Cross-Calibrated Multi-Platform Research Articles

Assessment of CCMP in Capturing High Winds with Respect to Individual Satellite Datasets

High-wind structures were identified in the Cross-Calibrated Multi-Platform (CCMP) ocean wind vector reanalysis for comparison with winds measured by satellite radiometers, scatterometers, and synthetic aperture radar (SAR) instruments from February to October 2023. The comparison aims to evaluate bias, uncertainty, and spatial correlations with the goal of enhancing the accuracy of ocean wind datasets during tropical cyclones (TCs). In 10° longitude × 10° latitude blocks, each containing a TC, Soil Moisture Active Passive (SMAP) and Advanced Microwave Scanning Radiometer 2 (AMSR2) winds are 6.5 and 4.8% higher than CCMP, while Advanced Scatterometer (ASCATB) is 0.8% lower. For extratropical cyclones, AMSR2 and SMAP also show stronger winds with a 5% difference, and ASCATB is about 0.3% weaker compared to CCMP. The comparison between SAR and CCMP for TC winds, sampled at the locations and time frames of SAR tiles, indicates that SAR winds around TCs are about 9% higher than CCMP winds. Using empirically defined TC structural indices, we find that the TCs observed by CCMP are shifted in locations and lack a compact core region. A Random Forest (RF) regressor was applied to TCs in CCMP with corresponding SAR observations, nearly correcting the full magnitude of low bias in CCMP statistically, with a 15 m/s correction in the core region. The hierarchy of importance among the predictors is as follows: CCMP wind speed (62%), distance of SAR pixels to the eye region (21%) and eye center (7%), and distance of CCMP pixels to the eye region (5%) and eye center (5%).

Sea Surface Wind Speed Estimation From the Combination of Satellite Scatterometer and Radiometer Parameters

AbstractSatellite research on global sea surface winds is crucial for understanding and monitoring the dynamics of earth's oceans, providing valuable insights into weather patterns, climate changes, and oceanographic processes. This study investigates the fusion of active (microwave scatterometer) and passive (microwave radiometer) satellite data using Machine learning (ML) for sea surface wind speed estimation. Employing Random Forest Regression (RF), Convolutional Neural Network Regression (CNN), and Multiple Linear Regression (MLR). Evaluation against reference data sets (Advanced Scatterometer, ERA5, Cross‐Calibrated Multi‐Platform (CCMP), buoy wind speeds) highlights the robustness of the proposed models. The research findings indicate that both RF and CNN exhibit superior accuracy in the active, passive, and joint active‐passive models compared to the simplistic MLR model. The joint models of the three regression methods outperform the individual active or passive models. The root mean square deviation (RMSD) accuracy of RF and CNN joint models, when compared with ASCAT, is in the order of 0.7 m/s, and when compared with buoys, the RMSD accuracy is around 1.1 m/s. The ML models, especially RF and CNN, demonstrate superior performance, providing accurate and reliable estimations crucial for meteorological and oceanographic applications. These findings underscore the potential operational use of ML techniques in enhancing remote sensing applications.

Analysis of Wind Energy Potential in Sri Lankan Waters Based on ERA5 (ECMWF Reanalysis v5) and CCMP (Cross-Calibrated Multi-Platform)

Analysis of Wind Energy Potential in Sri Lankan Waters Based on ERA5 (ECMWF Reanalysis v5) and CCMP (Cross-Calibrated Multi-Platform)

MJO-Induced Warm Pool Eastward Extension Prior to the Onset of El Niño: Observations from 1998 to 2019

