Oceansat-2 Scatterometer Research Articles

Dual-Mode Sea Ice Extent Retrieval for the Rotating Fan Beam Scatterometer

Scatterometers are highlighted in polar applications, such as sea ice extent retrieval. There are currently three types of spaceborne scatterometer in operation, among which the rotating pencil beam scatterometer and the rotating fan beam scatterometer have similar rotating observation geometry, but different beam sampling. However, it is difficult to objectively evaluate the performance of the two rotating beam scatterometers using the obtained data. This is because there are significant differences in their system parameters, which in turn affects the objectivity of the evaluation. Considering the high flexibility of the rotating fan beam scatterometer, this study proposes a dual-mode sea ice extent retrieval method for the rotating fan beam scatterometer. The dual modes refer to the rotating fan beam mode (or full incidence mode) and the equivalent rotating pencil beam mode (or single incidence mode). The two modes share the same system and spatiotemporal synchronous backscatter measurements provide the possibility of objectively comparing the rotating pencil beam and rotating fan beam scatterometers. The comparison, validation, and evaluation of the dual-mode sea ice extent of China France Oceanography Satellite Scatterometer (CSCAT) were performed. The results indicate that the sea ice extent retrieval of the equivalent rotating pencil beam mode of the rotating fan beam scatterometer is realizable, and compared to the existing rotating pencil beam scatterometers (such as the OceanSat Scatterometer on ScatSat-1, OSCAT, on ScatSat-1, and the Hai Yang 2B Scatterometer, HSCAT-B), the derived sea ice extent is closer to that of Advanced Microwave Scanning Radiometer 2 (AMSR2). For the two modes of CSCAT, when compared to AMSR2, the sea ice extent of the CSCAT full incidence mode has smaller values of root mean squared error (RMSE), error-of-ice (EI), and ice edge location distance (LD) than those of the CSCAT single incidence mode. These suggest that the rotating fan beam scatterometer shows better observation abilities for sea ice extent than the rotating pencil beam scatterometers.

Endeavours of Scatterometer Satellite (SCATSAT-1) in earth exploration: An overview of products, applications and emerging trends

The SCATSAT-1 (Scatterometer Satellite) was launched by ISRO (Indian Space Research Organisation) on September 26, 2016 from the Satish Dhawan Space Centre, Sriharikota, India. With nearly five years of its journey, the Ku-band (13.5 GHz) based SCATSAT-1 made a profound impact on many scientific domains such as ocean-atmosphere dynamics, soil moisture and vegetation dynamics, climate change, hydrology and polar sea-ice and snowmelt studies. As a successor of the Oceansat-2 Scatterometer (OSCAT), the SCATSAT-1 supports the long-term analysis in climate studies, crop yield prediction, and forecasting analysis. In addition, the SCATSAT-1 provides the four different levels of data products at an enhanced resolution to improve the scope of the scatterometer in different applications. Recently the SCATSAT-1 has been explored in many emerging applications apart from oceanography e.g., crop growth, snow cover analysis, jute crop detection and river level estimation with advanced algorithms i.e., machine learning-based classification, information fusion, and super-resolution mapping. Therefore, it is desired to summarise all operational SCATSAT-1 products, applications, and their emerging trends at the global level in the various scientific domains. This paper has summarized the progress made by SCATSAT-1 in different scientific domains since its launch. A meta-analysis has also been conducted in this paper (using the SCOPUS database) to analyse the current research status of SCATSAT-1 in terms of study area targets. This study highlights the features, challenges, and future directions for the scatterometer improvements.

An Advanced Ku-Band Fine-Resolution and High-Sensitivity Wind Scatterometer

An advanced Ku-band pencil-beam Scatterometer has been developed having a scanning 1.4m diameter Cassegrain antenna, for providing 3km x 20km (for HH beam) and 3km x 23km (for VV beam) fundamental range-slice resolution, a marked improvement over its predecessor Scatsat-1, which provided 6km x 46km (for HH beam) and 6km x 70km (for VV beam). This instrument will carry forward the legacy of data services from the Indian Space Research Organization’s OScat (Oceansat-2 Scatterometer, which operated in orbit from 2009 to 2014) and Scatsat-1, which operated from September 2016 to April 2021, with improved spatial resolution, noise-equivalent normalized radar cross-section (NEσ°) and the sensitivity parameter Kp. The improvement in NEσ° and in the Signal-to-Noise Ratio (SNR) is of the order of 4.5dB, and of Kp by 2.5dB over a Scatsat-1 equivalent resolution cell. Equivalently, over its own range-slice resolution, the new sensor will match Scatsat’s measurement sensitivity. This article elaborates on the system design of the instrument, the performance improvements over Scatsat-1 and the payload characterization methods.

