European Centre For Medium-Range Weather Forecasts Research Articles

Assessment of ERA5 derived zenith tropospheric delay data over East African region

The Global Navigation Satellite Systems (GNSS) provide valuable data for computing accurate and reliable Zenith Tropospheric Delay (ZTD) products used in GNSS precise positioning, climate, and meteorological studies. However, inconsistent GNSS data streaming in the East African region, caused by poor internet connectivity, equipment failure, and electrical power outages, results in data gaps that pose an impact on the applications of the ZTD products. To ensure continuous ZTD data availability, alternative approaches are necessary to fill the gaps. This paper evaluates the accuracy and feasibility of computing ZTD over East Africa using the European Centre for Medium-Range Weather Forecasts (ECMWF) 5th Re-Analysis (ERA5) model. The ERA5 ZTD is compared with ZTD data from 13 GNSS stations for the years 2013 to 2020 from the Nevada Geodetic Laboratory (NGL) products. Seven statistical evaluation metrics, including mean bias (MnB), root mean squared error (RMSE), Pearson correlation coefficient (R), coefficient of determination (R2), refined index of agreement (IA), Nash–Sutcliffe coefficient of efficiency (NSE), and Taylor skill score (TSS) were used to determine the agreement between ERA5 and GNSS tropospheric products. The results indicate that ERA5ZTD products match well with GNSS ZTD products, with an average MnB of −0.80 mm, RMSE of 8.94 mm, R of 0.976, R2 of 0.952, IA of 0.986, TSS of 0.975, and NSE of 0.945. The high accuracy and stability of ERA5 data in computing ZTD make it an excellent alternative source of ZTD data to augment GNSS ZTD products for positioning and meteorological applications in the East African region.

Assessment of Sensitivity and Validity Hydrodynamic Model in Cisadane Using Delft3d Flow Model

The National Capital Integrated Coastal Development (NCICD) program was prioritized as a disaster reduction technology within the annual target of BRIN in 2022. It is a part of the North Java Coastal Integrated Development Program project. A coastal reservoir development plan at the Cisadane River estuary is one of the outcomes. In this case, hydrodynamic modelling is needed to acquire hydrodynamic properties under the existing condition of Cisadane downstream, and it applied the Delft3D-FLOW model. The bathymetric data used in this simulation was obtained from the assimilation of national bathymetric data with a grid spacing of 180 m and field survey data with a horizontal resolution of 10 m and transect spacing of 200 m seaward of the Cisadane estuary. It has interpolated in a regular structured grid with 100 m and 50 m grid spacing. In addition, other data used are three hourly wind data downloaded from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the daily discharge of the Cisadane River in 2021. Astronomical water level data generated from TPXO 7.2 and TPXO 8 were used in the boundary conditions. These data are simulated using the two and three-dimensional flow model Delft3D-FLOW during November 1 – 15, 2021, using four manning coefficients (n) values, which are 0.03; 0.035; 0.04; and 0.045. The validation formulation used Normalization Root Mean Square Error (NRMSE), resulting in 5.3 - 9.6% for all the manning values and domains. The highest correlation coefficient of current velocity (u-v components) is acquired in the 10 km2 domain with 0.797 for the L3 (bottom layer).

Measurement of Downwelling Radiance Using a Low-Cost Compact Fourier-Transform Infrared System for Monitoring Atmospheric Conditions

Temperature and water vapor play crucial roles in the Earth’s climate system, and it is important to understand and monitor the variation in the thermodynamic profile within the lower troposphere. Among various observation platforms for understanding the vertical structure of temperature and humidity, ground-based Fourier-transform infrared (FTIR) can provide detailed information about the lower troposphere by complementing the limitations of radiosonde or satellite methods. However, these ground-based systems have limitations in terms of cost, operation, and mobility. Herein, we introduce a cost-effective and easily deployable FTIR observation system designed to enhance monitoring capabilities for atmospheric conditions. The atmospheric downwelling radiance spectrum of sky is measured by applying a real-time radiative calibration using a blackbody. From the observed radiance spectrum, the thermodynamic profile (temperature and the water vapor mixing ratio) of the lower troposphere was retrieved using an algorithm based on the optimal estimation method (OEM). The retrieved vertical structure results in the lower troposphere were similar to the fifth-generation reanalysis database (ERA-5) of the European Center for Medium-range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction final analysis (NCEP FNL). This provides a potential possibility for monitoring atmospheric conditions by a compact FTIR system.

