Numerical Weather Prediction Research Articles

Capturing the Variability of the Nocturnal Boundary Layer through Localized Perturbation Modeling

Abstract A single-column model is used to investigate regime transitions within the stable atmospheric boundary layer, focusing on the role of small-scale fluctuations in wind and temperature dynamics and of turbulence intermittency as triggers for these transitions. Previous studies revealed abrupt near-surface temperature inversion transitions within a limited wind speed range. However, representing these transitions in numerical weather prediction (NWP) and climate models is a known difficulty. To shed light on boundary layer processes that explain these abrupt transitions, the Ekman layer height and its correlation with regime shifts are analyzed. A sensitivity study is performed with several types of perturbations of the wind and temperature tendencies, as well as with the inclusion of intermittent turbulent mixing through a stochastic stability equation. The effect of small fluctuations of the dynamics on regime transitions is thereby quantified. The combined results for all tested perturbation types indicate that small-scale phenomena can drive persistent regime transitions from very to weakly stable regimes, but for the opposite direction, no evidence of persistent regime transitions was found. The inclusion of intermittency prevents the model from getting trapped in the very stable regime, thus preventing the so-called ”runaway cooling”, an issue for commonly used short-tail stability functions. The findings suggest that using stochastic parameterizations of boundary layer processes, either through stochastically perturbed tendencies or parameters, is an effective approach to represent sharp transitions in the boundary layer regimes and is, therefore, a promising avenue to improve the representation of stable boundary layers in NWP and climate models.

Evaluating Numerical Weather Prediction Models in the Middle Atmosphere Using Coherent Oceanic Acoustic Noise Observations

AbstractWe develop a method to assess numerical weather prediction (NWP) model performances from the mid‐stratosphere to the mesosphere‐lower thermosphere (30–120 km), through comparisons between observed and simulated amplitude of oceanic infrasound known as microbaroms. We adapt a recently published array processing algorithm, the multichannel maximum‐likelihood (MCML), to the 360‐observations of microbaroms. We simulate infrasound propagation using a source model and atmospheric specifications prescribed by NWP models. As this study paves the way for the assimilation of microbarom observations in these models, we assess the different components of our method. The sensitivity of the NWP model assessments to the acoustic propagation is investigated. We demonstrate the limitations of a parametrized attenuation solely driven by atmospheric fields at the infrasound station (a method previously used due to its computational efficiency) by comparing it with an explicit simulation retaining the whole 3D atmospheric fields. Importantly, in the microbarom simulations, we account for the array response to allow one‐to‐one comparisons with observations. We also highlight an observed intermittent semi‐diurnal periodicity, whose occurrence depends on middle‐atmospheric conditions, pointing at arrivals from the mesosphere and lower thermosphere. Hence, its variability needs to be accounted for in the simulations. We use a circular optimal transport metric to quantify differences between simulated and observed microbarom azimuthal distributions in a systematic way. We present NWP models relative performances diagnostics over periods of interest, including a sudden stratospheric warming in January 2021. We discuss how our approach provides insights into the model performances in the middle atmosphere.

Water vapor Raman lidar observations from multiple sites in the framework of WaLiNeAs

Abstract. During the Water Vapor Lidar Network Assimilation (WaLiNeAs) campaign, eight lidars specifically designed to measure water vapor mixing ratio (WVMR) profiles were deployed on the western Mediterranean coast. The main objectives were to investigate the water vapor content during case studies of heavy-precipitation events in the coastal western Mediterranean and assess the impact of high spatiotemporal WVMR data on numerical weather prediction forecasts by means of state-of-the-art assimilation techniques. Given the increasing occurrence of extreme events due to climate change, WaLiNeAs is the first program in Europe to provide network-like, simultaneous and continuous water vapor profile measurements over a period of 3–4 months. This paper focuses on the WVMR profiling datasets obtained from three of the lidars run by the French part of the WaLiNeAs team. These three lidars were deployed in the cities of Coursan, Le Grau-du-Roi and Cannes. This measurement setup enabled monitoring of the water vapor content of the lower troposphere over periods of 3 months in fall and winter 2022, with some interruptions, and 4 months in summer 2023. The lidars measured the WVMR profiles from the surface up to approximately 6–10 km at nighttime and 1–2 km during daytime. They had a vertical resolution of 100 m and a time resolution between 15 and 30 min, and they were selected to meet the needs of weather forecasting with an uncertainty lower than 0.4 g kg−1. The paper presents details about the instruments, the experimental strategy and the datasets provided. The final dataset (https://doi.org/10.25326/537; Chazette et al., 2023) is divided into two sub-datasets: the first with a time resolution of 15 min, which contains a total of 26 423 WVMR vertical profiles, and the second with a time resolution of 30 min to improve the signal-to-noise ratio and signal altitude range.

