ECMWF Re-Analysis Research Articles

Analyzing AIS and wave hindcast data for global wave scatter diagrams with seasonality

Global scatter diagrams are used to estimate possible ship motions in a specified route and are required for comprehensive cargo-loading considerations of merchant ships. This study aims to update global wave scatter diagrams using the latest wave hindcast and Automatic Identification System (AIS) data. The AIS data for over 7500 merchant ships were collected between 2015 and 2022. The wave hindcast data ECMWF Reanalysis v.5 (ERA5), preferred for its recent coverage, were adjusted based on CCI satellite data. The duration of the AIS data is much shorter than the design lifetime of merchant ships, which is generally 25 years. This study found almost linear relationships between the maximum significant wave heights in storms and the generalized extreme value (GEV) parameters representing the distribution of encountered significant wave heights, with adjustments made for seasonality and tropical cyclones. By simulating the parameters based on these relationships and the wave hindcast data over 25 years, the encountered significant wave height distributions over 25 years were obtained. The results indicated a reduction in extremely significant wave heights compared with the BMT-GWS, in agreement with previous studies. This study also found that the difference in the wave period distribution between natural and encountered sea states was small in most sea areas for all significant wave height levels. Taking advantage of the abundant data volume of ERA5, this study adopted an empirical distribution of the wave period conditioned by the significant wave height. The novelty of this study is that we obtained wave scatter diagrams globally by month to consider the seasonal effect in detail and evaluated the confidence interval of the encountered wave height in its probability tail. In addition, this study quantifies the uncertainty of the wave height distribution by Monte Carlo simulation, which is not within the scope of previous studies on wave scatter diagrams based on the significant wave heights encountered by merchant ships around the world.

A Systematic Local View of the Long-Term Changes in the Atmospheric Energy Cycle

Abstract The global climatology and long-term changes in the atmospheric energy cycle in the boreal winter are analyzed using a local available potential energy framework using the fifth-generation ECMWF reanalysis data during 1979–2021. In contrast to the classic Lorenz energy cycle, this local framework provides the local information of available potential energy (APE) and its interactions with kinetic energy (KE), allowing the systematic study of barotropic and baroclinic processes. A further systematic decomposition of the APE and KE reservoirs into high- and low-frequency components is conducted to investigate the source and sink terms that account for their spatial variations and long-term changes. The climatology of the local energetic budget terms shows that the interplay between low-frequency APE and KE is mostly over the tropical and polar regions associated with the thermally direct circulation there. Interactions involving high-frequency components are mainly located in the storm-track regions. We show that under recent global warming, a prominent dipolar trend of changes is present over the Asia–Pacific region for all energy forms. The long-term changes in the energy budget reveal how high-frequency APE intensification is due to a stronger conversion from low-frequency APE upstream of the storm track and that this intensification can be compensated by eddy advection away from the storm track, rather than conversion to kinetic energy in the proximity of the mean jet. This provides evidence that a warmer climate substantially affects the energy cycle and, thus, the atmospheric circulation. Significance Statement Potential and kinetic energies are major components of atmospheric circulation. Those energies and their generation, dissipation, and conversions provide insight into the dynamics of atmospheric circulation. The Lorenz energy cycle, based on a global framework, has been fundamental in deepening our understanding of the atmosphere. However, here we use a systematic local framework to gain a better understanding of atmospheric circulation changes and how global warming has affected them. Our study finds a general intensification of the local energy cycle over the last few decades.

High Water Level Forecast Under the Effect of the Northeast Monsoon During Spring Tides

