Numerical Weather Prediction Models Research Articles

Why Moist Dynamic Processes Matter for the Sub‐Seasonal Prediction of Atmospheric Blocking Over Europe

AbstractIn recent years, there has been growing evidence that latent heat release in midlatitude weather systems such as warm conveyor belts (WCBs) contributes significantly to the onset and maintenance of blocking anticyclones (blocked weather regimes). Still, numerical weather prediction (NWP) and climate models struggle to correctly predict and represent atmospheric blocking in particular over Europe. Here, we elucidate the representation of WCB activity in 20 years of extended winter (1997–2017) of European Centre for Medium‐Range Weather Forecast's IFS reforecasts around the onset of blocking over Europe (EuBL) employing different perspectives. First, we show that the model struggles to predict EuBL onsets already at 10–14 days lead time in line with a misrepresentation of WCB activity in the ensemble mean. However, we also find cases with accurate EuBL forecasts even in pentad 4 (15–19 days). This subset of successful forecasts at extended‐range lead times goes in line with accurate WCB forecasts over the North Atlantic several days prior to the blocking onset. Second, investigating the time‐lagged relationship of blocking onset and WCB activity, we find that WCB activity over the North Atlantic emerges well prior to the onset of the block and that different pathways into EuBL exist in the reforecasts compared to reanalysis. Finally, we find indication of predictability associated with a Rossby wave train emerging from the North Pacific. Although our study can not disentangle the roles of intrinsic predictability limits and model deficiencies, we show that correct predictions of EuBL go along with distinct patterns of WCB activity.

Change in the distribution of heavy 1 h precipitation due to temperature changes in measured values, model reanalyses and model simulations of future climate

This article presents main results on how the 1 h precipitation distribution changes, focused on high percentiles, with increasing air temperature based on the data from the Czech territory. The air temperature, the dew point temperature (both measured at 2 m above ground) and the temperature of lifting condensation level are used as temperature characteristics. The change in the distribution of 1 h precipitation measurements is compared with the results of reanalyses based on simulations of numerical weather prediction model ALADIN-CZ and with the results of future climate simulations by climate model ALADIN-CLIMAT-CZ. In general, the increase in precipitation appears clearly in the very upper part of precipitation distribution for high percentiles. Values of the upper percentiles of precipitation increase up to a certain threshold temperature and then decrease, which is also found in other studies. This also applies to the simulations of future climate. The main difference in measured and model results appears at the highest temperature values, which may be implied by the very different number of the model and the measured data.

Statistical predictability of Euro-Mediterranean subseasonal anomalies: The TeWA approach

Abstract It is widely known from energy balances that global oceans play a fundamental role in atmospheric seasonal anomalies via coupling mechanisms. However, numerical weather prediction models still have limitations in long-term forecasting due to their nonlinear sensitivity to initial deep oceanic conditions. As the Mediterranean climate has highly unpredictable seasonal variability, we designed a complementary method by supposing that (1) delayed teleconnection patterns provide information about ocean–atmosphere coupling on subseasonal timescales through the lens of (2) partially predictable quasi-periodic oscillations, since (3) forecast signals can be extracted by smoothing noise in a continuous lead-time horizon. To validate these hypotheses, subseasonal predictability of temperature and precipitation was analyzed at 11 reference stations in the Mediterranean area in the 1993–2021 period. The novel method, presented here, consists of combining lag-correlated teleconnections (15 indices) with self-predictability techniques of residual quasi-oscillation based on Wavelet (cyclic) and ARIMA (linear) analyses. The prediction skill of this Teleconnection-Wavelet-ARIMA (TeWA) combination was cross-validated and compared to that of the SEAS5-ECMWF model (3 months ahead). Results show that the proposed TeWA approach improves the predictability of first-month temperature and precipitation anomalies by 50–70% compared with the forecast of SEAS5. On a moving-averaged daily scale, the optimum prediction window is 30 days for temperature and 16 days for precipitation. The predictable ranges are consistent with atmospheric bridges in teleconnection patterns (e.g., ULMO) and are reflected by spatial correlation with SST. Our results suggest that combinations of the TeWA approach and numerical models could boost new research lines in subseasonal-to-seasonal forecasting.

