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At least 253 records · Page 14

Low potassium enhances sodium uptake in red-beet under moderate saline conditions

Due to the discrepancy in metabolic sodium (Na) requirements between plants and animals, cycling of Na between humans and plants is limited and critical to the proper functioning of bio-regenerative life support systems, being considered for long-term human habitats in space (e.g., Martian bases). This study was conducted to determine the effects of limited potassium (K) on growth, Na uptake, photosynthesis, ionic partitioning, and water relations of red-beet (Beta vulgaris L. ssp. vulgaris) under moderate Na-saline conditions. Two cultivars, Klein Bol, and Ruby Queen were grown for 42 days in a growth chamber using a re-circulating nutrient film technique where the supplied K levels were 5.0, 1.25, 0.25, and 0.10 mM in a modified half-strength Hoagland solution salinized with 50 mM NaCl. Reducing K levels from 5.0 to 0.10 mM quadrupled the Na uptake, and lamina Na levels reached -20 g kg-1 dwt. Lamina K levels decreased from -60 g kg-1 dwt at 5.0 mM K to -4.0 g kg-1 dwt at 0.10 mM K. Ruby Queen and Klein Bol responded differently to these changes in Na and K status. Klein Bol showed a linear decline in dry matter production with a decrease in available K, whereas for cv. Ruby Queen, growth was stimulated at 1.25 mM K and relatively insensitive to a further decreases of K down to 0.10 mM. Leaf glycinebetaine levels showed no significant response to the changing K treatments. Leaf relative water content and osmotic potential were significantly higher for both cultivars at low-K treatments. Leaf chlorophyll levels were significantly decreased at low-K treatments, but leaf photosynthetic rates showed no significant difference. No substantial changes were observed in the total cation concentration of plant tissues despite major shifts in the relative Na and K uptake at various K levels. Sodium accounted for 90% of the total cation uptake at the low K levels, and thus Na was likely replacing K in osmotic functions without negatively affecting the plant water status, or growth. Our results also suggest that cv. Ruby Queen can tolerate a much higher Na tissue concentration than cv. Klein Bol before there is any growth reduction. Grant numbers: 12180.

NASA Center KSC↗

The effect of dDAVP with saline loading on fluid balance during LBNP and standing after 24-hr head-down bedrest

Shuttle astronauts currently drink approximately a quart of water with eight salt tablets before reentry to restore lost body fluid and thereby reduce the likelihood of cardiovascular instability and syncope during reentry and after landing. However, the saline loading countermeasure is not entirely effective in restoring orthostatic tolerance to preflight levels. We tested the hypothesis that the effectiveness of this countermeasure could be improved with the use of a vasopressin analog, 1-deamino-8-D-arginine vasopressin (dDAVP). The rationale for this approach is that reducing urine formation with exogenous vasopressin should increase the magnitude and duration of the vascular volume expansion produced by the saline load, and in so doing improve orthostatic tolerance during reentry and postflight.

NASA Discipline Number 14-10↗

Biodiversity of Saline and Brakish Marshes of the Indian River Lagoon: Historic and Current Patterns

The Indian River Lagoon (IRL) crosses a zone of climatic transition. Historically, marshes dominated saline and brackish environments in the north of the lagoon, while mangroves became important to the south. Distribution of marsh communities was influenced by hydrology, salinity, soil characteristics, and fire, as well as periodic freezes. Marshes of the IRL have been greatly modified since the 1940s. Despite significant modifications, marsh plant species have not been lost from the region, but community and landscape patterns have been greatly modified and ecosystem processes altered.

Schmalzer, Paul A.↗

The Aquarius Ocean Salinity Mission High Stability L-band Radiometer

The NASA Earth Science System Pathfinder (ESSP) mission Aquarius, will measure global ocean surface salinity with approx.120 km spatial resolution every 7-days with an average monthly salinity accuracy of 0.2 psu (parts per thousand). This requires an L-band low-noise radiometer with the long-term calibration stability of less than or equal to 0.15 K over 7 days. The instrument utilizes a push-broom configuration which makes it impractical to use a traditional warm load and cold plate in front of the feedhorns. Therefore, to achieve the necessary performance Aquarius utilizes a Dicke radiometer with noise injection to perform a warm - hot calibration. The radiometer sequence between antenna, Dicke load, and noise diode has been optimized to maximize antenna observations and therefore minimize NEDT. This is possible due the ability to thermally control the radiometer electronics and front-end components to 0.1 Crms over 7 days.