Abstract The Madden–Julian oscillation (MJO) and El Niño–Southern Oscillation (ENSO) are the two most important tropical phenomena that affect global weather and climate on intraseasonal and interannual time scales. Although they occur on different time scales, the MJO-induced sea surface temperature (SST) anomalies over the equatorial Pacific have spatial scales similar to SST anomalies prior to El Niño. This study aims to address the question of whether the MJO plays an important role in the warm pool eastward extension (WPEE) leading up to El Niño. We use over 20 years of satellite observations, including optimum interpolated SST, TRMM-GPM precipitation, and the cross-calibrated multiplatform (CCMP) surface winds from 1998 to 2019, to quantify the spatial structure and duration of the MJO-induced warm SST anomalies over the equatorial Pacific (10°S–10°N, 130°E–180°). The intensity of the MJO is measured by the total rain volume and average surface westerly wind speed throughout its convectively active phase. Results show that 1) 61% of the 98 MJO events induced a WPEE over 1000–3000 km along the equator, which can last beyond 15–30 days after the MJO precipitation ended; 2) the MJO events prior to El Niño are generally stronger and produce significant WPEE far beyond its annual cycle and increasing SST warming in the Niño-3.4 region; 3) consecutive MJO events can produce much stronger WPEE prior to El Niño, which are observed in all El Niño events from 1998 to 2019; and 4) more frequent and stronger MJO-induced WPEE occurs in March–May than other seasons. These results can help better understand the MJO–ENSO interaction and, ultimately, improve the prediction of El Niño onset.

ResNet Deep Learning‐Based Inversion Method for Sea Surface Wind Field

This paper presents a method to invert the sea surface wind field from Sentinel‐1 synthetic aperture radar (SAR) interferometric wide swath (IW) band mode ground range detected (GRD) vertical polarization (VV) data based on the deep residual network (ResNet). Compared with the traditional algorithm (i.e., using geophysical model functions (GMFs)), the wind field inversion based on the ResNet deep learning method solves the problem of separate wind direction and wind speed inversion. The ResNet model is developed based on the results of 212 VV polarized IW images and cross calibrated multiplatform (CCMP) data collected in 2021 in the offshore waters of China. The root mean square error, standard deviation, and bias of the inverse wind direction of the ResNet model are 12.5°, 9.8°, and 7.4°, respectively, and the root mean square error, standard deviation, and bias of the inverse wind speed are 2.33, 1.495, and −1.375. The ResNet is better than the conventional local gradient and C‐band model models, further confirming the advantages shown by the deep learning algorithm in nonlinear fitting and its potential application in sea surface wind field inversion.

Evaluation of Blended Wind Products and Their Implications for Offshore Wind Power Estimation

The Cross-Calibrated Multi-Platform (CCMP) wind analysis is a satellite-based blended wind product produced using a two-dimensional variational method. The current version available publicly is Version 2 (CCMP2.0), which includes buoy winds in addition to satellite winds. Version 3 of the product (CCMP3.0) is being produced with several improvements in analysis algorithms, without including buoy winds. Here, we compare CCMP3.0 with a special version of CCMP2.0 that did not include buoy winds, so both versions are independent of buoy measurements. We evaluate them using wind data from buoys around the coasts of the United States and discuss the implications for the wind power industry and offshore wind farms. CCMP2.0 uses ERA-Interim 10 m winds as the background to fill observational gaps. CCMP3.0 uses ERA5 10 m neutral winds as the background. Because ERA5 winds are biased towards lower values at higher wind conditions, CCMP3.0 corrected this bias by matching ERA5 wind speeds with satellite scatterometer wind speeds using a histogram matching method. Our evaluation indicates that CCMP3.0 has better agreement with the independent buoy winds, primarily for higher winds (>10 m/s). This is reflected by the higher correlation and lower root-mean-squared differences of CCMP3.0 versus buoy winds, especially for higher wind conditions. For the U.S. coastal region (within 200 km), the mean wind speed of CCMP3.0 is enhanced by 1–2%, and the wind speed standard deviation is enhanced by around 3–5%. These changes in wind speed and its standard deviation from CCMP2.0 to CCMP3.0 cause an 8–12% increase in wind power density. The wind power density along the U.S. coastal region is also correlated with various climate indices depending on locations, providing a useful approach for predicting wind power on subseasonal to interannual timescales.

