ECMWF Re-Analysis Research Articles

Sub-Daily Performance of a Convection-Permitting Model in Simulating Decade-Long Precipitation over Northwestern Türkiye

One of the main differences between regional climate model and convection-permitting model simulations is not just how well topographic characteristics are represented, but also how deep convection is treated. The convection process frequently occurs within hours, thus a sub-daily scale becomes appropriate to evaluate these changes. To do this, a series of simulations has been carried out at different spatial resolutions (0.11 and 0.025) using the COSMO-CLM (CCLM) climate model forced by the ECMWF Reanalysis v5 (ERA5) between 2011 and 2020 over a domain covering northwestern Türkiye. Hourly precipitation and heavy precipitation simulated by both models were compared with the observations by Turkish State Meteorological Service (TSMS) stations and Integrated Multi-satellitE Retrievals for GPM (IMERG). Subsequently, we aimed to identify the reasons behind these differences by computing several atmospheric stability parameters and conducting event-scale analysis using atmospheric sounding data. CCLM12 displays notable discrepancies in the timing of the diurnal cycle, exhibiting a premature shift of several hours when compared to the TSMS. CCLM2.5 offers an accurate representation of the peak times, considering all hours and especially those occurring during the wet hours of the warm season. Despite this, there is a tendency for peak intensities to be overestimated. In both seasons, intensity and extreme precipitation are highly underestimated by CCLM12 compared to IMERG. In terms of statistical metrics, the CCLM2.5 model performs better than the CCLM12 model under extreme precipitation conditions. The comparison between CCLM12 and CCLM2.5 at 12:00 UTC reveals differences in atmospheric conditions, with CCLM12 being wetter and colder in the lower troposphere but warmer at higher altitudes, overestimating low-level clouds and producing lower TTI and KI values. These conditions can promote faster air saturation in CCLM12, resulting in lower LCL and CCL, which foster the development of low-level clouds and frequent low-intensity precipitation. In contrast, the simulation of higher TTI and KI values and a steeper lapse rate in CCLM2.5 enables air parcels to enhance instability, reach the LFC more rapidly, increase EL, and finally promote deeper convection, as evidenced by higher CAPE values and intense low-frequency precipitation.

High‐Resolution Analysis of Severe Heat Wave Dynamics and Thermal Discomfort Across India

ABSTRACTThe study explores variability and dynamical characteristics of heatwaves during March–June for 1990–2020 over India. Normalised Tmax anomaly is used to identify different heatwave spells in vulnerable regions of North‐central India (NCI) and Southeast coast of India (SECI) using India Meteorological Department (IMD, 1° × 1° resolution) observations, Indian Monsoon Data Assimilation and Analysis (IMDAA, 0.12° × 0.12°), and ECMWF Reanalysis v5 (ERA5, 0.25° × 0.25°). Results highlight that IMDAA exhibited a total 202 days (181 days) heatwaves duration in NCI (SECI) regions while ERA5 exhibited a total 132 days (89 days), respectively, compared with those of IMD (195 and 163 days). The primary heatwave periods for NCI (10 April to 20 June) and SECI region (1 May to 10 June) are well captured by IMDAA, unlike ERA5. The average length of the heatwave is 7.8, 7.5, and 7.76 days (8.15, 7.72, and 6.1 days) over NCI (SECI) in IMD, IMDAA, and ERA5, respectively. The high heat stress is more frequent in SECI than in the NCI region and is common during May–June (May only), as seen in IMDAA (ERA5). The middle to upper‐level anticyclone over NCI is stronger than SECI during heatwaves. Heat advection with stronger 850‐hPa north‐westerlies (~10 ms−1) abates sea breeze in the coastal region, aiding longer heatwaves in the SECI region. Ascending motion induced by surface heating is confined to the lower levels due to the subsidence by the upper‐level anomalous anticyclone, stagnating higher temperatures in the lower atmosphere, depicting a heat dome. The surface temperatures are slightly higher in NCI (31°C–39°C) than in SECI (30°C–37°C). However, the double moist heat dome in SECI has witnessed higher heat stress conditions than NCI. Higher relative humidity in the SECI region is contributed by maritime winds from the Bay of Bengal and Arabian Sea, soil moisture, and so forth. The study highlights the value of atmospheric moisture in differentiating the study regions for heat stress conditions.

