precipitation (PPT), temperature, solar radiation, and satellite-derived fractional absorbed photosynthetically active radiation (fAPAR). For the 1997–2004 period, we found that on average approximately 58 Pg C year−1 was fixed by plants, and approximately 95% of this was returned back to the atmosphere via Rh. Another 4%, or 2.5 Pg C year−1 was emitted by biomass burning; the remainder consisted of losses from fuel wood collection and subsequent burning. At a global scale, burned area and total fire emissions were largely decoupled from year to year. Total carbon emissions tracked burning in forested areas (including deforestation fires in the tropics), whereas burned area was largely controlled by savanna fires that responded to different environmental and human factors. Biomass burning emissions showed large interannual variability with a range of more than 1 Pg C year−1, with a maximum in 1998 (3.2 Pg C year−1) and a minimum in 2000 (2.0 Pg C year−1).


2020 ◽  
Vol 8 (12) ◽  
pp. 1039
Author(s):  
Ben Timmermans ◽  
Andrew G. P. Shaw ◽  
Christine Gommenginger

Measurements of significant wave height from satellite altimeter missions are finding increasing application in investigations of wave climate, sea state variability and trends, in particular as the means to mitigate the general sparsity of in situ measurements. However, many questions remain over the suitability of altimeter data for the representation of extreme sea states and applications in the coastal zone. In this paper, the limitations of altimeter data to estimate coastal Hs extremes (<10 km from shore) are investigated using the European Space Agency Sea State Climate Change Initiative L2P altimeter data v1.1 product recently released. This Sea State CCI product provides near complete global coverage and a continuous record of 28 years. It is used here together with in situ data from moored wave buoys at six sites around the coast of the United States. The limitations of estimating extreme values based on satellite data are quantified and linked to several factors including the impact of data corruption nearshore, the influence of coastline morphology and local wave climate dynamics, and the spatio-temporal sampling achieved by altimeters. The factors combine to lead to considerable underestimation of estimated Hs 10-yr return levels. Sensitivity to these factors is evaluated at specific sites, leading to recommendations about the use of satellite data to estimate extremes and their temporal evolution in coastal environments.


2021 ◽  
Author(s):  
Marta Ramirez ◽  
Melisa Menendez ◽  
Guillaume Dodet

&lt;p&gt;The knowledge of ocean extreme wave climate is of significant importance for a number of coastal and marine activities (e.g. coastal protection, marine spatial planning, offshore engineering). This study uses the recently released Sea State CCI v1 altimeter product to analyze extreme wave climate conditions at global scale. The dataset comprises 28-years inter-calibrated and denoised significant wave height data from 10 altimeter missions.&lt;/p&gt;&lt;p&gt;First, a regional analysis of the available satellite information of extreme waves associated with both, tropical and extratropical cyclones, is carried out. As tropical cyclones, we analyze two intense events which affected the Florida Peninsula and Caribbean Islands: Wilma (in October 2005) and Irma (in August 2017) hurricanes. As extratropical cyclones, we focused on the extreme waves during the 2013-2014 winter season along the Atlantic European coasts. The extreme waves associated with these events are identified in the satellite dataset and are compared with in situ and high-resolution simulated data. The analysis of the satellite data during the storm tracks and its comparison against other data sources indicate that satellite data can provide added value for the analysis of extreme wave conditions that caused important coastal damages.&lt;/p&gt;&lt;p&gt;After assessing the quality of extreme wave data measured by altimeters from this regional analysis, we explore a method to characterize wave height return values (e.g. 50yr return period significant wave height) from the multi-mission satellite data. The method is validated through comparisons with return values estimated from long-term wave buoy records. The extreme analysis is based on monthly maxima of satellite significant wave height computed over marine areas of varying extensions and centered on a target location (e.g. the wave buoy location for comparison and validation of the method).&amp;#160; The extension of the areas is defined from a seasonal study of the spatial correlation and the error metrics of the satellite data against the selected coastal location. We found a threshold of 0.85 correlation as the isoline to select the satellite data subsample (i.er. larger areas to select satellite maxima are found during winter seasons). Finally, a non-stationary extreme model based on GEV distribution is applied to obtain quantiles of low probability. Outcomes from satellite data are validated against extreme estimates from buoy records.&lt;/p&gt;