Moist processes in NCUM global forecasts during the boreal summer monsoon

The objective of this study is to evaluate the performance of the National Center for Medium-Range Weather Forecasting Unified model (NCUM) global version in representing the moist processes during active and break monsoon periods over the Indian region. We employed the moist static energy (MSE) equation, a process-oriented diagnostic (POD), to examine the relationship between small-scale convection and large-scale circulation patterns in the forecast for eight monsoon seasons spanning from June to September 2015 to 2022. Results are validated by utilizing the European Center for Medium-Range Weather Forecasting Reanalysis (ERA5) data products. By employing PODs, we aimed to identify the influence of various MSE terms on rainfall evolution, particularly over the Central Indian (CI) region. We also investigated the systematic errors in certain processes that contribute to rainfall biases. Additionally, the study explored the moisture-convection relationship and compared the sensitivity of free tropospheric moisture to deep convection in the NCUM forecasts against the reanalysis data. The findings indicate weaknesses in the model's sensitivity to moisture and its ability to represent the rainfall-column water vapor (CWV) relationship highlighting areas where further improvements may be necessary to enhance the model's performance in monsoon forecasting. Despite the presence of systematic biases in the model's mean state of rainfall, such as dry biases over the Bay of Bengal and wet biases over the eastern Indian Ocean (EIO), the results obtained from the MSE diagnostics aligned with the existing understanding of monsoon dynamics. Specifically, the study emphasized the significance of horizontal advection of MSE as a leading term preceding the peak active/break conditions over the CI region. Hence, these findings not only showcase the usefulness of PODs in examining the strengths and weaknesses of numerical weather prediction (NWP) models but also serve as a verification tool for assessing model skills, leading to improved understanding and development of NWP models.

Surface Air‐Pressure Measurements From Space Using Differential Absorption Radar on the Right Wing of the 60 GHz Oxygen Band

AbstractSurface air pressure is one of the most important parameters used in Numerical Weather Prediction (NWP) models. Although it has been measured using weather stations on the ground for many decades, the numbers of measurements are sparse and concentrated on land. Few measurements from buoys and ships are available over ocean. Global measurements can only be achieved by using remote sensing from Space, which is challenging; however, a novel design using Differential Absorption Radar (DAR) can provide a potential solution. The technique relies on two facts: first the electromagnetic fields are absorbed mainly by oxygen and water vapor, and second that oxygen is well mixed. In this work we discuss a space‐borne concept, which aims at providing, over the ocean, consistent, and regular observations for determining surface air pressure from space by a design of a multi‐tone radar operating on the upper wing of the O2 absorption band with tones from 64 to 70 GHz. Simulations of radar vertical profiles based on the output of a state of‐the‐art microphysical retrievals applied to the A‐Train suite of sensors are exploited to establish the performance of such a system for surface pressure determination. In particular the identification and quantification of errors introduced by the presence of water vapor, cloud liquid water and rain water and the potential of a correction via the three‐tone method is discussed. Errors introduced by surface measurement noise and temperature profile uncertainties are discussed as well. Results show that accuracy between 2 and 5 hPa is at reach.

A Convolutional Neural Network for Tropical Cyclone Wind Structure Identification in kilometre-scale forecasts

Abstract Detection and tracking of Tropical Cyclones (TCs) in numerical weather prediction model outputs is essential for many applications, such as forecast guidance and real-time monitoring of events. While this task has been automated in the 90s with heuristic models, relying on a set of empirical rules and thresholds, the recent success of machine learning methods to detect objects in images opens new perspectives. This paper introduces and evaluates the capacity of a convolutional neural network based on the U-Net architecture to detect the TC wind structure, including maximum wind speed area and hurricane-force wind speed area, in the outputs of the convective-scale Arome model. A dataset of 400 Arome forecasts over the West Indies domain has been entirely hand labeled by experts, following a rigorous process to reduce heterogeneities. The U-Net performs well on a wide variety of TC intensities and shapes, with an average intersection-over-union metric around 0.8. Its performances however strongly depend on the TC strength, the detection of weak cyclones being more challenging since their structure is less well defined. The U-Net also significantly outperforms an operational heuristic detection model, with a significant gain for weak TCs, while running much faster. In a last part, the capacity of the U-Net to generalize on slightly different data is demonstrated in the context of a domain change and a resolution increase. In both cases, the pre-trained U-Net achieves similar performances as on the original dataset.