Investigating the Underlying Mechanisms of Monsoon Season Heavy Precipitation in Central Asian High Mountain Areas

Abstract In this study, fifth major global reanalysis produced by ECMWF (ERA5) reanalysis data from 1979 to 2022 were utilized to investigate extreme precipitation in the central Asian high mountain (CAHM) region, comprising the Pamir Plateau and western, central, and eastern Tianshan regions. This study found that westerlies and monsoons are the primary drivers of extreme precipitation, with distinct mechanisms in the southwestern and northeastern CAHM (divided at approximately 79°E). In the southwestern CAHM, a weak Indian summer monsoon (ISM) leads to negative potential height anomalies, enhancing meridional water vapor flux from the Bay of Bengal and Arabian Sea, thereby increasing precipitation. Conversely, extreme precipitation is associated with the negative phase of the Silk Road pattern in the northeastern CAHM. While the East Asian summer monsoon (EASM) plays a lesser role, it influences water vapor supplies and atmospheric circulation in the southwestern CAHM and modulate meridional wind position in the northeastern CAHM with the ISM, contributing to extreme precipitation. Seasonal analysis revealed May as the peak for extreme precipitation in the southwestern CAHM region, while extreme precipitation in the northeastern CAHM region peaked in the midmonsoon months (June and July) due to the synergy between monsoons and westerlies of different strengths passing through the CAHM. Significance Statement This study explores the occurrence and mechanisms of extreme precipitation in CAHM and explores their four key regions’ water vapor transport pathways during the monsoon period. CAHM has a sensitive and vulnerable ecosystem highlighting the importance of understanding their processes, including the roles of westerlies and monsoons. This is crucial for detecting abnormal water cycles under climate change, such as flood prevention, and enhancing mountain weather forecasting abilities for the monsoon period.

Retrieval of Cloud Macro‐Physical Properties Using theFY‐4A Advanced Geostationary Radiation Imager (AGRI) and the Geostationary Interferometric Infrared Sounder (GIIRS)

AbstractThis study presents a novel approach for conducting all‐day retrieval of cloud macro‐physical properties (single‐layer cloud phase, cloud top height, and cloud base height for optical thickness less than 10) using the Advanced Geostationary Radiation Imager (AGRI) and the Geostationary Interferometric Infrared Sounder (GIIRS) onboard the geostationary meteorological satellite Fengyun‐4A based on machine learning methods. Model accuracy was compared after integrating ECMWF Reanalysis v5 (ERA‐5) data, atmospheric temperature and moisture profiles, and GIIRS clear‐column radiance. Results demonstrate that integrating GIIRS clear‐column radiances can enhance the precision of cloud phase classification and the retrieval of cloud macro‐physical properties. This effectively replaces the role of atmospheric temperature and humidity profiles, which are typically required for thermal infrared remote sensing retrieval. Moreover, the issue of delayed acquisition of ERA‐5 atmospheric temperature and humidity profiles is mitigated, enabling near real‐time and all‐day retrieval of cloud macro‐physical properties.

The differences between remote sensing and in situ air pollutant measurements over the Canadian oil sands

