Regional Numerical Weather Prediction Model Research Articles

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.

Assimilation of Satellite Lightning Data in a Storm-Scale Numerical Weather Prediction System Using a 3D-EnVar Data Assimilation Scheme

Abstract The objective of this study is to prepare the assimilation of the Meteosat Third Generation Lightning Imager (MTG-LI) observations in the regional numerical weather prediction model AROME-France using a 3D-EnVar data assimilation algorithm. As opposed to the current operational configuration of AROME-France data assimilation system, the hydrometeor specific contents are added in the control variable and are thus updated during the assimilation process. Consequently, a lightning observation operator based on the specific contents of snow and graupel is used. As the first MTG satellite was launched in December 2022, the real MTG-LI observations are not available yet, and proxy spaceborne lightning observations generated from ground-based lightning detection network are used. We assess the forecast skills of experiments assimilating lightning in a close to operational configuration by comparing the results to a reference experiment that does not assimilate lightning. In the studied cases, the frequency bias and fraction skill score are improved for brightness temperatures forecasts lower than 280 K up to 4 h after the assimilation, implying a better description of the general cloud cover. The convective cores, identified by brightness temperatures lower than 220 K, are better captured in the first forecast hour but this improvement is not maintained through time and quickly fades.

Impact of transport model resolution and a priori assumptions on inverse modeling of Swiss F-gas emissions

Abstract. Inverse modeling is a widely used top-down method to infer greenhouse gas (GHG) emissions and their spatial distribution based on atmospheric observations. The errors associated with inverse modeling have multiple sources, such as observations and a priori emission estimates, but they are often dominated by the transport model error. Here, we utilize the Lagrangian particle dispersion model (LPDM) FLEXPART (FLEXible PARTicle Dispersion Model), driven by the meteorological fields of the regional numerical weather prediction model COSMO. The main sources of errors in LPDMs are the turbulence diffusion parameterization and the meteorological fields. The latter are outputs of an Eulerian model. Recently, we introduced an improved parameterization scheme of the turbulence diffusion in FLEXPART, which significantly improves FLEXPART-COSMO simulations at 1 km resolution. We exploit F-gas measurements from two extended field campaigns on the Swiss Plateau (in Beromünster and Sottens), and we conduct both high-resolution (1 km) and low-resolution (7 km) FLEXPART transport simulations that are then used in a Bayesian analytical inversion to estimate spatial emission distributions. Our results for four F-gases (HFC-134a, HFC-125, HFC-32, SF6) indicate that both high-resolution inversions and a dense measurement network significantly improve the ability to estimate spatial distribution of the emissions. Furthermore, the total emission estimates from the high-resolution inversions (351 ± 44 Mg yr−1 for HFC-134a, 101 ± 21 Mg yr−1 for HFC-125, 50 ± 8 Mg yr−1 for HFC-32, 9.0 ± 1.1 Mg yr−1 for SF6) are significantly higher compared to the low-resolution inversions (20 %–40 % increase) and result in total a posteriori emission estimates that are closer to national inventory values as reported to the UNFCCC (10 %–20 % difference between high-resolution inversion estimates and inventory values compared to 30 %–40 % difference between the low-resolution inversion estimates and inventory values). Specifically, we attribute these improvements to a better representation of the atmospheric flow in complex terrain in the high-resolution model, partly induced by the more realistic topography. We further conduct numerous sensitivity inversions, varying different parameters and variables of our Bayesian inversion framework to explore the whole range of uncertainty in the inversion errors (e.g., inversion grid, spatial distribution of a priori emissions, covariance parameters like baseline uncertainty and spatial correlation length, temporal resolution of the assimilated observations, observation network, seasonality of emissions). From the abovementioned parameters, we find that the uncertainty of the mole fraction baseline and the spatial distribution of the a priori emissions have the largest impact on the a posteriori total emission estimates and their spatial distribution. This study is a step towards mitigating the errors associated with the transport models and better characterizing the uncertainty inherent in the inversion error. Improvements in the latter will facilitate the validation and standardization of national GHG emission inventories and support policymakers.

