Using saildrones to validate arctic sea-surface salinity from the smap satellite and from ocean models

The Arctic Ocean is one of the most important and challenging regions to observe—it experiences the largest changes from climate warming, and at the same time is one of the most difficult to sample because of sea ice and extreme cold temperatures. Two NASA-sponsored deployments of the Saildrone vehicle provided a unique opportunity for validating sea-surface salinity (SSS) derived from three separate products that use data from the Soil Moisture Active Passive (SMAP) satellite. To examine possible issues in resolving mesoscale-to-submesoscale variability, comparisons were also made with two versions of the Estimating the Circulation and Climate of the Ocean (ECCO) model (Carroll, D; Menmenlis, D; Zhang, H.). The results indicate that the three SMAP products resolve the runoff signal associated with the Yukon River, with high correlation between SMAP products and Saildrone SSS. Spectral slopes, overall, replicate the-2.0 slopes associated with mesoscale-submesoscale variability. Statistically significant spatial coherences exist for all products, with peaks close to 100 km. Based on these encouraging results, future research should focus on improving derivations of satellite-derived SSS in the Arctic Ocean and integrating model results to complement remote sensing observations

Saildrone: adaptively sampling the marine environment

Author Posting. © American Meteorological Society, 2020. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Bulletin of the American Meteorological Society 101(6), (2020): E744-E762, doi:10.1175/BAMS-D-19-0015.1.From 11 April to 11 June 2018 a new type of ocean observing platform, the Saildrone surface vehicle, collected data on a round-trip, 60-day cruise from San Francisco Bay, down the U.S. and Mexican coast to Guadalupe Island. The cruise track was selected to optimize the science team’s validation and science objectives. The validation objectives include establishing the accuracy of these new measurements. The scientific objectives include validation of satellite-derived fluxes, sea surface temperatures, and wind vectors and studies of upwelling dynamics, river plumes, air–sea interactions including frontal regions, and diurnal warming regions. On this deployment, the Saildrone carried 16 atmospheric and oceanographic sensors. Future planned cruises (with open data policies) are focused on improving our understanding of air–sea fluxes in the Arctic Ocean and around North Brazil Current rings.The Saildrone data collection mission was sponsored by the Saildrone Award, an annual data collection mission awarded by Saildrone Inc., and the Schmidt Family Foundation. The research was funded by the NASA Physical Oceanography Program Grant 80NSSC18K0837 and 80NSSC18K1441. The work by T. M. Chin, J. Vazquez-Cuerzo, and V. Tsontos was carried out at the Jet Propulsion Laboratory (JPL), California Institute of Technology, under a contract with the National Aeronautics and Space Administration (NASA). Piero L.F. Mazzini was supported by California Sea Grant Award NA18OAR4170073. We thank CeNCOOS for providing the HF radar data in the Gulf of the Farallones. Jose Gomez-Valdes was supported by CONACYT Grant 257125, and by CICESE. Work by Joel Scott and Ivona Cetinic was supported through NASA PACE. The work by Lisan Yu was supported by NOAA Ocean Observing and Monitoring Division under Grant NA14OAR4320158

Sea surface temperature intercomparison in the framework of the Copernicus Climate Change Service (C3S)

A joint effort between the Copernicus Climate Change Service (C3S) and the Group for High Resolution Sea Surface Temperature (GHRSST) has been dedicated to an intercomparison study of eight global gap-free Sea Surface Temperature (SST) products to assess their accurate representation of the SST relevant to climate analysis. In general, all SST products show consistent spatial patterns and temporal variability during the overlapping time period (2003-2018). The main differences between each product are located in western boundary current and Antarctic Circumpolar Current regions. Linear trends display consistent SST spatial patterns among all products and exhibit a strong warming trend from 2012 to 2018 with the Pacific Ocean basin as the main contributor. SST discrepancy between all SST products is very small compared to the significant warming trend. Spatial power spectral density shows that the interpolation into 1o spatial resolution has negligible impacts on our results. The global mean SST time series reveals larger differences among all SST products during the early period of the satellite era (1982-2002) when there were fewer observations, indicating that the observation frequency is the main constraint of the SST climatology. The maturity matrix scores, which present the maturity of each product in terms of documentation, storage, and dissemination but not the scientific quality, demonstrate that ESA-CCI and OSTIA SST are well documented for users' convenience. Improvements could be made for MGDSST and BoM SST. Finally, we have recommended that these SST products can be used for fundamental climate applications and climate studies (e.g. El Nino)

