European Centre For Medium-Range Weather Forecasts Research Articles

Online Model Error Correction With Neural Networks in the Incremental 4D‐Var Framework

AbstractRecent studies have demonstrated that it is possible to combine machine learning with data assimilation to reconstruct the dynamics of a physical model partially and imperfectly observed. The surrogate model can be defined as an hybrid combination where a physical model based on prior knowledge is enhanced with a statistical model estimated by a neural network (NN). The training of the NN is typically done offline, once a large enough data set of model state estimates is available. By contrast, with online approaches the surrogate model is improved each time a new system state estimate is computed. Online approaches naturally fit the sequential framework encountered in geosciences where new observations become available with time. In a recent methodology paper, we have developed a new weak‐constraint 4D‐Var formulation which can be used to train a NN for online model error correction. In the present article, we develop a simplified version of that method, in the incremental 4D‐Var framework adopted by most operational weather centers. The simplified method is implemented in the European Center for Medium‐Range Weather Forecasts (ECMWF) Object‐Oriented Prediction System, with the help of a newly developed Fortran NN library, and tested with a two‐layer two‐dimensional quasi geostrophic model. The results confirm that online learning is effective and yields a more accurate model error correction than offline learning. Finally, the simplified method is compatible with future applications to state‐of‐the‐art models such as the ECMWF Integrated Forecasting System.

The impact of Aeolus winds on near-surface wind forecasts over tropical ocean and high-latitude regions

Abstract. To detect global wind profiles and improve numerical weather prediction (NWP), the European Space Agency (ESA) launched the Aeolus satellite carrying a spaceborne Doppler wind lidar in 2018. After the successful launch, the European Centre for Medium-Range Weather Forecasts (ECMWF) performed the observing system experiments (OSEs) to evaluate the contribution of Aeolus data to NWP. This study aims to assess the impact of Aeolus wind assimilation in the ECMWF model on near-surface (10 m height) wind forecasts over tropical ocean regions by taking buoy measurements for reference and over high-latitude regions by taking weather station data for reference for the year 2020. The assessments were conducted mainly through inter-comparison analysis. The results show that Aeolus data assimilation has a limited impact on sea surface wind forecasts for tropical regions when compared with buoy measurements. For the high-latitude regions in the Northern Hemisphere, Aeolus is able to improve near-surface wind forecasts. This positive impact is more evident as the forecast time step is extended, during the first half year of 2020 and during the winter months. In addition, the v component tends to benefit more from the Aeolus observations than the u component. For the Southern Hemisphere, a few error reductions are observed but exist randomly. Overall, this in situ data-based assessment expands our understanding of the role of Aeolus data assimilation with the global NWP model in predicting near-surface wind for tropical oceans and high-latitude regions.

Availability of Free-Space Laser Communication Link with the Presence of Clouds in Tropical Regions

Free-space laser communication (lasercom), a great application of using free-space optics (FSO) for satellite communication, has been gaining significant attraction. However, despite of great potential of lasercom, its performance is limited by the adverse effects of atmospheric turbulence and cloud attenuation, which directly affect the quality and availability of lasercom links. The paper, therefore, concentrates on evaluating the cloud attenuation in the FSO downlinks between satellite and ground stations in tropical regions. The meteorological ERA-Interim database provided by the European Center for Medium-Range Weather Forecast (ECMWF) from 2015 to 2020 is used to get the cloud database in several areas in tropical regions. This study proposed a novel probability density function of cloud attenuation, which is validated by using a well-known curve-fitting method. Moreover, we derive a closed-form of satellite-based FSO link availability by applying the site diversity technique to improve the system performance. Numerical results, which demonstrate the urgency of the paper, reveal that the impact of clouds on tropical regions is more severe than in temperate regions.