Abstract. Ground-based remote sensing instruments have been widely used for atmospheric research, but applications for air quality monitoring remain limited. Compared to an in situ instrument that provides air quality conditions at the ground level, most remote sensing instruments (nadir viewing) are sensitive to a broad range of altitudes, often providing only integrated column observations. These column data can be more difficult to interpret and to relate to surface values and hence to “nose-height-level” health factors. This research utilized ground-based remote sensing and in situ air quality observations in Canada's Athabasca oil sands region to investigate some of their differences. Vertical column densities (VCDs) of SO2 and NO2 retrieved by Pandora spectrometers located at the Oski-Otin site at Fort McKay (Alberta, Canada) from 2013–2019 were analyzed along with measurements of SO2 and NO2 surface concentrations and meteorological data. Aerosol optical depth (AOD) observations by a CIMEL sunphotometer were compared with surface PM2.5 data. The Oski-Otin site is surrounded by several large bitumen mining operations within the Athabasca oil sands region with significant NO2 emissions from the mining fleet. Two major bitumen upgraders that are 20 km south-east of the site have total SO2 and NO2 emissions of about 40 and 20 kt yr−1, respectively. It was demonstrated that remote sensing data from Pandora and CIMEL combined with high-vertical-resolution wind profiles can provide information about pollution sources and plume characteristics. Elevated SO2 VCDs were clearly observed for times with south and south-eastern winds, particularly at 200–300 m altitude (above ground level). High NO2 VCD values were observed from other directions (e.g., north-west) with less prominent impacts from 200–300 m winds. In situ ground observations of SO2 and NO2 show a different sensitivity to wind profiles, indicating they are less sensitive to elevated plumes than remote sensing instruments. In addition to measured wind data and lidar-observed boundary layer height (BLH), modelled wind profiles and BLH from ECMWF Reanalysis v5 (ERA5) have been used to further examine the correlation between column and surface observations. The results show that the height of emission sources (e.g., emissions from high stacks or near the surface) will determine the ratio of measured column and surface concentration values (i.e., could show positive or negative correlation with BLH). This effect will have an impact on the comparison between column observations (e.g., from the satellite or ground-based remote sensing instruments) with surface in situ measurements. This study explores differences between remote sensing and in situ instruments in terms of their vertical, horizontal, and temporal sampling differences. Understanding and resolving these differences are critical for future analyses linking satellite, ground-based remote sensing and in situ observations in air quality monitoring and research.

Assessing the ducting phenomenon and its potential impact on Global Navigation Satellite System (GNSS) radio occultation refractivity retrievals over the northeast Pacific Ocean using radiosondes and global reanalysis

Abstract. In this study, high-resolution radiosondes from the Marine Atmospheric Radiation Measurement (ARM) Global Energy and Water Experiment (GEWEX) Cloud System Study (GCSS) Pacific Cross-section Intercomparison (GPCI) Investigation of Clouds (MAGIC) field campaign and ECMWF Reanalysis version 5 (ERA5) global reanalysis data are used to assess characteristics of the elevated ducting layer along a transect over the northeastern Pacific Ocean from Los Angeles, California, to Honolulu, Hawaii. The planetary boundary layer (PBL) height (PBLH) increases as the strength of the refractivity gradient decreases westward along the transect. The thickness of the prevailing ducting layer remains remarkably consistent (∼ 110 m) in the radiosonde data. On the other hand, the ERA5 reanalysis generally resolves the ducting features well, but it underestimates the ducting height and strength, especially over the trade cumulus region near Hawaii. A simple two-step end-to-end simulation is used to evaluate the impact of the elevated ducting layer on radio occultation (RO) refractivity retrievals. A systematic negative refractivity bias (N bias) below the ducting layer is observed throughout the transect, peaking (−5.42 %) slightly below the PBLH and gradually decreasing towards the surface (−0.5 %). The N bias shows a strong positive correlation with the ducting strength. The ERA5 data underestimate the N bias, with the magnitude of the underestimation increasing westward along the transect.

Climate Analysis for Tourist Comfort Level in Belitong Geopark

Abstract Tourism, a sector with significant potential to be developed into a source of regional income, is one of the focuses of the Ministry of Tourism and Creative Economy/Tourism and Creative Economy Agency (Kemenparekraf/Baparekraf) in their quest for sustainable tourism in Indonesia. Their efforts have resulted in Belitong Geopark being designated as a UNESCO Global Geopark., a new environmentally-based tourism branding. The comfort of the climate and the natural beauty of the tourist attractions play a crucial role in influencing tourists’ decisions to visit a destination. The Tourism Climate Index (TCI) and the Thermal Humidity Index (THI) are employed in this research to analyze the climate comfort level for tourism. (1) The factors that make up the climate comfort index, including precipitation, temperature, sunshine duration, wind speed and cloud coverage, are components of the thermal, physical, and aesthetic dimensions used to assess climate comfort levels. The climate data from 1991 to 2020 were obtained from the H.AS Hanandjoeddin Meteorological Station, cooperation rain-gauge stations from 2014 to 2020, and ECMWF reanalysis data with a data resolution of 0.25° x 0.25° for 1991-2020. According to the THI values in the Belitung Geopark, they are in the comfort category all year round, with the Tajam Mountain and Lumut Hill geosites showing the lowest THI values and having an extremely comfortable THI category, respectively. In comparison, the TCI results indicate that the best time to travel to Geopark Belitung is between December and June, mainly with TCI values in the excellent and perfect categories. In conclusion, climatic conditions in Belitung Geopark support tourism activities all year round.

