Advanced Scatterometer Research Articles

Variations in Antarctic Peninsula snow liquid water during 1999–2017 revealed by merging radiometer, scatterometer and model estimations

The Antarctic Peninsula (AP) has recorded one of the strongest warming rates on the Earth since the 1950s, which, however, turned into a cooling trend since 1999. Snow liquid water emerges in the snowpacks in the AP during melt seasons. Monitoring snow liquid water on the AP is critical for evaluating its impacts on the stability, surface mass and energy balances of ice shelves. Most of our knowledge about the occurrence of snow liquid water (OLW) stems from satellite observations and model simulations. However, the relative performances of OLW products derived from these measurements have never been evaluated due to the lack of in-situ snow wetness measurements. We ranked the OLW determined by the radiometer (i.e., Special Sensor Microwave Imager), scatterometer (i.e., Quick Scatterometer and Advanced Scatterometer) and the Regional Atmospheric Circulation Model (RACMO2) based on categorical triple collocation (CTC) which can identify the most accurate observation with unknown true values. The CTC fusion product was generated by adopting the best source for each pixel during 1999–2017. The cumulative liquid water volume simulated by RACMO2 has significantly declined (at the 99% confidence level) by 210 Gt decade−1, while the decreasing trends in CTC-fused liquid water days (−1.18 days decade−1) and cumulative wet surface (−1.69 × 106 days km2 decade−1) were not statistically significant. The significantly negative trend in eastern AP snow liquid water agreed well with the simultaneous cooling, and may be linked with the increased adjacent sea ice.

Spatiotemporal variability and decadal trends of snowmelt processes on Antarctic sea ice observed by satellite scatterometers

Abstract. The timing and intensity of snowmelt processes on sea ice are key drivers determining the seasonal sea-ice energy and mass budgets. In the Arctic, satellite passive microwave and radar observations have revealed a trend towards an earlier snowmelt onset during the last decades, which is an important aspect of Arctic amplification and sea-ice decline. Around Antarctica, snowmelt on perennial ice is weak and very different than in the Arctic, with most snow surviving the summer. Here we compile time series of snowmelt onset dates on seasonal and perennial Antarctic sea ice from 1992 to 2014/15 using active microwave observations from the European Space Agency's (ESA) European Remote Sensing (ERS) 1 and 2 missions (ERS-1 and ERS-2), Quick Scatterometer (QSCAT), and Advanced Scatterometer (ASCAT) radar scatterometers. We define two snowmelt transition stages: a weak backscatter rise, indicating the initial warming and destructive metamorphism of the snowpack (pre-melt), followed by a rapid backscatter rise, indicating the onset of thaw–freeze cycles (snowmelt). Results show large interannual variability, with an average pre-melt onset date of 29 November and melt onset of 10 December, respectively, on perennial ice, without any significant trends over the study period, consistent with the small trends of Antarctic sea-ice extent. There was a latitudinal gradient from early snowmelt onsets in mid-November in the northern Weddell Sea to late (end of December) or even absent snowmelt conditions in the southern Weddell Sea. We show that QSCAT Ku-band-derived (13.4 GHz signal frequency) pre-melt and snowmelt onset dates are earlier by 20 and 18 d, respectively, than ERS and ASCAT C-band-derived (5.6 GHz) dates. This offset has been considered when constructing the time series. Snowmelt onset dates from passive microwave observations (37 GHz) are later by 14 and 6 d than those from the scatterometers, respectively. Based on these characteristic differences between melt onset dates observed by different microwave wavelengths, we developed a conceptual model which illustrates how the seasonal evolution of snow temperature profiles may affect different microwave bands with different penetration depths. These suggest that future multi-frequency active and passive microwave satellite missions could be used to resolve melt processes throughout the vertical snow column of thick snow on perennial Antarctic sea ice.