Climatology of Planetary Boundary Layer Height over Jiangsu, China, Based on ERA5 Reanalysis Data

Based on the hourly ERA5 reanalysis dataset of the European Centre for Medium-Range Weather Forecasts (ECMWF) from 1 January 1979 to 31 December 2019, the climatology of the planetary boundary layer height (PBLH) in Jiangsu, China, is studied. The PBLH based on ERA5 is verified by using radiosonde data, and the results show that the PBLH based on ERA5 fits very well with the PBLH diagnosed by the radiosonde data. Overall, the daytime average PBLH is between 700 and 1200 m, which is higher in the north and lower in the south. It is between 100 and 400 m at night, and it is lower in the north and higher in the south. The PBLH exhibits complex spatiotemporal variation. In the daytime, the PBLH in inland areas is highest in spring, followed by fall and summer, and lowest in winter. At night, the seasonal variation in the PBLH is less obvious. The seasonal variation in the PBLH in coastal areas is higher in fall and winter and lower in spring and summer. The PBLH shows an obvious diurnal cycle, usually reaching its peak at 14:00 (LST) or 15:00 (LST). The diurnal cycle of the PBLH is significantly positively correlated with the near-surface temperature and wind speed and significantly negatively correlated with the relative humidity and lower tropospheric stability. Over these 41 years, the daytime PBLH has increased significantly in most areas. The increase in the PBLH can be attributed to the increase in near-surface temperature and the decrease in near-surface relative humidity and lower tropospheric stability.

Spatiotemporal Variability of Hot Days in Association with the Large-Scale Atmospheric Drivers over Vietnam

The severe heatwaves and hot spells in Vietnam were observed more frequently in intensity and duration due to global warming and climate change impacts. The hot days and extreme summer events make the weather harsh and significantly affect human health and the environment. This study presents the spatiotemporal distribution of the number of hot days (NHDs) in Vietnam. The variability of NHD in seven climate subregions is also examined in association with the large-scale drivers. The European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis version 5 (ERA5) data for the period 1981–2020 are used. Principal component analysis is also applied to the observed monthly NHD to obtain spatial patterns and time series. The results show that the NHD in the Highland and South subregions from March to May is linked with the subtropical high associated with decreased 500hPa-level vertical velocity (VV500). From May to June, the North and Central subregions seem related to deepening the Asiatic low and enhancement of southwest flows across the Indochina Peninsula. Finally, both increased southwest flows and decreased VV500 can partly contribute to the intensification of NHD in the North and Central subregions during July and August. The long trends of NHD are also examined. The results reveal that the increasing trends in NHD occur in most subregions, except for the Central Highland, and changing trends of NHD in June greatly contribute to the annual trend of NHD. Finally, the examinations with the El Niño-Southern Oscillation events show that NHD is significantly higher in El Niño events than in La Niña events in March and April for the Northwest, South Central, Central Highlands, and South, in May and June for all subregions, and in July and August for only the Red River Delta subregion. This suggests that ENSO can provide the potential for improving seasonal climate forecasts and mitigating natural disaster risks for the community.

Terrestrial water storage anomaly estimating using machine learning techniques and satellite‐based data (a case study of Lake Urmia Basin)

AbstractIn this study, the Terrestrial Water Storage Anomaly (TWSA) in the Lake Urmia Basin (LUB) was obtained by using the GRACE satellites. The whole study area was covered by 10 GRACE/GFO pixels, and the TWSA value was calculated for these 10 pixels. Examining the changing trend showed that the value of TWSA in the LUB has a decreasing trend and fluctuates from approximately −200 to +200 compared to the average value. The TWSA was modelled using the group method of data handling (GMDH) by considering six different parameters obtained from the European Centre for Medium‐Range Weather Forecasts (ECMWF) Reanalysis V5 (ERA5) and the Global Land Data Assimilation System version 2.0 (GLDAS V2.0). Finally, the best models with two to six input variables were selected. Among the models with different inputs, the GMDH Model 2, with three inputs, had the best performance compared to the other models. After choosing the best inputs in calculating the TWSA value by the GMDH, the TWSA value was also modelled by using the Adaptive Neuro‐Fuzzy Inference System (ANFIS), the Extreme Learning Machine (ELM) and the Artificial Neural Network (ANN). The results showed that GMDH not only outperformed other models but also provided some simple equations to apply in practical tasks.