Probabilistic weather forecasting with machine learning.

Weather forecasts are fundamentally uncertain, so predicting the range of probable weather scenarios is crucial for important decisions, from warning the public about hazardous weather to planning renewable energy use. Traditionally, weather forecasts have been based on numerical weather prediction (NWP)1, which relies on physics-based simulations of the atmosphere. Recent advances in machine learning (ML)-based weather prediction (MLWP) have produced ML-based models with less forecast error than single NWP simulations2,3. However, these advances have focused primarily on single, deterministic forecasts that fail to represent uncertainty and estimate risk. Overall, MLWP has remained less accurate and reliable than state-of-the-art NWP ensemble forecasts. Here we introduce GenCast, a probabilistic weather model with greater skill and speed than the top operational medium-range weather forecast in the world, ENS, the ensemble forecast of the European Centre for Medium-Range WeatherForecasts4. GenCast is an ML weather prediction method, trained on decades of reanalysis data. GenCast generates an ensemble of stochastic 15-day global forecasts, at 12-h steps and 0.25° latitude-longitude resolution, for more than 80 surface and atmospheric variables, in 8 min. It has greater skill than ENS on 97.2% of 1,320 targets we evaluated and better predicts extreme weather, tropical cyclone tracks and wind power production. This work helps open the next chapter in operational weather forecasting, in which crucial weather-dependent decisions are made more accurately and efficiently.

Sensitivity of mesoscale modeling to urban morphological feature inputs and implications for characterizing urban sustainability

We examine the differences in meteorological output from the Weather Research and Forecasting (WRF) model run at 270 m horizontal resolution using 10 m, 100 m and 1 km resolution 3D neighborhood morphological inputs and with no morphological inputs. We find that the spatial variability in temperature, humidity, and other meteorological variables across the city can vary with the resolution and the coverage of the 3D urban morphological input, and that larger differences occur between simulations run without 3D morphological input and those run with some type of 3D morphology. We also find that the inclusion of input-building-defined roughness length calculations would improve simulation results further. We show that these inputs produce different patterns of heat wave spatial heterogeneity across the city of Washington, DC. These findings suggest that understanding neighborhood level urban sustainability under extreme heat waves, especially for vulnerable neighborhoods, requires attention to the representation of surface terrain in numerical weather models.

A Lightweight Transformer-Based Spatiotemporal Analysis Prediction Algorithm for High-Dimensional Meteorological Data

High-dimensional meteorological data offer a comprehensive overview of meteorological conditions. Nevertheless, predicting regional high-dimensional meteorological data poses challenges due to the vast scale and rapid changes. Apart from slow conventional numerical weather prediction methods, recently developed deep learning methods often fail to fully integrate spatial information of the high-dimensional data and require a significant amount of computational resources. This paper presents the spatiotemporal analysis fitting prediction algorithm (SA-Fit), an approximation algorithm for regional high-dimensional meteorological data prediction. SA-Fit proposes two key designs to achieve efficient prediction of the high-dimensional data. SA-Fit introduces a lightweight Transformer-based spatiotemporal analysis network to encode spatiotemporal information, which can integrate the interaction information between different coordinates in the data. Furthermore, SA-Fit introduces explicit functions with a lasso penalty to fit variations in high-dimensional meteorological data, achieving the prediction of a large amount of data with minimal prediction values. We performed experiments using the ERA5 dataset from the Shanghai and Xi’an regions. The experimental results show that SA-Fit is comparable to other advanced deep learning prediction methods in overall prediction performance. SA-Fit shortens training time and significantly reduces model parameters while using the Transformer structure to ensure prediction accuracy.