Assimilation of SMOS brightness temperatures in the ECMWF Integrated Forecasting System

The assimilation of Soil Moisture and Ocean Salinity (SMOS) brightness temperature (TB) data in numerical weather prediction systems influences the state of the soil, which in turn affects the exchange of energy and water fluxes between the soil and the near‐surface atmosphere, with potential implications for the prediction of atmospheric variables. In this paper, the impact of assimilating SMOS TB alone or in combination with screen‐level observations and Advanced Scatterometer (ASCAT) soil moisture retrievals is assessed. Independent quality controlled insitu soil moisture observations belonging to several networks included in the International Soil Moisture Network, were used to validate the quality of both the new soil moisture analyses and the skill to predict soil moisture up to 5 days ahead. The impact on atmospheric variables is indirect and was evaluated through computation of the forecast skill at different lead times. The analysis period was selected to be around the boreal summer, a period of the year when evaporatranspiration fluxes are stronger, and when it is therefore expected that the assimilation of remote‐sensing data provides the largest impact on the state of the soil. The results show that the soil moisture state benefits from the direct assimilation of SMOS TB, especially in better representing the temporal variations of soil moisture. The skill of atmospheric variables is mainly driven by the screen‐level observations. Despite the clear benefits to the soil state, remote‐sensing data need to be used with screen‐level variables to add value to the state of the atmosphere, pointing to inconsistencies in the physical coupling between the land and near‐surface components of the ECMWF Earth system.

Using Cosmic-Ray Neutron Probes in Validating Satellite Soil Moisture Products and Land Surface Models

Soil moisture content is one of the most important parameters of hydrological studies. Cosmic-ray neutron sensing is a promising proximal soil moisture sensing technique at intermediate scale and high temporal resolution. In this study, we validate satellite soil moisture products for the period of March 2015 and December 2018 by using several existing Cosmic Ray Neutron Probe (CRNP) stations of the COSMOS database and a CRNP station that was installed in the south part of Turkey in October 2016. Soil moisture values, which were inferred from the CRNP station in Turkey, are also validated using a time domain reflectometer (TDR) installed at the same location and soil water content values obtained from a land surface model (Noah LSM) at various depths (0.1 m, 0.3 m, 0.6 m and 1.0 m). The CRNP has a very good correlation with TDR where both measurements show consistent changes in soil moisture due to storm events. Satellite soil moisture products obtained from the Soil Moisture and Ocean Salinity (SMOS), the METOP-A/B Advanced Scatterometer (ASCAT), Soil Moisture Active Passive (SMAP), Advanced Microwave Scanning Radiometer 2 (AMSR2), Climate Change Initiative (CCI) and a global land surface model Global Land Data Assimilation System (GLDAS) are compared with the soil moisture values obtained from CRNP stations. Coefficient of determination ( r 2 ) and unbiased root mean square error (ubRMSE) are used as the statistical measures. Triple Collocation (TC) was also performed by considering soil moisture values obtained from different soil moisture products and the CRNPs. The validation results are mainly influenced by the location of the sensor and the soil moisture retrieval algorithm of satellite products. The SMAP surface product produces the highest correlations and lowest errors especially in semi-arid areas whereas the ASCAT product provides better results in vegetated areas. Both global and local land surface models’ outputs are highly compatible with the CRNP soil moisture values.

Characterizing ERA-Interim and ERA5 surface wind biases using ASCAT

Abstract. This paper analyzes the differences between ERA-Interim and ERA5 surface winds fields relative to Advanced Scatterometer (ASCAT) ocean vector wind observations, after adjustment for the effects of atmospheric stability and density, using stress-equivalent winds (U10S) and air–sea relative motion using ocean current velocities. In terms of instantaneous root mean square (rms) wind speed agreement, ERA5 winds show a 20 % improvement relative to ERA-Interim and a performance similar to that of currently operational ECMWF forecasts. ERA5 also performs better than ERA-Interim in terms of mean and transient wind errors, wind divergence and wind stress curl biases. Yet, both ERA products show systematic errors in the partition of the wind kinetic energy into zonal and meridional, mean and transient components. ERA winds are characterized by excessive mean zonal winds (westerlies) with too-weak mean poleward flows in the midlatitudes and too-weak mean meridional winds (trades) in the tropics. ERA stress curl is too cyclonic in midlatitudes and high latitudes, with implications for Ekman upwelling estimates, and lacks detail in the representation of sea surface temperature (SST) gradient effects (along the equatorial cold tongues and Western Boundary Current (WBC) jets) and mesoscale convective airflows (along the Intertropical Convergence Zone and the warm flanks for the WBC jets). It is conjectured that large-scale mean wind biases in ERA are related to their lack of high-frequency (transient wind) variability, which should be promoting residual meridional circulations in the Ferrel and Hadley cells.