ECMWF and UK Met Office Offshore Blowing Winds: Impact of Horizontal Resolution and Coastal Orography

AbstractWe analyze the performance of the European Centre for Medium‐Range Weather Forecasts (ECMWF) and UK Met Office (UKMO) meteorological models in predicting offshore blowing wind in coastal areas. Our attention is mainly on the Mediterranean coast, up to 200 km distance from the shore. We compare forecast neutral winds with Advanced Scatterometer measurements. The results indicate that the ECMWF forecasts systematically underestimate wind speed with respect to scatterometer data, while the UKMO model tends to overestimate. A cross‐analysis suggests that, better than fetch, model biases are a function of the model horizontal discretization, hence of the number of grid steps the wind runs over the sea. The steepness and roughness of the land orography before entering the sea, together with the related drag parameters, appear to have a strong role in determining the coastal offshore wind values. Surface drag over land tends to reduce the related wind speed, and it takes a few grid points to adjust to the smooth sea surface conditions. This is supported by a detailed study with a high resolution grid, but different orography resolutions. In these simulations, practically identical coastal wind speed distributions are found when the subgrid orography schemes are switched off. Vertical cross‐sections of potential temperature and wind in bora and mistral conditions illustrate the strong role of gravity waves, wind channeling and turbulent diffusion in coastal numerical weather prediction. Besides local wave modeling, this may be relevant for offshore wind farms, typically situated within 5–50 km from the coast.

Downscaling of wind fields on the east coast of India using deep convolutional neural networks and their applications in storm surge computations

Abstract Downscaling is reconstructing data from low to high resolution, capturing local effects and magnitudes. Widely employed methods for downscaling are dynamic and statistical methods with pros and cons. With ample data, machine learning (ML) and deep learning (DL) techniques can be employed to learn mapping from low to high resolution. This article investigates convolutional neural network capabilities for downscaling winds. The speed and direction of the wind are guided by a complex relation among pressure, Coriolis force, friction, and temperature, which leads to highly nonlinear wind patterns and poses a significant challenge for downscaling. The problem can be formulated as a super-resolution technique called a super-resolution convolutional neural network (SRCNN) for data reconstruction. Few variations of SRCNN are studied for wind downscaling. Six years of European Centre for Medium-Range Weather Forecasts (ECMWF) wind datasets along the east coast of India are used in the current study and are downscaled up to four times. Downscaled winds provide better results than traditional interpolation methods. Simulations for an extreme event are conducted with SRCNN downscaled winds and are compared against interpolation methods and original data. The numerical simulation results show that DL-based methods provide results closer to the ground truth than interpolation methods.

Machine learning methods to predict sea surface temperature and marine heatwave occurrence: a case study of the Mediterranean Sea

Abstract. Marine heatwaves (MHWs) have significant social and ecological impacts, necessitating the prediction of these extreme events to prevent and mitigate their negative consequences and provide valuable information to decision-makers about MHW-related risks. In this study, machine learning (ML) techniques are applied to predict sea surface temperature (SST) time series and marine heatwaves in 16 regions of the Mediterranean Sea. ML algorithms, including the random forest (RForest), long short-term memory (LSTM), and convolutional neural network (CNN), are used to create competitive predictive tools for SST. The ML models are designed to forecast SST and MHWs up to 7 d ahead. For each region, we performed 15 different experiments for ML techniques, progressively sliding the training and the testing period window of 4 years from 1981 to 2017. Alongside SST, other relevant atmospheric variables are utilized as potential predictors of MHWs. Datasets from the European Space Agency Climate Change Initiative (ESA CCI SST) v2.1 and the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis from 1981 to 2021 are used to train and test the ML techniques. For each area, the results show that all the ML methods performed with minimum root mean square errors (RMSEs) of about 0.1 °C at a 1 d lead time and maximum values of about 0.8 °C at a 7 d lead time. In all regions, both the RForest and LSTM consistently outperformed the CNN model across all lead times. LSTM has the highest predictive skill in 11 regions at all lead times. Importantly, the ML techniques show results similar to the dynamical Copernicus Mediterranean Forecasting System (MedFS) for both SST and MHW forecasts, especially in the early forecast days. For MHW forecasting, ML methods compare favorably with MedFS up to 3 d lead time in 14 regions, while MedFS shows superior skill at 5 d lead time in 9 out of 16 regions. All methods predict the occurrence of MHWs with a confidence level greater than 50 % in each region. Additionally, the study highlights the importance of incoming solar radiation as a significant predictor of SST variability along with SST itself.