Sensitivity Analysis of C and Ku-Band Scatterometers for River Water Level Estimation

Water-level monitoring is essential for analyzing hydrological extremes, environmental flows, sustainable development and management of water resources. Satellite altimeters have operationally been used to estimate river water levels (e.g., SARAL-Altika, Jason-2/3) with poor revisit times (10&#x2013;35 days). In this current study, we propose a method to estimate river water levels using scatterometer data with 2-day temporal resolution. Sensitivity analysis of radar frequency (Ku- and C-bands) for river water-level estimation was carried out. The concept of change in radar backscatter with an increase in soil wetness is used to develop a model function between basin water index (BWI) and observed river stage. High-resolution OceanSat Scatterometer (OSCAT) (Ku-band) and advanced scatterometer (ASCAT) (C-band) scatterometer image reconstruction (SIR), digital elevation model, and observed water stage datasets have been used in this study. We observed that C-band scatterometer shows better results than Ku-band in terms of Pearson correlation coefficient (<inline-formula> <tex-math notation="LaTeX">$R$ </tex-math></inline-formula>), Nash&#x2013;Sutcliffe Efficiency (NSE), and root mean square error (RMSE), which can be attributed to higher soil wetness sensitivity of the C-band compared with Ku-band scatterometers. <inline-formula> <tex-math notation="LaTeX">$R$ </tex-math></inline-formula> ranged between 0.93 and 0.97 for the C-band and between 0.89 and 0.95 for the Ku-band data. Similarly, NSE ranged from 0.84 to 0.95 for the C-band and between 0.56 and 0.95 for the Ku-band data over selected study sites in the Brahmaputra river basin. Model accuracy was found to be adequate for monitoring of river stage from a remote sensing platform. This study shows that scatterometer observations can be used for operational water-level estimations, which can be useful for water resource management.

Assimilation of GNSS reflectometry delay‐Doppler maps with a two‐dimensional variational analysis of global ocean surface winds

AbstractDirect remote‐sensing observations (e.g., radar backscatter, radiometer brightness temperature, or radio occultation bending angle) are often more effective for use in data assimilation (DA) than the corresponding geophysical retrievals (e.g., ocean surface winds, soil moisture, or atmospheric water vapor). In the particular case of Global Navigation Satellite System Reflectometry (GNSS‐R), the lower‐level delay‐Doppler map (DDM) observable shows a complicated relationship with the ocean surface wind field. Prior studies have demonstrated DA using GNSS‐R wind retrievals inferred from DDMs. The complexity of the DDM dependence on winds, however, suggests that the alternative approach of ingesting DDM observables directly into DA systems, without performing a wind retrieval, may be beneficial. We demonstrate assimilation of DDM observables from the NASA Cyclone Global Navigation Satellite System (CYGNSS) mission into global ocean surface wind analyses using a two‐dimensional variational analysis method. Bias correction and quality‐control methods are described. Several models for the required observation‐error covariance matrix are developed and evaluated, with the conclusion that a diagonal matrix performs as well as a fully populated matrix empirically tuned to a large ensemble of CYGNSS observation data. The 10‐m surface winds from the European Centre for Medium‐Range Weather Forecasts (ECMWF) operational forecast are used as the background (i.e., prior in the variational analysis). Results are compared with independent scatterometer (the advanced scatterometer (ASCAT), the oceansat‐2 Scatterometer (OSCAT)) winds. For one month (June 2017) of data, the root‐mean‐square difference (RMSD) was reduced from 1.17 to 1.07 m·s−1 and bias from −0.14 to −0.08 m·s−1 for the wind speed at the specular point. Within a 150‐km wide swath along the specular point track, the RMSD was reduced from 1.20 to 1.13 m·s−1. These RMSD and bias statistics are smaller than other CYGNSS wind products available at this time.