The association of heatwave with drowning mortality in five provinces of China

Drowning is a serious public health problem in the world. Several studies have found that ambient temperature is associated with drowning, but few have investigated the effect of heatwave on drowning. This study aimed to explore the associations between heatwave and drowning mortality, and further estimate the mortality burden of drowning attributed to heatwave in China. Drowning mortality data were collected in 71 prefectures in China during 2013–2018 from provincial vital register system. Meteorological data at the same period were collected from European Centre for Medium-Range Weather Forecasts (ECMWF). A distributed lag non-linear model (DLNM) was first to explore the association between heatwave and drowning mortality in each prefecture. Secondly, the prefecture-specific associations were pooled using meta-analysis. Finally, attributable fractions (AFs) of drowning deaths caused by heatwave were estimated. Compared to normal day, the mortality risk of drowning significantly increased during heatwave (RR = 1.20, 95%CI: 1.18–1.23). Higher risks were observed in males (RR = 1.23, 95%CI: 1.20–1.27) than females (RR = 1.18, 95%CI: 1.13–1.23), in children aged 5–14 years old (RR = 1.24, 95%CI: 1.15–1.33) than other age groups, in urban city (RR = 1.32, 95%CI: 1.28–1.36) than rural area (RR = 1.09, 95%CI: 1.07–1.12) and in Jilin province (RR = 2.85, 95%CI: 1.61–5.06) than other provinces. The AF of drowning deaths due to heatwave was 11.4 % (95%CI: 10.0 %–12.9 %) during heatwave and 1.0 % (95%CI: 0.9 %–1.1 %) during study period, respectively. Moreover, the AFs during study period were higher for male (1.2 %, 95%CI: 1.0 %–1.3 %), children 5–14 years (1.1 %, 95%CI: 0.7 %–1.6 %), urban city (1.6 %, 95%CI: 1.4 %–1.8 %) than their correspondents. These differences were also observed in AFs during heatwave. We found that heatwave may significantly increase the mortality risk of drowning mortality, and its mortality burden attributable to heatwave was noteworthy. Targeted intervention should be carried out to decrease drowning mortality during heatwave.

Forecast evaluation of the North Pacific jet stream using AR Recon dropwindsondes

AbstractThe term jet stream generally refers to a narrow region of intense winds near the top of the midlatitude or subtropical troposphere. It is in the midlatitude jet stream where instabilities and waves may develop into synoptic‐scale systems, which in turn makes accurately resolving the structure of the jet stream and associated features critical for atmospheric development, predictability, and impacts, such as extreme precipitation and winds. Using dropwindsonde observations collected during the Atmospheric River Reconnaissance (AR Recon) campaign from 2020 to 2022, this study assesses the North Pacific jet stream structure in the European Centre for Medium‐Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS). Results show that the IFS has a slow‐wind bias on the lead times assessed, with the strongest winds (≥50 m·s−1) having a bias of up to −1.88 m·s−1 on forecast day 4. Also, the IFS cannot resolve the sharp potential vorticity (PV) gradient across the jet stream and tropopause, and this PV gradient weakens with forecast lead time. Cases with larger wind biases are characterized by higher PV biases and PV biases tend to be larger for cases with a higher horizontal PV gradient. These results suggest that further model‐based experiments are needed to identify and address these biases, which could ultimately yield increased forecast accuracy.

Evaluating the Precipitation Biases over the Western Periphery of the Sichuan Basin with the ECMWF Operational Forecast Model

Abstract Based on the hourly merged precipitation product, the performance of the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) in simulating the diurnal variations of precipitation during warm season over the western periphery of the Sichuan basin (SCB) has been evaluated, and the underlying physical causes associated with the wet biases have also been investigated. The results show that the IFS well reproduces the spatial distributions of precipitation amount, frequency, and intensity over the SCB, as well as their diurnal variations, but the simulated precipitation peaks earlier than the observation with notable wet biases over the western periphery of the SCB. In addition, the strong wet biases exhibit notable regional differences over the western periphery of the SCB. The simulated wet biases over the southwestern periphery of the SCB expand westward to higher altitudes along the windward slope, with the maximum wet biases occurring at night. The westward expansion of the simulated stronger upward motions results in a westward shift of precipitation. However, the simulated precipitation over the northwestern periphery of the SCB has small difference in terms of the location; hence, the overestimated precipitation is associated with the stronger atmospheric instability, resulting from the higher potential temperature and the larger specific humidity near the surface. The findings revealed in this study indicate that the ECMWF forecast shows distinct uncertainties over the different complex terrain, and thus offers a promising way forward for improvements of model physical processes.