One of the manifests of air-sea interactions is the change in sea level due to meteorological forcing through wind stress and atmospheric pressure. When meteorological conditions conducive to water level increase coincide with high tides during spring tides, the sea level may rise higher than expected and pose a flood risk to coastal low-lying areas. In Hong Kong, specifically when the northeast monsoon coincides with the higher spring tides in late autumn and winter, and sometimes even compounded by the storm surge brought by late-season tropical cyclones (TCs), the result may be coastal flooding or sea inundation. Aiming at forecasting such sea level anomalies on the scale of hours and days with local tide gauges using a flexible and computationally efficient method, this study adapts a data-driven method based on empirical orthogonal functions (EOF) regression of non-uniformly lagged regional wind field from ECMWF Reanalysis v5 (ERA5) to capture the effects from synoptic weather evolution patterns, excluding the effect of TCs. Local atmospheric pressure and winds are also included in the predictors of the regression model. Verification results show good performance in general. Hindcast using ECMWF forecasts as input reveals that the reduction of mean absolute error (MAE) by adding the anomaly forecast to the existing predicted astronomical tide was as high as 30% in February on average over the whole range of water levels, as well as that compared against the Delft3D forecast in a strong northeast monsoon case. The EOF method generally outperformed the persistence method in forecasting water level anomaly for a lead time of more than 6 h. The performance was even better particularly for high water levels, making it suitable to serve as a forecast reference tool for providing high water level alerts to relevant emergency response agencies to tackle the risk of coastal inundation in non-TC situations and an estimate of the anomaly contribution from the northeast monsoon under its combined effect with TC. The model is capable of improving water level forecasts up to a week ahead, despite the general decreasing model performance with increasing lead time due to less accurate input from model forecasts at a longer range. Some cases show that the model successfully predicted both positive and negative anomalies with a magnitude similar to observations up to 5 to 7 days in advance.

Comparing Satellite, Reanalysis, Fused and Gridded (In Situ) Precipitation Products Over Türkiye

ABSTRACTPrecipitation is the fundamental source for various research areas, including hydrology, climatology, geomorphology, and ecology, serving essential roles in modelling, distribution, and process analysis. However, the accuracy and precision of spatially distributed precipitation estimates is a critical issue, particularly for daily scale and topographically complex areas. Although many datasets have been developed based on different algorithms and sources are developed for this purpose, determining which of these datasets best reflects actual conditions is quite challenging. This study, hence, aims to compare the 25 global distributed precipitation estimates (gridded, satellite, model, and fused) concerning 221 ground‐based observations based on the ranking of 18 continuous (evaluation statistics), eight categorical (precipitation indices), and two seasonality metric (high and low precipitation). Upon examining the results, gridded and model precipitation data including APHRODITE (Asian Precipitation—Highly‐Resolved Observational Data Integration Towards Evaluation), CPC (Global Unified Gauge‐Based Analysis of Daily Precipitation), ERA5‐Land (ECMWF Reanalysis 5th Generation for Lands), and CFSR (Climate Forecast System Reanalysis) occupy the top four positions in continuous metrics. In contrast, satellite data such as PERSIANN‐PDIR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks), CMORPH (Climate Prediction Center morphing method), IMERG (The Integrated Multi‐Satellite Retrievals for GPM), and TRMM‐TMPA (Tropical Rainfall Measuring Mission/Multi‐satellite Precipitation Analysis) dominate in the top four positions in categorical metrics. For seasonality of high and low precipitation, fused, gridded, and reanalyses products such as CPC, MSWEP (Multi‐Source Weighted‐Ensemble Precipitation, version 2), HydroGFD (Hydrological Global Forcing Data), CFSR rank among top four. Based on the first five rankings of all metrics, fused (multiple sourced) and gridded datasets accurately reflect the actual situations compared to other precipitation products. Reanalysis (model) and satellite‐based follow this rank, respectively. The results clearly indicate that fused precipitation derived products from multiple sources offer better accuracy and precision in representing the spatial distribution of precipitation on a daily scale.

Intercomparison of reanalysis and satellite precipitation products in endorheic and exorheic basins on the Tibetan Plateau