3D wind observations with a compact mobile lidar based on tropo- and stratospheric aerosol backscatter

Abstract. We present the first measurements of simultaneous horizontal and vertical winds using a new lidar system developed at the Leibniz Institute of Atmospheric Physics in Kühlungsborn, Germany (54.12° N, 11.77° E), for the concept of Vertical And Horizontal COverage by LIdars (VAHCOLI). We describe the technical details of a multi-field-of-view (MFOV) upgrade, which allows the measurement of wind dynamics in the transition region from microscale to mesoscale (103–104 m). The method was applied at the edge of a developing high-pressure region, covering altitudes between 3 and 25 km. Comparisons between the lidar measurements and data from the European Centre for Medium-Range Weather Forecasts (ECMWF) show excellent agreement for the meridional wind component along the north beam of the lidar, which is better than 0.30±0.33 m s−1, while along the south beam, a higher deviation with -0.93±0.73 m s−1 is observed. Measurements of vertical wind show a significant underestimation of this component by ECMWF. Comparison of Aeolus winds to the lidar winds projected to the Aeolus viewing direction shows good agreement, with results better than -0.12±3.31 m s−1. The capability of the MFOV lidar to explore small-scale asymmetries in the wind field is shown by a comparison of the north and south field of view, where we observe a wind asymmetry in the meridional winds, which is also present in ECMWF but underestimated by a factor of approximately 4.

The Contribution of Aeolus Wind Observations to ECMWF Sea Surface Wind Forecasts

AbstractAeolus is the first satellite mission focusing on wind profile detection from near the surface to about 30 km in height on a global scale. This study evaluates the contribution of Aeolus winds to sea surface wind forecasts geographically by further analyzing the Observing System Experiments from the European Centre for Medium‐Range Weather Forecasts (ECMWF) with scatterometer winds from the meteorological operational satellites (assimilated into the model) and the Haiyang‐2B satellite (not assimilated into the model). The findings indicate that Aeolus has the ability to reduce the root‐mean‐square difference between scatterometer winds and background forecasts (short‐range) by about 0.05%–0.16% on average for climatic regions, except for the meridional wind component in the tropics. Also, Aeolus can generally reduce zonal biases of the background forecasts, while its beneficial impact on meridional biases mainly occurs in the Northern Hemisphere extratropics and tropics. For medium‐range forecast assessments, as the forecast step extends up to day 5, the positive impact of Aeolus on sea surface wind forecasts becomes more evident and is even greater than 3%, especially for extratropical ocean regions in the Southern Hemisphere. Furthermore, the impact of Aeolus shows seasonal variation, with a substantial positive impact from September 2019 to February 2020 and a negative impact mainly in March, April, and May 2020.

Validation of precipitation data generated by ERA5 reanalysis for the Mirim-São Gonçalo watershed, Brazil

The transboundary Mirim-São Gonçalo watershed (MSGW) is of a paramount importance for the development of its region, mainly for the agricultural sector. Obtaining climate data is fundamental in the management of water resources, as it allows estimating missing data, making long-term forecasts and checking possible trends. The European Center for Medium-Range Weather Forecasts (ECMWF) produces numerical weather forecasts, being the fifth generation of reanalysis (ERA5) the most recent. This work aimed to evaluate the efficiency of ERA5 in representing precipitation for the Brazilian side of MSGW, comparing its grid points data with the observed ones from rain gauges. For this purpose, the ERA5 and observed monthly climatological 1981-2020 rain data of the MSGW was calculated. The stationarity of the data was evaluated by applying the Mann-Kendall’s (MK) and Sen’s Slope (SS) tests, while the data homogeneity was verified with the Pettitt (Pt) test. For data validation, the Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE) and Pearson's correlation coefficient (rpearson) were calculated. MK and SS tests verified that there is no significant trend in the majority of the precipitation series, while the Pt test pointed out that there is no significant changing point (5% of significance level). The error measures (MAPE and RMSE) were low while the rpearson presented high correlation values (strong and positive), indicating the good performance and the potential of the ERA5 model in representing the precipitation data in the MSGW.

