Centre forMedium-Range Weather Forecasts Research Articles

Climate Variability of Air Temperature and Its Warming Trends in the Arabian Gulf

Over a 41-year period (1979–2019), the climate of the Arabian Gulf (Gulf) exhibited notable variability. This variation, influenced by mesoscale and globalscale meteorological and climatic determinants, showed temperature fluctuations, predominantly in the Northern Gulf (N Gulf). The N Gulf recorded an average temperature of 25.9 °C, a noticeable 3 °C higher than the broader Gulf average. This period marked extreme temperature records, with a peak at 53.8 °C in July 2016, paralleling the highest known temperatures in Asia. Data reveals a warming trend (0.1–0.6 °C per decade) in the region, with the N Gulf’s rise surpassing global trends. The years 2014–2019 were notably warmer, impacting overall trends, and 1997 was the transitional year shifting from cooler to warmer climates. The N Gulf’s seasonal dynamics are also transforming, with extended summers and shortened winters. In this study, we employed empirical orthogonal function (EOF) decomposition analysis to examine the interannual to multidecadal fluctuations in air temperature within the Gulf from 1979 to 2019, using hourly air temperature data from the Watch Forcing Dataset (WFDE5) generated from the fifth generation (ERA5) European Centre for Medium-Range Weather Fore-casts (ECMWF). Through this method, we discerned three principal EOF modes, which together accounted for 82% of the air temperature variability in the Gulf, highlighting external influences such as North Atlantic Oscillation (NAO), El Nino-Southern Oscillation (ENSO), and Atlantic Multidecadal Oscillation (AMO). The data for these external influences were sourced from the National Oceanic and Atmospheric Administration (NOAA) Physical Sciences Laboratory. These findings stress the pressing need for detailed climate studies and mitigation strategies for a rapidly warming Gulf region, especially the N Gulf.

Use of CAMS near Real-Time Aerosols in the HARMONIE-AROME NWP Model

Near real-time aerosol fields from the Copernicus Atmospheric Monitoring Services (CAMS), operated by the European Centre for Medium-Range Weather Forecasts (ECMWF), are configured for use in the HARMONIE-AROME Numerical Weather Prediction model. Aerosol mass mixing ratios from CAMS are introduced in the model through the first guess and lateral boundary conditions and are advected by the model dynamics. The cloud droplet number concentration is obtained from the aerosol fields and used by the microphysics and radiation schemes in the model. The results show an improvement in radiation, especially during desert dust events (differences of nearly 100 W/m2 are obtained). There is also a change in precipitation patterns, with an increase in precipitation, mainly during heavy precipitation events. A reduction in spurious fog is also found. In addition, the use of the CAMS near real-time aerosols results in an improvement in global shortwave radiation forecasts when the clouds are thick due to an improved estimation of the cloud droplet number concentration.

Warm conveyor belt characteristics and impacts along the life cycle of extratropical cyclones: case studies and climatological analysis based on ERA5

Abstract. This study presents a systematic investigation of the characteristics and meteorological impacts of warm conveyor belts (WCBs). For this purpose, we compile a new WCB climatology (1980–2022) of trajectories calculated with the most recent reanalysis dataset ERA5 from the European Centre for Medium-Range Weather Forecasts (ECMWF). Based on this new climatology, two-dimensional masks are defined that represent the inflow, ascent, and outflow locations of WCBs. These masks are then used to objectively quantify the key characteristics (intensity, ascent rate, and ascent curvature) and meteorological impacts (precipitation and potential vorticity (PV) anomalies) of WCBs in order to (i) attribute them to different stages in the life cycle of the associated cyclones and to (ii) evaluate differences in the outflow of the cyclonic and anticyclonic branches. The approach was applied globally, but this study focuses on the North Atlantic, one of the regions where WCBs ascend most frequently. The method is first tested and illustrated through three case studies of well-documented cyclones, revealing both the similarities and the case-to-case variability in the evolution of the WCB characteristics and impacts. We then extend the analysis to about 5000 cyclones that occurred in winter between 1980–2022 in the North Atlantic. The case studies and the climatological analysis both show that WCBs are typically most intense (in terms of air mass transported, ascent rate, precipitation rate, and volume) during the intensification period of the associated cyclone. The northward displacement along the storm track and diabatic PV production lead to an increase in low-level PV in the region of WCB ascent during the cyclone life cycle. The negative PV anomaly at upper levels, associated with the WCB outflow, remains relatively constant. The investigation of the WCB branches reveals an increasing intensity of the cyclonic WCB branch with time, linked to the increasing strength of the cyclonic wind field around the cyclone. Due to a lower altitude, the outflow of the cyclonic WCB branch is associated with a weaker negative PV anomaly than the anticyclonic one, which ascends to higher altitudes. In summary, this study highlights the distinct evolution of WCB characteristics and impacts during the cyclone life cycle and the marked differences between the cyclonic and anticyclonic branches.

