Soil Moisture Active Passive Satellite Research Articles

Improved sea surface salinity data for the Arctic Ocean derived from SMAP satellite data using machine learning approaches

Salinity is among the key climate characteristics of the World Ocean. During the last 15 years, sea surface salinity (SSS) is measured using satellite passive microwave sensors. Standard retrieving SSS algorithms from remote sensing data were developed and verified for the most typical temperature and salinity values of the World Ocean. However, they have far lower accuracy for the Arctic Ocean, especially its shelf areas, which are influenced by large river runoff and have low typical temperature and salinity values. In this study, an improved algorithm has been developed to retrieve SSS in the Arctic Ocean during ice-free season, based on Soil Moisture Active Passive (SMAP) mission data, and using machine learning approaches. Extensive database of in situ salinity measurements in the Russian Arctic seas collected during multiple field surveys is applied to train and validate the machine learning models. The error in SSS retrieval of the developed algorithm compared to the standard algorithm reduced from 3.15 to 2.15 psu, and the correlation with in situ data increased from 0.82 to 0.90. The obtained daily SSS fields are important to improve accurate assessment of spatial and temporal variability of large river plumes in the Arctic Ocean.

Application of a two source energy balance model coupled with satellite based soil moisture and thermal infrared data

Satellite retrievals of thermal infrared data from the Earth’s surface when combined with the surface energy balance equation are widely used to monitor land surface evapotranspiration (ET). Unfortunately thermal infrared based models using this approach have a systematic positive bias with ET during soil water stress conditions. The Two-source Energy Balance model using information on Soil Moisture as another key constraint (TSEB-SM) improves model performance and utility. Here, high resolution satellite soil moisture data from SMAP (Soil Moisture Active Passive) were used to provide soil water boundary conditions for the TSEB-SM model. This SMAP driven TSEB-SM approach was applied in the Heihe river basin in China across a wide range of soil moisture conditions using MODIS thermal infrared land surface temperature (LST) data. The ET estimates from TSEB-SM were validated using flux tower measurements over the main land cover types in the study area and compared with the results from the original TSEB model. The comparison showed that the TSEB-SM approach reduced the RMSE error in latent heat flux estimates by 5 % to 20 % at satellite overpass time, and TSEB-SM outperformed the original TSEB model in daily ET estimates (RMSE values of 1.0 mm/d versus 1.5 mm/d, respectively) across all validation sites. The magnitude of the TSEB-SM improvements were larger early and late in the growing season and in arid areas, consistent with soil moisture improving estimates of ET particularly under times of higher water stress. The use of SMAP satellite soil moisture data with the TSEB-SM model increased the reliability of ET estimation and provides an improved capacity for continuos monitoring of drought effects on the global water cycle.

Late-fall satellite-based soil moisture observations show clear connections to subsequent spring streamflow

Because runoff production is more efficient over wetter soils, and because soil moisture has an intrinsic memory, soil moisture information can potentially contribute to the accuracy of streamflow predictions at seasonal leads. In this work, we use surface (0–5 cm) soil moisture retrievals obtained with the National Aeronautics and Space Administration’s Soil Moisture Active Passive satellite instrument in conjunction with streamflow measurements taken within 236 intermediate-scale (2000–10,000 km2) unregulated river basins in the conterminous United States to show that late-fall satellite-based surface soil moisture estimates are indeed strongly connected to subsequent springtime streamflow. We thus show that the satellite-based soil moisture retrievals, all by themselves, have the potential to produce skillful seasonal streamflow predictions several months in advance. In poorly instrumented regions, they could perform better than reanalysis soil moisture products in this regard.

Oceanic response to typhoons in the Northwest Pacific using Aquarius and SMAP data (2011–2020)