Assimilation of Satellite Soil Moisture for Improved Atmospheric Reanalyses

AbstractA newly developed, weakly coupled land and atmosphere data assimilation system for NASA’s Global Earth Observing System model is presented, and used to demonstrate the benefit of assimilating satellite soil moisture into an atmospheric reanalysis. Specifically, Advanced Scatterometer and Soil Moisture Ocean Salinity soil moisture retrievals are assimilated into a system that uses the same model, atmospheric assimilation system, and atmospheric observations as the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). The atmosphere is sensitive to soil moisture only under certain conditions. Hence, while the globally averaged model improvements were small, regionally, the soil moisture assimilation induced some substantial improvements. For example, in a large region spanning from western Europe across southern Russia, the soil moisture assimilation decreased the RMSE against independent station observations of daily maximum 2-m temperature () by up to 0.4 K, and of 2-m specific humidity (q2m) by up to 0.5 g kg−1. Over all available stations, the mean RMSE was reduced from 2.82 to 2.79 K, while the mean q2m RMSE was reduced from 1.25 to 1.20 g kg−1. The soil moisture assimilation also reduced the mean RMSE across 29 flux tower sites from 34.2 to 32.6 W m−2 for latent heating, and from 37.7 to 36.5 W m−2 for sensible heating. For all variables evaluated, the soil moisture assimilation improved the model at monthly to seasonal, rather than daily, time scales. Based on the above experiments, it is recommended that satellite soil moisture be assimilated into future reanalyses, including the follow-on to MERRA-2.

Improving surface soil moisture estimates in West Africa through GRACE data assimilation

Recent annual trends of precipitation and terrestrial water storage (TWS) in West Africa have been increasing over the past decade. Despite a significant impact of soil moisture on the TWS in West Africa, there is little research on the recent spatial and temporal behaviors of surface soil moisture (SSM) along with the hydrological trends and variability in West Africa. In this study, we assimilate TWS estimates from the Gravity Recovery and Climate Experiment (GRACE) mission into the Catchment Land Surface Model (CLSM) and evaluate its impacts on SSM simulations for the years, 2002–2017. The evaluation is performed using reference datasets: the African Monsoon Multidisciplinary Analysis (AMMA) in situ soil moisture observations, three currently available microwave satellite SSM observations from the Advanced Scatterometer (ASCAT), the Soil Moisture Ocean Salinity (SMOS), and the Soil Moisture Active Passive (SMAP) satellites and also the triple collocation analysis (TCA). Overall, modeled SSM shows good agreement with the reference datasets in terms of the anomaly SSM correlations. However, both modeled and ASCAT SSM are limited in their representation of the drying rates, as observed by ground observations, SMOS and SMAP estimates. Further, GRACE data assimilation results in improved SSM simulations in the humid regions with large annual TWS variability. This study demonstrates the utility of land data assimilation to inform hydrological conditions in West Africa, where soil moisture monitoring is necessary for water resource and livestock management.