Study Region: Endorheic and exorheic basins of the Tibetan Plateau (TP). Study Focus: Reanalysis and satellite precipitation products provide alternatives for regions of sparse ground precipitation observation, but pose a tough task to select a suitable one for the TP. This study conducts a multi-scale evaluation of six reanalysis and satellite precipitation products in endorheic and exorheic basins using water balance and extended triple collocation (ETC) methods. The reanalysis precipitation products include ECMWF Re-Analysis version 5 (ERA5-Land), China Meteorological Forcing Dataset (CMFD), Global Land Data Assimilation Systems (GLDAS), and High-resolution Precipitation dataset for the Third Pole region (TPHiPr). The satellite precipitation data include Global Precipitation Measurements (GPM) and Tropical Rainfall Measuring Mission (TRMM) products. New Hydrological Insights for the Region: The precipitation products vary in accuracy from basin to basin, with better performance in exorheic than endorheic basins. Reanalysis-based ERA5-land, TPHiPr, and CMFD perform well in most basins at annual scale, among which TPHiPr performs best at daily scale. At regional scale, GPM performs well in endorheic region, and ERA5-land in exorheic region. While all the products increase significantly in accuracy from basin to regional scale in endorheic region, ERA5-land shows best performance at annual and multi-year scales in the entire region. Our findings provide valuable supports for precipitation product selection in the Tibetan endorheic and exorheic basins.

STFM: Accurate Spatio-Temporal Fusion Model for Weather Forecasting

Meteorological prediction is crucial for various sectors, including agriculture, navigation, daily life, disaster prevention, and scientific research. However, traditional numerical weather prediction (NWP) models are constrained by their high computational resource requirements, while the accuracy of deep learning models remains suboptimal. In response to these challenges, we propose a novel deep learning-based model, the Spatiotemporal Fusion Model (STFM), designed to enhance the accuracy of meteorological predictions. Our model leverages Fifth-Generation ECMWF Reanalysis (ERA5) data and introduces two key components: a spatiotemporal encoder module and a spatiotemporal fusion module. The spatiotemporal encoder integrates the strengths of convolutional neural networks (CNNs) and recurrent neural networks (RNNs), effectively capturing both spatial and temporal dependencies. Meanwhile, the spatiotemporal fusion module employs a dual attention mechanism, decomposing spatial attention into global static attention and channel dynamic attention. This approach ensures comprehensive extraction of spatial features from meteorological data. The combination of these modules significantly improves prediction performance. Experimental results demonstrate that STFM excels in extracting spatiotemporal features from reanalysis data, yielding predictions that closely align with observed values. In comparative studies, STFM outperformed other models, achieving a 7% improvement in ground and high-altitude temperature predictions, a 5% enhancement in the prediction of the u/v components of 10 m wind speed, and an increase in the accuracy of potential height and relative humidity predictions by 3% and 1%, respectively. This enhanced performance highlights STFM’s potential to advance the accuracy and reliability of meteorological forecasting.

Comparison of temperature, pressure and specific humidity derived from Sentinel-6 with ERA5 and radiosonde

The temperature, pressure and specific humidity data of Sentinel-6 (S6) products are compared with the ECMWF Reanalysis v5 (ERA5) and radio sounding in 2022. The overall STD of temperature, pressure and specific humidity for S6-ERA5 and S6-Radiosonde are 0.74 ℃, 0.45 hPa, 0.3 g/kg, 1.33 ℃, 0.51 hPa, 0.34 g/kg respectively. The study found that the consistency of these three atmospheric parameters between S6 and ERA5 is better than that between S6 and radiosonde. In the aspect of temporal characteristics, the specific humidity STD of S6-ERA5 decrease successively in summer, autumn, spring and winter. In the aspect of spatial characteristics, S6 has better retrieval ability of temperature values in the troposphere, but the maximum negative bias value of temperature between S6 and ERA5 is −1.57 ℃ at 41.7 km. The pressure bias values of S6 and ERA5 fluctuate greatly below the troposphere. In addition, the STD values of S6-ERA5 and S6-Radiosonde specific humidity data both show the decreasing characteristics in sequence from low to high latitudes.