Typhoons, such as tropical cyclones, can produce a variety of ocean responses through drastic changes in atmospheric and oceanic environments. However, the uncertainty in satellite salinity data increases during the passage of a typhoon and may limit its potential application. To investigate whether the satellite salinity data can explain oceanic responses to typhoons in the Northwest Pacific, we validated the satellite salinity using Argo float data for the past decade (2011–2020). The Soil Moisture Active Passive (SMAP) and Aquarius salinity were relatively accurate in subtropical regions at low latitudes under high sea surface temperature conditions in summer. This demonstrates the validity of the satellite salinity data in typhoon studies. We analyzed the oceanic responses to 20 representative typhoons over the past decade. Both the Aquarius and SMAP satellites observed a decrease in the SSS on the left side of the typhoon in contrast to the high salinity on the right side of the typhoon. The locations of SSS freshening coincided with those of higher precipitation to the left of the typhoon centers. We also observed that the higher the precipitation rate, the lower the satellite salinity. The ratio of the salinity freshening to the precipitation rate was significant at approximately –0.0401 psu mm-1 h-1. Changes in the vertical profiles of the Argo data supported this partial freshening of salinity as well as the characteristic surface cooling and deepening of the mixed layer after the passage of the typhoon. We further demonstrated that the atmospheric environments in a rotated coordinate system along the typhoon paths showed clear salinity freshening in the forward and slightly left sides of the typhoon center. The spatial distinction of the wind and precipitation fields along the typhoon paths induced the characteristic synoptic response of salinity prior to the arrival of each typhoon. Our results provide reasonable observational evidence of oceanic responses to typhoons in the Northwest Pacific and contribute to the understanding of atmospheric and oceanic processes related to tropical storms.

Downscaling and validating SMAP soil moisture using a machine learning algorithm over the Awash River basin, Ethiopia.

Microwave remote sensing instrument like Soil Moisture Active Passive ranging from 1 cm to 1 m has provided spatial soil moisture information over the entire globe. However, Soil Moisture Active Passive satellite soil moisture products have a coarse spatial resolution (36km x 36km), limiting its application at the basin scale. This research, subsequently plans to; (1) Evaluate the capability of SAR for the retrieval of surface roughness variables in the Awash River basin; (2) Measure the performance of Random Forest (RF) regression model to downscale SMAP satellite soil moisture over the Awash River basin; (3) validate downscaled soil moisture data with In-situ measurements in the river basin. Random Forest (RF) based downscaling approach was applied to downscale satellite-based soil moisture product (36km x 36km) to fine resolution (1km x 1km). Fine spatial resolution (1km) soil moisture data for the Awash River basin was generated. The downscaled soil moisture product also has a strong spatial correlation with the original one, allowing it to deliver more soil moisture information than the original one. In-situ soil moisture and downscaled soil moisture had a 0.69 Pearson correlation value, compared to a 0.53 correlation between the original and In-situ soil moisture. In-situ soil moisture measurements were obtained from the Middle and Upper Awash sub-basins for validation purposes. In the case of Upper Awash, downscaled soil moisture shows a variation of 0.07 cm3 /cm3, -0.036 cm3 /cm3, and 0.112 cm3 /cm3 with Root Mean Square Error, Bias error, and Unbiased Root Mean Square Error respectively. Following that, the accuracy of downscaled soil moisture against the Middle Awash Sub-basin reveals a variance of 0.1320 cm3 /cm3, -0.033 cm3 /cm3, and 0.148 cm3 /cm3 with Root Mean Square Error, Bias error, and Unbiased Root Mean Square Error respectively. Future studies should take into account the temporal domain of Soil Moisture Active Passive satellite soil moisture product downscaling over the study region.

Intensification of “river in the sea” along the western Bay of Bengal Coast during two consecutive La Niña events of 2020 and 2021 based on SMAP satellite observations

A narrow strip of low salinity water that hugs the western Bay of Bengal (BoB) is known as a “river in the sea” (RIS). During the autumns of 2020 and 2021, Soil Moisture Active Passive (SMAP) identified an intensification of the RIS that was characterized by a continuous, uniform strip of low salinity water (<31 psu) that was 100 km wide extending along the western BoB coast to south of Sri Lanka. The analysis shows that the La Niña events in 2020–2021 were responsible for this intensification. During the La Niña events of 2020 and 2021, cyclonic circulation anomalies were generated along the BoB coast, causing a stronger Eastern Indian Coastal Current (EICC). Meanwhile, the northern BoB received increased river discharge during the summer monsoons of the two years. The stronger EICC and increased river discharge together led to anomalous freshwater transport that contributed to the intensification of the RIS. Notably, the RIS extended far southward during La Niña events of 2016 and 2017. However, the spatial structure of the RIS differed sharply between 2016–2017 and 2020–2021. The results indicate that pairs of eddies in the western BoB steered the freshwater offshore by several hundred kilometers. As a result, offshore extending tongue-shaped freshwater plumes were formed that inhibited the formation of an organized RIS structure along the western BoB coast during 2016–2017. This study highlights that the year-to-year variability of the RIS was significantly influenced by the La Niña events and modulated by mesoscale processes.