Comparison of seven prognostic tools to identify low-risk pulmonary embolism in patients aged <50 years

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Identification of Sea Surface Temperature and Sea Surface Salinity Fronts along the California Coast: Application Using Saildrone and Satellite Derived Products

Coastal upwelling regions are one of the most dynamic areas of the world&rsquo;s oceans. The California and Baja California Coasts are impacted by both coastal upwelling and the California Current, leading to frontal activity that is captured by gradients in both Sea Surface Temperature (SST) and Sea Surface Salinity (SSS). Satellite data are a great source of spatial data to study fronts. However, biases near coastal areas and coarse resolutions can impair its usefulness in upwelling areas. In this work gradients in SST from NASA Multi-Scale Ultra-High Resolution (MUR) and in two SSS products derived from the Soil Moisture Active Passive (SMAP) NASA mission are compared directly with gradients derived from the Saildrone uncrewed vehicles to validate the gradients as well as to assess their ability to detect known frontal features. The three remotely sensed data sets (MURSST/JPL, SMAP SSS/RSS, SMAP SSS) were co-located with the Saildrone data prior to the calculation of the gradients. Wavelet analysis is used to determine how well the satellite derived SST and SSS products are reproducing the Saildrone derived gradients. Overall results indicate the remote sensing products are reproducing features of known areas of coastal upwelling. Differences between the SST and SSS gradients are mainly associated with the limitations of the microwave derived SSS coverage near land and its reduced spatial resolution. The results are promising for using remote sensing data sets to monitor frontal structure along the California Coast and the application to long term changes in coastal upwelling and dynamics

Using Saildrones to Validate Satellite-Derived Sea Surface Salinity and Sea Surface Temperature along the California/Baja Coast

Traditional ways of validating satellite-derived sea surface temperature (SST) and sea surface salinity (SSS) products by comparing with buoy measurements, do not allow for evaluating the impact of mesoscale-to-submesoscale variability. We present the validation of remotely sensed SST and SSS data against the unmanned surface vehicle (USV)—called Saildrone—measurements from the 60 day 2018 Baja California campaign. More specifically, biases and root mean square differences (RMSDs) were calculated between USV-derived SST and SSS values, and six satellite-derived SST (MUR, OSTIA, CMC, K10, REMSS, and DMI) and three SSS (JPLSMAP, RSS40, RSS70) products. Biases between the USV SST and OSTIA/CMC/DMI were approximately zero, while MUR showed a bias of 0.3 °C. The OSTIA showed the smallest RMSD of 0.39 °C, while DMI had the largest RMSD of 0.5 °C. An RMSD of 0.4 °C between Saildrone SST and the satellite-derived products could be explained by the diurnal and sub-daily variability in USV SST, which currently cannot be resolved by remote sensing measurements. SSS showed fresh biases of 0.1 PSU for JPLSMAP and 0.2 PSU and 0.3 PSU for RMSS40 and RSS70 respectively. SST and SSS showed peaks in coherence at 100 km, most likely associated with the variability of the California Current System

SMAP and CalCOFI Observe Freshening during the 2014–2016 Northeast Pacific Warm Anomaly

Data from NASA&rsquo;s Soil Moisture Active Passive Mission (SMAP) and from the California Cooperative Oceanic Fisheries Investigations (CalCOFI) were used to examine the freshening that occurred during 2015&ndash;2016 in the Southern California Current System. Overall, the freshening was found to be related to the 2014&ndash;2016 Northeast Pacific Warm Anomaly. The primary goal was to determine the feasibility of using SMAP data to observe the surface salinity signal associated with the warming and its coastal impact. As a first step, direct comparisons were done with salinity from the CalCOFI data at one-meter depth. During 2015, SMAP was saltier than CalCOFI by 0.5 Practical Salinity Units (PSU), but biases were reduced to &lt;0.1 PSU during 2016. South of 33&deg;N, and nearer to the coast where upwelling dominates, SMAP was fresher in 2015 by almost 0.2 PSU. CalCOFI showed freshening of 0.1 PSU. North of 33&deg;N, SMAP and CalCOFI saw significant freshening in 2016, SMAP by 0.4 PSU and CalCOFI by 0.2 PSU. Differences between SMAP and CalCOFI are consistent with the increased stratification in 2015 and changes in the mixed layer depth. SMAP observed freshening that reached the Baja California Coast