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.

Large‐Amplitude Inertia Gravity Waves Over Syowa Station: Comparison of PANSY Radar and ERA5 Reanalysis Data

AbstractWe examined large‐amplitude inertia gravity waves (GWs) over Syowa Station, Antarctica using the PANSY (Program of the Antarctic Syowa MST/IS) radar data and the latest reanalysis (ECMWF reanalysis v5; ERA5) from October 2015 to September 2016. Focusing on large‐amplitude events with large absolute momentum flux (AMF), hodograph analysis was applied to both data to estimate the wave parameters. It showed that the inertia GWs with a downward phase velocity becomes dominant in the stratosphere. Although their vertical wavelengths got shorter with altitude, their intrinsic periods and horizontal wavelengths got longer with altitude. In addition, their southward propagation was predominant in the stratosphere. Although height dependence of the estimated wave parameters is consistent with previous studies investigating inertia GWs over Syowa Station, some features specific to large‐amplitude inertia GWs were also observed. The GW features obtained from PANSY were mostly consistent with those from ERA5 except for their amplitudes. Comparison of AMF between PANSY and ERA5 indicated that ERA5 significantly underestimated the AMF by a factor of 5 between 5 and 12.5 km altitudes and more above 12.5 km. Difference of horizontal and vertical wind power spectra between PANSY and ERA5 is quantitatively consistent with the difference of AMF and its height dependence. It was found that underestimation of vertical wind spectra primarily contributed to the underestimation of AMF in ERA5. The greater underestimation of AMF in the stratosphere might be due to larger vertical grid spacing in ERA5 and the shorter vertical wavelengths of the dominant GWs in the stratosphere.

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.

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.

Revealing the synergistic contribution of PWV and CAPE to extreme precipitation throughout China

Precipitable water vapor (PWV) and convective available potential energy (CAPE) are two key variables affecting the formation and persistence of rainfall. Most previous studies only independently and qualitatively analyzed the long-term relationship between PWV/CAPE and extreme precipitation, but their synergistic contribution of PWV and CAPE to extreme precipitation (EP) was rarely investigated. Therefore, a novel model named PCEP (comprising PWV, CAPE, and EP), that integrates Global Navigation Satellite System (GNSS)-derived PWV and the fifth-generation ECMWF reanalysis (ERA5)-derived CAPE, is proposed in this study, where the joint contribution of PWV and CAPE on EP was investigated. Precipitation data is derived from the China Meteorological Administration (CMA). The daily PWV, CAPE and precipitation from 219 GNSS collocated meteorological stations in China from 2011 to 2018 were collected to perform the PCEP model. The tendency and periodic information of the three variables were revealed based on the multichannel singular spectral analysis and Lomb-scale methods. Moreover, the power-law relationships of PWV to loge(EP) varied with the seasons in China, while the linear relationships existed between loge(CAPE) and loge(EP). Finally, to solve the collinearity issue among PWV, CAPE and EP that occurred on the regional scale, the partial least square (PLS) method was first introduced to investigate their relationships on the seasonal scale. The PLS method found that PWV and CAPE also complement each other to EP on the seasonal scale, on the basis of regional complementarity. Additionally, the qualitative relationship between PWV, CAPE and the frequency of EP was elucidated. These results showed that the close relationships between high PWV and CAPE and the intensity and frequency of EP events on the annual and seasonal scales throughout China.

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%.