Role of Land–Atmosphere Interaction in the 2016 Northeast Asia Heat Wave: Impact of Soil Moisture Initialization

AbstractThe impact of soil moisture initialization on the 2016 Northeast Asian heat wave was explored using the Land Information System (LIS) Weather Research and Forecasting (WRF) model. To obtain advanced soil moisture estimates, various meteorological variables from the Global Data Assimilation System analysis and Integrated Multi‐Satellite Retrievals for Global Precipitation Measurement observation data were used as atmospheric forcings for the offline simulation of the Noah land surface model (Noah‐LSM). Based on the LIS, Soil Moisture Active Passive satellite were assimilated in the Noah LSM simulation. The assimilated soil moisture estimates revealed the drier land surface conditions over Northeast Asia compared with the product from the National Centers for Environmental Prediction Final Analysis (FNL) and were used as the initial condition of the WRF model. The WRF experiment initialized by the assimilated soil moisture product (LIS experiment) exhibited the observed surface air temperature (SAT) and 500 hPa geopotential height (500 GPH) over Northeast Asia compared with the result from the experiment initialized by the FNL (CTL experiment). At Week 1, the LIS experiment simulated warmer SAT than that from the CTL experiment, which was induced by the negative anomaly of latent heat flux over Mongolia. Then, 500 GPH became strong and spatially expanded in response to the thermal low induced by the warmer SAT, and the SAT was further increased during Weeks 2 and 3.

Ocean Salinity Retrieval and Prediction for Soil Moisture Active Passive Satellite Using Data-to-Data Translation

Sea surface salinity (SSS) is a key parameter in physical oceanography, global hydrological, biochemical cycles, and climate change studies. L-band radiometers onboard satellites, such as Soil Moisture Active Passive (SMAP), Soil Moisture and Ocean Salinity (SMOS), have played a crucial role in monitoring and analyzing the global SSS in the past decades. This study presents a method to retrieve and predict the SMAP SSS through data-to-data translation (D2D) based on conditional generative adversarial networks. The model was constructed from the polarized brightness temperatures, differences in the polarized brightness temperatures from the L band of the SMAP satellite, and sea surface temperature and sea surface wind speed from the European Center for Medium-Range Weather Forecasts data from April 2015 to July 2020, and applied to produce SMAP SSS. A comparison between the SMAP salinity and the D2D-generated SMAP salinity showed excellent agreement, evaluated through bias = 0.016, root mean square error (RMSE) = 0.173 in practical salinity unit (psu) units, and correlation coefficient (CC) = 0.985. Furthermore, a comparison between the D2D-generated and buoy-observed salinities showed good agreement (bias = -0.031 psu and RMSE = 0.196 psu, CC = 0.971). Additionally, the results of the one-month prediction model were also in good agreement with the SMAP SSS (bias = 0.028 psu and RMSE = 0.218 psu, CC = 0.977). Consequently, the D2D-based model can be effectively used to generate SMAP SSS information and can be applied to various microwave satellites.

A Novel Dual-Branch Neural Network Model for Flood Monitoring in South Asia Based on CYGNSS Data

Microwave remote sensing is widely applied in flood monitoring due to its independence from severe weather conditions, which usually restrict the usage of optical sensors. However, it is challenging to track the variation process of flood events in a timely manner by traditional active and passive microwave techniques, since they cannot simultaneously provide measurements with high spatial and temporal resolution. The emerging Global Navigation Satellite System Reflectometry (GNSS-R) technique with high spatio-temporal resolution offers a new solution to the dynamic monitoring of flood inundation. Considering the high sensitivity of GNSS-R signals to flooding, this paper proposes a dual-branch neural network (DBNN) with a convolution neural network (CNN) and a back propagation (BP) neural network for flood monitoring. The CNN module is used to automatically extract the abstract features from delay-Doppler maps (DDMs), while the BP module is fed with GNSS-R typical features, such as surface reflectivity and power ratio, as well as vegetation information from Soil Moisture Active Passive satellite (SMAP) data. In the experiments, the superiority of the DBNN method is firstly demonstrated by comparing it with the surface reflectivity and power ratio methods. Then, the spatio-temporal variation process of the 2020 South Asian flood events is analyzed by the proposed method based on Cyclone Global Navigation Satellite System (CYGNSS) data. The understanding of flood change processes could help enhance the capacity for resisting flood disasters.