Comparing global and regional downscaled NWP models for irradiance and photovoltaic power forecasting: ECMWF versus AROME

Inspecting the literature, much effort has been placed on the verification of irradiance forecasts from numerical weather prediction (NWP) models, as such forecasts are thought to have profound implications on the photovoltaic (PV) power forecasts, which in turn affects grid operators' confidence in integrating such power into the electricity grid. However, perhaps due to the proprietary nature of PV plants and lack of access to state-of-the-art NWP model output, only few have had the chance to conduct head-to-head comparisons of global mesoscale and regional downscaled NWP models, in terms of how their irradiance forecast inaccuracies propagate to PV power forecasts. In this regard, this work presents such a study, in which irradiance and PV power forecasts from the European Centre for Medium-Range Weather Forecasts' High-Resolution (HRES) and Météo-France's Application of Research to Operations at Mesoscale (AROME) models are thoroughly verified against the ground-based measurements from 32 research-grade radiometry stations and 94 actual PV plants in Hungary. A wide range of techniques and case studies concerning verification is herein considered, including variance ratio analysis, Murphy–Winkler decomposition, point-versus-areal verification, and seasonal verification. Despite that the results are too numerous to be summarized in a few sentences, the overarching observation from the verification exercise is that the performance of irradiance forecasts can only be used to infer that of PV power forecasts to a certain extent, which contrasts the conventional wisdom.

A Study on the Influence of Air Pollution on Temperature Forecast Skill Based on Operational Weather Forecast in BTH Region

Surface air temperature is a comprehensive function of aerosols in the atmosphere and various weather factors. However, there is no real-time aerosol concentration feedback in most operational numerical weather prediction (NWP) models. This raises a scientific question of how abnormal changes in air pollutants in a short period of time will affect the temperature prediction skill of NWP models. Thus, the study was carried out to investigate the possible influence of air pollution on the temperature forecast skill based on the operational NWP model over the Beijing–Tianjin–Hebei (BTH) region during January–February 2020. The results show that the average concentrations of PM2.5, SO2, NO2 and CO over the BTH region in February were smaller than those in January by 38.5%, 35.1%, 48.0% and 33.1%, respectively. Simultaneously, the forecast skills for surface temperature in February from both regional (RMAPS, Rapid-refresh Multi-scale Analysis and Prediction System) and global (ECMWF, European Centre for Medium-Range Weather Forecasts) operational NWP models improved markedly compared with that in January. In both models, the underestimation of maximum temperature and the overestimation of minimum temperature in most cities over the BTH region in February were significantly reduced. With the 24 h (24 h) forecast lead time, the RMSE (root mean square error) of BTH daily mean, maximum and minimum temperature prediction in February based on RMAPS were 17.3%, 9.8% and 21.6% lower than that in January, respectively. These are generally consistent with the other statistical indices such as deviation and regression coefficient. As the forecast lead time extended to 48 h and 72 h forecast, the phenomena still existed and were also evident in the ECMWF model. The improvement of temperature forecast skill of NWP models may be attributed to the unexpected dramatical reduction of air pollutants. Less aerosols during the daytime allow more solar radiation reaching the surface and cause a warming in the near-surface temperature, while less aerosols during the nighttime favor the outgoing long-wave radiation and then lead to a cooling near the ground.

The Local Unscented Transform Kalman Filter for the Weather Research and Forecasting Model

In this study, the local unscented transform Kalman filter (LUTKF) proposed in the previous study estimates the state of the Weather Research and Forecasting (WRF) model through local analysis. Real observations are assimilated to investigate the analysis performance of the WRF-LUTKF system. The WRF model as a regional numerical weather prediction (NWP) model is widely used to explain the atmospheric state for mesoscale meteorological fields, such as operational forecasting and atmospheric research applications. For the LUTKF based on the sigma-point Kalman filter (SPKF), the state of the nonlinear system is estimated by propagating ensemble members through the unscented transformation (UT) without making any linearization assumptions for nonlinear models. The main objective of this study is to examine the feasibility of mesoscale data assimilations for the LUTKF algorithm using the WRF model and real observations. Similar to the local ensemble transform Kalman filter (LETKF), by suppressing the impact of distant observations on model state variables through localization schemes, the LUTKF can eliminate spurious long-distance correlations in the background covariance, which are induced by the sampling error due to the finite ensemble size; therefore, the LUTKF used in the WRF-LUTKF system can efficiently execute the data assimilation with a small ensemble size. Data assimilation test results demonstrate that the LUTKF can provide reliable analysis performance in estimating the WRF model state with real observations. Experiments with various ensemble size show that the LETKF can provide better estimation results with a larger ensemble size, while the LUTKF can achieve accurate and reliable assimilation results even with a smaller ensemble size.