Opposite effects of aerosols and meteorological parameters on warm clouds in two contrasting regions over eastern China

Abstract. The sensitivity (S) of cloud parameters to the influence of different aerosol and meteorological parameters has in most previous aerosol–cloud interaction (aci) studies been addressed using traditional statistical methods. In the current study, relationships between cloud droplet effective radius (CER) and aerosol optical depth (AOD, used as a proxy for cloud condensation nuclei, CCN), i.e., the sensitivity (S) of CER to AOD, are investigated with different constraints of AOD and cloud liquid water path (LWP). In addition to traditional statistical methods, the geographical detector method (GDM) is applied in this study to quantify the relative importance of the effects of aerosol and meteorological parameters, as well as their interaction, on S. Moderate Resolution Imaging Spectroradiometer (MODIS) C6 L3 data and European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-5 reanalysis data, for the period from 2008 to 2022, were used to investigate aci over eastern China. Two contrasting areas were selected: the heavily polluted Yangtze River Delta (YRD) and a relatively clean area over the East China Sea (ECS). Linear regression analysis shows that CER decreases with the increase in AOD (negative S) in the moderately polluted atmosphere (0.1<AOD<0.3) over the ECS, whereas, in contrast, CER increases with increasing AOD (positive S) in the polluted atmosphere (AOD>0.3) over the YRD. Evaluation of S as function of the LWP shows that in the moderately polluted atmosphere over the ECS, S is negative in the LWP interval [40 g m−2, 200 g m−2], and the sensitivity of CER to AOD is substantially stronger as LWP is larger. In contrast, in the polluted atmosphere over the YRD, S is positive in the LWP interval [0 g m−2, 120 g m−2] and does not change notably as function of LWP in this interval. The study further shows that over the ECS, the CER is larger for higher low tropospheric stability (LTS) and relative humidity (RH) but lower for higher pressure vertical velocity (PVV). Over the YRD, there is no significant influence of LTS on the relationship between CER and AOD. The GDM has been used as an independent method to analyze the sensitivity of cloud parameters to AOD and meteorological parameters (RH, LTS and PVV). The GDM has also been used to analyze the effects of interactions between two parameters and thus obtain information on confounding meteorological effects on the aci. Over the ECS, cloud parameters are sensitive to almost all parameters considered except for cloud top pressure (CTP), and the sensitivity to AOD is larger than that to any of the meteorological factors. Among the meteorological factors, the cloud parameters are most sensitive to PVV and least sensitive to RH. Over the YRD, the explanatory power of the sensitivity of cloud parameters to AOD and meteorological parameters is much smaller than over the ECS, except for RH, which has a statistically significant influence on CTP and can explain 74 % of the variation of CTP. The results from the GDM analysis show that cloud parameters are more sensitive to the combination of aerosol and a meteorological parameter than to each parameter alone, but confounding effects due to co-variation of both parameters cannot be excluded.

Analytical and adaptable initial conditions for dry and moist baroclinic waves in the global hydrostatic model OpenIFS (CY43R3)

Abstract. This article presents a description of an analytical, stable, and flexible initial background state for both dry and moist baroclinic wave simulation on an aquaplanet in order to test the dynamical core of numerical weather prediction models and study the dynamics and evolution of extratropical cyclones. The initial background state is derived from an analytical zonal wind speed field, or jet structure, and the hydrostatic primitive equations for moist adiabatic and frictionless flow in spherical coordinates. A baroclinic wave can develop if a perturbation is added to the zonal wind speed field. This new baroclinic wave configuration has been implemented in the Open Integrated Forecasting System (OpenIFS) CY43R3, a global numerical weather prediction model developed by the European Centre for Medium-Range Weather Forecasts. In total, seven parameters can be used to control the generation of the initial background state and hence the development of the baroclinic waves in the OpenIFS configuration file: the jet's width, the jet's height, the maximum zonal mean wind speed of the jet, the horizontal mean of the surface virtual temperature, the surface relative humidity, the lapse rate, and the surface roughness. Nine dry and nine moist initial background states have been generated to test their stability without perturbations. The meteorological stability of the initial states is investigated by examining the spatial distributions of the equivalent potential temperature, the absolute vorticity, and the Brunt–Väisälä frequency. Moreover, the root mean square error (RMSE) of the zonal wind speed has been computed to assess their numerical stability. Finally, six dry and six moist initial background state have been used with an unbalanced perturbation to ensure that the baroclinic life cycles that develop are physically realistic. The resulting baroclinic wave is shown to be sensitive to the jet's width. This configuration for baroclinic wave simulations will be used to create a large ensemble of baroclinic life cycles to study how extratropical cyclones may evolve in the future.