Impact of profile-averaged soil ice fraction on passive microwave brightness temperature Diurnal Amplitude Variations (DAV) at L-band

The dynamic change of frozen soil is crucial to land-surface modeling, carbon feedback studies, ground engineering (e.g., constructions), and microwave remote sensing. L-Band satellite missions Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) are currently exploited to characterize soil into freeze or thaw (FT) states. However, brightness temperatures (TB) at L-band contain more information besides the FT state, particularly over permafrost or seasonally frozen soil, which has not been explored via current retrieval algorithms. To examine the potential for L-band TB observations, we define an index called Profile-Averaged Frozen Soil Fraction (Ff) related to Diurnal Amplitude Variation (DAV) of TB (i.e., ΔTB) based on the optical depth of the frozen soil column. We evaluated Ff inferred from the 0th-order microwave transfer model with the SMAP L1c product, the ground-based European Space Agency L-Band Radiometer III (ELBARA-III) TB observations, and temperature profiles collected at the Maqu station in northeastern Tibet. While there is a clear relationship between Ff and ΔTB, no apparent link exists with the ice content fraction (Ffi) within a fixed-depth soil column. The proposed model certifies that the profile-averaged soil ice content Ff relates to the dynamic microwave penetration depth by math and field measurement. The model reproduces well ΔTB in Period Freezing but has problems in Period Thawing when melted surface water obstruct the microwave signals. Our findings can be used to exploit ΔTB between 6 am and 6 pm, as a typically overpassing time by the SMOS and SMAP satellites, for estimating Ff, which can be further applied in weather/climate forecasting and for improving land-surface modeling.

A model predictive control for attitude stabilization and spin control of a spacecraft with a flexible rotating payload

Many Earth Observation missions, implementing space-based microwave sensing techniques for collecting surface information, employ spinning sensors to cover large swaths of terrestrial areas, thus improving the rate at which global maps of those measured data are generated. These spacecraft (as Soil Moisture Active Passive (SMAP) developed by NASA or Copernicus Imaging Microwave Radiometer (CIMR) currently under development by Thales Alenia Space) consist of a main non-spun platform and a rotating part composed of an antenna boom, a deployable reflector and a rotation mechanism. As the reflector is designated to rotate about the nadir axis producing conically scanned antenna beams with precise surface incidence angle, the payload pointing accuracy needs to be addressed at both spin subsystem and platform level. In this work, a representative model of the dynamic behaviour of SMAP satellite is developed as a study case to design the proposed control strategies; in particular, a SMAP-like payload structural model is built using FEM commercial codes. The spacecraft is equipped with a Reaction Wheels Assembly (RWA) to accomplish both momentum compensation for the spun element and three-axis attitude control and a motor for the spin mechanism. The objective of the study is to develop the spacecraft control architecture in the frame of Model Predictive Control (MPC) theory. MPC refers to a class of algorithms in which the control action is obtained by computing an open-loop optimal sequence of control moves over a predefined time horizon; moreover, the ability to set constraints on process inputs and outputs directly in the problem formulation allows to account for actuators’ limits. In the study two operative phases of the satellite are addressed: the Spin-up, in which the 6-meter diameter antenna is spun-up to the operative condition of 14.6 RPM, and the Science Phase, in which precise nadir pointing and stability of the flexible system must be kept for acquiring high-resolution measurements. To this purpose, control–structure interaction between attitude/spin control system and flexible dynamics, as well as system’s imbalances, are carefully addressed by the proposed control architecture. The nonlinear in-orbit dynamics of the flexible spacecraft is then used to evaluate the performance of the MPC controller in terms of pointing accuracy and robustness to uncertainties.