SMAP and CalCOFI Observe Freshening during the 2014–2016 Northeast Pacific Warm Anomaly

Data from NASA&#8217;s Soil Moisture Active Passive Mission (SMAP) and from the California Cooperative Oceanic Fisheries Investigations (CalCOFI) were used to examine the freshening that occurred during 2015&#8315;2016 in the Southern California Current System. Overall, the freshening was found to be related to the 2014&#8315;2016 Northeast Pacific Warm Anomaly. The primary goal was to determine the feasibility of using SMAP data to observe the surface salinity signal associated with the warming and its coastal impact. As a first step, direct comparisons were done with salinity from the CalCOFI data at one-meter depth. During 2015, SMAP was saltier than CalCOFI by 0.5 Practical Salinity Units (PSU), but biases were reduced to &lt;0.1 PSU during 2016. South of 33&#176;N, and nearer to the coast where upwelling dominates, SMAP was fresher in 2015 by almost 0.2 PSU. CalCOFI showed freshening of 0.1 PSU. North of 33&#176;N, SMAP and CalCOFI saw significant freshening in 2016, SMAP by 0.4 PSU and CalCOFI by 0.2 PSU. Differences between SMAP and CalCOFI are consistent with the increased stratification in 2015 and changes in the mixed layer depth. SMAP observed freshening that reached the Baja California Coast

Complex empirical orthogonal functions analysis of ERS-1 and TOPEX/POSEIDON combined altimetric data in the region of the Algerian current

13 pages, 12 figures, 2 tablesMaps of sea level anomalies (SLA) relative to the 1993 annual mean sea level combine the data from the two altimetric missions, ERS-1 and TOPEX/POSEIDON, during the overlap period (October 1992 to December 1993). These regular maps in space and time of residual sea level every 10 days on a 0.2° regular grid are used in the region of the Algerian current where the mesoscale eddies are of primary importance to the circulation of all the Mediterranean water masses. They are first compared with ERS-1 along-track scanning radiometer sea surface temperature images to get information on two anticyclonic eddies produced by instabilities of the Algerian current and visible in both infrared and altimetric data sets. Then, an analysis of complex empirical orthogonal functions (CEOFs) is performed on the SLA data set to see the correlation of the different dynamic features of the observed variability. The CEOF analysis is applied to the complex time series formed from the original SLA time series and their Hubert transforms to separate the variability into spatially coherent modes. The spatially correlated signal in the study area (0–15°E and 35°–40°N) was found to be dominated by the first two CEOFs. These first two modes explain nearly 85% of the variability, with 80% of the total variance for the first one and 5% of the total variance for the second one. The temporal phase of the first mode indicates that a constant frequency of one cycle per year is clearly dominant, corresponding to the seasonal signal. The strongest amplitude is obtained in the southern part of the channel of Sardinia and south of the Strait of Sicily. The temporal amplitude and the temporal phase of the second mode show a periodicity of about 6 months which appears to be associated with the variability of the Algerian current as the phase isolines are parallel to the mean current path along the Algerian coast. The strongest amplitude of the second mode is located near the African coast at ∼4°E and 8°E. These two points of high variability could correspond to eddy detachments from the main curren

Evaluation of the Multi-Scale Ultra-High Resolution (MUR) Analysis of Lake Surface Temperature

Obtaining accurate and timely lake surface water temperature (LSWT) analyses from satellite remains difficult. Data gaps, cloud contamination, variations in atmospheric profiles of temperature and moisture, and a lack of in situ observations provide challenges for satellite-derived LSWT for climatological analysis or input into geophysical models. In this study, the Multi-scale Ultra-high Resolution (MUR) analysis of LSWT is evaluated between 2007 and 2015 over a small (Lake Oneida), medium (Lake Okeechobee), and large (Lake Michigan) lake. The advantages of the MUR LSWT analyses include daily consistency, high-resolution (~1 km), near-real time production, and multi-platform data synthesis. The MUR LSWT versus in situ measurements for Lake Michigan (Lake Okeechobee) have an overall bias (MUR LSWT-in situ) of −0.20 °C (0.31 °C) and a RMSE of 0.86 °C (0.91 °C). The MUR LSWT versus in situ measurements for Lake Oneida have overall large biases (−1.74 °C) and RMSE (3.42°C) due to a lack of available satellite imagery over the lake, but performs better during the less cloudy 15 July–30 September period. The results of this study highlight the importance of calculating validation statistics on a seasonal and annual basis for evaluating satellite-derived LSWT