ECMWF Data Research Articles

Agrometeorological modeling to estimate soybean productivity in the Southern Cone of Rondônia

Agrometeorological modeling is crucial to predict the impact of climatic conditions on agricultural production. However, its application in large regions faces challenges due to the lack of weather stations and limited access to real-time data. In this study, the multiplicative agrometeorological model was used to estimate soybean productivity in the seven municipalities of the Southern Cone of Rondônia in the 2020/21 to 2022/23 harvests. This model considers the relationships between actual and potential evapotranspirations and water conditions during different phenological stages. Ten-year water balances were developed for each municipality, using air temperature data from ERA5-Land from the ECMWF and precipitation data estimated by NASA’s GPM_v06 satellite. The results showed satisfactory performance of the model, with coefficients of determination ranging from 0.48 to 0.88 and agreement indices from 0.41 to 0.90 compared to the data observed by IBGE.

Superobbing and Thinning Scales for All-Sky Humidity Sounder Assimilation

Abstract Humidity sounder radiances are currently thinned to 110-km spacing prior to assimilation at ECMWF and used with no averaging applied. In this paper, the thinning scale and possible averaging of all-sky humidity sounder observations into “superobs” are considered. The short- and medium-range forecast impacts of changing the thinning and averaging scales of humidity sounder radiances prior to the data assimilation are investigated separately and then together. Superobbing acts as a low-pass filter and provides smoother images of departures, decreasing the effective sensor noise and thus the standard deviation of background departures, marginally for 183-GHz channels (5%–15%) and significantly for 118-GHz channels (5%–55%). Observations are thus more representative of the model effective resolution, with a better utilization of total information content than thinning native-resolution radiances, as less information is discarded. Whether changed in isolation or combination, the additions of data via superobbing and finer thinning are both shown to markedly improve background fits to independent observations, indicative of better short-range forecasts of humidity and improved winds via the 4D-Var tracer effect. Wind forecasts in the Southern Hemisphere are slightly improved in the medium range. A final configuration is tested at the resolution of the current operational model, with humidity sounder radiances averaged into 50-km superobs with 70-km spacing. This provides about 140% more radiances for assimilation and more finely detailed maps to analyze mesoscale features. The forecast impact of this change is larger in testing with higher model and data assimilation resolutions, showing the scale dependence of such decisions and the expected performance in an operational configuration. Significance Statement This paper investigates thinning and averaging scales for humidity-sounding microwave observations in the ECMWF data assimilation system. The introduction of spatial averaging shows a positive impact, as does the assimilation of observations with finer spacing. These changes permit more total information on humidity into the system, and both are beneficial for short-range forecasts of humidity and winds in the mid- to upper troposphere. The results highlight the interplay between spatial scales of observations and those of the analysis system, with possibilities for improved utilization in this particular case. This is expected to remain a key consideration in assimilation systems going forward, given the continued increases in the resolution of assimilation systems and forecast models.

Probabilistic Neural Networks for Ensemble Postprocessing

Abstract Accurate temperature forecasts are critical for various industries and sectors. We propose a probabilistic neural network (PNN), an extension of the distributional regression network (DRN), for 2-m temperature forecasts, consisting of three variants with different inputs and target variables. The first variant, standardized anomaly probabilistic neural network (SAPNN), employs a two-step approach involving standardized anomalies and global PNN modeling to effectively capture underlying features and anomalies. The second variant, PNN with geographical predictors (PNNGE), incorporates raw and static geographical predictors to enhance predictive performance. The third variant, PNN with station one-hot encoding (PNNEN), utilizes raw with station one-hot encoding predictors to represent geographical information effectively. We compare three PNN variants with two benchmarks: 1) standardized anomaly model output statistics (SAMOS) and 2) three DRN variants identical to those applied to PNN. These evaluations utilize ECMWF data from 2019 to 2020 at 6-h intervals up to 72 h over Hebei, China. Results show that SAPNN and PNNGE are better than SAMOS, while PNNEN notably exhibits a significant 14% improvement in the continuous ranked probability skill score (CRPSS). Moreover, the PNN variants exhibit comparable or superior performance to DRN regarding forecast accuracy, CRPSS, and reliability, showcasing a better-calibrated spread–error relationship. This study highlights the value of the proposed PNN variants with a distribution output in capturing nonlinear relationships within different sources of predictors and improving temperature forecast skills. Significance Statement This study aims to improve the accuracy, skills, and reliability of 2-m temperature forecasts, which are crucial in agriculture and energy management. To achieve this, we extend a popular artificial intelligence framework and explore four data sources with two schemes to systematically compare the predictive performances in making temperature forecasts. The findings of this research are vital as they offer novel ways to improve forecast skills. Imagine having a weather app that is significantly more accurate, enabling you to plan your day better. This study is about discovering innovative approaches to enhance forecast skills and reliability, which could benefit various aspects of our daily lives. One of the new methods even exhibits a 14% improvement in forecast skills.