Pellerano, Fernando A.↗

Microwave Remote Sensing Modeling of Ocean Surface Salinity and Winds Using an Empirical Sea Surface Spectrum

Active and passive microwave remote sensing techniques have been investigated for the remote sensing of ocean surface wind and salinity. We revised an ocean surface spectrum using the CMOD-5 geophysical model function (GMF) for the European Remote Sensing (ERS) C-band scatterometer and the Ku-band GMF for the NASA SeaWinds scatterometer. The predictions of microwave brightness temperatures from this model agree well with satellite, aircraft and tower-based microwave radiometer data. This suggests that the impact of surface roughness on microwave brightness temperatures and radar scattering coefficients of sea surfaces can be consistently characterized by a roughness spectrum, providing physical basis for using combined active and passive remote sensing techniques for ocean surface wind and salinity remote sensing.

Sea surface spectrum↗

Improving an Atlantic Fisheries DSS using Sea Surface Salinity Data from NASA's Aquarius Mission

This report assesses the capacity of incorporating NASA#s Aquarius SSS (sea surface salinity) data into the SMAST (School of Marine Science and Technology) DSS for Fisheries Science. This data will enhance the SMAST DSS by providing SSS over a large area. Aquarius is a focused satellite mission designed to measure global SSS. SSS mapping is limited because conventional in situ SSS sampling is too sparse to give a large-scale view of the salinity variability. Aquarius will resolve missing physical processes that link the water cycle, the climate, and the ocean. The SMAST Fisheries program provides a DSS for fisheries science. It collects fisheries and environmental data, integrates them into a suite of data assimilation ocean models, and provides hindcasts, nowcasts, and forecasts for fisheries research, fisheries management, and the fishery industry. Currently, SMAST is using SSS data from the National Oceanic and Atmospheric Administration#s National Data Buoy Center. The SMAST DSS would be enhanced with SSS data from the Aquarius mission.

Guest, DeNeice↗

Microstrip Antenna for Remote Sensing of Soil Moisture and Sea Surface Salinity

This compact, lightweight, dual-frequency antenna feed developed for future soil moisture and sea surface salinity (SSS) missions can benefit future soil and ocean studies by lowering mass, volume, and cost of the antenna system. It also allows for airborne soil moisture and salinity remote sensors operating on small aircraft. While microstrip antenna technology has been developed for radio communications, it has yet to be applied to combined radar and radiometer for Earth remote sensing. The antenna feed provides a key instrument element enabling high-resolution radiometric observations with large, deployable antennas. The design is based on the microstrip stacked-patch array (MSPA) used to feed a large, lightweight, deployable, rotating mesh antenna for spaceborne L-band (approximately equal to 1 GHz) passive and active sensing systems. The array consists of stacked patches to provide dual-frequency capability and suitable radiation patterns. The stacked-patch microstrip element was designed to cover the required L-band center frequencies at 1.26 GHz (lower patch) and 1.413 GHz (upper patch), with dual-linear polarization capabilities. The dimension of patches produces the required frequencies. To achieve excellent polarization isolation and control of antenna sidelobes for the MSPA, the orientation of each stacked-patch element within the array is optimized to reduce the cross-polarization. A specialized feed-distribution network was designed to achieve the required excitation amplitude and phase for each stacked-patch element.

Ramhat-Samii, Yahya↗

Solutions Network Formulation Report. Integrating Salinity Measurements from Aquarius into the Harmful Algal Blooms Observing System

This Candidate Solution suggests the use of Aquarius sea surface salinity measurements to improve the NOAA/NCDDC (National Oceanic and Atmospheric Administration s National Coastal Data Development Center) HABSOS (Harmful Algal Blooms Observing System) DST (decision support tool) by enhancing development and movement forecasts of HAB events as well as potential species identification. In the proposed configuration, recurring salinity measurements from the Aquarius mission would augment HABSOS sea surface temperature and in situ ocean current measurements. Thermohaline circulation observations combined with in situ measurements increase the precision of HAB event movement forecasting. These forecasts allow coastal managers and public health officials to make more accurate and timely warnings to the public and to better direct science teams to event sites for collection and further measurements.

Anderson, Daniel↗

Sea Surface Salinity Variability from Simulations and Observations: Preparing for Aquarius

Oceanic fresh water transport has been shown to play an important role in the global hydrological cycle. Sea surface salinity (SSS) is representative of the surface fresh water fluxes and the upcoming Aquarius mission scheduled to be launched in December 2010 will provide excellent spatial and temporal SSS coverage to better estimate the net exchange. In most ocean general circulation models, SSS is relaxed to climatology to prevent model drift. While SST remains a well observed variable, relaxing to SST reduces the range of SSS variability in the simulations (Fig.1). The main objective of the present study is to simulate surface tracers using a primitive equation ocean model for multiple forcing data sets to identify and establish a baseline SSS variability. The simulated variability scales are compared to those from near-surface argo salinity measurements.