Constraining the inner boundaries of COCONUT through plasma beta and Alfvén speed

Space weather modelling has been gaining importance due to our increasing dependency on technology sensitive to space weather effects, such as satellite services, air traffic and power grids. Improving the reliability, accuracy and numerical performance of space weather modelling tools, including global coronal models, is essential to develop timely and accurate forecasts and to help partly mitigate the space weather threat. Global corona models, however, require accurate boundary conditions, for the formulations of which we have very limited observational data. Unsuitable boundary condition prescriptions may lead to inconsistent features in the solution flow field and spoil the code's accuracy and performance. In this paper, we develop an adjustment to the inner boundary condition of the COolfluid COrona uNstrUcTured (COCONUT) global corona model to better capture the dynamics over and around the regions of stronger magnetic fields by constraining the plasma beta and the Alfvén speed. Using data from solar observations and solar atmospheric modelling codes such as Bifrost, we find that the baseline homogeneous boundary condition formulations for pressure and density do not capture the plasma conditions physically accurately. We develop a method to adjust these prescribed pressure and density values by placing constraints on the plasma beta and the Alfvén speed that act as proxies. We demonstrate that we can remove inexplicable fast streams from the solution by constraining the maximum Alfvén speed and the minimum plasma beta on the boundary surface. We also show that the magnetic topology is not significantly affected by this treatment otherwise. The presented technique shows the potential to ease the modelling of solar maxima, especially removing inexplicable features while, at the same time, not significantly affecting the magnetic field topology around the affected regions.

A revised ocean mixed layer model for better simulating the diurnal variation in ocean skin temperature

Abstract. Sea surface temperature (SST) is a crucial parameter in climate, weather, and ocean sciences due to its decisive role in ocean–atmosphere interactions. Identifying errors in the prognostic scheme used by the current European Centre for Medium-range Weather Forecasts (ECMWF) model for predicting the diurnal variation in ocean skin temperature led to a revisit and revision of the ocean mixed layer (OML) model. Validation of the revised model was conducted by comparing simulated temperatures at the sub-skin level and 1 m depth with observations from shipborne infrared measurements and buoys. These comparisons revealed a strong correlation, with an absolute mean deviation of less than 0.1 K and a standard deviation under 0.5 K, which are found to be comparable to errors in satellite observations of SST. Given that these results are derived from the same model simulations, the error statistics for the simulated skin temperature and its diurnal variation should have the same degree of accuracy. Furthermore, the simulation results closely align with anticipated solar radiation distributions, whereas ERA5 ocean skin temperature shows a significant lack of alignment with solar radiation. Consequently, the revised OML model shows promising potential for improving the simulation of diurnal SST variations in weather and climate models.

Joint Short-term Power Forecasting of Hydro-Wind-Photovoltaic Considering Spatiotemporal Delay of Weather Processes