Generation of Non-Linear Technique Based 6 Hourly Wind Reanalysis Products Using SCATSAT-1 and Numerical Weather Prediction Model Outputs

We combined observations of ocean surface winds from Indian SCATterometer SATellite-1 (SCATSAT-1) with a background wind field from a numerical weather prediction (NWP) model available at National Centre for Medium-Range Weather Forecast (NCMRWF) to generate a 6-hourly gridded hybrid wind product. A distinctive feature of the study is to produce a global gridded wind field from SCATSAT-1 scatterometer passes with spatio-temporal data gaps at regular synoptic hours relevant for forcing models and other NWP studies. We are following the concept from the modern particle filter technique, which does not represent the model probability density function (PDF) as Gaussian. We generated the 6-hourly hybrid winds for 2018 and validated them using the wind speed from daily gridded level-4 SCATSAT-1 winds (L4AW), Cross Calibrated Multi-Platform (CCMP) dataset and global buoy data from National Data Buoy Centre (NDBC). The results suggest the potential of the technique to produce scatterometer winds at the desired temporal frequency with significantly less noise and bias along the swath. The study shows that the generated hybrid winds are of prime quality compared with the already existing daily products available from Indian Space Research Organization (ISRO).

Seasonal Variation of the Land Breeze System in the Southwestern Bay of Bengal and Its Influence on Air‐Sea Interactions

AbstractThis study examines the seasonal variability of the Land Breeze System (LBS) in the Bay of Bengal (BoB) using hourly moored buoy data, coastal radar data, atmospheric reanalysis data, and 6‐hourly satellite‐based Cross‐Calibrated Multi‐Platform (CCMP) wind velocity data. We first provide an overview of the LBS for the entire BoB, then focus on the pronounced LBS in southwestern BoB and its impact on the near‐surface current field and latent heat flux (LHF). We show that the LBS in the southwestern BoB exhibits a maximum diurnal wind speed amplitude of ∼2 m s−1 with seaward nearshore winds best developed in the morning hours. The geographical coverage is maximum in July and August and minimum in December and January. During its peak phase in July–August, the signature of the LBS in the southwestern BoB extends up to 600 km offshore, occupies ∼20% of the basin, and accounts for approximately 15% of the seasonal mean wind speed variance. The near‐surface current field shows a rapid response to the diurnal wind speed variations, with an eastward current observed between 1000–1800 IST, a westward current between 1800–0800 IST, and a diurnal range of 12 cm s−1. LHF shows well‐defined diurnal variability in response to diurnal LBS wind speed variability, with a morning maximum, evening minimum, and a diurnal range of 35 W m−2. We also find that the large‐scale seasonal winds are the main factor in determining the annual variability in strength and geographical coverage of the LBS in the southwestern BoB.

Risk assessment of marine disasters in fishing ports of Qinhuangdao, China

The data of the Cross-Calibrated Multi-Platform (CCMP) wind field was used as the background wind field to reconstruct the Yellow Sea and Bohai Sea wind fields for 30 years from 1991 to 2020. The observed and reconstructed wind fields were in good alignment. Reconstructed wind-field data were used to drive the SWAN model. In comparison, the calculated significant wave heights and measured data were in good agreement. Based on the SWAN calculation results, the wave elements of the 10-year return period were calculated to provide the input boundary conditions for the MIKE21 SW model. By calculating the wave fields near the fishing ports, the hidden dangers of marine disasters in six fishing ports in Qinhuangdao were analyzed. The results show that there are hidden dangers of marine disasters in the Xinkaikou fishing port; Daihekou, Yanghekou and Shanhaiguan fishing ports all have potential marine disasters risks. In addition, no hidden dangers of marine disasters in the Xieliangkou fishing port and Fishing vessel wharf port were identified.