A review of the BuFeng-1 GNSS-R mission: calibration and validation results of sea surface and land surface

ABSTRACT In this paper, we will conclude the results of Bufeng-1 (BF-1) A/B data processing, calibration workflow, and validation of the calibrated sea surface winds, land surface soil moisture, and sea surface height measurements. Since 2019, the BF-1 mission has operated in-orbit for over 4 years. The Earth reflected delay Doppler maps (DDMs) are continuously collected to perform global sea surface and land observations. At the same time, the intermediate frequency (IF) raw data are also obtained for 12 seconds every pass in diagnostic mode. To begin with, a brief description of the spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) technique will be provided in the introduction. Next, we will present the overview of Chinese BF-1 mission and the data specifications used in our research. In the next section, the BF-1 mission-related spaceborne power calibration and validation are presented to show the support to power DDM observable production for sea surface and land surface applications. Then, the status of Chinese Beidou System (BDS) Equivalent Isotropic Radiated Power (EIRP) acquisition programme is then introduced. Furthermore, the latest sea surface height (SSH) measurements results including two modes (group delay and carrier phase) and wind speed derivation based on machine learning (ML) method will be spatial-temporal aligned and validated with auxiliary datasets including Denmark Technology University (DTU) mean sea surface (MSS) products and European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis. The previous published results of sea surface winds retrieval under Hurricane conditions and soil moisture retrieval are also reviewed for the BF-1 mission applications. Finally, the conclusion of BF-1 derived results will be discussed to draw out ongoing/future works.

Spatial-temporal characteristics analysis of solar irradiance forecast errors in Europe and North America

Accurate photovoltaic (PV) power forecast is crucial to power systems. Solar irradiance forecast is the fundamentals of PV power forecast. Current researches merely focus on improving the forecast accuracy. There hasn't been comprehensive study on the spatial-temporal characteristics of solar irradiance forecast errors. Thus, this paper analyzed the error characteristics of solar irradiance forecast provided by European Centre for Medium-Range Weather Forecasts (ECMWF), one of the most accurate numerical weather prediction (NWP) products. ECMWF's forecasts were compared with ERA5 solar irradiance reanalysis data to reveal the error distribution characteristics. Four-year (2017–2020) data from the geographical region bounded by 63°N, −126°W, 21°S, and 36°E (covering most parts of Europe and North America) were studied. Experiments show that solar irradiance forecast errors peak at noon of the local time. Furthermore, correlations between solar irradiance forecast errors and other weather variables were also revealed. Experiments show that solar irradiance forecast errors have negative correlation with low cloud cover forecast errors and positive correlation with air temperature forecast errors. The possible reasons for the correlation relationship were also analyzed in detail. This paper systematically reveals the spatial-temporal characteristics of solar irradiance forecast errors and provides a useful guideline for solar PV system operation.

Evaluation of downward and upward solar irradiances simulated by the Integrated Forecasting System of ECMWF using airborne observations above Arctic low-level clouds

Abstract. The simulations of upward and downward irradiances by the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts are compared with broadband solar irradiance measurements from the Arctic CLoud Observations Using airborne measurements during polar Day (ACLOUD) campaign. For this purpose, offline radiative transfer simulations were performed with the ecRad radiation scheme using the operational IFS output. The simulations of the downward solar irradiance agree within the measurement uncertainty. However, the IFS underestimates the reflected solar irradiances above sea ice significantly by −35 W m−2. Above open ocean, the agreement is closer, with an overestimation of 28 W m−2. A sensitivity study using measured surface and cloud properties is performed with ecRad to quantify the contributions of the surface albedo, cloud fraction, ice and liquid water path and cloud droplet number concentration to the observed bias. It shows that the IFS sea ice albedo climatology underestimates the observed sea ice albedo, causing more than 50 % of the bias. Considering the higher variability of in situ observations in the parameterization of the cloud droplet number concentration leads to a smaller bias of −27 W m−2 above sea ice and a larger bias of 48 W m−2 above open ocean by increasing the range from 36–69 to 36–200 cm−3. Above sea ice, realistic surface albedos, cloud droplet number concentrations and liquid water paths contribute most to the bias improvement. Above open ocean, realistic cloud fractions and liquid water paths are most important for reducing the model–observation differences.