Validation of NASA SMAP Satellite Soil Moisture Products over the Desert of Kuwait

The goal of this study is to validate and analyze NASA’s Soil Moisture Active Passive (SMAP) products over the desert of Kuwait. The study period was between April 2015 and April 2020. The study domain includes a mission candidate calibration/validation (Cal/Val) site that comprises six permanent soil moisture stations used to verify SMAP estimates. In addition, intensive field campaigns were conducted within and around the candidate Cal/Val site during the study period to collect additional thermogravimetric samples. The mean difference (MD), root mean squared difference (RMSD), unbiased root mean square difference (ubRMSD), and correlation coefficient (R) were computed to assess the agreement between SMAP SM products and in situ observations. The comparison of the six ground station sensors’ observations with the thermogravimetric samples led to an absolute mean bias (AMB) of 0.034 m3 m−3, which was then used to calibrate the sensors and bias-correct their measurements. The temporal consistency of the readings from the test site and calibrated sensors was assessed using the mean relative difference (MRD) and its standard deviation of relative difference (SDRD). Using a sampling density analysis, it was determined that a minimum of four ground stations would be required to validate the test site. Furthermore, the consistency between SMAP satellite soil moisture data and those derived from the Soil Moisture and Ocean Salinity (SMOS) satellite operated by the European Space Agency, and their agreement with in situ samples, was analyzed. The comparison of SMAP and SMOS soil moisture data with in situ observations showed that both satellites successfully captured the spatial and temporal distribution of soil moisture. For SMAP and SMOS, the lowest ubRMSE statistics were 0.043 m3 m−3 and 0.045 m3 m−3, respectively, which are slightly higher than the mission target of 0.04 m3 m−3.

Climate change-induced peatland drying in Southeast Asia

When organic peat soils are sufficiently dry, they become flammable. In Southeast Asian peatlands, widespread deforestation and associated drainage create dry conditions that, when coupled with El Niño-driven drought, result in catastrophic fire events that release large amounts of carbon and deadly smoke to the atmosphere. While the effects of anthropogenic degradation on peat moisture and fire risk have been extensively demonstrated, climate change impacts to peat flammability are poorly understood. These impacts are likely to be mediated primarily through changes in soil moisture. Here, we used neural networks (trained on data from the NASA Soil Moisture Active Passive satellite) to model soil moisture as a function of climate, degradation, and location. The neural networks were forced with regional climate model projections for 1985–2005 and 2040–2060 climate under RCP8.5 forcing to predict changes in soil moisture. We find that reduced precipitation and increased evaporative demand will lead to median soil moisture decreases about half as strong as those observed during recent El Niño droughts in 2015 and 2019. Based on previous studies, such reductions may be expected to accelerate peat carbon emissions. Our results also suggest that soil moisture in degraded areas with less tree cover may be more sensitive to climate change than in other land use types, motivating urgent peatland restoration. Climate change may play an important role in future soil moisture regimes and by extension, future peat fire in Southeast Asian peatlands.

Use of soil moisture active passive satellite data and WorldClim 2.0 data to predict the potential distribution of visceral leishmaniasis and its vector Lutzomyia longipalpis in Sao Paulo and Bahia states, Brazil.

Visceral leishmaniasis (VL) is a neglected tropical disease transmitted by Lutzomyia longipalpis, a sand fly widely distributed in Brazil. Despite efforts to strengthen national control programs reduction in incidence and geographical distribution of VL in Brazil has not yet been successful; VL is in fact expanding its range in newly urbanized areas. Ecological niche models (ENM) for use in surveillance and response systems may enable more effective operational VL control by mapping risk areas and elucidation of eco-epidemiologic risk factors. ENMs for VL and Lu. longipalpis were generated using monthly WorldClim 2.0 data (30-year climate normal, 1-km spatial resolution) and monthly soil moisture active passive (SMAP) satellite L4 soil moisture data. SMAP L4 Global 3-hourly 9-km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data V004 were obtained for the first image of day 1 and day 15 (0:00-3:00 hour) of each month. ENM were developed using MaxEnt software to generate risk maps based on an algorithm for maximum entropy. The jack-knife procedure was used to identify the contribution of each variable to model performance. The three most meaningful components were used to generate ENM distribution maps by ArcGIS 10.6. Similar patterns of VL and vector distribution were observed using SMAP as compared to WorldClim 2.0 models based on temperature and precipitation data or water budget. Results indicate that direct Earth-observing satellite measurement of soil moisture by SMAP can be used in lieu of models calculated from classical temperature and precipitation climate station data to assess VL risk.

Exceptional heat and atmospheric dryness amplified losses of primary production during the 2020 U.S. Southwest hot drought.