Numerical prediction of fog: A novel parameterization for droplet formation

AbstractA novel aerosol activation scheme was developed and implemented into the WRF Thompson–Eidhammer Aerosol‐Aware microphysical module. While in most of the numerical weather prediction (NWP) models the activation is based on updraft velocity, even in the fog, the new parameterization considers the local rate of cooling and water vapor flux in the evaluation of the aerosol activation rate. In addition, diagnostic variables are added to evaluate the visibility reduction due to formation of haze droplets. The impact of changing the activation scheme is demonstrated by a case study of a well‐observed fog event. Data observed during the fog event campaign at Budapest and observed at standard meteorological stations are compared with the output of the numerical model. The results are summarized as follows: (i) the inhomogeneous spatial and temporal distribution of the number concentration of droplets is an inherent characteristic of the new parameterization scheme. (ii) Compared to the prior parameterization based on updraft velocity, the new parameterization scheme increases the number concentration of the droplets significantly, especially at the top of the fog. As a consequence, it reduces the downward short‐wave radiation flux prolonging the lifetime of the fog by about 30–60 min. (iii) Analyses reveal that earlier dissipation of the fog comparing to the observed data cannot be explained only by overestimation of the downward short‐wave radiation flux. (iv) The new method is able to evaluate the reduction of visibility due to haze.

Convection‐Permitting ICON‐LAM Simulations for Renewable Energy Potential Estimates Over Southern Africa

AbstractRenewable energy is recognized in Africa as a means for climate change mitigation, but also to provide access to electricity in sub‐Saharan Africa, where three‐quarters of the global population without electricity resides. Reliable and highly resolved renewable energy potential (REP) information is indispensable to support power plants expansion. Existing atmospheric data sets over Africa that are used for REP estimates are often characterized by data gaps, or coarse resolution. With the aim to overcome these challenges, the ICOsahedral Nonhydrostatic (ICON) Numerical Weather Prediction (ICON‐NWP) model in its Limited Area Mode (ICON‐LAM) is implemented and run over southern Africa in a hindcast dynamical downscaling setup at a convection‐permitting 3.3 km horizontal resolution. The simulation time span covers contrasting solar and wind weather years from 2017 to 2019. To assess the suitability of the novel simulations for REP estimates, the simulated hourly 10 m wind speed (sfcWind) and hourly surface solar irradiance (rsds) are extensively evaluated against a large compilation of in situ observations, satellite, and composite data products. ICON‐LAM reproduces the spatial patterns, temporal evolution, the variability, and absolute values of sfcWind sufficiently well, albeit with a slight overestimation and a mean bias (mean error (ME)) of 1.12 m s−1 over land. Likewise the simulated rsds with an ME of 50 W m−2 well resembles the observations. This new ICON simulation data product will be the basis for ensuing REP estimates that will be compared with existing lower resolution data sets.

Optimal hybrid power dispatch through smart solar power forecasting and battery storage integration

This study presents a strategy to optimize hybrid power system dispatch for commercial sectors in South Africa while utilizing the day-ahead method to forecast solar photovoltaic (PV) power. The approach utilizes numerical weather prediction (NWP) models obtained from open weather maps and incorporates power plant specifications to generate predictions of the PV power plant’s output. These predictions are then integrated into an optimal control strategy incorporating battery storage. The use of optimal algorithms helps manage PV power plant curtailment during periods of over-generation. It is crucial to optimize PV power systems and ensure a continuous power supply for solar power plants, even during unfavorable weather conditions. Besides, the study develops a model that solves the challenging questions of combining solar power forecasting with an optimal dispatch and demand management scheme. Therefore, there is a need to incorporate battery storage systems through the developed optimal control method to maximize the energy from the PV system and minimize the power from the utility grid. The obtained results demonstrate the effectiveness of the developed model. The winter season presented a lower MAE of 21 kW, an RMSE of 35.4 kW, and a MAPE of 3,1% for PV power output forecasting, showing that the errors during prediction are lower compared to other seasons. It has been observed that 60% of the load is supplied through a combination of PV power and battery storage. Therefore, evidence of the developed optimal hybrid power dispatch with an innovative solar power forecasting model suggests that accurate forecasting can improve system planning and mitigate the necessity of procuring grid power at high electricity prices.