GloWS-Net: A Deep Learning Framework for Retrieving Global Sea Surface Wind Speed Using Spaceborne GNSS-R Data

Spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) is a new remote sensing technology that uses GNSS signals reflected from the Earth’s surface to estimate geophysical parameters. Because of its unique advantages such as high temporal and spatial resolutions, low observation cost, wide coverage and all-weather operation, it has been widely used in land and ocean remote sensing fields. Ocean wind monitoring is the main objective of the recently launched Cyclone GNSS (CYGNSS). In previous studies, wind speed was usually retrieved using features extracted from delay-Doppler maps (DDMs) and empirical geophysical model functions (GMFs). However, it is a challenge to employ the GMF method if using multiple sea state parameters as model input. Therefore, in this article, we propose an improved deep learning network framework to retrieve global sea surface wind speed using spaceborne GNSS-R data, named GloWS-Net. GloWS-Net considers the fusion of auxiliary information including ocean swell significant wave height (SWH), sea surface rainfall and wave direction to build an end-to-end wind speed retrieval model. In order to verify the improvement of the proposed model, ERA5 and Cross-Calibrated Multi-Platform (CCMP) wind data were used as reference for extensive testing to evaluate the wind speed retrieval performance of the GloWS-Net model and previous models (i.e., GMF, fully connected network (FCN) and convolutional neural network (CNN)). The results show that, when using ERA5 winds as ground truth, the root mean square error (RMSE) of the proposed GloWS-Net model is 23.98% better than that of the MVE method. Although the GloWS-Net model and the FCN model have similar RMSE (1.92 m/s), the mean absolute percentage error (MAPE) of the former is improved by 16.56%; when using CCMP winds as ground truth, the RMSE of the proposed GloWS-Net model is 2.16 m/s, which is 20.27% better than the MVE method. Compared with the FCN model, the MAPE is improved by 17.75%. Meanwhile, the GloWS-Net outperforms the FCN, traditional CNN, modified CNN (MCNN) and CyGNSSnet models in global wind speed retrieval especially at high wind speeds.

ASSESSMENT OF WIND ENERGY POTENTIAL IN THE BIEN DONG SEA USING CCMP SATELLITE DATA

This study assessesed the wind energy potential in the Bien Dong Sea by using the Cross-Calibrated Multi-Platform (CCMP) satellite wind data from 1991 to 2020. The results showed that the wind energy potential at 10 m height above ground is not high in the offshore areas with annual mean wind speeds ranging from 5 to 8 m/s. At heights of 100 m, 150 m, and 200 m, the greatest wind energy potential is found in the offshore areas of the North Bien Dong Sea Region, Binh Dinh - Ninh Thuan, Binh Thuan - Ca Mau, and the central part of the Gulf of Tonkin. Especially, the wind energy is quite high in the Sea of Ninh Thuan to Ba Ria - Vung Tau with annual mean wind speeds of 8 to 10 m/s, and the annual mean wind power density of 500 to 700 W/m2 at a height of 100 m. On the seasonal scale, the wind energy potential of January and October is greater than that of April and July for the orthern offshore areas while the wind energy potential of January is the greatest, followed by July and transition months for the southern offshore areas.

Adjustment of extreme wind speed in regional climate downscaling over southwestern South Atlantic

AbstractIn this study, we estimated near‐surface wind speed 100‐year return values over the southwestern South Atlantic Ocean, using present‐day (1979–2018) simulations from the regional climate models (RCMs) WRF and RegCM4. Extreme wind events, associated with hazardous conditions over coastal and oceanic areas, must be well represented in numerical models for risk assessment, and few studies focused on the added value offered by RCMs to wind extremes. Events were selected with the peaks‐over‐threshold method and extremes were calculated by fitting peaks to a generalized Pareto distribution. For the assessment of model performance, we used the satellite‐based dataset from the Cross‐Calibrated Multi Platform (CCMP), which has great agreement with in situ observations. While modern reanalysis underestimated higher wind speed quantiles, the CCMP was able to represent these quantiles. Dynamical downscaling with the WRF (RegCM4) indicates an underestimation (overestimation) of wind speed for upper quantiles. To mitigate the effects of these differences in the extreme value estimate, we applied a linear adjustment in the simulated wind speed using the CCMP as reference. This application reduced the bias for higher wind speeds in simulations for all regions, except over the coastal area near Argentina and Uruguay, where downscaling already realistically represents extreme events. The spatial distribution of the simulated extremes is compatible with previous results based on reanalysis and satellite data, although with finer‐scale structure, especially over the southern South Atlantic, a region frequently affected by cyclone occurrence and extreme near‐surface winds. The extreme wind speed maps estimated for 100 years reflect these conditions with values reaching around 30 m·s−1 in the area, even after the wind adjustment. Besides, the fine‐scale features aggregate important new information related to extreme winds in the region, which is a relevant added value in the study of return values estimates.