ERAstar: A High-Resolution Ocean Forcing Product

To address the growing demand for accurate high-resolution ocean wind forcing from the ocean modeling community, we develop a new forcing product, ERA*, by means of a geolocated scatterometer-based correction applied to the European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis or ERA-interim (hereafter referred to as ERAi). This method successfully corrects for local wind vector biases present in the ERAi output globally. Several configurations of the ERA* are tested using complementary scatterometer data [advanced scatterometer (ASCAT)-A/B and oceansat-2 scatterometer (OSCAT)] accumulated over different temporal windows, verified against independent scatterometer data [HY-2A scatterometer (HSCAT)], and evaluated through spectral analysis to assess the geophysical consistency of the new stress equivalent wind fields (U10S). Due to the high quality of the scatterometer U10S, ERA* contains some of the physical processes missing or misrepresented in ERAi. Although the method is highly dependent on sampling, it shows potential, notably in the tropics. Short temporal windows are preferred, to avoid oversmoothing of the U10S fields. Thus, corrections based on increased scatterometer sampling (use of multiple scatterometers) are required to capture the detailed forcing errors. When verified against HSCAT, the ERA* configurations based on multiple scatterometers reduce the vector root-mean-square difference about 10% with respect to that of ERAi. ERA* also shows a significant increase in small-scale true wind variability, observed in the U10S spectral slopes. In particular, the ERA* spectral slopes consistently lay between those of HSCAT and ERAi, but closer to HSCAT, suggesting that ERA* effectively adds spatial scales of about 50 km, substantially smaller than those resolved by global numerical weather prediction (NWP) output over the open ocean (about 150 km).

Calibration and Cross Validation of Global Ocean Wind Speed Based on Scatterometer Observations

AbstractGlobal ocean wind speed observed from seven different scatterometers, namely, ERS-1, ERS-2, QuikSCAT, MetOp-A, OceanSat-2, MetOp-B, and Rapid Scatterometer (RapidScat) were calibrated against National Data Buoy Center (NDBC) data to form a consistent long-term database of wind speed and direction. Each scatterometer was calibrated independently against NDBC buoy data and then cross validation between scatterometers was performed. The total duration of all scatterometer data is approximately 27 years, from 1992 until 2018. For calibration purposes, only buoys that are greater than 50 km offshore were used. Moreover, only scatterometer data within 50 km of the buoy and for which the overpass occurred within 30 min of the buoy recording data were considered as a “matchup.” To carry out the calibration, reduced major axis (RMA) regression has been applied where the regression minimizes the size of the triangle formed by the vertical and horizontal offsets of the data point from the regression line and the line itself. Differences between scatterometer and buoy data as a function of time were investigated for long-term stability. In addition, cross validation between scatterometers and independent altimeters was also performed for consistency. The performance of the scatterometers at high wind speeds was examined against buoy and platform measurements using quantile–quantile (Q–Q) plots. Where necessary, corrections were applied to ensure scatterometer data agreed with the in situ wind speed for high wind speeds. The resulting combined dataset is believed to be unique, representing the first long-duration multimission scatterometer dataset consistently calibrated, validated and quality controlled.

Scatterometer-derived long-term (2000 to 2017) variability in snowmelt and freeze in river basins of the Indian Himalayas

We present the scatterometer-derived snowmelt and freeze variability data for three river basins in the Indian Himalayas (the third pole)—knowledge about which is important for studying the climate and water resources of the area. We document the timing and duration of snowmelt/freeze across the Indian Himalayas. The analysis includes data from QuikSCAT (2000 to 2009), Oceansat Scatterometer (OSCAT) (2010 to 2013), Advanced Scatterometer (ASCAT) (2014 to 2016), and SCATSAT (2017). We utilize Ku-band HH-polarized data from QuikSCAT, OSCAT, and SCATSAT and C-band VV-polarized data from ASCAT and use the adaptive/variable threshold method based on winter mean and standard deviation. The analysis is based on the sensitivity of the backscatter coefficient to the presence of liquid water content in the snow caused due to melting. Past air temperature data available for seven Himalayan stations are used to validate the methodology. The melt/freeze detection is limited to areas where snow is available in that year. The majority of melt onset in the study area takes place in April. Melt onset for higher-elevation zones for the Indus, Ganges, and Brahmaputra basins varies from April 19 to May 20, April 17 to April 30, and April 7 to April 28, respectively. Major freeze onset for these basins is in October. The outputs generated from the analysis will be available on Bhuvan portal under the National Information System for Climate and Environment Studies program of Indian Space Research Organisation.