Regional integrated energy systems (RIES) represented by hydro, wind, and solar energy are crucial avenues for future clean energy development, fully leveraging their complementarity can facilitate the orderly supply of renewable energy, significantly enhance the level of new energy consumption, and make outstanding contributions to achieving carbon neutrality goals. However, run-of-river hydropower (RHP), wind power (WP), and photovoltaic (PV) are affected by the chaotic characteristics of the weather system, with significant random fluctuations, and their output characteristics have significant heterogeneity, which brings a big challenge for the complementary coordinated scheduling. The high-accuracy power prediction of RHP, WP, and PV in the region is one of the key means for solving the above problems. To meet this challenge, by analyzing the spatio-temporal correlation properties between weather processes and station output, a novel joint prediction framework for short-term power forecasting (STPF) of regional hydro-wind-PV clusters is proposed. Firstly, to solve the problem of significant heterogeneity of water, wind, and solar, a Differentiated Spatio-Temporal Mixture Network (DSTMN) is proposed, which differentially encodes the weather and power information at multiple locations, enabling the extraction of common features while preserving their specificities, which addresses the difficulty of representing the inherent correlation patterns between wide-area meteorological information and heterogeneous energy sources. Secondly, to further explore the spatio-temporal correlation between meteorological information and power information, to improve the forecasting performance of the model, a spatio-temporal-lagged correlation (STLC) that can quantify their spatio-temporal delays is proposed. By analyzing the correlation characteristics between regional grid numerical weather prediction (NWP) information and station output at different spatial and temporal scales, the optimal spatial location and delay time of NWP inputs under the current scenario are determined for each station. Based on this, the best NWP scene probabilistic transfer model is established based on the Rank Bayesian Ensemble (RBE) method. By fitting the conditional probability distribution of the current best NWP spatiotemporal location and the 2nd day’s best NWP spatiotemporal location, providing a reliable input for the STPF model. Finally, a case study with 164 hydro-wind-solar stations in China demonstrates the effectiveness of the proposed method. Specifically, we achieved an accuracy improvement of 0.46% — 11.03% on the 2nd day of forecasting compared to benchmark methods.

Incorporating long-term numerical weather forecasts to quantify dynamic vulnerability of irrigation supply system: A case study of Shihmen Reservoir in Taiwan

Incorporating long-term numerical weather forecasts to quantify dynamic vulnerability of irrigation supply system: A case study of Shihmen Reservoir in Taiwan

Sensitivity Analysis and Validation of WRF-ARW Parameterizations for Potential Solar and Wind Energy Sources over Malaysia

Abstract This study investigates how accurately the Weather Research and Forecasting (WRF) simulation anticipates the viability of renewable energy options in Malaysia, with specific attention paid to wind and solar power potential. Due to Malaysia’s transition toward sustainable energy, precise forecasting is essential for grid stability and efficient integration. The research used simulations across East and West Malaysia for October 2022 with varying parameterization schemes to determine the most suitable configurations for accurate energy predictions. Eleven unique scheme permutations were analyzed against Mesonet, ERA5-Land, and Centre for Marine and Coastal Studies (CEMACS) datasets. The results revealed that certain permutations, specifically run 9 and run 10, significantly reduced the root-mean-square error (RMSE) for predicting radiation and wind speed outputs, indicating higher accuracy in simulations. The two best runs were then validated for November 2022 and used to estimate wind (run 10: WRF single-moment 6-class microphysics, RRTMG longwave radiation, Dudhia shortwave radiation, unified Noah land surface model, MYJ PBL, Eta surface layer, and new Tiedtke cumulus schemes) and solar photovoltaic (run 9: Morrison 2-moment microphysics, CAM longwave radiation, RRTMG shortwave radiation, unified Noah land surface model, MYJ PBL, and Eta surface layer schemes) potential maps over CEMACS and Kuching locations. Solar photovoltaic energy was estimated at a total of 3165 and 3379 kWh for CEMACS and Kuching, respectively, and a good potential overall, ranging from about 5500 to 7881 kWh, while wind energy had a much more variability appearance of 115 936 and 6250 kWh for CEMACS and Kuching, respectively. This study highlights the potential for targeted weather forecasting models to enhance the reliability and integration of renewable energy sources into national grids. Significance Statement This study explores the effective use of weather forecasting models to enhance renewable energy generation in Malaysia, a region increasingly focusing on sustainable energy solutions. By analyzing and validating various weather model configurations, this research provides vital information for selecting optimal settings for accurate predictions of solar and wind energy potential. This crucial advancement, which effectively integrates renewable energy into power grids to reliably provide a steady and efficient flow of energy, is essential for moving progressively closer to a more sustainable and eco-friendly energy. The results of this study both enhance scientific comprehension of atmospheric simulations utilized in energy sectors and carry meaningful consequences for policy formation and renewable infrastructure evolution within tropical regions.