Improving the Accuracy of the Cross-Calibrated Multi-Platform (CCMP) Ocean Vector Winds

The Cross-Calibrated Multi-Platform (CCMP) Ocean vector wind analysis is a level-4 product that uses a variational method to combine satellite retrievals of ocean winds with a background wind field from a numerical weather prediction (NWP) model. The result is a spatially complete estimate of global ocean vector winds on six-hour intervals that are closely tied to satellite measurements. The current versions of CCMP are fairly accurate at low to moderate wind speeds (<15 m/s) but are systematically too low at high winds at locations/times where a collocated satellite measurement is not available. This is mainly because the NWP winds tend to be lower than satellite winds, especially at high wind speed. The current long-term CCMP version, version 2.0, also shows spurious variations on interannual to decadal time scales caused by the interaction of satellite/model bias with the varying amount of satellite measurements available as satellite missions begin and end. To alleviate these issues, here we explore methods to adjust the source datasets to more closely match each other before they are combined. The resultant new CCMP wind analysis agrees better with long-term trend estimates from satellite observations and reanalysis than previous versions.

Decade-low aerosol levels over the Bohai and Yellow Seas amid the COVID-19 lockdown.

Coronavirus disease 2019 (COVID-19) has crucially influenced anthropogenic activities, which in turn impacts upon the environment. In this study, we investigated the variations on aerosol optical depth (AOD) at 550 nm over the Bohai Sea and Yellow Sea during the COVID-19 lockdown (from February to March in 2020) of China mainland based on Moderate-resolution Imaging Spectroradiometer (MODIS) observation by comparing with historical AOD records (2011–2019). Our results show that with the lockdown implementation, the decade-low AOD levels are achieved in February and March 2020 (0.39 ± 0.18 and 0.37 ± 0.19, respectively), which are 22% and 28% (p < 0.01) lower than the average AOD between 2011 and 2019 (0.50 ± 0.08 and 0.52 ± 0.05, respectively). After the lockdown restrictions were relaxed and industrial production gradually resumed, the AOD in April 2020 rebounded to the historical average level. Besides, compared with historical observations (2011–2019), the AOD temporal variability from February to April 2020 showed different pattern, with the decade-high increase from March to April (+0.11) and decade-low increase from February to March (-0.01). Independent observations and simulation, including fine particulate matter (PM2.5) from ground-based measurements, wind field from Cross-Calibrated Multi-Platform, satellite-derived aerosol type, and back trajectories calculation by Hybird Single Paricle Lagrangian Intergrated Trajectory (HYSPLIT) model, indicated that the above abnormal AOD variation can be attributed to reduction of anthropogenic emissions during the COVID-19 lockdown period. The results of this paper, therefore, indicate that aerosols over the Bohai and Yellow Seas are strongly influenced by human activities, and the public health events such as the epidemic may alter the intensity of human activities and thus the spatio-temporal pattern of aerosol over ocean. With the global spread of the epidemic and the corresponding significant changes in human behavior patterns (restrictions on human activities, etc.), more studies should be carried out in the future about the aerosol variability and its potential impact on the marine environment.