Evaluating Chinese HY-2B HSCAT Ocean Wind Products Using Buoys and Other Scatterometers

This letter preliminarily assesses the accuracy of ocean wind products from the Ku-band scatterometer (HSCAT) onboard the recently launched Chinese satellite HY-2B. The wind vectors derived from HSCAT during the period from November 15, 2018 to April 30, 2019 are evaluated. The reference wind data include in situ measurements from the offshore meteorological buoys of the National Data Buoy Center, USA, and westerlies mooring of the National Ocean Technology Center, China, and scatterometer winds from the European Advanced Scatterometer (ASCAT) and Indian SCATSAT-1 Oceansat Scatterometer (OSCAT). The spatial difference is limited to $25/\surd 2$ km, while the HSCAT winds are temporally collocated with buoys and other scatterometers by less than 0.5 and 1.5 h, respectively. The comparison results show that the HSCAT data have a root-mean-square error (RMSE) of 0.95–1.20 m/s (14.7°–25.7°) regarding the wind speed and direction, respectively, indicating consistency between the HSCAT winds and the reference. Furthermore, better agreement is found for the HY-2B HSCAT winds processed using the Pencil-beam Wind Processor (PWP) algorithm, regarding wind speed (RMSE of 0.95–1.07 m/s) and particularly with respective to the wind direction (RMSE of 14.7°–19.6°), both satisfying the mission specification (< 2 m/s and < 20 for wind speed and direction, respectively). The encouraging validation results over the first 5 months demonstrate that the HY-2B HSCAT wind products will be useful for the scientific community.

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.

Validation and Impact of SCATSAT-1 Scatterometer Winds

Scatterometer winds are useful in weather forecasting and cyclone monitoring related activities. The Indian Space Research Organization has launched SCATSAT-1, which is a follow-up mission of the Oceansat-2 scatterometer in September 2016. It operates in the Ku-band frequency and provides wind vector data over oceanic region from sun synchronous polar orbit. SCATSAT-1 wind vector data are validated against buoy observations, other scatterometer observations and numerical weather prediction (NWP) analysis. It is found that SCATSAT observations are of good quality in comparison with other similar types of measurements. Impact of assimilation of these observations in NWP forecasts is investigated using the National Centre for Medium Range Weather Forecasting Global Forecast System (NGFS). The NGFS model employs a three-dimensional variational-ensemble Kalman filter (3D Var-EnKF) hybrid assimilation system with a deterministic model of T-1534 resolution and 80 member ensembles of T-574 resolution. Impact of assimilation of SCATSAT-1 observations in prediction of the Kyant, Nada and Varadha cyclones formed in the Bay of Bengal region and impact on operational forecasts are discussed. Assimilation of SCATSAT observations produced marginal improvement in general NWP forecast statistics and improvement is observed in cyclone track forecasts if observations are available near the cyclone position at analysis time.

Bio-optical response of surface waters to wind-induced upwelling off the Kanyakumari coast

This paper investigates the impact of wind-induced coastal upwelling on bio-optical properties over waters off the Kanyakumari coast for two consecutive winters (October 2011 to March 2012 and October 2012 to March 2013). Level-2 wind datasets from Oceansat-2 Scatterometer (12.5 km) have been used to estimate rate of upwelling using a published approach. Moderate-resolution Imaging Spectroradiometer on-board Aqua and Terra space-platform has been used to obtain bio-optical datasets (level-3, 4 km) that include chlorophyll-a (Chl-a) and particulate organic carbon (POC) concentration, absorption due to phytoplankton at 443 nm (aph_443), absorption due to detritus and gelbstoff at 443 nm (adg_443) and remote sensing reflectance (Rrs) at multiple wavelengths. Wind speed resulting in upwelling is found to be in range of 6.95 m s−1–18.67 m s−1. Phytoplankton bloom occurs when upwelling intensity is ranging between approximately 0.90 m/day – 3.50 m/day at a lag of about 2–3 days. Chl-a, aph_443, adg_443 and POC concentrations show simultaneous increment after upwelling and Chl-a is found to be the major parameter controlling other bio-optical properties of the study region. The waters belong to Case-1 type mainly co-dominated by phytoplankton and CDOM with very low concentrations of suspended particulate matter. Additionally, a noticeable drop in SST ranging between 0.55 °C to 1.50 °C has been observed at the same day of upwelling when upwelling intensity is around 1.5 m/day or above.

