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The discovery of the 2012 extreme melt event across almost the entire surface of the Greenland ice sheet is presented. Data from three different satellite sensors – including the Oceansat‐2 scatterometer, the Moderate‐resolution Imaging Spectroradiometer, and the Special Sensor Microwave Imager/Sounder – are combined to obtain composite melt maps, representing the most complete melt conditions detectable across the ice sheet. Satellite observations reveal that melt occurred at or near the surface of the Greenland ice sheet across 98.6% of its entire extent on 12 July 2012, including the usually cold polar areas at high altitudes like Summit in the dry snow facies of the ice sheet. This melt event coincided with an anomalous ridge of warm air that became stagnant over Greenland. As seen in melt occurrences from multiple ice core records at Summit reported in the published literature, such a melt event is rare with the last significant one occurring in 1889 and the next previous one around seven centuries earlier in the Medieval Warm Period. Given its rarity, the 2012 extreme melt across Greenland provides an exceptional opportunity for new studies in broad interdisciplinary geophysical research. Key Points Satellites reveal the 2012 extreme melt across most of the Greenland ice sheet In‐situ temperature data and field observations confirm the melt event Extreme melt is rare: last one in 1889, and next previous ~7 centuries earlier

The sea surface wind products retrieved from HY-2 scatterometer have become better over time, and it has contributed significantly to the development of forecasting abilities. This article compares the sea surface wind products obtained from HSCAT with buoy winds from NDBC during long time series to validate the results, and uses the global MPAS-GSI with unstructured spherical grids for assimilating and forecasting. The observation errors employed in this study are 2.5m s -1 since the greatest RMSEs of the u- and v-component are less than 1.7m s -1 . The simulations with or without assimilation are analysed and validated with ERA5 reanalysis in several selected locations, proving that assimilating HSCAT wind products can indeed enhance the forecast skill of surface wind.

In this study, global satellite data were analyzed to determine trends in oceanic wind speed and significant wave height over the 33-year period from 1985 to 2018. The analysis uses an extensive database obtained from 31 satellite missions comprising three types of instruments—altimeters, radiometers, and scatterometers. The analysis shows small increases in mean wind speed and significant wave height over this period, with larger increases in extreme conditions (90th percentiles). The largest increases occur in the Southern Ocean. Confidence in the results is strengthened because the wind speed trends are confirmed by all three satellite systems. An extensive set of sensitivity analyses confirms that both the mean and 90th percentile trends are robust, with only small impacts caused by satellite calibration and sampling patterns.

SCATSAT-1 is the Indian Space Research Organisation’s (ISRO’s) newest Ku-band scatterometer which was launched on 26 September 2016 from ISRO’s space-port Sriharikota on-board the PSLV C35 mission. It is an advanced follow-on of OSCAT, ISRO’s first Scatterometer in space on-board the Oceansat-2 satellite, which ceased to operate in April 2014. OSCAT had been a globally acclaimed sensor during its lifetime. The data from SCATSAT-1 exhibit superior quality, and will not only serve the operational wind and weather prediction community in the years to come, but also hold the promise of securing a place in the long-term climate data records. SCATSAT-1 is a standalone scatterometer mission atop the Indian Mini Satellite (IMS-2) bus. The scatterometer payload is a two-beam, dual-polarized, conically scanning, pencil beam, real-aperture radar which measures near-surface wind vectors over ocean exploiting Bragg scattering resonance at Ku-band. It has been developed in ISRO’s Space Applications Centre, Ahmedabad in less than two and half years to replace OSCAT. Although it inherits the instrument specifications from OSCAT, several enhancements have been made in its hardware as well as in the payload characterization from the purview of miniaturization and performance improvement over OSCAT. This article highlights the hardware improvements, the payload characterization methods devised, and the performance enhancements of SCATSAT-1 over OSCAT. The in-orbit performance of SCATSAT-1 is also discussed.

Recent technological advancements in the past decades have been driven by the miniaturisation of devices using surfaces with nano-scale features. These advancements require fast, large area measurement techniques that can be used in process control to detect surface contaminations or to monitor fabrication quality. Here we present a modified version of the scanning coherent Fourier scatterometer with multiple beams that can be used to scan larger areas without increasing the scan time or decreasing the spatial resolution.