Jacob, S. Daniel↗

A Proposed Extension to the Soil Moisture and Ocean Salinity Level 2 Algorithm for Mixed Forest and Moderate Vegetation Pixels

The Soil Moisture and Ocean Salinity (SMOS)mission, launched in November 2009, provides global maps of soil moisture and ocean salinity by measuring the L-band (1.4 GHz) emission of the Earth's surface with a spatial resolution of 40-50 km.Uncertainty in the retrieval of soilmoisture over large heterogeneous areas such as SMOS pixels is expected, due to the non-linearity of the relationship between soil moisture and the microwave emission. The current baseline soilmoisture retrieval algorithm adopted by SMOS and implemented in the SMOS Level 2 (SMOS L2) processor partially accounts for the sub-pixel heterogeneity of the land surface, by modelling the individual contributions of different pixel fractions to the overall pixel emission. This retrieval approach is tested in this study using airborne L-band data over an area the size of a SMOS pixel characterised by a mix Eucalypt forest and moderate vegetation types (grassland and crops),with the objective of assessing its ability to correct for the soil moisture retrieval error induced by the land surface heterogeneity. A preliminary analysis using a traditional uniform pixel retrieval approach shows that the sub-pixel heterogeneity of land cover type causes significant errors in soil moisture retrieval (7.7%v/v RMSE, 2%v/v bias) in pixels characterised by a significant amount of forest (40-60%). Although the retrieval approach adopted by SMOS partially reduces this error, it is affected by errors beyond the SMOS target accuracy, presenting in particular a strong dry bias when a fraction of the pixel is occupied by forest (4.1%v/v RMSE,-3.1%v/v bias). An extension to the SMOS approach is proposed that accounts for the heterogeneity of vegetation optical depth within the SMOS pixel. The proposed approach is shown to significantly reduce the error in retrieved soil moisture (2.8%v/v RMSE, -0.3%v/v bias) in pixels characterised by a critical amount of forest (40-60%), at the limited cost of only a crude estimate of the optical depth of the forested area (better than 35% uncertainty). This study makes use of an unprecedented data set of airborne L-band observations and ground supporting data from the National Airborne Field Experiment 2005 (NAFE'05), which allowed accurate characterisation of the land surface heterogeneity over an area equivalent in size to a SMOS pixel.

Panciera, Rocco↗

Retrieving Marine Inherent Optical Properties from Satellites Using Temperature and Salinity-dependent Backscattering by Seawater

Time-series of marine inherent optical properties (IOPs) from ocean color satellite instruments provide valuable data records for studying long-term time changes in ocean ecosystems. Semi-analytical algorithms (SAAs) provide a common method for estimating IOPs from radiometric measurements of the marine light field. Most SAAs assign constant spectral values for seawater absorption and backscattering, assume spectral shape functions of the remaining constituent absorption and scattering components (e.g., phytoplankton, non-algal particles, and colored dissolved organic matter), and retrieve the magnitudes of each remaining constituent required to match the spectral distribution of measured radiances. Here, we explore the use of temperature- and salinity-dependent values for seawater backscattering in lieu of the constant spectrum currently employed by most SAAs. Our results suggest that use of temperature- and salinity-dependent seawater spectra elevate the SAA-derived particle backscattering, reduce the non-algal particles plus colored dissolved organic matter absorption, and leave the derived absorption by phytoplankton unchanged.

Passive Remote Sensing↗

The Impact of Satellite Sea Surface Salinity for Prediction of the Coupled Indo-Pacific System

Here we assess the impact of satellite sea surface salinity (SSS) observations on seasonal to interannual variability of tropical Indo-Pacific Ocean dynamics as well as on dynamical ENSO forecasts. The baseline experiment assimilates satellite sea level (SL), sea surface temperature (SST), and in situ subsurface temperature and salinity observations (Tz, Sz). These baseline experiments are then compared with experiments that additionally assimilate Aquarius (version 5.0 Lilly and Lagerloef, 2008) and SMAP (version 2.0 Meissner and Wentz, 2016) SSS. Twelve-month forecasts are initialized for each month from September 2011 to September 2017. We find that including satellite SSS significantly improves NINO3.4 sea surface temperature anomaly validation over 0-8 month forecast lead-times and removing the salty bias from SMAP data helps to extend useful forecasts out to 12 month lead-times.