WAM and WAVEWATCH-III intercomparison studies in the North Indian Ocean using Oceansat-2 Scatterometer winds

This paper presents the intercomparison of wave hindcasts using the third-generation models WAM and WAVEWATCH-III for the North Indian Ocean over 1° × 1° (latitude × longitude) grid resolutions, which reveals the first assessment of their relative performance through intercomparison of the model results. Hindcast wave parameters such as significant wave height, mean wave period, and swell wave height obtained from the simulations using Oceansat-2 scatterometer winds are analyzed to understand the quality and variability associated with the individual model outputs in the Indian Ocean. WAM and WAVEWATCH-III intercomparison studies are carried out for four different cases (January and June 2010, and January and June 2011). A comparative study of the relative performances of these two models is evaluated through extensive and robust statistical error analysis. Based on both qualitative and quantitative assessment of the model results, this study clearly indicates that both WAM and WAVEWATCH-III performed well in the common model domain using Oceansat-2 scatterometer winds, and they can be confidently used for long-term hindcasting in the North Indian Ocean, which will be very useful for most of the user community dealing with various coastal/offshore activities. The study also suggests that it would be preferable to consider available long-term wave measurements both in deep and coastal waters of the North Indian Ocean to validate and intercompare WAM and WAVEWATCH-III further.

A combined passive–active microwave retrieval of ocean surface wind speed from SARAL-AltiKa microwave radar altimeter and radiometer

SARAL (Satellite with Argos and AltiKa) is the first satellite radar altimetry mission to fly a Ka-band instrument (AltiKa), which has completed 5 years in orbit on 25th February 2018. Ka-band SARAL-AltiKa suffers larger signal attenuation due to atmospheric water content and also exhibits a different relationship with wind speed than at Ku and C band. Here an augmentation of our earlier geophysical model function (GMF) that was developed based on first principles for wind speed retrieval from SARAL-AltiKa is proposed. This GMF is further augmented here, taking advantage of the co-aligned passive microwave radiometer making measurements of brightness temperature (BT) along with the primary active radar AltiKa measurements of radar backscatter (RB) and significant wave height (SWH) in conjunction with a large matched database of wind speed from Jason-2, respectively. The retrieved wind speed from currently modified algorithm provides an accuracy of 1.34 ms−1 in root mean square deviation with global buoys. Finally, the new algorithm has been used successfully in demonstrating its applicability in capturing regional to global wind field features and compared with other independent satellite measurements like OceanSat scatterometer, Jason-2 altimeter and DMSP-SSM/I radiometer.

Estimation of size of tropical cyclones in the North Indian Ocean using Oceansat-2 scatterometer high-resolution wind products

Tropical cyclone (TC) is one of the most intense weather hazards, especially for the coastal regions, as it causes huge devastation through gale winds and torrential floods during landfall. Thus, accurate prediction of TC is of great importance to reduce the loss of life and damage to property. Most of the cyclone track prediction model requires size of TC as an important parameter in order to simulate the vortex. TC size is also required in the impact assessment of TC affected regions. In the present work, the size of TCs formed in the North Indian Ocean (NIO) has been estimated using the high resolution surface wind observations from oceansat-2 scatterometer. The estimated sizes of cyclones were compared to the radius of outermost closed isobar (ROCI) values provided by Joint Typhoon warning Center (JTWC) by plotting their histograms and computing the correlation and mean absolute error (MAE). The correlation and MAE between the OSCAT wind based TC size estimation and JTWC-ROCI values was found 0.69 and 33 km, respectively. The results show that the sizes of cyclones estimated by OSCAT winds are in close agreement to the JTWC-ROCI. The ROCI values of JTWC were analyzed to study the variations in the size of tropical cyclones in NIO during different time of the diurnal cycle and intensity stages.

Validation of global sea‐level pressure fields calculated from Oceansat‐2 scatterometer wind data using the UWPBL model

Sea level pressure fields were estimated from the Oceansat‐2 scatterometer (OSCAT) wind measurements using the University of Washington Planetary Boundary Layer (UWPBL) model, and the estimations were evaluated for the year 2011. The Data Interpolating Variational Analysis (DIVA) technique was used for the generation of daily global composites from the along‐track pressure field values. The sea level pressure field data were compared with in‐situ pressure observations (of which there were 220 818 in total) over the global oceans. Scatterometer‐derived pressure fields are in good agreement with the in‐situ measurements, with a bias of 0.31hPa, a correlation coefficient of 0.918 and a standard deviation ratio of 0.398. The accuracy of the estimations shows that global pressure fields calculated using OSCAT winds are suitable for use in synoptic studies/global oceanographic studies.