Multi‐Decadal Soil Moisture and Crop Yield Variability—A Case Study With the Community Land Model (CLM5)

AbstractWhile the impacts of climate change on global food security have been studied extensively, the capability of emerging tools that couple land surface processes and crop growth in reproducing inter‐annual yield variability at regional scale remains to be tested rigorously. In this study, we analyzed the effects of weather variations between years (1999–2019) on regional crop productivity for two agriculturally managed regions with contrasting climate and cropping conditions: the German state of North Rhine‐Westphalia (DE‐NRW) and the Australian state of Victoria (AUS‐VIC), using the latest version of the Community Land Model (CLM5) and the WFDE5 (WATCH Forcing Data methodology applied to ECMWF reanalysis version 5) reanalysis. Overall, the simulation results were able to reproduce the total annual crop yields of certain crops, while also capturing the differences in total yield magnitudes between the domains. However, the simulations showed limitations in correctly capturing inter‐annual differences of crop yield compared to official yield records, which resulted in relatively low correlation coefficients between 0.07 and 0.39 in AUS‐VIC and between 0.11 and 0.42 in DE‐NRW. The mean absolute deviation of simulated winter wheat yields was up to 4.6 times lower compared to state‐wide records from 1999 to 2019. Our results suggest the following limitations of CLM5: (a) limitations in simulating yield responses from plant hydraulic stress; (b) errors in simulating soil moisture contents compared to satellite‐derived data; and (c) errors in the representation of cropland in general, for example, crop parameterizations and human influences.

Wave Downscaling Approach with TCN model, Case Study in Bengkulu, Indonesia

When conducting marine operations that rely on wave conditions, such as maritime trade, the fishing industry, and ocean energy, accurate wave downscaling is important, especially in coastal locations with complicated geometries. Traditional approaches for wave downscaling are usually obtained by performing nested simulations on a high-resolution local grid from global grid information. However, this approach requires high computation resources. In this paper, to downscale global wave height data into a high-resolution local wave height with less computation resources, we propose a machine learning-based approach to downscaling using the Temporal Convolutional Network (TCN) model. To train the model, we obtain the wave dataset using the SWAN model in a local domain. The global datasets are taken from the ECMWF Reanalysis (ERA-5) and used to train the model. We choose the coastal area of Bengkulu, Indonesia, as a case study. The results of TCN are also compared with other models such as LSTM and Transformers. It showed that TCN demonstrated superior performance with a CC of 0.984, RMSE of 0.077, and MAPE of 4.638, outperforming the other models in terms of accuracy and computational efficiency. It proves that our TCN model can be alternative model to downscale in Bengkulu’s coastal area.

Comparative analysis of the spatiotemporal patterns and trends of three types of summer heatwaves in China (1981–2020) using a biometeorological index

AbstractUnder global warming, heatwaves (HWs) are exerting increasing impacts on human life. In order to explore the thermal sensation of HWs under the combined influence of air temperature, humidity, radiation and wind speed, this study utilized the hourly Universal Thermal Climate Index (UTCI) data from the ECMWF Reanalysis v5 (ERA5) to define daytime, night‐time and compound HWs, and then comparatively analysed their occurrences in China during 1981–2020. Results revealed that daytime HWs affect the majority of regions in China with the exception of the Tibetan Plateau, while both night‐time and compound HWs mainly strike the Yangtze River Basin and the Guangdong–Guangxi region in southeastern China. Additionally, it is noteworthy that a substantial portion of regions experiencing night‐time HWs also encounter compound HWs. The frequency and intensity of all three types of HWs show increasing trends generally in regions where HWs occur. The spatial coverage of HWs grows at rates of 8.05%/10a, 2.11%/10a and 1.62%/10a for the daytime, night‐time and compound types, respectively. For each summer month, the spatial coverage of daytime HWs notably surpasses that of night‐time and compound HWs. Further classifying HWs with intensity, it is found that the spatial coverage of both moderate and severe HW events exhibits discernible rising trends across all three types of HWs. Furthermore, regions with higher HW frequencies are more likely to experience severe HWs as well as record‐breaking ones. Additionally, the spatial coverage of daytime, night‐time and compound record‐breaking HWs is expanding.