Estimating reservoir evaporation using numerical weather prediction: Omo Gibe III reservoir in Ethiopia

Abstract Water resource management plays a crucial role in promoting sustainable development and protecting the environment. However, estimating evaporation rates in complex terrains, such as the Omo Gibe III Reservoir area, poses significant challenges due to limited data availability and the influence of natural formations on local weather patterns. To solve this problem Weather Research and Forecasting (WRF) model employed. The WRF model employs the European Centre for Medium-Range Weather Forecasts (ECMWF) Fifth generation of atmospheric reanalysis (ERA5) climate dataset to simulate key meteorological parameters. The simulation period covers 2014 to 2020, with a one-month spin-up period (December 1–30, 2013) to ensure model stability. To evaluate the model performance, various metrics such as Mean Squared Error (MSE), Nash-Sutcliffe Efficiency (NSE), Pearson correlation coefficient (r), Kling-Gupta Efficiency (KGE), and Mean Absolute Error (MAE) are employed. Reservoir evaporation is estimated by employing the mass transfer method and using WRF simulated meteorological variables. The findings highlight the WRF model-based estimatessuperiority over the MOD16 dataset, displaying lower MSE (85.22 versus 168.13) and higher NSE (0.91 versus 0.82), and signifying better agreement with observed evaporation patterns. Both models exhibit robust positive correlations with observed data (r: WRF − 0.97, MOD16–0.98), effectively capturing overall trends. The WRF model calculates a mean monthly average evaporation rate of 72.79 mm, which falls between Wolaita Station’s estimate (76.70 mm) and is lower than MOD16 (86.61 mm). The standard deviation values indicate that MOD16 exhibits the highest variability (36.67 mm), whereas the WRF model-based estimates(26.58 mm) and estimates of observed Station at Wolaita (30.91 mm) show comparatively lower variability, suggesting more consistent estimates. The research emphasizes the importance of the WRF model in estimating evaporation rates at Gibe III Reservoir, assisting in water allocation, reservoir operation, and environmental preservation. Furthermore, its applicability in climate resilience and sustainable development.

Probing the Atmospheric Precipitable Water Vapor with SOFIA, Part. IV. Water Vapor Estimates from FORCAST Grism Spectra

SOFIA was an airborne observatory for far-infrared astronomy stationed at the Armstrong Flight Research Center in Palmdale, CA, USA. Although SOFIA flew at altitudes of ∼41,000 ft, any far-infrared observations from within the Earth’s atmosphere are nevertheless hampered by water vapor absorbing the astronomical signal. The primary atmospheric parameter governing absorption at far-infrared wavelengths is the total upward precipitable water vapor (PWV). In this paper we present a method of deriving PWV values directly from low resolution (R ∼ 100–200) mid-infrared (5–40 μm) spectroscopic observations and apply it to low resolution grism spectra obtained with the FORCAST instrument on-board SOFIA. We then compare these values with those determined from the fifth European Re-analysis (ERA5) of the global atmospheric parameters provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) for the time and altitude corresponding to the SOFIA observations. In general, we find a very good correlation between the ERA5-ECMWF values and the values derived from the FORCAST grism spectra, especially for high signal-to-noise ratio data. These results indicate that ERA5-ECMWF PWV values can be used to generate the telluric corrections for FORCAST imaging data as well as grism spectra for which the PWV values cannot be determined directly. We also derive the resolving power of the various grism and slit width combinations for FORCAST. Our results will be useful for reprocessing the FORCAST data in the SOFIA archive.