Oceansat-2 Scatterometer (OSCAT) Wind Fields Over the Global Oceans

Global 2-day mean wind fields of Oceansat-2 scatterometer (OSCAT) were generated using variational data analysis method for the period 2010–2013. The resultant OSCAT gridded wind data with a resolution of 0.5° × 0.5° was validated using in situ observations from buoys and Advanced Scatterometer (ASCAT) for different global ocean basins and wind speed ranges. Globally, a bias of − 0.5 ms−1 for u and 0.1 ms−1 for v and root mean square difference (RMSD) of 1.95 ms−1 for u and 1.7 ms−1 for v was observed. Inter-comparison in different oceanic basins revealed that the gridded OSCAT data is in concurrence with in situ measurements. Inter-comparison between OSCAT and ASCAT for different seasons showed a correlation greater than 0.75, bias in the range of − 1 to 1 ms−1 and RMSD less than 2.5 ms−1; for both u and v wind components on a global scale. The resultant OSCAT gridded winds can be used to generate value added products like wind stress and curl for the global oceans.

Effectiveness of Using Multisatellite Wind Speed Estimates to Construct Hourly Wind Speed Datasets with Diurnal Variations

AbstractThis study used an optimum interpolation method (OIM) to construct a sea surface wind speed (SSW) dataset with hourly resolution based on merged wind speed analysis products from four satellites [AMSR-2, ASCAT, OceanSat Scatterometer (OSCAT), and WindSat]. To validate this hourly SSW dataset, the OIM dataset was compared with observations obtained from moored buoys. These buoy observations were also compared with the products of each of the four satellites individually. The root-mean-square differences and the correlation coefficients between the buoy observations and the OIM dataset indicated that the accuracy of the dataset was slightly lower than that of the single-satellite products. However, a spectrum analysis at the buoy locations indicated that the OIM dataset was capable of resolving diurnal variations in wind speed, which was a result not reproduced by the single-satellite products. In addition, the study also found that the hourly dataset with diurnal variation was effective in obtaining accurate daily mean values by reducing the sampling error. A comparison of daily mean wind speeds derived from satellite observations with those obtained from buoy observations demonstrated that greater accuracy in daily mean SSW data could be achieved using multisatellite observations in comparison with single-satellite observations. Therefore, the application of multisatellite observations that have different observation times could be a useful and effective approach with which to construct datasets with high temporal resolution and to improve the accuracy of daily mean values.

Extension of the QuikSCAT Sea Ice Extent Data Set With OSCAT Data

The Ku-band Oceansat-2 Scatterometer (OSCAT) is very similar to the Quick Scatterometer (QuikSCAT), which operated from 1999 to 2009. OSCAT continues the Ku-band scatterometer data record through 2014 with an overlap of 19 days with QuikSCAT’s mission in 2009. This letter discusses a particular climate application of the time series for sea ice extent observation. In this letter, a QuikSCAT sea ice extent algorithm is modified for OSCAT. Gaps in OSCAT data are accounted for and filled in to support sea ice extent mapping. The OSCAT sea ice extent data are validated with QuikSCAT and Special Sensor Microwave/Imager sea ice extent data.

Offshore wind power resource assessment using Oceansat-2 scatterometer data at a regional scale

In the offshore region the scarcity of in situ wind data in space proves to be a major setback for wind power potential assessments. Satellite data effectively overcomes this setback by providing continuous and total spatial coverage. The study intends to assess the offshore wind power resource of the Karnataka state, which is located on the west coast of India. Oceansat-2 scatterometer (OSCAT) wind data and GIS based methodology were adopted in the study.The OSCAT data accuracy was assessed using INCOIS Realtime All Weather Station (IRAWS) data. Wind speed maps at 10m, 90m and wind power density maps using OSCAT data were developed to understand the spatial distribution of winds over the study area. Bathymetric map was developed based on the available foundation types and demarking various exclusion zones to help in minimizing conflicts. The wind power generation capacity estimation performed using REpower 5MW turbine, based on the water depth classes was found to be 9,091MW in Monopile (0–35m), 11,709MW in Jacket (35–50m), 23,689MW in Advanced Jacket (50–100m) and 117,681MW in Floating (100–1000m) foundation technology. In Indian scenario major thrust for wind farm development in Monopile region is required. Therefore as first phase of development, if 10% of the estimated potential in the region can be developed then, 116% of energy deficit for FY 2011–12 could be met. Also, up to 79% of the anticipated energy deficit for the FY 2014–15 of the Karnataka state could be achieved.