Evaluation of NCEP-CFSv2, ERA5, and CCMP wind datasets against buoy observations over Zhejiang nearshore waters

Numerical models are indispensable tools that allow analysing and predicting the dynamic processes of ocean shelf seas. Selecting the most reliable wind product is crucial for reducing model biases. The five buoy observation stations in the coastal waters of Zhejiang, China, continuously recorded wind field data from 1 June 2018 to 31 May 2019, which provided an opportunity to evaluate the most widely used wind products: the Climate Forecast System Version 2 (CFSv2), Cross-Calibrated Multi-Platform (CCMP), and Medium-Range Weather Forecasts (ECMWF) latest reanalysis (ERA5). Our results suggested that in different seasons, regardless of the water region, the wind speeds of the three wind products in autumn and winter were relatively closer to the observations than those during spring and summer because of higher wind speeds. Statistical analysis indicated that wind products performed better over the open waters than nearshore waters. In nearshore, wind products performed worse over the region with more complex topography than relatively simple surrounding terrain. Overall, the ERA5 wind field slightly outperformed the other wind products. Although preliminary, our work is indispensable to ensure high model performance. • The most widely used wind field products were evaluated against observations. • Wind products perform better over open waters than those over nearshore waters. • The Medium-Range Weather Forecasts latest reanalysis is closer to the observations. • Three wind products perform better in autumn and winter than in spring and summer.

Preliminary Estimate of CFOSAT Satellite Products in Tropical Cyclones

The China France Oceanography Satellite (CFOSAT) was launched on October 29, 2018, and is equipped with two sensors: a space-borne rotating fan beam scatterometer (CSCAT) and a space-borne surface wave investigation and monitoring (SWIM) radar. The CSCAT is dedicated to monitoring sea surface winds (SSWs), and the SWIM radar is designed to measure surface ocean waves. In this study, CSCAT SSW products during global tropical cyclones (TCs) were validated using wind data from multiple sources, including European Center for Medium-Range Weather Forecasts (ECMWFs) reanalysis (ERA5), cross-calibrated multi-platform (CCMP), soil moisture active passive (SMAP), WindSat, HY-2B, advanced scatterometer (ASCAT), and buoy wind. Wave parameters (significant wave height (SWH), dominant direction, and wavelength) were validated using wave data from ERA5, Jason3, and the HY-2B altimeter. The results show the following: 1) errors of the CFOSAT wind speed (WS) and SWH increase with the enhancement of TC intensity; 2) the WS accuracy of wind vector cells (WVCs) near the nadir is better than that at the nadir and the edge of the swath; 3) the wind direction (WD) correlation between the CSCAT and ERA5, ASCAT, and WindSat is more obvious at high WSs (>20 m/s) than at lower WSs (<6 m/s); 4) the accuracy of the SWIM off-nadir SWH product is lower than that of the nadir SWH; and 5) a triple collocation comparison among the CFOSAT SSW, ERA5 SSW, and WindSat SSW indicates that the CFOSAT SSW provides the best performance.

GNSS-R Wind Speed Retrieval of Sea Surface Based on Particle Swarm Optimization Algorithm

Spaceborne global navigation satellite system reflectometry (GNSS-R) techniques have been developed for sea surface wind speed retrieval in recent years. In order to utilize both the leading edge slope (LES) and normalized bistatic radar cross-section (NBRCS), the minimum variance estimator (MVE) is used in the cyclone GNSS (CYGNSS) algorithm for wind retrieval. However, due to the high correlation of two observables, the root mean square error (RMSE) of the MVE estimated winds is not improved significantly. In this article, a new method by combining retrievals from delay-Doppler map (DDM) observables based on particle swarm optimization (PSO) is proposed. LES and NBRCS observables from CYGNSS V2.1 products are used, and then wind retrievals from them are combined by PSO. In order to validate the performance, European Center for Medium-Range Weather Forecasts (ECMWF) and cross-calibrated multi-platform (CCMP) ocean surface wind vector analysis product 10-m ocean surface wind products are used as ground truth. The results show that, when using ECMWF winds, the RMSE of MVE retrievals is 2.21 m/s, while that of PSO is 1.95 m/s: an improvement of 12%; when using CCMP winds, the RMSE of MVE retrievals is 2.15 m/s, while that of PSO is 1.92 m/s: an improvement of 11%. Therefore, we conclude that the PSO algorithm is an improvement on the state-of-the-art MVE GNSS-R-based wind speed retrieval techniques. However, the PSO-based wind retrievals show the dependence on the GPS block type and the CYGNSS satellite identifier that the MVE-based techniques suffer from.