Interactive 3D Visual Analysis of Weather Prediction Data Reveals Midlatitude Overshooting Convection during the CIRRUS-HL Field Experiment

Abstract In summer 2021, microphysical properties and climate impact of high- and midlatitude ice clouds over Europe and the North Atlantic were studied during the Cirrus High Latitude (CIRRUS-HL) airborne field campaign. The related forecasting and flight planning tasks provided a testbed for interactive 3D visual analysis. Operational analyses and forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) were visualized with the open-source software “Met.3D.” A combination of traditional 2D displays with innovative 3D views in the interactive visualization framework facilitated rapid and comprehensive exploration of the NWP data. By this means, the benefit of interactive 3D visual forecast products in the routine flight planning procedure was evaluated. Here, we describe the use of 3D tropopause and cloud visualizations during a convective event over the Alps, which became one of the CIRRUS-HL observation targets. For the planning of the research flight on 8 July 2021, our analysis revealed that simulated strong convective updrafts locally disturb the tropopause and inject ice water across the dynamical tropopause into the lower stratosphere. The presented example provides a novel 3D perspective of convective overshooting in a global NWP model and its impact on the tropopause and lower stratosphere. The case study shall encourage the atmospheric science community to further evaluate the use of modern 3D visualization capabilities for NWP analysis.

Verification of Some Weather Elements Forecasts from BSC-DREAM8b V2 Model

Abstract The aim of this study is to verify 2-meter temperature and mean sea level pressure forecasts from BSC-DREAM8B V2.0 model, which is a short-term numerical forecasting model against observed data from the Iraqi Meteorological Organization & Seismology constitution and modelled data (ERA-Interim) reanalysis from the European Centre for Medium Range Weather Forecasts (ECMWF) for the period 1-3 June 2012 for four selected stations in Iraq, they are Mosul, Baghdad, Rutba and Basra which represent northern, central, western and southern of Iraq respectively by using some statistical methods including Mean Absolute Error (MAE), Bias Error (BE) and Correlation Coefficient (R). The results showed that the BSC-DREAM8B V2.0 model worked well in representing the daily temperature variation and with relatively good compatibility with the ERA-I modelled data, however, the latter showed lower absolute error rates. The two modelled data showed the highest absolute error rate for Baghdad station whereas BSC showed lower values for Basra station and different values for Rutba and Mosul, and the predicted values were overestimated for all stations and for both models except for Rutba station where ERA-I data showed underestimated value. As for the surface pressure at mean sea level, the BSC-DREAM8B V2.0 model showed its ability to simulate the daily change of pressure and worked in a good consistency with ERA-I modelled data with few hetopascal as mean absolute error. Both modelled data showed underestimated values for all stations, and thus, BSC modelled data is better for simulating temperature and mean sea level pressure in low elevated regions compared to ERA-Interim data and vice versa.

Parametric Postprocessing of Dual-Resolution Precipitation Forecasts

Abstract All major weather centers issue ensemble forecasts, which differ both in ensemble size and spatial resolution, even while covering the same domain. These parameters directly determine both the forecast skill of the prediction and the computation cost. In the past few years, the plans for upgrading the configuration of the Integrated Forecast System of the European Centre for Medium-Range Weather Forecasts (ECMWF) from a single forecast with 9-km resolution and a 51-member ensemble with 18-km resolution induced an extensive study of the forecast skill of both raw and postprocessed dual-resolution predictions comprising ensemble members of different horizontal resolutions. We investigate the predictive performance of the censored shifted gamma (CSG) ensemble model output statistic (EMOS) approach for statistical postprocessing with the help of dual-resolution, 24-h, precipitation accumulation ensemble forecasts over Europe with various forecast horizons. We consider the operational 50-member ECMWF ensemble as of high resolution and extend it with a low-resolution (29-km grid), 200-member experimental forecast. The investigated dual-resolution combinations consist of subsets of these two forecast ensembles with equal computational cost, which is equivalent to the cost of the operational ensemble. Our case study verifies that, compared with the raw ensemble combinations, EMOS postprocessing results in a significant improvement in forecast skill and that skill is statistically indistinguishable between any of the analyzed mixtures of dual-resolution combinations. Furthermore, the semilocally trained CSG EMOS provides an efficient alternative to the state-of-the-art quantile mapping without requiring additional historical data.