A carbon sink-driven approach to estimate gross primary production from microwave satellite observations

Global estimation of Gross Primary Production (GPP) - the uptake of atmospheric carbon dioxide by plants through photosynthesis - is commonly based on optical satellite remote sensing data. This presents a source-driven approach since it uses the amount of absorbed light, the main driver of photosynthesis, as a proxy for GPP. Vegetation Optical Depth (VOD) estimates obtained from microwave sensors provide an alternative and independent data source to estimate GPP on a global scale, which may complement existing GPP products. Recent studies have shown that VOD is related to aboveground biomass, and that both VOD and temporal changes in VOD relate to GPP. In this study, we build upon this concept and propose a model for estimating GPP from VOD. Since the model is driven by vegetation biomass, as observed through VOD, it presents a carbon sink-driven approach to quantify GPP and, therefore, is conceptually different from common source-driven approaches. The model developed in this study uses single frequencies from active or passive microwave VOD retrievals from C-, X- and Ku-band (Advanced Scatterometer (ASCAT) and Advanced Microwave Scanning Radiometer for Earth Observation (AMSR-E)) to estimate GPP at the global scale. We assessed the ability for temporal and spatial extrapolation of the model using global GPP from FLUXCOM and in situ GPP from FLUXNET. We further performed upscaling of in situ GPP based on different VOD data sets and compared these estimates with the FLUXCOM and MODerate-resolution Imaging Spectroradiometer (MODIS) GPP products. Our results show that the model developed for individual grid cells using VOD and change in VOD as input performs well in predicting temporal patterns in GPP for all VOD data sets. For spatial extrapolation of the model, however, additional input variables are needed to represent the spatial variability of the VOD-GPP relationship due to differences in vegetation type. As additional input variable, we included the grid cell median VOD (as a proxy for vegetation cover), which increased the model performance during cross validation. Mean annual GPP obtained for AMSR-E X-band data tends to overestimate mean annual GPP for FLUXCOM and MODIS but shows comparable latitudinal patterns. Overall, our findings demonstrate the potential of VOD for estimating GPP. The sink-driven approach provides additional information about GPP independent of optical data, which may contribute to our knowledge about the carbon source-sink balance in different ecosystems.

Evaluation of the Performance of SM2RAIN-Derived Rainfall Products over Brazil

Microwave-based satellite soil moisture products enable an innovative way of estimating rainfall using soil moisture observations with a bottom-up approach based on the inversion of the soil water balance Equation (SM2RAIN). In this work, the SM2RAIN-CCI (SM2RAIN-ASCAT) rainfall data obtained from the inversion of the microwave-based satellite soil moisture (SM) observations derived from the European Space Agency (ESA) Climate Change Initiative (CCI) (from the Advanced SCATterometer (ASCAT) soil moisture data) were evaluated against in situ rainfall observations under different bioclimatic conditions in Brazil. The research V7 version of the Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis (TRMM TMPA) was also used as a state-of-the-art rainfall product with an up-bottom approach. Comparisons were made at daily and 0.25° scales, during the time-span of 2007–2015. The SM2RAIN-CCI, SM2RAIN-ASCAT, and TRMM TMPA products showed relatively good Pearson correlation values (R) with the gauge-based observations, mainly in the Caatinga (CAAT) and Cerrado (CER) biomes (R median > 0.55). SM2RAIN-ASCAT largely underestimated rainfall across the country, particularly over the CAAT and CER biomes (bias median < −16.05%), while SM2RAIN-CCI is characterized by providing rainfall estimates with only a slight bias (bias median: −0.20%), and TRMM TMPA tended to overestimate the amount of rainfall (bias median: 7.82%). All products exhibited the highest values of unbiased root mean square error (ubRMSE) in winter (DJF) when heavy rainfall events tend to occur more frequently, whereas the lowest values are observed in summer (JJA) with light rainfall events. The SM2RAIN-based products showed larger contribution of systematic error components than random error components, while the opposite was observed for TRMM TMPA. In general, both SM2RAIN-based rainfall products can be effectively used for some operational purposes on a daily scale, such as water resources management and agriculture, whether the bias is previously adjusted.