Momentum Transport in Heterogeneous Forest Canopies

This study investigates the impact of spatial heterogeneity on momentum transport within forest canopies through wind tunnel experiments using 1:200 scale forest models. The models, crafted from 10 Pores Per Inch reticulated foam, emulate a leaf area index of 5.3 and include alternating patches and gaps of various sizes. Statistical results of the mean velocity profiles and velocity standard deviations show that the canopies develop a mixing layer. By employing lacunarity analysis to quantify spatial heterogeneity, we establish that the heterogeneity scale effectively represents variations in canopy height. The success of the lacunarity analysis as a metric is particularly noteworthy, providing a robust and practical measure of heterogeneity that can be easily applied in future research. Control volume analysis reveals that horizontal and vertical momentum advection terms rise as canopy heterogeneity increases, emphasizing its critical role in heterogeneous canopies and the possibility of describing this role using the lacunarity scale. The gaps also give rise to pressure terms through the local pressure gradient at each pattern. The study highlights the higher influence of gap size over heterogeneity scale on momentum flux. These insights contribute to improved parameterization of heterogeneous canopies in numerical weather prediction models, aiding in better representation of sub-grid scale processes and enhancing our understanding of canopy-atmosphere interactions.

Retrieval of refractivity fields from GNSS tropospheric delays: theoretical and data-based evaluation of collocation methods and comparisons with GNSS tomography

This paper focuses on the retrieval of refractivity fields from GNSS measurements by means of least-squares collocation. Collocation adjustment estimates parameters that relate delays and refractivity without relying on a grid. It contains functional and stochastic models that define the characteristics of the retrieved refractivity fields. This work aims at emphasizing the capabilities and limitations of the collocation method in modeling refractivity and to present it as a valuable alternative to GNSS tomography. Initially, we analyze the stochastic models in collocation and compare the theoretical errors of collocation with those of tomography. We emphasize the low variability of collocation formal variances/covariances compared to tomography and its lower dependence on a-priori fields. Then, based on real and simulated data, we investigate the importance of station resolution and station heights for collocation. Increasing the network resolution, for example, from 10 to 2 km, results in improved a-posteriori statistics, including a 10% reduction in the error statistic for the retrieved refractivity up to 6 km. In addition, using additional stations at higher altitudes has an impact on the retrieved refractivity fields of about 1 ppm in terms of standard deviation up to 6 km, and a bias reduction of more than 3 ppm up to 3 km. Furthermore, we compare refractivity fields retrieved through tomography and collocation, where data of the COSMO weather model are utilized in a closed-loop validation mode to simulate tropospheric delays and validate the retrieved profiles. While tomography estimates are less biased, collocation captures relative changes in refractivity more effectively among the voxels within one height level. Finally, we apply tomography and collocation to test their capabilities to detect an approaching weather front. Both methods can sense the weather front, but their atmospheric structures appear more similar when the GNSS network has a well-distributed height coverage.

Soil Moisture-Cloud-Precipitation Feedback in the Lower Atmosphere From Functional Decomposition of Satellite Observations.

The feedback of topsoil moisture (SM) content on convective clouds and precipitation is not well understood and represented in the current generation of weather and climate models. Here, we use functional decomposition of satellite-derived SM and cloud vertical profiles (CVP) to quantify the relationship between SM and the vertical distribution of cloud water in the central US. High-dimensional model representation is used to disentangle the contributions of SM and other land-surface and atmospheric variables to the CVP. Results show that the sign and strength of the SM-cloud-precipitation feedback varies with cloud height and time lag and displays a large spatial variability. Positive anomalies in antecedent 7-hr SM and land-surface temperature enhance cloud reflectivity up to 4 dBZ in the lower atmosphere about 1-3km above the surface. Our approach presents new insights into the SM-cloud-precipitation feedback and aids in the diagnosis of land-atmosphere interactions simulated by weather and climate models.

Lightning-Fast Convective Outlooks: Predicting Severe Convective Environments With Global AI-Based Weather Models.