Effects of Initial and Boundary Conditions on Heavy Rainfall Simulation over the Yellow Sea and the Korean Peninsula: Comparison of ECMWF and NCEP Analysis Data Effects and Verification with Dropsonde Observation

Effects of Initial and Boundary Conditions on Heavy Rainfall Simulation over the Yellow Sea and the Korean Peninsula: Comparison of ECMWF and NCEP Analysis Data Effects and Verification with Dropsonde Observation

Investigating the impact of coupling HARMONIE-WINS50 (cy43) meteorology to LOTOS-EUROS (v2.2.002) on a simulation of NO2 concentrations over the Netherlands

Abstract. Meteorological fields calculated by numerical weather prediction (NWP) models drive offline chemical transport models (CTMs) to solve the transport, chemical reactions, and atmospheric interaction over the geographical domain of interest. HARMONIE (HIRLAM ALADIN Research on Mesoscale Operational NWP in Euromed) is a state-of-the-art non-hydrostatic NWP community model used at several European weather agencies to forecast weather at the local and/or regional scale. In this work, the HARMONIE WINS50 (cycle 43 cy43) reanalysis dataset at a resolution of 0.025° × 0.025° covering an area surrounding the North Sea for the years 2019–2021 was coupled offline to the LOTOS-EUROS (LOng-Term Ozone Simulation-EURopean Operational Smog model, v2.2.002) CTM. The impact of using either meteorological fields from HARMONIE or from ECMWF on LOTOS-EUROS simulations of NO2 has been evaluated against ground-level observations and TROPOMI tropospheric NO2 vertical columns. Furthermore, the difference between crucial meteorological input parameters such as the boundary layer height and the vertical diffusion coefficient between the hydrostatic ECMWF and non-hydrostatic HARMONIE data has been studied, and the vertical profiles of temperature, humidity, and wind are evaluated against meteorological observations at Cabauw in The Netherlands. The results of these first evaluations of the LOTOS-EUROS model performance in both configurations are used to investigate current uncertainties in air quality forecasting in relation to driving meteorological parameters and to assess the potential for improvements in forecasting pollution episodes at high resolutions based on the HARMONIE NWP model.

The Role of Atmospheric Stabilities and Moisture Convergence in the Enhanced Dry Season Precipitation over Land from 1979 to 2021

Abstract Between 1979 and 2021, global ocean regions experienced a decrease in dry season precipitation, while the trend over land areas varied considerably. Some regions, such as southeastern China, the Maritime Continent, eastern Europe, and eastern North America, showed a slight increasing trend in dry season precipitation. This study analyzes the potential mechanisms behind this trend by using the fifth major global reanalysis produced by ECMWF (ERA5) data. The analysis shows that the weakening of downward atmospheric motions played a critical role in enhancing dry season precipitation over land. An atmospheric moisture budget analysis revealed that larger convergent moisture fluxes lead to an increase in water vapor content below 400 hPa. This, in turn, induced an unstable tendency in the moist static energy profile, leading to a more unstable atmosphere and weakening downward motions, which drove the trend toward increasing dry season precipitation over land. More water vapor in the low troposphere is because of higher moisture convergence and moisture transport from ocean to land regions. In summary, this study demonstrates the intricate elements involved in altering dry season rainfall trends over land, which also emphasizes the importance of comprehending the spatial distribution of the wet-get-wetter and dry-get-drier paradigm. Significance Statement This study found that global land precipitation during the dry season slightly increased from 1979 to 2021, while precipitation over oceans declined. Moist static energy analysis showed a trend toward less stability in areas with increased dry season precipitation and the opposite trend in regions with declining precipitation. Water vapor content trends and dynamic components were the primary controlling mechanism for precipitation trends. Furthermore, the hotspots with pronounced increases or decreases in dry season precipitation reflect local circulation changes influenced by anthropogenic or natural factors.