Accuracy Assessment of HY-2A Scatterometer Wind Measurements During 2011–2017 by Comparison With Buoys, ASCAT, and ERA-Interim Data

Chinese satellite HY-2A, carrying a Ku-band scatterometer (HSCAT), was launched in August 2011. This letter validates wind vectors retrieved by the HSCAT during October 2011–July 2017 by comparing with wind data recorded by global moored buoys, the Advanced Scatterometer (ASCAT), and ERA-Interim. HSCAT wind data are collocated with moored-buoy observations made by the National Data Buoy Center, tropical atmosphere ocean/triangle trans-ocean buoy network, pilot research moored array in the Tropical Atlantic, and research moored array for African–Asian–Australian monsoon analysis and prediction. Only buoys located offshore are selected. Their spatial and temporal differences are limited to 25 km and 30 min. The results show that the HSCAT wind speed has bias of 0.09–0.28 m/s and root-mean-square error (RMSE) of 1.24–1.52 m/s, which satisfies the mission specification of less than 2 m/s. The HSCAT wind direction has bias of 0.8°–0.9° and RMSE of 21.5°–25.8°, which is close to the mission specification of less than 20°. The HSCAT wind retrievals are overestimates at wind speeds lower than 5 m/s. The RMSEs of the HSCAT wind speed and direction decrease with increasing wind speed. Wind vectors from HSCAT and ASCAT are compared using spatial and temporal windows of 0.1° and 10 min. Results show the consistency of the wind speed; RMSEs of wind speed and direction are 1.50 m/s and 22.90°. The comparison of HSCAT and ERS-Interim wind data shows that the HSCAT provides global ocean wind data at the level required for data application.

A Bayesian Estimation Technique for Improving the Accuracy of SCATSAT-1 Winds in Rainy Conditions

In this study, we present a mean-of-posteriors Bayesian estimation technique for improving rain contaminated wind speed estimates from the SCATSAT-1 scatterometer, particularly at low and moderate wind speeds. The performance of the algorithm is assessed using the SCATSAT-1 Version 1.1.3 data, collocated National Centre for Medium Range Weather Forecasting (NCMRWF) analysis winds, measurements from the Advanced Scatterometer (ASCAT), and buoy measurements for a 6-month period from April to September 2018. The validation results show a reduction in wind speed bias from 2.6 to $-$ 0.1 m/s and a reduction in root mean square difference (RMSD) from 4.8 to 2.9 m/s relative to NCMRWF. The results from comparisons with ASCAT and buoy data are also qualitatively similar; the RMSD relative to ASCAT reduces from 2.9 to 1.8 m/s after correction, whereas the RMSD relative to buoy reduces from 4.1 to 2.2 m/s. Different methods such as histogram comparisons, across-track, wind speed dependent, and spatial variation of wind speed errors have also been presented to demonstrate the overall improvement achieved by this technique. This algorithm presents two main advantages: 1) the errors in wind speed estimates are significantly lower after correction and 2) this methodology can be applied on both the inner and outer SCATSAT-1 swath.

A Monte Carlo based adaptive Kalman filtering framework for soil moisture data assimilation

The main sources of global soil moisture information are remote sensing observations and land surface model estimates. Data assimilation (DA) aims at optimally combining these data sources through statistical merging. To properly parameterize such merging, one needs to obtain accurate knowledge of model and observation uncertainties, which is the crux of a successful DA system. In this paper, we propose a new Monte Carlo based adaptive Kalman filtering framework (MadKF) that estimates model and observation uncertainties (Q and R) and updates soil moisture forecasts simultaneously. Spatially distributed uncertainties are estimated by applying triple collocation analysis (TCA) to Monte Carlo simulations of the model open-loop, the model analysis, and the observation time series at each grid cell. Error cross-covariances, which are inevitable between these time series, are diagnosed from their ensembles and used to iteratively correct biases they cause in Q and R estimates, and hence, in the Kalman filter gain. The proposed MadKF is tested in a synthetic environment and by assimilating real satellite soil moisture retrievals from the Advanced SCATterometer (ASCAT) into the Antecedent Precipitation Index (API) model forced with daily aggregated satellite precipitation. Synthetic experiments indicate a good convergence of Q and R estimates. Internal DA diagnostics, i.e., the innovation auto-correlation (IAC) and the variance of the normalized innovations, asymptotically converge to their desired values, which indicates that the filter is operating near its optimum and reliably estimates analysis uncertainty. Real-data experiments assimilating ASCAT observations into the API model further indicate that the MadKF is robust against observation error auto-correlations, which typically cause problems in conventional IAC-tuning based adaptive filtering approaches. A performance evaluation over 264 in situ sites within the contiguous United States shows that the MadKF leads to a significant skill gain in surface soil moisture estimation. The MadKF is also potentially applicable when assimilating Level 1 backscatter or brightness temperature observations, or when updating/assimilating other geophysical variables.