Response of upper tropospheric water vapor to global warming and ENSO

The upper tropospheric water vapor is a key component of Earth's climate. Understanding variations in upper tropospheric water vapor and identifying its influencing factors is crucial for enhancing our comprehension of global climate change. While many studies have shown the impact of El Niño-Southern Oscillation (ENSO) and global warming on water vapor, how they affect the upper tropospheric water vapor remains unclear. Long-term, high-precision ERA5 specific humidity data from the European Centre for Medium-Range Weather Forecasts (ECMWF) provided the data foundation for this study. On this basis, we successfully obtained the patterns of global warming (Independent Component 1, IC1) and ENSO (Independent Component 2, IC2) by employing the strategy of independent component analysis (ICA) combined with non-parametric optimal dimension selection to investigate the upper tropospheric water vapor variations and responses to ENSO and global warming. The results indicate that global warming and ENSO are the primary factors contributing to water vapor variations in the upper troposphere, achieving the significant correlations of 0.87 and 0.61 with water vapor anomalies respectively. Together, they account for 86% of the global interannual variations in water vapor. Consistent with previous studies, our findings also find positive anomalies in upper tropospheric water vapor during El Niño years and negative anomalies during La Niña years. Moreover, the influence extent of ENSO on upper tropospheric water vapor varies with the changing seasons.

Increasing frequency and lengthening season of western disturbances are linked to increasing strength and delayed northward migration of the subtropical jet

Abstract. Western disturbances (WDs) are cyclonic storms that travel along the subtropical jet, bringing the majority of seasonal and extreme precipitation to the Hindu Kush, Karakoram, and western Himalaya in the winter months. They are a vital component of the region's water security. Although typically most common in the winter, WDs can also interact with the summer monsoon, leading to catastrophic consequences. These seem to be happening more frequently, and along with increasingly harsh winter seasons, questions are now being asked about how climate change is affecting WD frequency and intensity in both summer and winter seasons. An analysis of 17 previous studies assessing trends in WD frequency revealed no consensus, at least in part because they quantified trends in different regions, seasons, and time periods. In this study, a more robust approach is used, quantifying trends in WD frequency and intensity by region and month using a track catalogue derived from 70 years of European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5) data. Winter WDs have increased significantly over the western and central Himalaya and the Hindu Kush in the last 70 years. This trend is attributed to a strengthening of the subtropical jet. The WD season has also significantly lengthened with WDs becoming far more common in May, June, and July, months where they were previously rare. For example, WDs have been twice as common in June in the last 20 years than during the previous 50. This is attributed to a delayed northward retreat of the subtropical jet, which historically has occurred before the onset of the summer monsoon. The most important implication is that the frequency of “monsoonal” WDs is increasing significantly, and therefore, due to climate change, catastrophic events like the 2013 Uttarakhand floods and the 2023 north India floods are becoming much more frequent.

A new implementation of FLEXPART with Enviro-HIRLAM meteorological input, and a case study during a heavy air pollution event

ABSTRACT We integrated Enviro-HIRLAM (Environment-High Resolution Limited Area Model) meteorological output into FLEXPART (FLEXible PARTicle dispersion model). A FLEXPART simulation requires meteorological input from a numerical weather prediction (NWP) model. The publicly available version of FLEXPART can utilize either ECMWF (European Centre for Medium-range Weather Forecasts) Integrated Forecast System (IFS) forecast or reanalysis NWP data, or NCEP (U.S. National Center for Environmental Prediction) Global Forecast System (GFS) forecast or reanalysis NWP data. The primary benefits of using Enviro-HIRLAM are that it runs at a higher resolution and accounts for aerosol effects in meteorological fields. We compared backward trajectories generated with FLEXPART using Enviro-HIRLAM (both with and without aerosol effects) to trajectories generated using NCEP GFS and ECMWF IFS meteorological inputs, for a case study of a heavy haze event which occurred in Beijing, China in November 2018. We found that results from FLEXPART were considerably different when using different meteorological inputs. When aerosol effects were included in the NWP, there was a small but noticeable difference in calculated trajectories. Moreover, when looking at potential emission sensitivity instead of simply expressing trajectories as lines, additional information, which may have been missed when looking only at trajectories as lines, can be inferred.