Severe convective storms are among the most dangerous weather phenomena and accurate forecasts mitigate their impacts. The recently released suite of AI-based weather models produces medium-range forecasts within seconds, with a skill similar to state-of-the-art operational forecasts for variables on single levels. However, predicting severe thunderstorm environments requires accurate combinations of dynamic and thermodynamic variables and the vertical structure of the atmosphere. Advancing the assessment of AI-models toward process-based evaluations lays the foundation for hazard-driven applications. We assess the forecast skill of the top-performing AI-models GraphCast, Pangu-Weather and FourCastNet for convective parameters at lead-times up to 10days against reanalysis and ECMWF's operational numerical weather prediction model IFS. In a case study and seasonal analyses, we see the best performance by GraphCast and Pangu-Weather: these models match or even exceed the performance of IFS for instability and shear. This opens opportunities for fast and inexpensive predictions of severe weather environments.

Insights from Nowcasting and Mesoscale Research Working Group Projects of the World Weather Research Programme

Abstract Insights from Forecast Demonstration Projects and Research Development Projects, training workshops and symposia, conducted between 2000 and 2024 are summarized. The projects were organized by the Nowcasting and Mesoscale Research Working Group of the World Weather Research Program of the World Meteorological Organization. The objective was to advance, promote and build capacity in nowcasting and very short-range forecasting. The projects were associated with the Olympic Games, emergency management and aviation services. They brought international experts together to work in a collaborative fashion. Extensive interaction with end-users and decision-makers expanded and extended the scope of services from traditional weather hazards (heavy rain, wind, hail, lightning) to include specific user needs (e.g., visibility in complex terrain or airport runways, periods of calm winds or light rain, heat stress). Substantial progress has been made in many areas including: advanced radar nowcasting algorithms, stochastic nowcasts, kilometric and hectometric numerical weather prediction models; blending of observations and models and multi-model systems. Verification was a key and valuable component of the projects quantifying the results. Also, the types of services have expanded to include both summer and winter services, complex terrain and urban environments, air transport, air quality, hydrology and health. Insights are presented in all aspects of nowcasting and very short-range forecasting from end-user decision-making, critical role of the forecaster, forecast systems (models, heuristics, observations), to science and knowledge gaps.

Observation and Numerical Simulation of a Windshear Case at an Airport in the Qinghai-Tibet Plateau

This paper documents a windshear case for an airport in the Qinghai-Tibet Plateau and explores, for the first time, the capability for high-resolution numerical weather simulation of the wind shear features. The windshear appears to be associated with pulses of the wind speed in a low-level easterly jet. The features are basically reproduced quite well with the high-resolution numerical model, though some discrepancies are identified, such as the maximum wind speed of the easterly jet and the magnitude of the eddy dissipation rate as compared with the actual Doppler LIDAR observations. Statistical analysis has been performed between the observation and the simulation results. The sensitivity of the modeling result to the choice of turbulence parameterization scheme has also been studied. The study result shows that it is possible to forecast the windshear feature using a high-resolution numerical weather prediction model for an airport in the complex terrain of the Plateau.

Characterization of Mycelium Biocomposites under Simulated Weathering Conditions.

Expanded polystyrene (EPS) remains a popular packaging material despite environmental concerns such as pollution, difficulty to recycle, and toxicity to wildlife. The goal of this study is to evaluate the potential of an ecofriendly alternative to traditional EPS composed of a mycelium biocomposite grown from agricultural waste. In this material, the mycelium spores are incorporated into cellulosic waste, resulting in a structurally sound biocomposite completely enveloped by mycelium fibers. One of the main criteria for shipping applications is the ability of a material to withstand extreme weather conditions. Accordingly, this study focused on evaluating a commercially available mycelium material before and after exposure to various weathering conditions, including high and low temperatures at different humidity levels. Fourier transform infrared spectroscopy (FTIR) was performed to examine any transformations in the mycelium structure and composition, whereas scanning electron microscopy (SEM) was used to reveal any changes in the morphology. Similarly, thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) analyses were conducted to evaluate the thermal behavior, whereas mechanical properties were measured by using shore hardness and Izod Impact testing. Although some irreversible changes were observed due to the exposure to high temperatures, the material exhibited good thermal stability and impact resistance. FTIR analysis demonstrated small changes in the biocomposite structure and protein rearrangement as a result of weathering, whereas SEM revealed some cracking in the cellulose substrate. A combination of low temperatures and humidity resulted in significant moisture absorption, as indicated by TGA and DSC. This in turn decreased the hardness of the fibers by nearly 2-fold; however, the impact strength of the entire biocomposite remained unchanged. Overall, these results provide important insight into the structure-property relationships of mycelium-based materials.