Author(s):  
Cristina Izaguirre ◽  
Fernando J. Mendez ◽  
Melisa Menendez ◽  
Alberto Luceño ◽  
Inigo J. Losada

2020 ◽  
Vol 70 (7) ◽  
pp. 965-976
Author(s):  
Marco J. Vega ◽  
Oscar Alvarez-Silva ◽  
Juan C. Restrepo ◽  
Juan C. Ortiz ◽  
Luis J. Otero

1998 ◽  
Vol 11 (8) ◽  
pp. 1859-1873 ◽  
Author(s):  
Catherine Gautier ◽  
Peter Peterson ◽  
Charles Jones

Abstract Novel ways of monitoring the large-scale variability of the southwest monsoon in the Indian Ocean are presented using multispectral satellite datasets. The fields of sea surface temperature (SST), surface latent heat flux (LHF), net surface solar radiation (SW), precipitation (P), and SW − LHF over the Indian Ocean are analyzed to characterize the seasonal and interannual variability with special emphasis on the period 1988–90. It is shown that satellite data are able to make a significant contribution to the multiplatform strategy necessary to describe the large-scale spatial and temporal variability of air–sea interactions associated with the Indian Ocean Monsoon. The satellite data analyzed here has shown for the first time characteristics of the interannual variability of air–sea interactions over the entire Indian Ocean. Using monthly means of SST, LHF, SW, P, and the difference SW − LHF, the main features of the seasonal and interannual variability of air–sea interactions over the Indian Ocean are characterized. It is shown that the southwest monsoon strongly affects these interactions, inducing dramatic exchanges of heat between air and sea and large temporal variations of these exchanges over relatively small timescale (with regards to typical oceanic timescales). The analyses indicate an overall good agreement between satellite and in situ (ship) estimates, except in the southern Indian Ocean, where ship sampling is minimal, the disagreement can be large. In the latitudinal band of 10°N–15°S, differences in climatological in situ estimates of surface sensible heat flux and net longwave radiation has a larger influence on the net surface heat flux than the difference between satellite and in situ estimates of SW and LHF.


2021 ◽  
Author(s):  
Benjamin Phillips ◽  
Jonathan Higham ◽  
Andrew Plater ◽  
Nicoletta Leonardi ◽  
Daniel Arribas-Bel ◽  
...  

&lt;p&gt;Safe port operations require accurate information on vessel location, routine monitoring and maintenance of navigation channels, and accurate information on coastal hydrodynamics. &amp;#160;Accurate bathymetric data enables port operators to have a high level of confidence in assuring sufficient water depth for vessels, and to effectively direct surveying and dredging operations to maintain navigation routes. However, this is not readily facilitated for nearshore approaches where migrating sandbanks and shoals pose a hazard to shipping.&lt;/p&gt;&lt;p&gt;In this presentation, we present an innovative and novel data assimilation method of combining satellite data, hydrodynamic model (Delft3D) outputs and land-based radar data using machine learning and advanced statistical methods (Dynamic Mode Decomposition). To assimilate these data we use machine learning and statistical methods to detect &quot;patterns&quot; or &quot;modes&quot; in near- and far-field wave climate that are attributable to sub- and intertidal bathymetry and changes therein. We then combine the dominant modes into a low-order representation of the system, providing informed estimates of spatial resolutions and temporal scales where no measurements are physically performed. Satellite data and associated hydrodynamic model outputs are used to provide information on wave direction and height for the offshore-nearshore approaches while land-based marine radar located in the appropriate position provide wave data at higher temporal and more local spatial resolution.&amp;#160;&lt;/p&gt;&lt;p&gt;The data nexus we present in this presentation demonstrates significant improvements in capability above and beyond the use of a given technology in isolation.&lt;/p&gt;


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