Clear Air Turbulence Resolved by Numerical Weather Prediction Model Validated by Onboard and Virtual Flight Data

AbstractA clear air turbulence (CAT) occurred on 30 December 2020 over Tokyo, Japan. The CAT was largely generated by breaking Kelvin‐Helmholtz (KH) instability waves in the free atmosphere. A regional numerical weather prediction model simulated the event with fine resolution (35 m). Onboard‐recorded flight data and a flight simulation were utilized to validate the meteorological simulation. The locations of the reproduced strong turbulence agree well with the regions where flights encountered turbulence on that day. In a simulation with the finest resolution, the KH waves and their breaking were resolved. When the resolution was finer, the turbulent eddies were stronger, causing meteorological effects on the airplanes. The response of a virtual airplane to the simulated turbulence was estimated using a flight simulation. By comparing onboard‐recorded data with virtual flight data, we confirm that turbulent eddies are reasonably reproduced.

北京主要气传致敏花粉年浓度峰值日期预测

PDF HTML阅读 XML下载 导出引用 引用提醒 北京主要气传致敏花粉年浓度峰值日期预测 DOI: 10.5846/stxb202202220416 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 北京市科技计划课题（Z191100009119013） Prediction of date of annual maximum concentration of main airborne allergenic pollen in Beijing Author: Affiliation: Fund Project: Beijing Municipal Science and Technology Project(Z191100009119013) 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:花粉是我国北方引发过敏性鼻炎最主要过敏原，花粉症发病期与花粉浓度高峰期吻合。基于北京地区2012至2020年花粉季多站、逐日分类花粉浓度观测数据分析，得出北京地区花粉浓度在3月上旬至5月中旬（可进一步划分为3月中旬至4月上旬和4月下旬至5月上旬两个高峰期）和8月中旬至9月中旬分别存在两个高峰期，第一个高峰期内优势致敏花粉种类为柏科、杨柳科和松科，第二个高峰期内优势致敏花粉种类为桑科、菊科蒿属和藜科。根据优势致敏花粉年浓度峰值日期观测数据，使用与花粉采样站点位置相匹配的逐日气象观测数据累积值，基于作物模型概念和模糊逻辑原理建立了北京地区主要气传致敏花粉年浓度峰值日期预测模型。经检验，柏科、杨柳科、松科、桑科、菊科蒿属和藜科花粉模型预测准确率分别为87.8%、80.0%、64.4%、86.7%、78.8%和81.8%。基于北京地区主要气传致敏花粉年浓度峰值日期预测模型可为本地花粉症防治提供理论参考。 Abstract:The prevalence and severity of pollen-induced pollinosis has been increasing yearly worldwide in recent years. Pollen is the main allergen causing allergic rhinitis in North China and the onset period of airborne allergenic pollen-induced pollinosis coincides with the peak period of pollen concentration. As one of the mega-cities in the northern region of China, Beijing is becoming increasingly aware of the problems caused by allergenic pollen. Based on the analysis of 12 pollen sampling stations daily classified pollen concentration observation data during pollen season in Beijing from 2012 to 2020, there were two peak periods of pollen concentration in Beijing from early March to mid-May (it could be further divided into two peak periods from mid-March to early April and from late April to early May) and from mid-August to mid-September, respectively. The dominant allergenic pollen species in the first peak period were Cupressaceae, Salicaceae (from early March to mid-April, the annual average concentration accounted for 39.1% and 18.2% respectively) and Pinaceae (from mid-April to early May, the annual average concentration accounted for 18.2%), and the dominant allergenic pollen species in the second peak period were Moraceae, Artemisia and Chenopodiaceae (from mid-August to mid-September, the annual average concentration accounted for 34.4%, 30.4% and 12.7% respectively). The annual maximum concentration of dominant airborne allergenic pollen in Beijing varied significantly among stations and pollen seasons, and fluctuated significantly. In contrast, the variation of the dates of annual maximum pollen concentration of the same species is relatively stable, and its prediction research work is more meaningful for pollen-induced pollinosis control. Based on the observation data of annual maximum concentration of dominant airborne allergenic pollen and the cumulative value of daily meteorological observation data matched with the location of pollen sampling stations, a prediction model of date of annual maximum concentration of main airborne allergenic pollen in Beijing is established based on the principle of crop growth model and fuzzy logic. The results showed that the prediction accuracy of pollen models of Cupressaceae, Salicaceae, Pinaceae, Moraceae, Artemisia and Chenopodiaceae were 87.8%, 80.0%, 64.4%, 86.7%, 78.8% and 81.8%, respectively. Using a model based on fuzzy logic principle and driven by the cumulative values of daily meteorological elements for pollen annual maximum concentration date, combined with the high-resolution regional numerical weather prediction model in Beijing, we can make a reasonable prediction of the time of maximum pollen concentration of different airborne allergenic pollens and provide a theoretical reference for the prevention and control of local airborne allergenic pollen-induced pollinosis. 参考文献 相似文献 引证文献