Impact of Different Wind Representations on Resonant Ocean Near-inertial Motions in the Gulf of Mexico

This study investigates the resonated diurnal impact of the surface wind forcing from several atmospheric wind products, including CCMP (Cross-Calibrated Multi-Platform), ERA Interim, NCEP-2 (NCEP-DOE AMIP-II Reanalysis), MERRA (Modern-Era Retrospective analysis for Research and Application), NARR (Northern American Regional Reanalysis) and NAM (North American Mesoscale Forecast System), on the wind-driven near-inertial ocean motions in the northern Gulf of Mexico by numerical simulation, and in comparison, with NDBC buoy surface observed winds. Our analyses show that the near-inertial wind power input, which can affect vertical mixing in the water column, is closely associated with the variability of land-sea breeze by various wind products. In comparison with buoy observations in the northern Gulf of Mexico, the MERRA has the worst land-sea breeze representation, while the NCEP2 and CCMP, which are chosen to represent the coarsest and medium–high horizontal resolution, have the best performance. We further show that by comparison of the buoy data, that the near-inertial wind power input derived from all the wind products is underestimated by 30–50% approximately.

Potential Temporal and Spatial Trends of Oceanographic Conditions with the Bloom of Ulva Prolifera in the West of the Southern Yellow Sea

Based on National Oceanic and Atmospheric Administration/Advanced Very High-Resolution Radiometer (NOAA/AVHRR) remote sensing and Cross-Calibrated Multi-Platform (CCMP) wind field data from 2007 to 2019, oceanographic conditions are analysed, respectively, in the Source Area (SA) and Typical Bloom Area (TBA) of Ulva prolifera (U. prolifera) in the west of the Southern Yellow Sea (SYS) using Sea Surface Temperature (SST), Suspended Sediment Concentration (SSC) and Wind Speed over the years. The results indicate that the annual maximum SST Difference (SSTD) between U. prolifera SA and TBA is strongly consistent with the intensity of U. prolifera, and a high SST Warming Rate (WR) from May to July may constrain the U. prolifera blooms. The Taiwan Warm Current (TWC), crossing Yangtze River Estuary northward from March to April, leads to SST increasing in the SA and becomes a key trigger for the growth of U. prolifera in the early period. The amount of U. prolifera may decrease in the early period because of the lower light intensity with high SSC and turbidity in SA. The summer monsoon is one of determinants for the spread of U. prolifera, and the distribution of U. prolifera reaches its highest point with a higher mean wind speed in the TBA.

Wind Energy Potential Assessment Based-on WRF Four-Dimensional Data Assimilation System and Cross-Calibrated Multi-Platform Dataset

Indonesia has a target of achieving 23% of renewable energy share in the total energy mix in 2025. However, Indonesia does not have accurate and comprehensive data on renewable energy potentials, especially wind energy. This article aims to assess the theoretical potential of wind speed and to visualize the wind speed by province for the entire Indonesia. Our assessment relied on the Weather Research and Forecasting (WRF) model using Four-Dimensional Data Assimilation technique, also known as Nudging Newtonian relaxation. The robustness of our analysis is confirmed by using high-resolution data from the National Centers for Environmental Prediction–Final (NCEP - FNL) and Cross-Calibrated Multi-Platform (CCMP) Reanalysis satellite data. This study shows the WRF method is a feasible option to estimate wind speed data.