Sources of MJO teleconnection errors in the ECMWF extended‐range forecasts

AbstractThe European Centre for Medium‐range Weather Forecasts (ECMWF) extended‐range forecasts display large errors in the representations of Madden–Julian Oscillation (MJO) teleconnections over the North Atlantic with a strong underestimation of the impact of the MJO on the North Atlantic Oscillation (NAO) following an MJO in Phase 2–3. The origin of this error was investigated using a large set of re‐forecasts covering 20 years where part of the atmosphere was relaxed towards the ECMWF Reanalysis v5 (ERA5) reanalysis. These relaxation experiments show that relaxing the Tropics significantly improves the MJO teleconnections associated with Phase 2–3, with a reduction of about 50% in the amplitude error of the teleconnections. However, model errors outside the Tropics also play an important role. Results also suggest that errors in local processes reduce by about 20% the amplitude of the MJO teleconnections over the North Atlantic. Another example of a source of MJO teleconnection errors is the near surface over the Tibetan and Mongolian Plateaux. Nudging this area towards ERA5 improves the representation of the Pacific subtropical jet and the amplitude of MJO teleconnections associated with Phase 2–3 in the extended‐range re‐forecasts. Nudging the stratosphere exerts a comparatively weaker impact on the MJO teleconnections 11–15 days after an MJO in Phase 2–3. Overall, these experiments indicate that there are multiple sources of MJO teleconnection errors, making the representation of realistic MJO teleconnection by dynamical models a particularly challenging task.

Impact of Saharan dust outbreaks on short‐range weather forecast errors in Europe

AbstractMineral dust, the most abundant atmospheric aerosol by mass, interacts with radiation directly and alters cloud properties indirectly. Many operational numerical weather prediction models account for aerosol direct effects by using climatological mean concentrations and neglect indirect effects. This simplification may lead to shortcomings in model forecasts during outbreaks of Saharan dust towards Europe, when climatological mean dust concentrations deviate strongly from actual concentrations. This study investigates errors in model analyses and short‐range forecasts during such events. We investigate a pronounced dust event in March 2021 using the pre‐operational ICOsahedral Nonhydrostatic weather and climate model with Aerosols and Reactive Trace gases (ICON‐ART) with prognostic calculation of dust and the operational European Centre for Medium‐Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) model, which deploys a dust climatology. We compare model analysis and forecast with measurements from satellite and in situ instruments. We find that inclusion of prognostic aerosol and direct radiative effects from dust improves forecasts of surface radiation during clear‐sky conditions. However, dust‐induced cirrus clouds are strongly underestimated, highlighting the importance of representing indirect effects adequately. These findings are corroborated by systematic quantification of forecast errors against satellite measurements. For this we construct an event catalogue with 49 dust days over Central Europe between January 2018 and March 2022. We classify model cells by simulated and observed cloudiness and simulated dustiness in the total atmospheric column. We find significant overestimations of brightness temperature for cases with dust compared with cases without dust. For surface shortwave radiation, we find median overestimations of 6.2% during cloudy conditions with dust optical depth greater than 0.1, however these are not significant compared with cloudy conditions without dust. Our findings show that the pre‐operational ICON‐ART and the operational IFS model still do not reproduce cloudiness adequately during events with Saharan dust over Central Europe. Missing implementations of prognostic dust, particularly of indirect effects on cloud formation, lead to significant underestimations of cloudiness and potentially overestimations of surface radiation.