Improved Delineation of Urban Hydrological Processes in Coupled Regional Climate Models

AbstractAs the resolution of global and regional numerical weather prediction (NWP) models increases, urban parameterizations are increasingly important because urbanization has significant impacts on the surface energy and water balance. However, NWP models tend to fail to accurately simulate the surface latent heat flux, with most urban canopy models lacking or oversimplifying urban hydrological and vegetation processes. In this study, we develop an urban hydrology scheme and couple it with WRF/BEP + BEM, which improves the regional model performance in urban areas at high resolution. The evaluation results show improved model accuracy not only for the surface latent heat flux but also for the air temperature and humidity. The vertical section analysis suggests that the urban hydrology scheme can have impacts on the boundary layer to a height of 1,000 m. Consequently, improved precipitation simulation causes reasonable prediction of urban surface water depth, which is valuable for early warning of waterlogging.

Investigation of Maximum Hail-Size Forecasting Using Bulk Microphysics Schemes

Abstract This study uses the Weather Research and Forecasting (WRF) Model to investigate the performance of hail parameterizations of the WRF double-moment 7-class (WDM7), aerosol-aware Thompson (AAT), and National Taiwan University triple-moment (NTU3M) bulk microphysics schemes (BMSs) on a real case of a hailstorm initiated in Shandong Province, China. The maximum hail size is particularly evaluated because it is crucial to hail severity prediction, along with areal coverage and intensity of the 24-h solid precipitation during the simulations. Compared with the radar-derived maximum hail size, the objective analysis shows that the NTU3M scheme has the best score in the forecast skill of hail-fall coverage and size, while two BMSs with single-moment rimed ice species overestimate hail diameters aloft but underpredict the coverage at the surface. A deeper investigation suggests that the derived size tendencies from the three BMSs are comparable to the benchmark solutions from the detailed hailstone growth and melting models. The NTU3M scheme displays the most consistent size tendency of the maximum diameter with the benchmark solution in the growth processes. The behaviors of melted diameter by parameterizations are highly related to the treatments of number concentration, which are consistent with the predicted hail severity and coverage. Finally, the sensitivity study shows that increasing the model resolution does not improve the forecast of the maximum hail size, given the biases in the hail mass budget equations and the parameterization of particle size distribution, with single-moment rimed ice species of the AAT scheme. Significance Statement Improving hail-forecasting skill, including the size, severity, and the spatial and temporal coverage of hail fall, has become an important subject for numerical weather prediction models as the model resolution increases. The objective of this study is to investigate the fundamental differences in hail parameterizations of three bulk microphysics schemes that lead to differences in the prediction of severe hail events and the spatial coverage of hail fall, hopefully providing insights into hail prediction with a regional numerical weather prediction model in the future.

Do the large-eddy simulations yield deeper atmospheric boundary layers in comparison to the RANS model simulations?

In this article, we address the behaviour of a RANS and an LES model in the simulation of the diurnally evolving atmospheric boundary layer (ABL) over Thiruvananthapuram, a coastal station in India, for contrasting sky conditions during the passage of a tropical cyclone Ockhi. Based on the prevailing meteorological conditions over this experimental site, 5 December 2017 and 1 December 2017 are chosen as representative days for the clear-sky and cloudy conditions, respectively. The Consortium for small-scale modelling (COSMO), a regional numerical weather prediction model is chosen to represent the RANS technique, and a mesoscale-to-LES nested configuration of the Parallelized LES Model (PALM) is taken to represent the LES technique. The LES experiments are performed at 30 m and 100 m grid spacings. The main objective of these experiments is to assess the individual model biases of nested LES in conjunction with a RANS model with a special emphasis on the diurnal evolution of ABL heights. The ABL heights inferred from the LES experiments were found to be deeper than that in the RANS experiments. The decay of ABL during the evening hours was rapid in the LES experiments, whereas it was more smooth in the RANS experiment. The study also evaluates the sensitivity of the PALM to the grid spacing in a mesoscale-to-LES nesting configuration.