Effect of Carbon Dioxide, Methane and Nitrous Oxide with TCO and Solar Flux on the Earth’s Stratosphere Temperature in Mid-Latitude Region for Period 2000-2018

Abstract The stratosphere is the second layer of the atmosphere and the important of this layer is to contain the ozone layer which absorbs the UV radiation from the Sun. The main objective of this research is to study the effect of GHGs and TCO on the temperature of stratosphere layer in the presence of influence of solar radiation in mid-latitude region, specifically capital Beijing (latitude 40°N; longitude 116°E) in China are taken as a study case, because it is the most city in the world that emits greenhouse gases for the period (2000-2018) selected in this research. The data for GHGs (carbon dioxide, methane and nitrous oxide) are taken from Emissions Database for Global Atmospheric Research (EDGAR), the monthly average data for the total column ozone and temperature are taken from the European Centre for Medium Range Weather Forecasts (ECMWF). While the data for the solar radiation is taken from the NASA Goddard Institute for Space Studies. The data for Sunspot number is taken from Sunspot Index and Long-term Solar Observations (SILSO), all these data are taken for the period (2000-2018) above the capital Beijing. From data analysis and results it found that there is a cooling effect on the stratosphere layer due to increase in greenhouse gases and decrease in the total column ozone, as well as the decreasing in solar radiation leads to this cooling.

Formation of an unprecedented yellow snow episode in Xinjiang on December 1, 2018

On 1 December 2018, a heavy yellow snow fell in Urumqi (87°37′E, 43°47′N) — the largest city of northwest China's Xinjiang province, which was the first case that the yellow snow has been observed in winter. The air parcel trajectories obtained from Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model and the dust surface mass concentration from Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) were adopted to identify the potential sources and transport paths of pollutants responsible for this yellow snow episode. The meteorological situation and the European Center for Medium-Range Weather Forecasts (ECMWF) forecast products have been utilized to analyze the supportive meteorological conditions. The results showed that the heavy snow in Urumqi was contaminated by the yellow dust originated in Karamay of Xinjiang province. The strong surface winds in Karamay lifted large amounts of dust into the atmosphere. Then the airborne dusts were transported to Urumqi rapidly by strong low-level winds, where precipitation in connection with the upper trough and the cold front lead to the yellow snow episode. This study can provide important scientific significance for predicting this kind of event (yellow snow).

Numerical modeling of return period waves based on non-linear Boussinesq wave models to support tidal flood studies in the Kedungsepur area

A number of rapid developments and negative effects of climate change in coastal areas have forced its equilibrium system. As a result, tidal floods, high waves, and storms occur more frequently. The goal of this research is to examine nearshore hydrodynamic conditions by using results from the hindcasting procedure. A non-linear Boussinesq (BW) and Spectral Wave (SW) models are used in this study. The models use the manning coefficient, bathymetry data (BATNAS), significant wave height, and wave period calculated by using the hindcasting method. This research commenced by processing wind data from the European Center for Medium-Range Weather Forecasts (ECMWF) from the years 2009 to 2021 with an interval of 3 hours. Then, wave height for return periods 1-, 50- and 100-years are calculated using Gumbel and Fisher-Tippett type I distribution, respectively. The aforementioned approach produced wave heights of 4.23; 6.16; and 6.49 m for return periods 1, 50, and 100 years, respectively. By using this series of wave data, the models predicted that 1.2 m waves propagate close to the nearshore regions.