Statistical post-processing of numerical weather prediction data using distribution-based scaling for wind energy

The performance of numerical weather prediction models has improved dramatically recently. However, model biases remain a fundamental limitation prohibiting the direct implementation of model results. There are several ways to describe wind speed data. The Weibull and lognormal distributions are used to obtain the best-fit model for the wind speed data. This study aims to develop a statistical post-processing method based on the distribution-based scaling (DBS) approach, which scales NWP data to fit the distribution derived using recorded wind speed at that site location. The performance of the suggested method was evaluated using four different error measures. The optimal model is anticipated to have the lowest Mean Bias Error (MBE), Mean Absolute Error (MAE), Root Mean square Error (RMSE), and variance (s2) values. It was determined that employing a DBS strategy significantly improved the NWP by at least 75%.

Added Value of Three‐Dimensional Horizontal Winds From Geostationary Interferometric Infrared Sounder for Typhoon Forecast in a Regional NWP Model

AbstractHigh temporal resolution geostationary hyperspectral infrared sounders can simultaneously profile atmospheric temperature and moisture, and track moisture features to provide 3D horizontal winds in clear and partially cloudy scenarios. The thermodynamic information obtained has been integral to enhancing tropical cyclone (TC) forecasts. However, the potential benefits derived from the dynamic information provided by geostationary hyperspectral infrared sounders (GeoHIS) are yet to be fully comprehended for numerical weather prediction (NWP). With the re‐estimated observation error and multivariate hydrometeor background error covariance, the 3D horizontal winds from the Geostationary Interferometric Infrared Sounder (GIIRS) are synergistically assimilated with the hydrometeor information. The impact of the GIIRS‐derived 3D horizontal wind assimilation on Typhoon Maria (2018) and Lekima (2019) analysis and forecast are evaluated. The results show that the assimilating GIIRS‐derived 3D horizontal wind notably reduces the RMSEs of analysis and forecast, particularly in the U and V components. The improvement in accuracy of large‐scale fields substantially enhances the forecasts of typhoons' track and maximum wind speed, as well as the central sea‐level pressure. Moreover, the prediction of the spatial distribution and intensity for landfall precipitation is also improved. The detailed diagnoses of the Maria show that a more southerly subtropical high generated by the additional horizontal wind assimilation improves the rainfall spatial distribution, and the reasonable water vapor transportation caused by the improved dynamic conditions corrects the precipitation intensity. This work highlights the importance of dynamic information in TC forecasting and emphasizes the need for efficient and high‐precision 3D wind measurements to improve NWP.

Joint Retrieval of Multiple Species of Ice Hydrometeor Parameters from Millimeter and Submillimeter Wave Brightness Temperature Based on Convolutional Neural Networks

Submillimeter wave radiometers are promising remote sensing tools for sounding ice cloud parameters. The Ice Cloud Imager (ICI) aboard the second generation of the EUMETSAT Polar System (EPS−SG) is the first operational submillimeter wave radiometer used for ice cloud remote sensing. Ice clouds simultaneously contain three species of ice hydrometeors—ice, snow, and graupel—the physical distributions and submillimeter wave radiation characteristics of which differ. Therefore, jointly retrieving the mass parameters of the three ice hydrometeors from submillimeter brightness temperatures is very challenging. In this paper, we propose a multiple species of ice hydrometeor parameters retrieval algorithm based on convolutional neural networks (CNNs) that can jointly retrieve the total content and vertical profiles of ice, snow, and graupel particles from submillimeter brightness temperatures. The training dataset is generated by a numerical weather prediction (NWP) model and a submillimeter wave radiative transfer (RT) model. In this study, an end to end ICI simulation experiment involving forward modeling of the brightness temperature and retrieval of ice cloud parameters was conducted to verify the effectiveness of the proposed CNN retrieval algorithm. Compared with the classical Unet, the average relative errors of the improved RCNN–ResUnet are reduced by 11%, 25%, and 18% in GWP, IWP, and SWP retrieval, respectively. Compared with Bayesian Monte Carlo integration algorithm, the average relative error of the total content retrieved by RCNN–ResUnet is reduced by 71%. Compared with BP neural network algorithm, the average relative error of the vertical profiles retrieved by RCNN–ResUnet is reduced by 69%. In addition, this algorithm was applied to actual Advanced Technology Microwave Sounder (ATMS) 183 GHz observed brightness temperatures to retrieve graupel particle parameters with a relative error in the total content of less than 25% and a relative error in the profile of less than 35%. The results show that the proposed CNN algorithm can be applied to future space borne submillimeter wave radiometers to jointly retrieve mass parameters of ice, snow, and graupel.