High-precision tropospheric correction method for NRTK regions with significant height differences

Abstract In response to the issue of poor network real-time kinematic (NRTK) service performance in regions with significant height differences, an improved tropospheric height correction (ITHC) method is proposed. Precise point positioning (PPP) is employed to compute the troposphere delay at base stations. Subsequently, a tropospheric vertical profile fitting model (TVPFM) is established for the vertical reduction of the troposphere in regions with significant height differences. In this case, the tropospheric errors introduced by the height differences between the base and rover stations can be calculated. Finally, the tropospheric error can be corrected during the generation of virtual observations, ensuring high-accuracy positioning of NRTK rovers. With the troposphere delay computed based on the PPP approach, datum errors introduced by inaccurate tropospheric correction methods are mitigated. To reduce the dependence of the troposphere delay on height, ECMWF reanalysis v5 (ERA5) data are employed to fit the TVPFM. Experimental analysis demonstrates that the troposphere exhibits distinct vertical variation characteristics, allowing for its segmentation into three layers. Consequently, a piecewise TVPFM is established. Observations obtained from the continuously operating reference stations network located in Yunnan, China, are utilized for validation. The selected stations exhibit a maximum height difference of approximately 2 km. The experimental results exhibit a notable enhancement in correction accuracy with the ITHC in comparison to conventional correction methodologies. Specifically, the ambiguity fixing rate demonstrates a noteworthy improvement of 13.3%, accompanied by a substantial increase in positioning accuracy by 51.4%.

Occurrence and trends of historical tropical cyclone rainfall on near-coastal regions of Australia

Extreme rainfall driven by tropical cyclones (TCs) has profound effects on Australian coastlines at both local and regional scales. Here, we develop methods for comparing TC-driven widespread and localised rainfall on three broad coastal regions of tropical Australia (west, north and east). Trends, average recurrence intervals (ARIs) and the fractional contribution of TC rainfall are explored in three historical datasets: Australian Gridded Climate Data (AGCD), ECMWF Reanalysis (ver. 5, ERA5) and the Bureau of Meteorology Atmospheric high resolution Regional Reanalysis for Australia (ver. 1, BARRA1). Results for trends and ARIs between the different datasets are generally inconsistent and also differ between regions, partially owing to the short-term temporal records of some of the data as well as inconsistencies in extreme values between datasets. By contrast, there is a general agreement between all datasets on the fractional contribution of TC rainfall, signalling an increase in recent years. This result is considered together with the trend towards fewer TCs occurring in this region over recent decades, indicating a trend towards increased rainfall intensity per TC on average, assuming steady landfall rates. The methods developed here can be applied easily to other data types such as regional climate model experiments, facilitating a multiple lines of evidence approach that incorporates both observational-based and model-based data. This research is intended to help provide new methods and guidance for identifying trends in TC-driven extreme rainfall, relevant for enhanced planning and adaptation to the impacts of these extreme weather systems.

Single-instant spatial wave height forecast using machine learning: An image-to-image translation approach based on generative adversarial networks

An innovative single-instant approach for spatial significant wave height forecast based on a Generative Adversarial Networks (GANs) model is presented. The model's most distinctive feature is its ability to provide spatial wave height forecast with only a single snapshot of wind field input, eliminating the need for continuous wind field sequences while simultaneously maintaining high accuracy and enhanced efficiency. The proposed model architecture comprises an image-to-image translation generator and a patch-style discriminator, working in tandem to forecast high-quality wave height data. The model is trained using a 20-year ECMWF Reanalysis v5 (ERA5) wind and wave dataset. A vast sea area covering 15°-40°N, 105°-130°E in West Pacific is used for this study. The presented model is carefully tested against a 10-year period historical dataset and the results are discussed. The model exhibits outstanding overall performances with Mean Absolute Error (MAE) of 0.16 m, Root Mean Square Error (RMSE) of 0.24 m, and Mean Absolute Percentage Error (MAPE) of 13.4 %. The model proves particularly effective in forecasting extreme wave heights during tropical cyclones, capturing the structure of typhoon-driven waves and providing wave heights with favorable accuracy. It is also proficient at delivering satisfactory statistical wave height forecasts for long-term periods.