Soil moisture is a key environmental variable, important to e.g., farmers, meteorologists, and disaster management units. We fuse surface soil moisture (SSM) estimates from spatio-temporally complementary radar sensors through temporal filtering of their joint signal and obtain a kilometre-scale, daily soil water content product named SCATSAR-SWI. With 25 km Metop ASCAT SSM and 1 km Sentinel-1 SSM serving as input, the SCATSAR-SWI is globally applicable and achieves daily full coverage over operated areas. We employ a near-real-time-capable SCATSAR-SWI algorithm on a fused 3 year ASCAT-Sentinel-1-SSM data cube over Italy, obtaining a consistent set of model parameters, unperturbed by coverage discontinuities. An evaluation of a therefrom generated SCATSAR-SWI dataset, involving a 1 km Soil Water Balance Model (SWBM) over Umbria, yields comprehensively high agreement with the reference data (median R = 0.61 vs. in situ; 0.71 vs. model; 0.83 vs. ASCAT SSM). While the Sentinel-1 signal is attenuated to some extent, the ASCAT’s signal dynamics are fully transferred to the SCATSAR-SWI and benefit from the Sentinel-1 parametrisation. Using the SM2RAIN approach, the SCATSAR-SWI shows excellent capability to reproduce 5 day-accumulated rainfall over Italy, with R = 0.89 against observed rainfall. The SCATSAR-SWI is currently in preparation towards operational product dissemination in the Copernicus Global Land Service (CGLS).

The Winds and Currents Mission (WaCM) is a proposed approach to meet the need identified by the NRC Decadal Survey for the simultaneous measurements of ocean vector winds and currents. WaCM features a Ka-band pencil-beam Doppler scatterometer able to map ocean winds and currents globally. We review the principles behind the WaCM measurement and the requirements driving the mission. We then present an overview of the WaCM observatory and tie its capabilities to other OceanObs reviews and measurement approaches.

Large-scale changes in Arctic sea ice thickness, volume and multiyear sea ice (MYI) coverage with available measurements from submarine sonars, satellite altimeters (ICESat and CryoSat-2), and satellite scatterometers are summarized. The submarine record spans the period between 1958 and 2000, the satellite altimeter records between 2003 and 2018, and the scatterometer records between 1999 and 2017. Regional changes in ice thickness (since 1958) and within the data release area of the Arctic Ocean, previously reported by Kwok and Rothrock (2009 Geophys. Res. Lett. 36 L15501), have been updated to include the 8 years of CryoSat-2 (CS-2) retrievals. Between the pre-1990 submarine period (1958-1976) and the CS-2 period (2011-2018) the average thickness near the end of the melt season, in six regions, decreased by 2.0 m or some 66% over six decades. Within the data release area (∼38% of the Arctic Ocean) of submarine ice draft, the thinning of ∼1.75 m in winter since 1980 (maximum thickness of 3.64 m in the regression analysis) has not changed significantly; the mean thickness over the CS-2 period is ∼2 m. The 15 year satellite record depicts losses in sea ice volume at 2870 km3/decade and 5130 km3/decade in winter (February-March) and fall (October-November), respectively: more moderate trends compared to the sharp decreases over the ICESat period, where the losses were weighted by record-setting melt in 2007. Over the scatterometer record (1999-2017), the Arctic has lost more than 2 × 106 km2 of MYI-a decrease of more than 50%; MYI now covers less than one-third of the Arctic Ocean. Independent MYI coverage and volume records co-vary in time, the MYI area anomalies explain ∼85% of the variance in the anomalies in Arctic sea ice volume. If losses of MYI continue, Arctic thickness/volume will be controlled by seasonal ice, suggesting that the thickness/volume trends will be more moderate (as seen here) but more sensitive to climate forcing.

This study presents an objective scheme to construct hurricane bogus vortices based on satellite microwave scatterometer observations of sea surface wind vectors. When specifying a bogus vortex using Fujita’s formula, the required parameters include the center position and the radius of the maximum gradient of sea level pressure (R0). We first propose determining the tropical cyclone (TC) center position as the cyclonic circulation center obtained from sea surface wind observations and then establishing a regression model between R0 and the radius of 34-kt sea surface wind of scatterometer observations. The radius of 34-kt sea surface wind (R34) is commonly used as a measure of TC size. The center positions determined from HaiYang-2B/2C/2D Scatterometers, MetOp-B/C Advanced Scatterometers, and FengYun-3E Wind Radar compared favorably with the axisymmetric centers of hurricane rain/cloud bands revealed by Advanced Himawari Imager observations of brightness temperature for the western Pacific landfalling typhoons Doksuri, Khanun, and Haikui in 2023. Furthermore, regression equations between R0 and the scatterometer-determined radius of 34-kt wind are developed for tropical storms and category-1, -2, -3, and higher hurricanes over the Northwest Pacific (2022–2023). The bogus vortices thus constructed are more realistic than those built without satellite sea surface wind observations.

The present study discusses the application of near real-time ocean surface wind vectors retrieved from scatterometer instrument, on-board Indian polar satellite SCATSAT-1, for tropical cyclone (TC) analysis and prediction. The real-time tropical cyclogenesis prediction of cyclonic activities in the North Indian Ocean basin has been presented using SCATSAT-1 wind data. The study also demonstrates the utility of high-resolution surface wind products of the scatterometer in monitoring mesoscale-level features of TCs for centre determination, size estimation and analysis of asymmetric wind radii. Impact of SCATSAT-1 winds for TC prediction using numerical weather prediction model has also been discussed. The shortcomings of ocean surface wind observations from space-based scatterometers are addressed, in addition to the sensor requirements for future satellite missions.