Radar Outage Costs and the Value of Alternate Datasets

Abstract Quantifying the costs of radar outages allows value to be attributed to the alternate datasets that help mitigate outages. When radars are offline, forecasters rely more heavily on nearby radars, surface reports, numerical weather prediction models, and satellite observations. Monetized radar benefit models allow value to be attributed to individual radars for mitigating the threat to life from tornadoes, flash floods, and severe winds. Eighteen radars exceed $20 million in annual benefits for mitigating the threat to life from these convective hazards. The Jackson, MS radar (KJAN) provides the most value ($41.4 million), with the vast majority related to tornado risk mitigation ($29.4 million). During 2020-2023, the average radar is offline for 2.57% of minutes or 9.27 days per year, and experiences an average of 58.9 outages per year lasting 4.32 hours on average. Radar outage cost estimates vary by location and convective hazard. Outage cost estimates concentrate at the top, with 8, 2, 4, and 5 radars exceeding $1 million in outage costs during 2020, 2021, 2022, and 2023, respectively. The KJAN radar experiences outage frequencies of 4.92% and 5.50% during 2020 and 2023, resulting in outage cost estimates > $2 million both years. Combining outage cost estimates for all radars suggests that approximately $29.1 million in annual radar outage costs may be attributable as value to alternative datasets for helping to mitigate radar outage impacts.

Transformer-based nowcasting of radar composites from satellite images for severe weather

Abstract Weather radar data are critical for nowcasting and an integral component of numerical weather prediction models. While weather radar data provide valuable information at high resolution, their ground-based nature limits their availability, which impedes large-scale applications. In contrast, meteorological satellites cover larger domains but with coarser resolution. However, with the rapid advancements in data-driven methodologies and modern sensors aboard geostationary satellites, new opportunities are emerging to bridge the gap between ground- and space-based observations, ultimately leading to more skillful weather prediction with high accuracy. Here, we present a Transformer-based model for nowcasting ground-based radar image sequences using satellite data up to two hours lead time. Trained on a dataset reflecting severe weather conditions, the model predicts radar fields occurring under different weather phenomena and shows robustness against rapidly growing/decaying fields and complex field structures. Model interpretation reveals that the infrared channel centered at 10.3 μm (C13) contains skillful information for all weather conditions, while lightning data have the highest relative feature importance in severe weather conditions, particularly in shorter lead times. The model can support precipitation nowcasting across large domains without an explicit need for radar towers, enhance numerical weather prediction and hydrological models, and provide radar proxy for data-scarce regions. Moreover, the open-source framework facilitates progress towards operational data-driven nowcasting.

Forecasts of fog events in northern India dramatically improve when weather prediction models include irrigation effects

Dense wintertime fog regularly impacts Delhi, severely affecting road and rail transport, aviation and human health. Recent decades have seen an unexplained increase in fog events over northern India, coincident with a steep rise in wintertime irrigation associated with the introduction of double-cropping. Accurate fog forecasting is challenging due to a high sensitivity to numerous processes across many scales, and uncertainties in representing some of these in state-of-the-art numerical weather prediction models. Here we show fog event simulations over northern India with and without irrigation, revealing that irrigation counteracts a common model dry bias, dramatically improving the simulation of fog. Evaluation against satellite products and surface measurements reveals a better spatial extent and temporal evolution of the simulated fog events. Increased use of irrigation over northern India in winter provides a plausible explanation for the observed upward trend in fog events, highlighting the critical need for optimisation of irrigation practices.