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)

Ocean waves in the southwestern Atlantic Ocean: An evaluation of global wave models

AbstractIn situ wave observations are essential for implementing coastal management strategies, designing coastal and offshore structures and understanding many processes related to sedimentology and marine biology. Despite their importance, these observations are rather scarce in the coastal areas of southeastern South America. Moreover, the existing observations are discontinuous, with several gaps and no simultaneity. Today, global wave models have improved their performance by providing reliable wave parameters with high resolution and coverage even at near‐shore areas. In this work, wave parameters from five global models were compared with significant wave heights (HS), peak periods (T) and propagation direction at nine sites located at the Southwestern South Atlantic Ocean. The global products used were WAVERYS (Global Reanalysis Wav 001 032), IOWAGA (WAVEWATCH III Hindcast – WW3 – Global grid – CFSR), ERA5 (ECMWF reanalysis), NOAA WW3 – 30‐year Hindcast Phase 2 and NCEP/EMC Operational global wave model GFS – Wave. Mean arithmetic error (Bias), root mean square deviation, scatter index and the Pearson linear correlation coefficient (r) were assessed at each location where wave observations were available. The best agreements for HS were obtained at the deepest sites located farther from the coast. Lower statistical metrics were achieved at coastal sites where the bathymetry is roughly represented by the global models. The calculated correlation coefficients were found to be lower for T than for HS. Overall, T was slightly underestimated by global models. Wave propagation directions were excellently represented by global models at offshore sites. The WAVERYS model is the global product that best represents the wave parameters in the study area, probably due to its higher spatial resolution (0.2°) and more realistic atmospheric forcing (ERA5). In addition, it satisfactorily represents the temporal variability of HS in open water as well as swell characteristics at the outer the Río de la Plata.

Evaluation of precipitation extremes in ERA5 reanalysis driven regional climate simulations over the CORDEX-Australasia domain

Reanalysis-driven regional climate simulations using the Weather Research and Forecasting (WRF) model in New South Wales (NSW) and Australian Regional Climate Modelling (NARCliM) Version 2.0 are assessed for capturing precipitation extreme indices. Seven configurations of the WRF model driven by ECMWF (European Centre for Medium-Range Weather Forecasts) Reanalysis v5 (ERA5) for Australia from 1979 to 2020 at 20 km resolution are evaluated. We assess the spatiotemporal patterns of six selected Expert Team on Sector-Specific Climate Indices (ET-SCI) precipitation extremes by comparing regional climate model (RCM) simulations against gridded observations. The RCMs evaluated have varying levels of accuracy in simulating precipitation extremes. While they capture climatology and coefficient of variation of precipitation extremes relatively well, temporal correlation and trend reproduction present challenges. Some RCMs perform more effectively for specific extreme indices, while others encounter challenges in accurately replicating them. No single RCM excels in all aspects, highlighting the need to consider specific strengths when selecting RCMs for global climate model (GCM) driven simulations.

Development and preliminary validation of a land surface image assimilation system based on the Common Land Model

Abstract. Data assimilation is an essential approach to improve the predictions of land surface models. Due to the characteristics of single-column models, assimilation of land surface information has mostly focused on improving the assimilation of single-point variables. However, land surface variables affect short-term climate more through large-scale anomalous forcing, so it is indispensable to pay attention to the accuracy of the anomalous spatial structure of land surface variables. In this study, a land surface image assimilation system capable of optimizing the spatial structure of the background field is constructed by introducing the curvelet analysis method and taking the similarity of image structure as a weak constraint. The fifth-generation ECMWF Reanalysis – Land (ERA5-Land) soil moisture reanalysis data are used as ideal observation for the preliminary effectiveness validation of the image assimilation system. The results show that the new image assimilation system is able to absorb the spatial-structure information of the observed data well and has a remarkable ability to adjust the spatial structure of soil moisture in the land model. The spatial correlation coefficient between the model surface soil moisture and observation increased from 0.39 to about 0.67 after assimilation. By assimilating the surface soil moisture data and combining these with the model physical processes, the image assimilation system can also gradually improve the spatial structure of soil moisture content at a depth of 7–28 cm, with the spatial correlation coefficient between the model soil moisture and observation increased from 0.35 to about 0.57. The forecast results show that the positive assimilation effect could be maintained for more than 30 d. The results of this study adequately demonstrate the application potential of image assimilation system in short-term climate prediction.