Earth's ecosystems are increasingly threatened by “hot drought,” which occurs when hot air temperatures coincide with precipitation deficits, intensifying the hydrological, physiological, and ecological effects of drought by enhancing evaporative losses of soil moisture (SM) and increasing plant stress due to higher vapor pressure deficit (VPD). Drought‐induced reductions in gross primary production (GPP) exert a major influence on the terrestrial carbon sink, but the extent to which hotter and atmospherically drier conditions will amplify the effects of precipitation deficits on Earth's carbon cycle remains largely unknown. During summer and autumn 2020, the U.S. Southwest experienced one of the most intense hot droughts on record, with record‐low precipitation and record‐high air temperature and VPD across the region. Here, we use this natural experiment to evaluate the effects of hot drought on GPP and further decompose those negative GPP anomalies into their constituent meteorological and hydrological drivers. We found a 122 Tg C (>25%) reduction in GPP below the 2015–2019 mean, by far the lowest regional GPP over the Soil Moisture Active Passive satellite record. Roughly half of the estimated GPP loss was attributable to low SM (likely a combination of record‐low precipitation and warming‐enhanced evaporative depletion), but record‐breaking VPD amplified the reduction of GPP, contributing roughly 40% of the GPP anomaly. Both air temperature and VPD are very likely to continue increasing over the next century, likely leading to more frequent and intense hot droughts and substantially enhancing drought‐induced GPP reductions.

A Multiscale Deep Learning Model for Soil Moisture Integrating Satellite and In Situ Data

AbstractDeep learning (DL) models trained on hydrologic observations can perform extraordinarily well, but they can inherit deficiencies of the training data, such as limited coverage of in situ data or low resolution/accuracy of satellite data. Here we propose a novel multiscale DL scheme learning simultaneously from satellite and in situ data to predict 9 km daily soil moisture (5 cm depth). Based on spatial cross‐validation over sites in the conterminous United States, the multiscale scheme obtained a median correlation of 0.901 and root‐mean‐square error of 0.034 m3/m3. It outperformed the Soil Moisture Active Passive satellite mission's 9 km product, DL models trained on in situ data alone, and land surface models. Our 9 km product showed better accuracy than previous 1 km satellite downscaling products, highlighting limited impacts of improving resolution. Not only is our product useful for planning against floods, droughts, and pests, our scheme is generically applicable to geoscientific domains with data on multiple scales, breaking the confines of individual data sets.

Global sea surface salinity via the synergistic use of SMAP satellite and HYCOM data based on machine learning

Sea surface salinity (SSS) provides information on the variability of ocean dynamics (global water cycle and ocean circulation) and air-sea interactions, thereby contributing to the identification and prediction of significant changes in the global climate. Monitoring global SSS via satellite observations has been possible using L-band microwave radiometers since 2010; however, their performance is limited by their retrieval algorithms under conditions such as radio frequency interference, low sea surface temperatures, and strong winds. This study proposes a new global SSS model using multi-source data based on seven machine learning approaches: K-nearest neighbor, support vector regression, artificial neural network, random forest, extreme gradient boosting, light gradient boosting model, and gradient boosted regression trees (GBRT). Five Soil Moisture Active Passive (SMAP) products, Hybrid Coordinate Ocean Model (HYCOM) SSS, and four ancillary data were used as input variables. All models produced better performance than either SMAP or HYCOM SSS products, with the top performing GBRT model reducing the root mean square difference for the validation dataset from 1.062 to 0.259 practical salinity units compared to the SMAP SSS product. The improved SSS products had increased correlation with the in-situ data for both low- and high-salinity waters across all global oceans, thus further advancing the understanding and monitoring of global SSS.