SSS↗

The Impact of Satellite Sea Surface Salinity for Prediction of the Coupled Indo-Pacific System

We assess the impact of satellite sea surface salinity (SSS) observations on seasonal to interannual variability of tropical Indo-Pacific Ocean dynamics as well as on dynamical ENSO forecasts. Our coupled model is composed of a primitive equation ocean model for the tropical Indo-Pacific region that is coupled with the global SPEEDY atmospheric model (Molteni, 2003). The Ensemble Reduced Order Kalman Filter is used to assimilate observations to constrain dynamics and thermodynamics for initialization of the coupled model. The baseline experiment assimilates satellite sea level, SST, and in situ subsurface temperature and salinity observations. This baseline is then compared with experiments that additionally assimilate Aquarius (version 4.0) and SMAP (version 2.0) SSS. Twelve-month forecasts are initialized for each month from Sep. 2011 to Dec. 2016. We find that including satellite SSS significantly improves NINO 3.4 sea surface temperature anomaly validation after 1 out to 12 month forecast lead times. For initialization of the coupled forecast, the positive impact of SSS assimilation is brought about by surface freshening near the eastern edge of the western Pacific warm pool and density changes that lead to shallower mixed layer between 10 degrees South latitude-5 degrees North latitude. SST differences at initialization force wide-spread downwelling favorable curl over most of the tropical Pacific. Over an average forecast, SST remains warmer with SSS assimilation at the eastern edge of the warm pool. This warm SST propagates into the eastern Pacific and drags westerly wind anomalies eastward into the NINO 3.4 region. In addition, salting near the ITCZ (Intertropical Convergence Zone) leads to a deepening of the mixed layer and thermocline near 8 degrees North latitude. These patterns together lead to a funneling effect that provides the background state to amplify equatorial Kelvin waves. We show that the downwelling Kelvin waves are amplified by assimilating satellite SSS and lead to significantly improved forecasts particularly for the 2015 El Nino.

El Nino↗

The Impact of Satellite Sea Surface Salinity for Prediction of the Coupled Indo-Pacific System

We assess the impact of satellite sea surface salinity (SSS) observations on seasonal to interannual variability of tropical Indo-Pacific Ocean dynamics as well as on dynamical ENSO forecasts. Our coupled model is composed of a primitive equation ocean model for the tropical Indo-Pacific region that is coupled with the global SPEEDY atmospheric model (Molteni, 2003). The Ensemble Reduced Order Kalman Filter is used to assimilate observations to constrain dynamics and thermodynamics for initialization of the coupled model. The baseline experiment assimilates satellite sea level, SST, and in situ subsurface temperature and salinity observations. This baseline is then compared with experiments that additionally assimilate Aquarius (version 4.0) and SMAP (version 2.0) SSS. Twelve-month forecasts are initialized for each month from Sep. 2011 to Dec. 2016. We find that including satellite SSS significantly improves NINO3.4 sea surface temperature anomaly validation after 1 out to 12 month forecast lead times. For initialization of the coupled forecast, the positive impact of SSS assimilation is brought about by surface freshening near the eastern edge of the western Pacific warm pool and density changes that lead to shallower mixed layer between 10S-5N. SST differences at initialization force wide-spread downwelling favorable curl over most of the tropical Pacific. Over an average forecast, SST remains warmer with SSS assimilation at the eastern edge of the warm pool. This warm SST propagates into the eastern Pacific and drags westerly wind anomalies eastward into the NINO3.4 region. In addition, salting near the ITCZ leads to a deepening of the mixed layer and thermocline near 8N. These patterns together lead to a funneling effect that provides the background state to amplify equatorial Kelvin waves. We show that the downwelling Kelvin waves are amplified by assimilating satellite SSS and lead to significantly improved forecasts particularly for the 2015 El Nino.

SST↗

Assessment of Sea Surface Salinity Products Using a Coupled ENSO Prediction Model

We assess the impact of satellite sea surface salinity (SSS) observations on seasonal to interannual variability of tropical Indo-Pacific Ocean dynamics as well as on dynamical ENSO forecasts. Twelve-month forecasts are initialized for each month from September 2011 to September 2017. All experiments assimilate satellite sea level (SL), sea surface temperature (SST), and in situ subsurface temperature and salinity observations (T(sub z), S(sub z)). Additionally various satellite, blended, and in-situ SSS products are assimilated. Using our intermediate-complexity coupled model as a transfer function, we test if more mature SSS model algorithms actually improve ENSO forecast skill. We find that including satellite SSS significantly improves Niño3.4 sea surface temperature anomaly validation, more mature SSS model algorithms are generally improving ENSO forecasts over time, and more satellite SSS helps to extend useful forecasts.