False Alarm Causes and Wind Field Sensitivity Analysis of a Severe Rainfall Event in the Guangdong-Hong Kong-Macao Greater Bay Area Urban Cluster

On May 11, 2022, despite the favorable upper and lower-level circulation patterns of the high-altitude trough, shear line, and southwest jet stream, the urban cluster of the Guangdong-Hong Kong-Macao Greater Bay Area experienced light to moderate rainfall, deviating significantly from the forecasted heavy rain and local heavy rainstorm. This study explores the reasons for false alarms and predictability using ground observation data, radar data, ECMWF-ERA5 reanalysis field data, and ECMWF and CMA-TRAMS forecast data. The results indicate that the warm and moist airflow transported by the low-level jet stream was intercepted by the upstream MCS (mesoscale convective system) along the coastal area of western Guangdong, and inadequate conditions of negative vorticity dynamics led to insufficient moisture, thermodynamic, and dynamic conditions over the urban cluster, preventing the triggering of heavy precipitation. In addition, the 700 hPa westerly flow guiding the airflow and the stable low-level shear line, coupled with surface convergence lines, influenced the northward or southward movement of MCSs along the coastal and inland regions of western Guangdong. The weak and discontinuous intensity of echoes in the upstream Zhaoqing region further hindered the influence of surrounding echoes on the urban cluster. Numerical forecast models ECMWF and CMA-TRAMS overestimated the 850 hPa windspeed and 925 hPa meridional windspeed, resulting in the forecasted urban cluster experiencing heavy rain. Sensitivity tests of wind fields indicate that the 850 hPa wind field information is more sensitive to precipitation in the urban cluster. In this process, weak signal correction can be achieved in strong precipitation forecasts using the distinct signal of lower 850 hPa water vapor flux divergence compared to 925 hPa. Therefore, in the future, when the Guangdong-Hong Kong-Macao Greater Bay Area encounters similar warm-sector heavy rainfall events, adjustments to model forecasts can be made using specific 850 hPa elements such as wind speed, water vapor flux divergence, or specific humidity to enhance predictive accuracy.

Pesisir Barat Lampung Offshore Wind Characteristic in Local Renewable Energy Development Studies

Renewable energy is one of the most researched topics. One renewable energy source is wind energy. As with some other energy sources, there are 2 locations for wind energy conversion, onshore and offshore. Using offshore wind energy requires more accurate wind data due to the higher risk. The characteristics of the wind at sea are one of these data. This study used data sets from ECMWF and BMKG from 2001 to 2020 (20 years). This research aims to examine the wind characteristics in the offshore of Pesisir Barat Lampung. The wind characteristics being examined are wind speed, wind direction, wind blowing duration, and wind speed distribution. The maximum hourly mean wind speed in these waters is 16.35 m/s. The dominant wind direction is blowing from the South East (SE). The maximum average wind-blowing duration to SE direction is 11 hours based on ECMWF and 14 hours based on BMKG. Based on the Chi-Square and Kolmogorov-Smirnov distribution fitting methods, the wind speed distribution in these waters is best fitted by the Weibull distribution for the ECMWF and the Lognormal distribution for BMKG. Using these two data sources, on average, ECMWF provides wind speeds 5% to 20% higher than BMKG. In analyzing wind potential, BMKG data will certainly provide more critical results than ECMWF data. In the analysis of offshore support structures, ECMWF data will certainly produce more critical structures. While the calculation of potential is better using BMKG data. Wind power potential in Pesisir Barat offshore is in the range of 44.6 to 82.9 W/m2.

On-Orbit Calibration Method for Correction Microwave Radiometer of the HY-2 Satellite Constellation