Agricultural drought assessment based on multiple soil moisture products

In this study, we evaluated three soil moisture (SM) products (Advanced Microwave Scanning Radiometer-2 [AMSR2], Advanced SCATterometer [ASCAT], and European Reanalysis Interim [ERA-interim]) across Australia in four climate zones by comparing against the Australian Water Resources Assessment-Landscape (AWRA-L) SM products from July 2012 to June 2017. The ASCAT SM indicated better performance than other SM products over Australia. To evaluate the applicability and reliability for monitoring agricultural drought, an agricultural drought index, the Soil Water Deficit Index, was estimated from three SM products and compared with three commonly-used drought indices (atmospheric water deficit [AWD], Evaporative Stress Index, and Reconnaissance Drought Index). Volumetric contingency tables were compiled to quantitatively assess the performance of agricultural drought detection using various SM products compared with the AWD. All products had reliable drought detection capability over Australia based on the results of temporal evolution and contingency tables with a mean volumetric hit index of 0.700, 0.728, and 0.787 for AMSR2, ASCAT, and ERA-interim, respectively. The slight incapability of drought detection capability of SWDI in tropical region was low due to the variation in persistence times of moisture in the atmosphere and soil. Except arid zone, in all climate zones, the reliability of SM products for drought detection followed the following order ASCAT > ERA-interim > AMSR2.

Assessment of remotely sensed soil moisture products and their quality improvement: A case study in South Korea

Soil moisture (SM) retrieved from satellite observations has become available at a global scale with relatively high spatial–temporal resolution, and the satellite-derived SM can be useful data sources where in-situ measurements are scarce or not available. In this study, the SM data from two different satellite sensors, the Advanced Scatterometer (ASCAT) and Advanced Microwave Scanning Radiometer 2 (AMSR2), are evaluated through the comparison with in-situ observation collected from twelve sites over a three-year period (2013–2015) in South Korea. The results reveal that the ASCAT descending overpass (09:30, the local equatorial crossing time) shows a better correlation with the in-situ observation than the ascending overpass (21:30, the local equatorial crossing time), while no significant difference in performance is found for AMSR2. Moreover, ASCAT SM retrieval shows a generally better agreement with in-situ observation. Considering the spatial mismatch and different measurement depths, a cumulative distribution function (CDF) matching method, as well as an exponential filter method, are employed to improve the applicability of satellite-derived SM. Specifically, the observation operators based on CDF matching are derived to find the optimal temporal period and tested by cross-validation. It is found that the CDF matching method split into two groups (i.e., growing and non-growing season) outperforms the other temporal groups. Additionally, considering different observation depths between the in-situ (>10 cm) and the satellite products (the top soil layer), the root-zone SM (RZSM) is derived from satellite surface SM by using the exponential filter method. For this study, a characteristic time length (T) at each observation depth is optimized by maximizing the r value between the SWI and the in-situ observation. Although the optimal T value generally increases with observation depth, it is clearly seen that T values are highly location-dependent. Given an encouraging improvement of the satellite SM estimation when scaling and filtering method applied, the results obtained in this study show that the satellite SM products have the useful potential for operational applications.