Causes of the severe drought in Southwest China during the summer of 2022

In summer 2022, Southwest China (SWC) suffered its worst drought since 1980, with a rainfall deficit and an extremely high temperature anomaly. This had a severely harmful effect on agricultural production, water resources, and hydroelectric supply over SWC. Both a less-than-normal water vapor supply and warmer-than-normal temperature contribute to the precipitation decrease associated with drought. Using the observed station data from the China Meteorological Administration and the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA5) data, our results show that the 2022 summer drought over SWC was caused by both the less-than-normal water vapor supply and warmer-than-normal temperature, and the warmer temperature played a more important role, unlike in previous droughts. The warmer air temperature for the 2022 summer induced record-breaking potential evapotranspiration (PET) over SWC, reducing surface water and drying out soils. Meanwhile, the warmer air temperature made it difficult for water vapor to reach saturation, resulting in a decrease in precipitation over the whole of SWC. We found that such anomalous water vapor supply and temperature were closely related to the South Asian high (SAH) during summer 2022. The SAH was stronger and covered a larger area than during other summer SWC droughts since 1980. The anomalous 2022 summer SAH inhibited the water vapor transport from the tropical Indian Ocean to SWC and further caused a significant warming temperature anomaly by regulating the thickness of the layer between isobaric surfaces, which contributed to the record-breaking summer drought over SWC. Our study highlights the vital role played by warmer temperatures in the 2022 summer drought. Considering that the warming air temperature under greenhouse-gas forcing may cause more droughts like that in 2022 in the future, the 2022 drought may yield useful information that will provide insight into future droughts and raise awareness of the impact of temperature on extreme climate events.

Assimilation of the deep learning-corrected global forecast system fields into the regional model for improving medium-range persistent precipitation forecasts

The error of regional models gradually increases during long-term integration, and it can be reduced with the constraint of large-scale circulation by assimilating global forecast system (GFS) fields to improve the forecast performance. However, with increasing forecast time, the GFS forecast error also significantly increases and varies widely. Directly assimilating GFS fields into the regional model without processing may generate negative impacts, whereas employing postprocessing correction methods can effectively mitigate GFS forecast errors. In this study, the GFS data of European Centre for Medium-Range Weather Forecasts (ECMWF) are first corrected using a convolutional long- and short-term memory network method. The correction model can correct multiple forecasts simultaneously by training only one model, and the correction results are less erroneous than those obtained by training multiple correction models for the different forecast times and pressure layers. The corrected ECMWF forecast fields are then assimilated into Weather Research and Forecasting Model (WRF) using the nudging method. This assimilation method generally improves the forecasts for precipitation above the moderate rainfall level at forecast times of 4–7 days, and reduces the errors of surface wind speed forecasts. This study improves the accuracy of correction models and the forecasts of persistent precipitation weather event with regional model.

Deep Learning for Postprocessing Global Probabilistic Forecasts on Subseasonal Time Scales

Abstract Subseasonal weather forecasts are becoming increasingly important for a range of socioeconomic activities. However, the predictive ability of physical weather models is very limited on these time scales. We propose four postprocessing methods based on convolutional neural networks to improve subseasonal forecasts by correcting systematic errors of numerical weather prediction models. Our postprocessing models operate directly on spatial input fields and are therefore able to retain spatial relationships and to generate spatially homogeneous predictions. They produce global probabilistic tercile forecasts for biweekly aggregates of temperature and precipitation for weeks 3–4 and 5–6. In a case study based on a public forecasting challenge organized by the World Meteorological Organization, our postprocessing models outperform the bias-corrected forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF), and achieve improvements over climatological forecasts for all considered variables and lead times. We compare several model architectures and training modes and demonstrate that all approaches lead to skillful and well-calibrated probabilistic forecasts. The good calibration of the postprocessed forecasts emphasizes that our postprocessing models reliably quantify the forecast uncertainty based on deterministic input information in the form of ECMWF ensemble mean forecast fields only.