The HY-2D satellite was successfully launched in 2022, which marks the first phase of the HY-2 satellite constellation. In order to reduce the deviation of wet path delay (WPD) between different satellites in the HY-2 satellite constellation and increase precision in the correction microwave radiometer (CMR) products, on-orbit calibration must be performed to the brightness temperature (BT) of the CMR in this constellation. This study describes the principle and process of on-orbit calibration for CMR in detail. For the three satellites of the HY-2 satellite constellation, after cross-matching with each other within a limited spatio-temporal range, the HY-2B satellite with sounding on the global ocean is selected to the calibration source, calibrating BT from the CMR of the HY-2C and HY-2D satellites to the BT dimension of the HY-2B satellite CMR. To check on-orbit calibration, a retrieval algorithm is built using atmospheric profile data from ECMWF and BT data, obtained from the CMR of the HY-2B satellite; this is used to calculate the atmospheric water vapor (AWV) and WPD from the HY-2 satellite constellation. After on-orbit calibration to the CMRs of the HY-2 satellite constellation, the deviation between the CMR products of different satellites is significantly reduced by over 20%, and the RMS of WPD for the same type of products from the Jason-3 satellite is less than 1 cm. It may be concluded that on-orbit calibration improves the accuracy of AWV and WPD by normalizing the BT dimension for CMRs of the HY-2 satellite constellation, so this calibration method is effective and credible for enhancing the quality of altimeter products in the HY-2 satellite constellation.

The Synoptic Characteristics of Icing Events on Transmission Lines in Southern China

This study utilizes the ECMWF ERA5 climate reanalysis database and data from the Southern China Transmission Lines Icing Observation System and applies the T-mode principal component analysis, an objective synoptic pattern classification method, to investigate synoptic characteristics associated with transmission line icing events in southern China between 2014 and 2021. The findings reveal that Southern China’s winter synoptic conditions can be categorized into four patterns, with the predominant pattern featuring a centrally located 850 hPa high-pressure system in Inner Mongolia’s western region. This pattern facilitates the convergence of northwesterly cold air from the north and southwesterly moisture from the south over southern China, resulting in balanced conditions conducive to transmission line icing. Furthermore, during a specific icing event in Zhaoqing City, Guangdong Province, in February 2022, the atmospheric stratification exhibited a distinctive “cold–warm–cold” structure with a pronounced inversion layer, characteristic of freezing and rainy weather conditions that create the requisite environment for transmission line icing. Within the dominant icing synoptic pattern, a northwesterly airflow transports cold air from central Siberia to southern China, accompanied by two primary low-level water vapor transport pathways: one originating as a southwest low-level jet carrying moisture from the Bay of Bengal to the Chinese mainland, and the other stemming from the South China Sea. These findings provide valuable insights into the synoptic conditions contributing to transmission line icing events in the region.

Neural Network-Based Wind Measurements in Rainy Conditions Using the HY-2A Scatterometer

Wind measurement using spaceborne scatterometers has been used for various scientific and operational purposes. However, the major problem of such measurements is contamination by rain. To improve the wind measurement using the HY-2A scatterometer under rainy conditions, a neural network-based model was established in this study. The model is almost autonomous in that it only needs the backscatter coefficient measurement data and the observation geometry information from the HY-2A scatterometer itself. The model can distinguish between rain-contaminated wind pixels and rain-free wind pixels and significantly improve the accuracy of wind speed measurements using HY-2A scatterometer alone. TAO data and linearly calibrated ECMWF data were used in the study to validate the neural network-inverted wind speed. Under no rain conditions, the RMS of the neural network-inverted wind speed and TAO wind speed was 1.06 m/s, with a deviation of −0.21 m/s, which is a small difference from the standard method inverted wind speed. Under rain conditions, the RMS and deviation were 1.94 m/s and 0.66 m/s, respectively, which were better than the statistical results of the conventional maximum likelihood estimation method. The validated results using linearly calibrated data also indicate that the neural network-inverted wind speed is closer to the validation data under rain conditions.

Seasonal Effects of the Tibetan Plateau on Cyclonic Transient Eddies: A System-Centered View