Improvement of land surface model simulations over India via data assimilation of satellite-based soil moisture products

Realistic representation of surface states using the land surface model (LSM) is extremely challenging owing to human-induced changes and uncertainty in forcing data. In this study, we focus on two crucial objectives pertaining to hydrology namely (a) to understand the ability of different soil moisture (SM) products to improve simulation of unmodeled irrigation processes through data assimilation process, and (b) to learn the feasibility of these SM products to correct the spatial surface soil moisture artifacts caused due to error in precipitation forcing. The utility of SM products evaluated in the present study are retrieved from different satellite sensors and algorithms such as the active satellite-based the Advanced Scatterometer (ASCAT), merged SM from European Space Agency Climate Change Initiative (ESA CCI V4.2) and the latest passive microwave-based SMOS INRA-CESBIO (SMOS-IC) SM product. The results presented for three years (2010, 2011 and 2012) suggest that assimilation of ASCAT and CCI based products effectively captures the SM changes due to irrigation. Similarly, it corrects the spatial artifacts caused due to precipitation errors. However, the single sensor based products have a limitation in spatial samples per day which is critical to capture dynamic SM products over a larger area. Hence, blended products are more effective on a larger area to capture the dynamics in a more effective manner at daily temporal resolutions.

Comparação Climatológica do Vento na Superfície do Mar entre Duas Reanálises Atmosféricas Globais para a Margem Continental Leste/Sudeste Brasileira

Devido a relevância do entendimento da influencia dos fluxos atmosfericos sobre o estado do oceano, em especial, a modelagem e previsao das interacoes entre esses dois sistemas, o presente estudo objetivou comparar, climatologicamente, duas reanalises atmosfericas com relacao a energia dos ventos sobre a superficie do mar para margem continental leste/sudeste brasileira. Os produtos selecionados foram o Climate Forecast System Reanalysis (CFSR) e o ERA–Interim (ERAI), comparados entre si de 1979 a 2010 e validados de acordo com os dados remotos do Advanced Scatterometer (ASCAT) entre maio/2007 e dezembro/2010. Os resultados demonstraram claro padrao mais energetico do CFSR em relacao ao ERAI, seja espacial ou temporalmente, excecao feita a certas areas costeiras. Quantitativamente, o primeiro produto, em media, foi 13% mais intenso, bem como apresentou maior energia cinetica em 92% dos meses entre 1979 e 2010. O CFSR e o ERAI superestimaram os dados do ASCAT em 8% e 2%, respectivamente, mas se ajustaram satisfatoriamente bem, com baixos RMSE e altos coeficientes de correlacao. Sugere-se que a maior energia existente no CFSR esteja associada a sua maior resolucao espacial, o que possibilitaria maior amostragem dos fenomenos atmosfericos, e que o melhor ajuste do ERAI seja consequencia de seu metodo de assimilacao de dados mais robusto, o que compensaria a menor disponibilidade de dados in situ e remotos no Atlântico Sul.

Comparison of Oceansat-2 Scatterometer Wind Data with Global Moored Buoys and ASCAT Observation

The Oceansat-2 satellite was launched on 23 September 2009 by the Indian Space Research Organization (ISRO). In this study, the historic archived OSCAT wind vectors are compared with the global moored buoys’ wind observations, including the U.S. National Data Buoy Center (NDBC), the Tropical Atmosphere Ocean (TAO), the Pilot Research Moored Array in the Tropical Atlantic (PIRATA), the Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction (RAMA), and Advanced Scatterometer (ASCAT) wind data in the same period of OSCAT by calculating the statistical parameters, namely, the root mean square error (RMSE), bias (mean of residuals), and correlation coefficient (R) between the collocated data. The comparisons with the global moored buoys show that the OSCAT wind vectors are consistent with buoys’ wind measurements. The average errors of the OSCAT wind vectors are 1.20 m/s and 17.7°. The analysis of the OSCAT wind vector errors at different buoy wind speeds in bins of 1 m/s indicates that the accuracy of the OSCAT wind speed first increases and then decreases with the increasing wind speed. The comparisons of OSCAT wind vectors and ASCAT wind vectors show that the average RMSEs of their differences are 1.27 m/s and 20.17°. In general, the accuracies of the OSCAT wind vectors satisfy the general scatterometer’s mission requirement and are consistent with ASCAT wind data. OSCAT wind vectors can be used in the global change study by the combination with other scatterometer data.