Hackert, E.↗

Impact of Satellite Sea Surface Salinity Observations on ENSO Predictions from the GMAO S2S Forecast System

El Nino/Southern Oscillation (ENSO) has far reaching global climatic impacts and so extending useful ENSO forecasts would be of great benefit for society. However, one key variable that has yet to be fully exploited within coupled forecast systems is accurate estimation of near-surface ocean density. Satellite Sea Surface Salinity (SSS), combined with temperature, help to identify ocean density changes and associated mixing near the ocean surface. We assess the impact of satellite SSS observations for improving near-surface dynamics within ocean analyses and how these impact dynamical ENSO forecasts using the NASA GMAO (Global Modeling and Assimilation Office) Sub-seasonal to Seasonal (S2S_v2.1) coupled forecast system (Molod et al. 2018 - i.e. NASA's contribution to the NMME (National Multi-Model Ensemble) project). For all initialization experiments, all available along-track absolute dynamic topography and in situ observations are assimilated using the LETKF ( Local Ensemble Transform Kalman Filter) scheme similar to Penny et al., 2013. A separate reanalysis additionally assimilates Aquarius V5 (September 2011 to June 2015) and SMAP (Soil Moisture Active Passive) V4 (March 2015 to present) along-track data.We highlight the impact of satellite SSS on ocean reanalyses by comparing validation statistics of experiments that assimilate SSS versus our current prediction system that withholds SSS. We find that near-surface validation versus observed statistics for salinity are slightly degraded when assimilating SSS. This is an expected result due to known biases between SSS (measured by the satellite at approximately 1 centimeter) and in situ measurements (typically measured by Argo floats at 3 meters). On the other hand, a very encouraging result is that both temperature, absolute dynamic topography, and mixed layer statistics are improved with SSS assimilation. Previous work has shown that correcting near-surface density structure via gridded SSS assimilation can improve coupled forecasts. Here we present results of coupled forecasts that are initialized from the GMAO S2S reanalyses that assimilates/withholds along-track (L2) SSS. In particular, we contrast forecasts of the overestimated 2014 El Nino, the big 2015 El Nino, and the minor 2016 La Nina. For each of these ENSO scenarios, assimilation of satellite SSS improves the forecast validation. Improved SSS and density upgrades the mixed layer depth leading to more accurate coupled air/sea interaction.

Hackert, E.↗

Observing System Experiments for Evaluating the Impact of Satellite Sea Surface Salinity on Seasonal Predictions from the GMAO S2S System

We assess the impact of satellite sea surface salinity (SSS) observations on dynamical ENSO (El Nino-Southern Oscillation) forecasts. Assimilation of SSS improves the mixed layer depth (MLD) and modulates the Kelvin waves associated with ENSO. In column 2, the initialization differences between experiments that assimilate SSS minus those withholding SSS assimilation are presented. Column 3 shows examples of forecasts generated for the different phases of ENSO assimilating the different satellite SSS. In general, for all phases of ENSO, SSS assimilation improves forecasts. The far right column compares ensemble means for assimilation of individual and combined SMOS, Aquarius, SMAP (Soil Moisture and Ocean Salinity, NASA Aquarius, Soil Moisture Active Passive) SSS forecasts. Finally, the latest forecasts are presented comparing assimilation versus no- assimilation of satellite SSS for single forecasts over the last year.

Hackert, E.↗

The Aquarius ocean salinity mission high stability L-band radiometer

The NASA Earth Science System Pathfinder (ESSP) mission Aquarius, will measure global ocean surface salinity with ~120 km spatial resolution every 7-days with an average monthly salinity accuracy of 0.2 psu (parts per thousand) [1]. This requires an L-band low-noise radiometer with the long-term calibration stability of ≤0.15 K over 7 days. The instrument utilizes a push-broom configuration which makes it impractical to use a traditional warm load and cold plate in front of the feedhorns. Therefore, to achieve the necessary performance Aquarius utilizes a Dicke radiometer with noise injection to perform a warm – hot calibration. The radiometer sequence between antenna, Dicke load, and noise diode has been optimized to maximize antenna observations and therefore minimize NEDT. This is possible due the ability to thermally control the radiometer electronics and front-end components to 0.1 °Crms over 7 days.

Freedman, Adam↗