Abstract Using an objective feature-tracking algorithm and the fifth major global reanalysis produced by ECMWF data (ERA5), the seasonal behaviors of cyclonic transient eddies (cyclones) at different levels around the Tibetan Plateau (TP) were examined to understand the effects of the TP on cyclones. Results show that the TP tends to change the moving directions of the remote cyclones when they are close to the TP, with only 2% of the 250-hPa eastward-moving cyclones directly passing over the TP. The sudden reductions of their moving speeds and relative vorticity intensities around the TP suggest a suppression effect of the plateau. Over 70% of these cyclones perish over the TP regardless of the altitude. This percentage decreases to around 65% during summertime, exhibiting a weaker summer suppression effect. On the other hand, the TP has a stimulation effect on local cyclones through its dynamic forcing in winter, thermodynamic forcing in summer, and both forcings in the transitional seasons. The numbers of locally generated cyclones, especially at 500 hPa, just above the TP, are significantly larger than those of the remote cyclones during all seasons. Although about one-half of the local cyclones dissipate over the TP, the cyclones moving off the plateau significantly outnumber the moving-in cyclones, with the differences ranging from 0 to 6 cyclones per month. Only the 250-hPa wintertime moving-off cyclones are fewer than the cyclones entering the TP, which may be caused by the weaker stimulation effect and stronger suppression effect of the TP on the wintertime upper-level cyclones. Significance Statement Cyclonic transient eddies (cyclones), steered by westerly jet streams, can influence climate and induce extreme weather processes under certain conditions. The Tibetan Plateau (TP), the highest and largest obstacle embedded in the westerly jet streams, suppresses remote cyclones entering the TP region, destroying over 70% of these cyclones. However, because of the excitation effect of the TP on local cyclones, the numbers of cyclones moving off the TP are still larger than or equal to those of the moving-in cyclones, except at the upper levels in winter. This feature suggests that the TP cannot significantly decrease the total cyclone numbers in most cases, but it indeed weakens the mean intensity and moving speed of the cyclones.

Subseasonal Ensemble Prediction of Flash Droughts over China

Abstract Flash droughts have been occurring frequently worldwide, which has a serious impact on food and water security. The rapid onset of flash droughts presents a challenge to the subseasonal forecast, but there is limited knowledge about their forecast skills due to the lack of appropriate identification and assessment procedures. Here, we investigate the forecast skill of flash droughts over China with lead times up to 3 weeks by using hindcast datasets from the Subseasonal-to-Seasonal Prediction (S2S) project. The flash droughts are identified by using weekly soil moisture percentiles from two S2S forecast models (ECMWF and NCEP). The comparison with reanalysis shows that ECMWF and NCEP forecast models underestimate flash drought occurrence by 5% and 19% for lead 1 week. The national mean hit rates for flash droughts are 0.22 and 0.16 for ECMWF and NCEP models for lead 1 week, and they can reach 0.29 and 0.18 over South China. The ensemble of the two models increases equitable threat score (ETS) from ECMWF and NCEP models by 8% and 40% for lead 1 week. In terms of probabilistic forecast, ECMWF has a higher Brier skill score than NCEP, especially over eastern China, which is consistent with higher temperature and precipitation forecast skill. The multimodel ensemble has the highest Brier skill score. This study suggests the importance of multimodel ensemble flash drought forecasting. Significance Statement Flash droughts have raised considerable concern, but whether they can be predicted at subseasonal time scales remains unclear. This study evaluates forecast skill of flash droughts over China based on ECMWF and NCEP hindcast data. Focusing on the historical flash drought events identified by the onset speed and duration, it is found that the ECMWF model outperformed the NCEP model with higher hit rates, lower false alarm ratios, and higher equitable threat scores, especially during the first week. However, less than 30% of the drought events can be captured in most regions by both models. An ensemble of the two models showed skill improvement against the ECMWF model for both deterministic and probabilistic forecasts.

Why do inverse models disagree? A case study with two European CO2 inversions

Abstract. We present an analysis of atmospheric transport impact on estimating CO2 fluxes using two atmospheric inversion systems (CarboScope-Regional (CSR) and Lund University Modular Inversion Algorithm (LUMIA)) over Europe in 2018. The main focus of this study is to quantify the dominant drivers of spread amid CO2 estimates derived from atmospheric tracer inversions. The Lagrangian transport models STILT (Stochastic Time-Inverted Lagrangian Transport) and FLEXPART (FLEXible PARTicle) were used to assess the impact of mesoscale transport. The impact of lateral boundary conditions for CO2 was assessed by using two different estimates from the global inversion systems CarboScope (TM3) and TM5-4DVAR. CO2 estimates calculated with an ensemble of eight inversions differing in the regional and global transport models, as well as the inversion systems, show a relatively large spread for the annual fluxes, ranging between −0.72 and 0.20 PgC yr−1, which is larger than the a priori uncertainty of 0.47 PgC yr−1. The discrepancies in annual budget are primarily caused by differences in the mesoscale transport model (0.51 PgC yr−1), in comparison with 0.23 and 0.10 PgC yr−1 that resulted from the far-field contributions and the inversion systems, respectively. Additionally, varying the mesoscale transport caused large discrepancies in spatial and temporal patterns, while changing the lateral boundary conditions led to more homogeneous spatial and temporal impact. We further investigated the origin of the discrepancies between transport models. The meteorological forcing parameters (forecasts versus reanalysis obtained from ECMWF data products) used to drive the transport models are responsible for a small part of the differences in CO2 estimates, but the largest impact seems to come from the transport model schemes. Although a good convergence in the differences between the inversion systems was achieved by applying a strict protocol of using identical prior fluxes and atmospheric datasets, there was a non-negligible impact arising from applying a different inversion system. Specifically, the choice of prior error structure accounted for a large part of system-to-system differences.