Exploring the characteristics of Fengyun-4A Advanced Geostationary Radiation Imager (AGRI) visible reflectance using the China Meteorological Administration Mesoscale (CMA-MESO) forecasts and its implications for data assimilation

Abstract. The Advanced Geostationary Radiation Imager (AGRI) on board the Fengyun (FY)-4A geostationary satellite has provided high-spatiotemporal-resolution visible reflectance data since 12 March 2018. Data assimilation experiments under the framework of observing system simulation experiments have shown the great potential of these data to improve the forecasting skills of numerical weather prediction (NWP) models. To assimilate the AGRI visible reflectance in real-world cases, it is important to evaluate the quality and to quantify the observation errors in these data. In this study, the FY-4A AGRI channel 2 (0.55–0.75 µm) reflectance data (O) were compared with the equivalents (B) derived from the short-term forecasts of the China Meteorological Administration Mesoscale (CMA-MESO) model using the Radiative Transfer for the Television Infrared Observation Satellite Operational Vertical Sounder (RTTOV, v12.3). It is shown that the O–B biases could be used to reveal the abrupt change related to the measurement calibration processes. In general, the O–B departure was positively biased in most cases. Potential causes include the deficiencies of the NWP model, the forward-operator errors, and the unresolved aerosol processes. The relative biases of O–B computed from cloud-free and cloudy pixels were used to correct the systematic biases for the corresponding scenarios over land and sea surfaces separately. In general, the method effectively reduced the O–B biases. Moreover, the bias-correction method based on an ensemble forecast is more robust than a deterministic forecast due to the advantages of the former in dealing with uncertainties in cloud simulations. The findings demonstrate that analyzing the O–B biases has a potential to monitor the performance of the FY-4A AGRI visible instrument and to correct the systematic biases in the observations, which will facilitate the assimilation of these data in conventional data assimilation applications.

Effect of complex orography on numerical simulations of a downburst event in Spain

A downburst is a localized and intense downdraft of air that descends quickly from the middle troposphere and reaches the Earth's surface. It is frequently originated by a thunderstorm or a supercell. Downburst winds can cause significant damage to buildings, infrastructure, and pose a great threat to aviation traffic. On July 1, 2018, many supercells were spotted near the Zaragoza Airport (Spain), and at least one of them generated a downburst that affected the airport, causing significant damage in the surrounding area. This event is here simulated using the Weather Research and Forecasting (WRF-ARW) numerical weather prediction model. Three different WRF-ARW orography experiments are carried out to investigate if the region's complex orography has an important role in supercell and downburst development over the research area. One of the three experiments uses the default orography as control; another one uses a 90 % smoothed orography, and the third experiment is configured with a high-resolution dataset. Several atmospheric and convective variables are compared for each orography experiment. Results show that MUCAPE is clearly higher when the orography complexity is reduced. The smoothing process leads to a more uniform wind flow, contributing to the formation of numerous supercells. However, supercells channel through valleys and mountains in the control and high-resolution orography experiments, where the surface wind divergences are uniquely reproduced, and the highest reflectivity values are observed. Moisture advection from the Mediterranean Sea is essential in the process, reaching more deeply into the study region in the smoothed orography experiment due to the lack of orographic barriers. Orography affects dynamic and thermodynamic features, which have considerable effects on the formation and development of downbursts.