Coastal Iberia Summertime Low‐Level Flow Assessed From Scatterometers

AbstractIn summer, the prevailing northerly flow off west Iberia occasionally intensifies and weakens with significant consequences for weather conditions and marine ecology. High‐resolution Advanced SCATterometer (ASCAT) wind products on a 6.25 km grid (ASCAT‐6.25) were used to characterize wind maxima downwind to the major Iberian capes. Wind intensifications downwind Cape Finisterre are large and detached from the coast. They are considered as the response to a large feature in the coastal morphology. Intensifications downwind Cape Roca and Cape São Vicente are smaller and closer to the coast. These are triggered by interaction of supercritical or transcritical flow with topography. ASCAT‐6.25 evening and morning passes show two distinct regimes during intensification events: a southern regime with wind maximum moving toward the coast during the day and a northern regime in which this diurnal shift is not so clear. Another aspect that distinguishes these regimes is the later occurrence of the wind maximum in the southern portion of the coast. Also, wind relaxation events can be identified in ASCAT data. These are followed by a poleward flow of warm water that interrupts the upwelling conditions over west Iberia south of 41°N. Synoptic conditions favorable for the occurrence of intensifications and relaxations were obtained from NWP models. This study was supplemented by RapidScat observations during the summers of 2015 and 2016 at different phases of the diurnal cycle. RapidScat and ASCAT wind fields in combination with NWP data confirm that wind intensifications are multiday events, clearly distinct from the sea breeze circulation.

Extending the Record of Surface Melt on the Larsen C Ice Shelf

The first use of Advanced Scatterometer radar data to determine melt duration on an Antarctic ice shelf shows the season has decreased by up to 2 days per year during the extended 21st century record.

Assessment and inter-comparison of recently developed/reprocessed microwave satellite soil moisture products using ISMN ground-based measurements

Soil moisture (SM) is a key state variable in understanding the climate system through its control on the land surface energy, water budget partitioning, and the carbon cycle. Monitoring SM at regional scale has become possible thanks to microwave remote sensing. In the past two decades, several satellites were launched carrying on board either radiometer (passive) or radar (active) or both sensors in different frequency bands with various spatial and temporal resolutions. Soil moisture algorithms are in rapid development and their improvements/revisions are ongoing. The latest SM retrieval products and versions of products that have been recently released are not yet, to our knowledge, comprehensively evaluated and inter-compared over different ecoregions and climate conditions. The aim of this paper is to comprehensively evaluate the most recent microwave-based SM retrieval products available from NASA's (National Aeronautics and Space Administration) SMAP (Soil Moisture Active Passive) satellite, ESA's led mission (European Space Agency) SMOS (Soil Moisture and Ocean Salinity) satellite, ASCAT (Advanced Scatterometer) sensor on board the meteorological operational (Metop) platforms Metop-A and Metop-B, and the ESA Climate Change Initiative (CCI) blended long-term SM time series. More specifically, in this study we compared SMAPL3 V4, SMOSL3 V300, SMOSL2 V650, ASCAT H111, and CCI V04.2 and the new SMOS-IC (V105) SM product. This evaluation was achieved using four statistical scores: Pearson correlation (considering both original observations and anomalies), RMSE, unbiased RMSE, and Bias between remotely-sensed SM retrievals and ground-based measurements from >1000 stations from 17 monitoring networks, spread over the globe, disseminated through the International Soil Moisture Network (ISMN). The analysis reveals that the performance of the remotely-sensed SM retrievals generally varies depending on ecoregions, land cover types, climate conditions, and between the monitoring networks. It also reveals that temporal sampling of the data, the frequency of data in time and the spatial coverage, affect the performance metrics. Overall, the performance of SMAP and SMOS-IC products compared slightly better with respect to the ISMN in situ observations than the other remotely-sensed products.