Comparative Analysis of the Precipitation Difference Between Sanya Airport and Haikou Airport Caused by Typhoon 2117

By using ECMWF and FNI reanalysis data, airport self-observation precipitation data, satellite TBB data, and precipitation distribution data of Hainan Meteorological Bureau, the rainfall characteristics of Sanya Phoenix Airport and Haikou Meilan Airport on the day of Typhoon Lionrock on the 17th, autumn 2021 were analyzed. The results found that: (1) The lifting effect of the terrain and the invasion of weak cold air intensified the intensity of the rainstorm in Hainan. The rainstorm center was located on the windward slope of Wuzhishan Mountain. Fenghuang Airport was in the leeward slope environment, while Meilan Airport was in the windward slope. (2) The convergence of southerly air flow and southeast air flow and the abundant water vapor brought by the dynamic action of upper low-level jet stream are favorable conditions for triggering and maintaining heavy rain. (3) There was a negative correlation between heavy precipitation and brightening temperature of black body. The direct influence system of this weather process was mesoscale convective cloud cluster. (4) The rainstorm occurs in the area of dense θse line, which is conducive to the development of inclined vorticity. (5) The positive vertical helicity of 850hpa is a good indicator of precipitation in the next 6 hours.

VARIABILITY ANALYSIS OF SIGNIFICANT WAVE HEIGHTS AND WIND WAVES IN RIAU ARCHIPELAGO SEA PART ALKI 1

The Riau Archipelago Sea is part of the Indonesian Archipelago Sea Lane (ALKI) 1, with a very high intensity of crossing ships. Analysis of surface wind speed and significant wave height is the most important for the safety and performance of offshore shipping. This research aims to study wave characteristics and wind speed by identifying the main factors that affect significant wave height and surface wind speed. Dominant factors that affect significant wave height and wind speed are needed to decide on the safest path and the best time before crossing in ALKI-1. Temporal and spatial analysis of the seasonal variability of significant wave height and wind speed using ECMWF data for 18 years. The data used are significant wave height data and wind speed every 6 hours during the period 2000 – 2018. Three observation points are used for temporal analysis, it is found that significant wave height and wind speed are influenced by two main factors, namely MJO and Monsoon. Maximum significant wave height and wind speed that occurs in the SON period for points 3 and the DJF period at points 1 and 2. MJO affects directly from phases one to eight for observation points 1, 2, and 3. The 4, 7, and 8 MJO phases affect the value of wave height and wave speed significantly, and the 1, 2, and 5 MJO phases affect wave height and wind speed weakly.
 Keywords: ALKI 1, MJO, MJO phase, Monsoon, Significant wave height, Wind speed.

Retrieval and Assessment of Significant Wave Height from CYGNSS Mission Using Neural Network

In this study, we investigate sea state estimation from spaceborne GNSS-R. Due to the complex scattering of electromagnetic waves on the rough sea surface, the neural network approach is adopted to develop an algorithm to derive significant wave height (SWH) from CYGNSS data. Eighty-nine million pieces of CYGNSS data from September to November 2020 and the co-located ECMWF data are employed to train a three-hidden-layer neural network. Ten variables are considered as the input parameters of the neural network. Without the auxiliary of the wind speed, the SWH retrieved using the trained neural network exhibits a bias and an RMSE of −0.13 and 0.59 m with respect to ECMWF data. When considering wind speed as the input, the bias and RMSE were reduced to −0.09 and 0.49 m, respectively. When the incidence angle ranges from 35° to 65° and the SNR is above 7 dB, the retrieval performance is better than that obtained using other values. The measurements derived from the “Block III” satellite offer worse results than those derived from other satellites. When the distance is considered as an input parameter, the retrieval performances for the areas near the coast are significantly improved. A soft data filter is used to synchronously improve the precision and ensure the desired sample number. The RMSEs of the retrieved SWH are reduced to 0.45 m and 0.41 m from 0.59 m and 0.49 m, and only 16.0% and 14.9% of the samples are removed. The retrieved SWH also shows a clear agreement with the co-located buoy and Jason-3 altimeter data.