ASSESSMENT OF REANALYSIS DATA FOR SOLAR PV OUTPUT FORECASTING IN THE PHILIPPINES: CASE OF PANGASINAN, NEGROS OCCIDENTAL, AND DAVAO DEL NORTE

Abstract. The sustainable energy transition in the Philippines requires accurate forecasting of solar PV output to optimize energy efficiency and grid management. While existing studies have emphasized the positive correlation between solar irradiance and PV production, this study aims to explore whether forecasting improves with the inclusion of weather data. This research conducts a comparative analysis between relying solely on solar irradiance against integrating various weather parameters to enhance solar PV output forecasting. The study focuses on three distinct locations (Pangasinan, Negros Occidental, and Davao Del Norte) and employs two models per each site: Model 1 (M1), which relies only on solar irradiance as predictors, and Model 2 (M2), which incorporates solar irradiance and weather parameters. Using Fifth Generation ECMWF Reanalysis (ERA5) Data, Principal Component Analysis (PCA) is conducted on the significant weather parameters. Extreme Gradient Boosting (XGBoost) with 5-fold nested cross-validation is applied for solar PV output forecasting. Models are assessed using Mean Absolute Percentage Error (MAPE) and skill scores. Results showthat while solar irradiance alone suffices for predicting solar PV output in Negros Occidental, incorporating weather parameters improves forecasting accuracy in Davao Del Norte and Pangasinan. This paper recommends caution in generalizing the findings to different regions with varying weather patterns, as the forecasting performance of the models is influenced by data quality, specific location, and prevailing weather conditions.

Extraction of persistent lagrangian coherent structures for the pollutant transport prediction in the Bay of Bengal

Lagrangian Coherent Structures (LCS) are the hidden fluid flow skeletons that provide meaningful information about the Lagrangian circulation. In this study, we computed the monthly climatological LCSs (cLCS) maps utilizing 24 years (1994–2017) of HYbrid Coordinate Ocean Model (HYCOM) currents and ECMWF re-analysis winds in the Bay of Bengal (BoB). The seasonal reversal of winds and associated reversal of currents makes the BoB dynamic. Therefore, we primarily aim to reveal the cLCSs associated with seasonal monsoon currents and mesoscale (eddies) processes over BoB. The simulated cLCS were augmented with the complex empirical orthogonal functions to confirm the dominant lagrangian transport pattern features better. The constructed cLCS patterns show a seasonal accumulation zone and the transport pattern of freshwater plumes along the coastal region of the BoB. We further validated with the satellite imagery of real-time oil spill dispersion and modelled oil spill trajectories that match well with the LCS patterns. In addition, the application of cLCSs to study the transport of hypothetical oil spills occurring at one of the active oil exploration sites (Krishna-Godavari basin) was described. Thus, demonstrated the accumulation zones in the BoB and confirmed that the persistent monthly cLCS maps are reasonably performing well for the trajectory prediction of pollutants such as oil spills. These maps will help to initiate mitigation measures in case of any occurrence of oil spills in the future.

An inflection point-based method for estimating metrics of mangrove phenology combining climatic factors and Landsat NDVI time series

ABSTRACT The present research evaluated the prospects of utilizing rainfall and temperature combined with Landsat-8 derived HANTS (Harmonic Analysis of Time Series) reconstructed NDVI for estimating the metrics of the mangrove phenology. The selected period of the study was from 2013 to 2020 for the Pichavaram mangroves of Tamil Nadu. The NDVI and ERA5 (ECMWF Re-Analysis) datasets of rainfall and temperature were the input datasets for developing the new algorithm. The ‘z-score sum’ provided a measure of the cumulative impact of rainfall and temperature, displaying its most negative value coinciding with the peak positive value of the NDVI time series datasets. The algorithm developed for phenological metrics estimation identified the common inflection points of the z-score sum and NDVI curves. The temporal analysis of metrics revealed the average Length of Season (LoS) as 230 days. The metrics also identified the drought year 2016 with the shortest LoS and the least Gross Primary Productivity (GPP) values. The analysis showed the influences of the preceding year’s monsoon rainfall on the GPP values of the later part of the phenological cycle. The temperatures during the days of PoS were found to be the optimum temperature for the growth of mangroves.