A deep-learning model for rapid spatiotemporal prediction of coastal water levels

With the increasing impact of climate change and relative sea level rise, low-lying coastal communities face growing risks from recurrent nuisance flooding and storm tides. Thus, timely and reliable predictions of coastal water levels are critical to resilience in vulnerable coastal areas. Over the past decade, there has been increasing interest in utilizing machine learning (ML) based models for emulation and prediction of coastal water levels. However, flood advisory systems still rely on running computationally demanding hydrodynamic models. To alleviate the computational burden, these physics-based models are either run at small scales with high resolution or at large scales with low resolution. While ML-based models are very fast, they face challenges in terms of ensuring reliability and ability to capture any surge levels. In this paper, we develop a deep neural network for spatiotemporal prediction of water levels in coastal areas of the Chesapeake Bay in the U.S. Our model relies on data from numerical weather prediction models as the atmospheric input and astronomical tide levels, while its outputs are time series of predicted water levels at several tide gauge locations across the Chesapeake Bay. We utilized a CNN-LSTM setting as the architecture of the model. The CNN part extracts the features from a sequence of gridded wind fields and fuses its output to several independent LSTM units. The LSTM units concatenate the atmospheric features with respective astronomical tide levels and produce water level time series. The novel contribution of the present work is in spatiotemporality and in prioritization of the physical relationships in the model to maintain a high analogy to hydrodynamic modeling, either in the network architecture or in the selection of predictors and predictands. The results show that this setting yields a strong performance in predicting coastal water levels that cause flooding from minor to major levels. We also show that the model stands up successfully to the rigorous comparison with a high-fidelity ADCIRC model, yielding mean RMSE and correlation coefficient of 14.3 cm and 0.94, respectively, in two extreme cases, versus 12.30 cm and 0.96 for the ADCIRC model. The results highlight the practical feasibility of employing fast yet inexpensive data-driven models for resilient coastal management.

Temperature forecasts for the continental United States: a deep learning approach using multidimensional features

Accurate weather forecasts are critical for saving lives, emergency services, and future developments. Climate models such as numerical weather prediction models have made significant advancements in weather forecasts, but these models are computationally expensive and can be subject to inaccurate representations of complex natural interconnections. Alternatively, data-driven machine learning methods have provided new dimensions in assisting weather forecasts. In this study, we used convolutional neural networks (CNN) to assess how geopotential height at different levels of the troposphere may affect the predictability of extreme surface temperature (t2m) via two cases. Specifically, we analyzed temperature forecasts over the continental United States at lead times from 1 day to 30 days by incorporating z100, z200, z500, z700, and z925 hPa levels as inputs to the CNN. In the first case, we applied the framework to predict summer temperatures of 2012, which contributed to one of the extreme heatwave events in the U.S. history. The results show that z500 leads to t2m forecasts with relatively less root mean squared errors (RMSE) than other geopotential heights at most of the lead time under consideration, while the inclusion of more atmospheric pressure levels improves t2m forecasts to a limited extent. At the same lead time, we also predicted the z500 patterns with different levels of geopotential height and temperature as the inputs. We found that the combination of z500, t2m, and t850 (temperature at 850 hPa) is associated with less RMSE for the z500 forecasts compared to other inputs. In contrast to the 2012 summer, our second case examined the wintertime temperature of 2014 when the upper Midwest and Great Lakes regions experienced the coldest winter on record. We found that z200 contributes to better t2m predictions for up to 7-days lead times whereas z925 gives better results for z500 forecasts during this cold event. Collectively, the results suggest that for long-range temperature forecasts based on the CNN, including various levels of geopotential heights could be beneficial.

Comparison of COSMO and ICON-LAM high-resolution numerical forecast for Romanian territory: Case studies and evaluation

This paper aims to offer the first detailed inter-comparison of the performance from the numerical weather prediction models Consortium for Small-scale Modeling (COSMO) and Icosahedral Nonhydrostatic General Circulation Model, Limited Area Mode (ICON-LAM), integrated for the Romanian territory at the horizontal resolution of 2.8 km. As the ICON numerical model is set to replace COSMO, a comparison is carried out between the operational configurations of the two models. First, two cases with severe weather (strong atmospheric instability, observed heavy precipitation, and strong wind) are analyzed: February 3-6, 2020 (heavy precipitation [snow] and very strong wind) and May 3-5, 2020 (heavy precipitation). This is followed by a statistical inter-comparison between the two models for the summer of 2020 (June, July, August). A statistical evaluation of the forecast quality from the two models is performed objectively, through statistical scores computed using surface observations from all available meteorological stations on the Romanian territory. Stratifications depending on station altitude and location are carried out, with emphasis on stations of particular interest, depending on the evolution of the synoptic situation. Following the statistical evaluation, results for the precipitation forecast do not show significant improvement in favor of either model. However, ICON-2.8 km mostly outperforms COSMO-2.8 km for surface parameters.

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.

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.