Numerical Forecasting of Wind-Waves for Singapore Region

A numerical wave model has been set up and tuned for the Singapore region extending to the parts of the South-China Sea and Malacca Strait. The MIKE 21 spectral wave model was set up using ERA5/ECMWF wind and waves reanalysis data to compute the phase-averaged significant wave heights. The outputs of the calibrated model validates well with the ECMWF data within the domain. The model increases the resolution of available wave data in narrow channelled regions like in the Singapore Straits. When supplied with the wind forecast data the resulting model allows for high resolution forecasting of phase-averaged wave spectra in the Singapore region. The study enables setting up of a wind-wave forecast dissemination framework on a visualization and data-sharing platform or the maritime community.

Development of a hybrid wind field for modeling the tropical cyclone wave field

Due to the increasing trend of tropical cyclone occurrence in the recent decades, the necessity of developing models for forecasting and hindcasting these atmospheric-oceanic hazards, becomes more apparent. One of the uncertainties about the tropical cyclones is the wind field accuracy of modeling oceanic waves and flows. In this paper, six different wind fields have been examined for hindcasting the wave field of cyclone Gonu within the areas of the Arabian Sea and the Gulf of Oman. The wind field of ECMWF (ERA-Interim) and Climate Forecast System Reanalysis (CFSR) data, that obtained via weather research forecasting (WRF) mesoscale modeling and three wind fields achieved via Holland, Rankin, Young and Sobey (S&Y) parametric models have been investigated as well. The wave field modeling results have been compared with the third-generation SWAN model in two stations within the area of Chabahar Bay. The wind field results obtained via the reanalysis models and WRF one indicate an appropriate estimation of the wave characteristics and the maximum wave height at the measurement stations. In order to better evaluate the results of different wind fields, the maximum wave height maps have been studied in the simulation time period. The results indicated that the maximum wave height values obtained from the reanalysis data do not match the track and development direction of the cyclone Gonu. The mismatch between storm development track and maximum wave height in WRF data can also be observed. The maximum significant wave height obtained via the parametric wind fields is very compatible with the cyclone Gonu track. Examining the maximum wave heights obtained via different wind fields, along with comparing the wave characteristics at the measurement stations, highlights the effect of different wind fields in hindcasting the wave characteristics, especially on the northern coasts of the Gulf of Oman. Examination of the maximum wave heights obtained from different wind fields has been investigated in this study along with comparison of wave characteristics at measurement stations. Results indicated that the optimal approach for simulating the tropical cyclone's wave field is to use a hybrid one. Therefore, a hybrid wind field has been developed which includes the wind characteristics of the parametric model in the areas near the storm source and the WRF meso scale model in the outer zone. The hybrid wind field has led to an improvement in the characteristics of waves within deep and coastal waters.

ПРИМЕНЕНИЕ ИСКУССТВЕННЫХ НЕЙРОННЫХ СЕТЕЙ ДЛЯ ОПРЕДЕЛЕНИЯ ОБЩЕГО СОДЕРЖАНИЯ ВОДЯНОГО ПАРА В АТМОСФЕРЕ ПО ДАННЫМ МИКРОВОЛНОВОГО РАДИОМЕТРА МТВЗА-ГЯ СО СПУТНИКА “МЕТЕОР-М” № 2-2

The paper presents the application of the artificial neural network algorithm for the retrieval of total precipitable water in the atmosphere over water and land from the measurements of MTVZA-GYa microwave radiometer on board the Meteor-M No. 2-2 satellite. Satellite-based estimates of total precipitable water were compared with radiosonde and AERONET data, as well as with the ECMWF numerical weather prediction model output. According to the comparison, the root-mean-square error (RMSE) does not exceed 4.5 mm for radiosonde data and is less than 4 mm for the ECMWF and AERONET data. The best accuracy is provided over water with the RMSE not exceeding 3 mm. The total precipitable water estimates retrieved from MTVZA-GYa and NOAA-20/ATMS radiometer data are consistent over water, while the MTVZА-GYa based estimates are more accurate over land.