Evaluation of VIIRS Thermal Emissive Bands Long-Term Calibration Stability and Inter-Sensor Consistency Using Radiative Transfer Modeling

This study investigates the long-term stability of the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) moderate-resolution Thermal Emissive Bands (M TEBs; M12–M16) covering a period from February 2012 to August 2020. It also assesses inter-sensor consistency of the VIIRS M TEBs among three satellites (S-NPP, NOAA-20, and NOAA-21) over eight months spanning from 18 March to 30 November 2023. The field of interest is limited to the ocean surface between 60°S and 60°N, specifically under clear-sky conditions. Taking radiative transfer modeling (RTM) as the transfer reference, we employed the Community Radiative Transfer Model (CRTM) to simulate VIIRS TEB brightness temperature (BTs), incorporating European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis data as inputs. Our results reveal two key findings. Firstly, the reprocessed S-NPP VIIRS TEBs exhibit a robust long-term stability, as demonstrated through analyses of the observation minus background BT differences (O-B ∆BTs) between VIIRS measurements (O) and CRTM simulations (B). The drifts of the O-B BT differences are consistently less than 0.102 K/Decade across all S-NPP VIIRS M TEB bands. Notably, observations from VIIRS M14 and M16 stand out with drifts well within 0.04 K/Decade, reinforcing their exceptional reliability for climate change studies. Secondly, excellent inter-sensor consistency among these three VIIRS instruments is confirmed through the double-difference analysis method (O-O). This method relies on the O-B BT differences obtained from daily VIIRS operational data. The mean inter-VIIRS O-O BT differences remain within 0.08 K for all M TEBs, except for M13. Even in the case of M13, the O-O BT differences between NOAA-21 and NOAA-20/S-NPP have values of 0.312 K and 0.234 K, respectively, which are comparable to the 0.2 K difference observed in overlapping TEBs between VIIRS and MODIS. These disparities are primarily attributed to the significant differences in the Spectral Response Function (SRF) of NOAA-21 compared to NOAA-20 and S-NPP. It is also found that the remnant scene temperature dependence of NOAA-21 versus NOAA-20/S-NPP M13 O-O BT difference after accounting for SRF difference is ~0.0033 K/K, an order of magnitude smaller than the corresponding rates in the direct BT comparisons between NOAA-21 and NOAA-20/S-NPP. Our study confirms the versatility and effectiveness of the RTM-based TEB quality evaluation method in assessing long-term sensor stability and inter-sensor consistency. The double-difference approach effectively mitigates uncertainties and biases inherent to CRTM simulations, establishing a robust mechanism for assessing inter-sensor consistency. Moreover, for M12 operating as a shortwave infrared channel, it is found that the daytime O-B BT differences of S-NPP M12 exhibit greater seasonal variability compared to the nighttime data, which can be attributed to the idea that M12 radiance is affected by the reflected solar radiation during the daytime. Furthermore, in this study, we’ve also characterized the spatial distributions of inter-VIIRS BT differences, identifying variations among VIIRS M TEBs, as well as spatial discrepancies between the daytime and nighttime data.

Characterization of some secondary radio-climatic factors for reliable radio wave propagation and link’s design over North western Nigeria