Weddell Sea polynya analysis using SMOS–SMAP apparent sea ice thickness retrieval

Abstract. The Weddell Sea is known to feature large openings in its winter sea ice field, otherwise known as open-ocean polynyas. An area within the Weddell Sea region that has repeatedly featured open-ocean polynyas in the past is that which encompasses the Maud Rise seamount. Within this area, after 40 years of intermittent, smaller openings, a larger, more persistent polynya appeared in early September 2017​​​​​​​ and remained open for approximately 80 d until spring ice melt. In this study we present proof that polynya-favorable activity in the Maud Rise area is taking place more frequently and on a larger scale than previously assumed. By investigating thin (< 50 cm) apparent sea ice thickness (ASIT) retrieved from the satellite microwave sensors Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP), we find an anomaly of thin sea ice spanning an area comparable to the polynya of 2017 over Maud Rise which occurred in September 2018. In this paper, we look at sea ice above Maud Rise in August and September of 2017 and 2018 as well as all years from 2010 until 2020 in an 11-year time series. Using fifth-generation ECMWF Reanalysis (ERA5) surface wind reanalysis data, we corroborate previous findings (e.g., Campbell et al., 2019; Francis et al., 2019; Wilson et al., 2019) on the strong impact that storm activity can have on sea ice above Maud Rise and help consolidate the theory that the evolution of the Weddell Sea polynya is controlled by local atmospheric as well as oceanographic variability. Based on the results presented, we propose that the Weddell Sea polynya, rather than being a binary phenomenon with one principal cause, is a dynamic process caused by various different preconditioning factors that must occur simultaneously for it to appear and persist. Moreover, we show that rather than an abrupt stop to anomalous activity above Maud Rise in 2017, the very next year shows signs of polynya-favorable activity that, although insufficient to open the polynya, were present in the region. This phenomenon, as we have shown in the 11-year SMOS record, was not unique to 2018 and was also identified in 2010, 2013 and 2014. It is demonstrated that L-band microwave radiometry from the SMOS and SMAP satellites can provide additional useful information, which helps to better understand dynamic sea ice processes like polynya events when compared to the use of satellite sea ice concentration products alone.

A Combined Optimization‐Assimilation Framework to Enhance the Predictive Skill of Community Land Model

AbstractLand Surface Model (LSM) predictions are affected by unknown parameters, uncertain meteorological forcings, inaccurate initial conditions, and incomplete process representation. While parameter optimization (PO) algorithms have been used to estimate the model parameter for best model performance, the use of data assimilation (DA) has been receiving increased prominence to characterize the uncertainties in such models. However, it is still an open question that how DA techniques can be used together with PO algorithms to enhance the performance of LSMs for different applications. In this study, a combined framework composed of the PO algorithm—Adaptive Surrogate Modeling Based Optimization (ASMO) and the DA algorithm—Evolutionary Particle Filter with Markov Chain Monte Carlo (EPFM), is used to improve the soil moisture (SM) estimates of Community Land Model (CLM) across the Tibetan Plateau in China. The SM data from the Soil Moisture Active Passive satellite are used as the benchmark data for model improvement. To demonstrate the usefulness and effectiveness of the proposed approach, the simulated SM values are validated against the SM observations collected from the in situ networks in the Tibetan Plateau, China. The findings revealed that the joint application of ASMO and EPFM algorithms results in more accurate and reliable SM estimates of CLM compared to when they are solely employed. This study suggests that the combined optimization‐assimilation framework can be utilized for improving the predictive skill of the other large‐scale complex LSMs while accounting for the uncertainties associated with both model parameters and state variables.

Seasonal Variability of Sea Surface Salinity in the NW Gulf of Guinea from SMAP Satellite

The advent of satellite-derived sea surface salinity (SSS) measurements has boosted scientific study in less-sampled ocean regions such as the northwestern Gulf of Guinea (NWGoG). In this study, we examine the seasonal variability of SSS in the NWGoG from the Soil Moisture Active Passive (SMAP) satellite and show that it is well-suited for such regional studies as it is able to reproduce the observed SSS features in the study region. SMAP SSS bias, relative to in-situ data comparisons, reflects the differences between skin layer measurements and bulk surface measurements that have been reported by previous studies. The study results reveal three broad anomalous SSS features: a basin-wide salinification during boreal summer, a basin-wide freshening during winter, and a meridionally oriented frontal system during other seasons. A salt budget estimation suggests that the seasonal SSS variability is dominated by changes in freshwater flux, zonal circulation, and upwelling. Freshwater flux, primarily driven by the seasonally varying Intertropical Convergence Zone, is a dominant contributor to salt budget in all seasons except during fall. Regionally, SSS is most variable off southwestern Nigeria and controlled primarily by westward extensions of the Niger River. Anomalous salty SSS off the coasts of Cote d’Ivoire and Ghana especially during summer are driven mainly by coastal upwelling and horizontal advection.