CONUS404: The NCAR–USGS 4-km Long-Term Regional Hydroclimate Reanalysis over the CONUS

Abstract A unique, high-resolution, hydroclimate reanalysis, 40-plus-year (October 1979–September 2021), 4 km (named as CONUS404), has been created using the Weather Research and Forecasting Model by dynamically downscaling of the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis of the global climate dataset (ERA5) over the conterminous United States. The paper describes the approach for generating the dataset, provides an initial evaluation, including biases, and indicates how interested users can access the data. The motivation for creating this National Center for Atmospheric Research (NCAR)–U.S. Geological Survey (USGS) collaborative dataset is to provide research and end-user communities with a high-resolution, self-consistent, long-term, continental-scale hydroclimate dataset appropriate for forcing hydrological models and conducting hydroclimate scientific analyses over the conterminous United States. The data are archived and accessible on the USGS Black Pearl tape system and on the NCAR supercomputer Campaign storage system.

Can Sea Surface Waves Be Simulated by Numerical Wave Models Using the Fusion Data from Remote-Sensed Winds?

The purpose of our work is to investigate the performance of fusion wind from multiple remote-sensed data in forcing numeric wave models, and the experiment is described herein. In this study, 0.125° gridded wind fields at 12 h intervals were fused by using swath products from an advanced scatterometer (ASCAT) (a Haiyang-2B (HY-2B) scatterometer) and a spaceborne polarimetric microwave radiometer (WindSAT) during the period November 2019 to October 2020. The daily average wind speeds were compared with observations from National Data Buoy Center (NDBC) buoys from the National Oceanic and Atmospheric Administration (NOAA), yielding a 1.66 m/s root mean squared error (RMSE) with a 0.81 correlation (COR). This suggests that fusion wind was reliable for our work. The fusion winds were used for hindcasting sea surface waves by using two third-generation numeric wave models, denoted as WAVEWATCH-III (WW3) and Simulation Wave Nearshore (SWAN). The WW3-simulated waves in the North Pacific Ocean and the SWAN-simulated waves in the Gulf of Mexico were validated against the measurements from the NDBC buoys and the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA-5) for the period June−September 2020. The analysis of significant wave heights (SWHs) up to 9 m yielded a < 0.5 m RMSE with a > 0.8 COR for the WW3 and SWAN models. Therefore, it was believed that the accuracy of the simulation using the two numeric models was comparable with that forced by a numeric atmospheric model. An error analysis was systematically conducted by comparing the modeled WW3-simulated SWHs with the monthly average products from the HY-2B and a Jason-3 altimeter over global seas. The seasonal analysis showed that the differences in the SWHs (i.e., altimeter minus the WW3) were within ±1.5 m in March and June; however, the difference was quite significant in December. It was concluded that remote-sensed fusion wind can serve as a driving force for hindcasting waves using numeric wave models.

An evaluation of GNSS radio occultation atmospheric profiles from Sentinel-6

The Global Navigation Satellite System (GNSS) radio occultation (RO) significantly impacts climate change monitoring. GNSS RO is based mainly on the refraction of the GNSS signals transmitted by satellites and received by a receiver on a Low Earth Orbit (LEO) satellite. Sentinel-6 (S6) is the first satellite from the Sentinel missions to retrieve atmospheric parameters using the RO technique. Temperature, pressure, and water vapor profiles can be retrieved using S6 based on the refraction of GNSS signals. This research evaluated the distribution and coverage of GNSS RO data from S6. This research also aims to evaluate the results of GNSS RO products, especially temperature from S6, from January 2022 to June 2022. The results of S6 are compared with the results from the Meteorological Operational Polar Satellite series (MetOp-B and MetOp-C) and the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) dataset. Our results indicate that S6 allows for many RO observations in space by receiving signals from the Global Positioning System (GPS) and Russia's Global Navigation Satellite System (GLONASS). S6 provides monthly RO profiles from GPS that are greater than monthly RO profiles from GLONASS. Results show that S6 provides more GNSS RO data than MetOp-B and MetOp-C. The findings indicate that the distribution of GNSS RO events from S6 over the globe is uniform in longitude and non-uniform at latitude, with the minimum number at the poles. This paper makes a significant contribution to exploring the atmosphere with the S6 as a promising satellite for weather forecasting and monitoring climate change.