Seasonal forecasting of local-scale soil moisture droughts with Global BROOK90: a case study of the European drought of 2018

Abstract. Prolonged deficit of soil moisture can result in significant ecosystem and economical losses. General slowdown of vegetation growth and development, withering of foliage cover, reduction of carbon, nutrients and water cycling, increase of fire and insect outbreaks are just a few examples of soil moisture drought impacts. Thus, an early and timely warning via monitoring and forecast could help to prepare for a drought and manage its consequences. In this study, a new version of Global BROOK90, an automated framework to simulate water balance at any location, is presented. The new framework integrates seasonal meteorological forecasts (SEAS5 forecasting system) from European Centre for Medium-Range Weather Forecasts (ECMWF). Here we studied how well the framework can predict the soil moisture drought on a local scale. Twelve small European catchments (from 7 to 115 km2) characterized by various geographical conditions were chosen to reconstruct the 2018–2019 period, when a large-scale prolonged drought was observed in Europe. Setting the ERA5-forced soil moisture simulations as a reference, we analysed how the lead time of the SEAS5 hindcasts influences the quality of the soil moisture predictions under drought and non-drought conditions. It was found that the hindcasted soil moisture fits well with the reference model runs only within the first (in some cases until the second and third) month of lead time. Afterwards, significant deviations up to 50 % of soil water volume were found. Furthermore, within the drought period the SEAS5 hindcast forcings resulted in overestimation of the soil moisture for most of the catchment, indicating an earlier end of a drought period. Finally, it was shown that application of the probabilistic forecast using the ensembles' quantiles to account for the uncertainty of the meteorological input is reasonable only for a lead time of up to 3 months.

East Asia Atmospheric River Forecast With a Deep Learning Method: GAN‐UNet

AbstractAccurate forecasting of atmospheric rivers (ARs) holds significance in preventing losses from extreme precipitation. However, traditional numerical weather prediction (NWP) models are computationally expensive and can be limited in accuracy due to inaccurate physical parameter settings. To overcome these limitations, we propose a deep learning (DL) model, called GAN‐UNet, to forecast the AR occurrence, position, and intensity in East Asia. GAN‐UNet can capture the complex nonlinear relationship between the inputs at the past moment, including the vertically integrated water vapor transport (IVT), zonal wind at 850 hPa (U850), and meridional wind at 850 hPa (V850), and the forecast output (IVT, U850, or V850), whose results are comparable to NWP models. In addition, the average model (AM) by integrating the results generated by GAN‐UNet and European Centre for Medium‐Range Weather Forecasts (ECMWF) outperforms all the NWP models selected in this study, demonstrating its potential to improve the performance of NWP through the DL method. Specifically, the 5‐day average F1 scores of the AM are 0.777 and 0.845, whose values are significantly better than those obtained by ECMWF (0.712 and 0.794) in the two key regions of East Asia; The AM 5‐day average intersection over unions are 0.706 and 0.688 while the values of ECMWF are 0.675 and 0.64; in terms of intensity forecast, GAN‐UNet and AM exhibited lower differences in most of the intensity bins, except for the final bin with IVT more than 825 kg m−1 s−1. With this thorough analysis, GAN‐UNet is shown as an effective model to forecast ARs.

Spatial and Temporal Analysis of Carbon Dioxide Concentrations over Italy by means of OCO‑2 Satellite Data Series

Carbon dioxide is a greenhouse gas with sink and source related to natural cycles and anthropic activities. OCO‑2 is a NASA carbon dioxide dedicated mission launched in 2014 aimed to measure the CO2 concentrations in the atmosphere by recording sunlight reflected off the Earth and provides, at the state of the art, the highest spatial resolution for mapping CO2 at global scale. In this work, for the first time, we statistically analyse 8 years of OCO‑2 acquisitions over Italian territory, obtaining the main trend and the seasonal behaviour of CO2 over land. After data reprocessing and compensating on temporal frequency of OCO‑2 acquisitions over Italy, a mean of 21 ppm of increment in the period from 2015 to 2022 has been found. In the data time series, we also noticed a significant acceleration in the trend between 2019 and 2020 and a return to average values of the trend after the COVID19 pandemic lockdown. In addition, such trends have been compared with those achieved by the European Centre for Medium Range Weather Forecasts (ECMWF) model. The data time series was also used to perform a spatial analysis of areas characterized by lower/higherc CO2 concentrations to detect sinks/sources in Italy due to the land use. The analysis reveals that the North Italian regions, with more population and industries, are the source of CO2; moreover, the fundamental role of vegetation as a sink of CO2 is confirmed.