A good understanding of the behavior of the atmosphere is a key factor in radio wave propagation and link’s design. This study investigated the influence of refractivity gradient, effective earth radius factor (k-factor) and geo-climatic factor (K-factor) on radio communication over the cities of Kaduna, Katsina and Sokoto, in Northwestern parts of Nigeria. Forty-one year’s secondary data (1980–2020) of temperature, pressure and humidity at the surface 12, 100 and 250 m above ground level retrieved from European Centre for Medium-Range Weather Forecasts (ECMRWF) ERA-5 reanalysis were used for the study. The three secondary radio-climatic parameters under investigation were computed and analyzed using latest ITU-R (Rec.P.453-14 (08/2019)) Models. It was observed that secondary radio-climatic variables exhibit seasonal type dependence. The research also reveals that the secondary radio-climatic variables depend on location, with all the study locations having different values. The highest value of refractivity gradient obtained is about −75.06 (N-units/km) in the month of February in Sokoto while the least of about −162.65 (N-units/km) was recorded in Kaduna in September. The value of the refractivity gradient is higher during the dry season compared to the wet season months with Sokoto recording the highest values for more than half of the months, followed by Katsina. Kaduna recorded highest number of lowest values of refractivity gradient. For the geoclimatic factor, it was revealed that Kaduna has an average maximum geoclimatic factor of 14.0000E-5 in September and an average minimum of 9.6677E-05 in February. Furthermore, the obtained values of k factor values for the study areas are higher than the standard value of, 43 (1.33). The k-factor in Kaduna ranges from 1.69 to 2.16 with mean value of 1.85 for dry season while for the wet season it ranges from 2.66 to 4.12 with a mean value of 3.74; the overall mean value for Kaduna is 2.80. In Katsina, it ranges from 1.66 to 2.20 with mean value of 1.81 for dry season while for the wet season it ranges from 1.95 to 4.00 with a mean value of 3.27; the overall mean value is 2.54. In Sokoto it ranges from 1.60 to 2.30 with mean value of 1.78 for dry season while for the wet season it ranges from 1.72 to 3.45 with a mean value of 2.59; the overall mean value is 2.19. The interpretation of this is that, radio signal propagation in the Northwest region of Nigeria is super-refractive. The overall findings of this research are essential for estimating location-dependent fade margins, which are useful for the design of wireless communication links for the region.

Effects of Intraseasonal Oscillation on Timing and Subseasonal Predictability of Mei-yu Onset over the Yangtze River Basin

Abstract The time of rainy season onset is crucial information for policymakers, especially in densely populated regions such as the Yangtze River basin (YRB) in China. In this study, we proposed a new grid-based index to objectively detect mei-yu onset timing using reanalysis data and model predictions, and then we identified the key processes via which intraseasonal oscillation (ISO) affects the YRB mei-yu onset and its subseasonal predictability based on scale-decomposed moisture analysis. Climatologically, propagation of an ISO anticyclonic anomaly toward East China supports the moisture convergence required for rainy season onset over the YRB via interaction with the seasonal-mean moisture component. In the years of early mei-yu onset, the ISO was enhanced earlier in May and favored the moisture convergence anomaly in late May–early June, when the mei-yu started. In contrast, the enhanced ISO and associated moistening processes were observed later in June–early July in the years with delayed onset. The European Centre for Medium-Range Weather Forecasts and National Centers for Environmental Prediction models show skillful prediction of mei-yu onset at forecast lead times of 5–6 pentads, whereas the China Meteorological Administration model has limited skill of 3 pentads. The differences in model prediction skill are related to the accuracy of predicted moisture convergence anomalies induced by the ISO. The prediction bias in mei-yu onset timing (early or delayed) is also connected to bias in the occurrence timing of enhanced intraseasonal perturbations, suggesting the vital role of ISO in YRB mei-yu onset on the subseasonal time scale.

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.

Estimation of PM2.5 concentrations in North China with high spatiotemporal resolution using the ERA5 dataset and machine learning models

Previous studies have predominantly focused on developing high spatiotemporal resolution PM2.5 models utilizing moderate-resolution imaging spectroradiometer (MODIS) aerosol optical depth (AOD) products alongside certain meteorological factors. However, MODIS AOD is not continuous, and there is a problem of missing data, which is not conducive to the study of PM2.5 in areas without AOD. This study proposes a new method for estimating PM2.5 concentration in North China, which not only simplifies the complex process associated with the use of AOD, but also partially solves the issue of missing AOD data. This study proposes a machine learning (random forest, RF; back propagation neural network, BPNN) based method for estimating PM2.5 using meteorological parameters obtained from the fifth-generation reanalysis (ERA5) dataset released by the European Centre for Medium-Range Weather Forecasts and considering the effects of land cover type, digital elevation model, vegetation index and population data. The model performed well, with 10-fold cross-validation (coefficient of determination) R2 and (root-mean-square error) RMSE of 0.79/0.95 and 26.10/13.22 µg/m3, respectively, for BPNN and RF. The estimated hourly performance of the RF model in winter (00:00 to 23:00 BST) with an R2 ranging from 0.92 to 0.96, an RMSE of 11.45 to 16.70 µg/m3. RF model with the best performance and ERA5 were selected to build a high-resolution (0.25°×0.25°) hourly PM2.5 map (PMM), and the PMM was compared with CHAP, with R2 and RMSE of 0.75 and 20.62 µg/m3, respectively. This study further investigates the impacts of land cover types, digital elevation model, and land surface characteristics on the spatial distribution of PM2.5 in North China.

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.

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.

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.