Assessment of machine learning techniques for prediction of integrated water vapor using meteorological data

Weather and Climatological studies are very important in assessing atmospheric conditions like storms and cyclones. Integrated water vapor (IWV) is an important greenhouse gas in the atmosphere responsible for the Earth's radiative balance. Global Positioning System (GPS) observations have been used for monitoring the IWV variability. The IWV estimations are carried out using ground-based GPS observations at Hyderabad (17.4°N, 78.46°E), India using GAMIT software. GAMIT is GPS analysis software developed by MIT, USA. It takes input as GPS observation data containing pseudo ranges, navigation data containing ephemeris, clock errors, g-files with orbital information, and meteorological data like pressure, temperature, and relative humidity to calculate IWV. However, estimating IWV for forecasting applications is impossible with a GPS system. This paper introduces a methodology to predict IWV during normal days and severe cyclonic events using machine learning (ML) techniques. Rational quadratic Gaussian process regression (RQ-GPR) and neural network (NN) algorithms are considered for identifying suitable ML prediction algorithms over tropical conditions. Meteorological surface data like Pressure, Temperature, and relative humidity are given as input to the machine learning models. The IWV values computed from GPS are compared with the model's predicted values. RQ-GPR model is showing good accuracy with the IWV values computed from GPS against the NN model. The correlation coefficient (ρ) achieved for RQ-GPR is 0.93, and 0.86 is obtained for the NN model.
 The RMSE (Root Mean Square Error) of the predicted IWV value with RQ-GPR is better than the NN model. We have obtained mean square error (MSE) and mean absolute error (MAE) as 18.146 kg/m2 and 3.0762 kg/m2 for RQ-GPR and 27.509 kg/m2 and 3.9102 kg/m2 for the NN model which is showing RQ-GPR is a suitable model for forecasting applications. The HUDHUD cyclonic event that occurred in October 2014 is considered for testing the proposed ML algorithms. RQ-GPR model has better results in the Prediction of IWV than the NN model. The RMSE value obtained is 2.837 kg/m2 for RQ-GPR and 3.327 kg/m2 obtained from the NN model. The results indicate that the RQ-GPR model has more accuracy than the other IWV prediction models. The prediction results are helpful for meteorology, weather, and climatology studies and useful to improve the accuracy of the regional numerical weather prediction models.

Reducing the spin‐up of a regional NWP system without data assimilation

AbstractIn both operational and research settings, kilometre‐scale regional numerical weather prediction (NWP) models are often initialised by interpolating analyses from a global modelling system on to the finer regional grid. When initialised in this way (known as a cold start), the first few hours of the forecast are characterised by a rapid growth of precipitation as the system develops convective‐scale structures. This is the well‐known spin‐up problem. Spin‐up effects limit the utility of cold‐start models for short‐range forecasting of severe weather events and model development activities. In this article, we present a method for reducing the spin‐up of regional NWP models, without data assimilation (DA). The basis of our method is periodically to insert large‐scale information from global model analyses into a continuously cycling regional model, thus updating the large scales whilst preserving fine‐scale structures. We refer to this as a warm start. Our method is tested in a regional model over Darwin, for a 10‐week period in the 2016/2017 wet season. It is shown that warm‐starting indeed reduces the spin‐up of precipitation significantly in the first 6–12 hr of the forecast compared with cold‐starting. Full objective verification against both radar and satellite observations reveals that precipitation forecast skill is also improved during this time. In addition, the large scales remain closer to global analyses (taken as a best guess of reality) than in a free‐running model in which the large scales are updated only via the lateral boundary conditions. Our warm‐start technique thus provides a considerable improvement on standard cold‐starting and could also be integrated into a regional modelling system with full convective‐scale DA as a means of reducing analysis errors on large scales.

The Relationship between High-Presentation Asthma Days in Melbourne, Australia, and Modeled Thunderstorm Environments

Abstract Epidemic asthma events represent a significant risk to emergency services as well as the wider community. In southeastern Australia, these events occur in conjunction with relatively high amounts of grass pollen during the late spring and early summer, which may become concentrated in populated areas through atmospheric convergence caused by a number of physical mechanisms including thunderstorm outflow. Thunderstorm forecasts are therefore important for identifying epidemic asthma risk factors. However, the representation of thunderstorm environments using regional numerical weather prediction models, which are a key aspect of the construction of these forecasts, have not yet been systematically evaluated in the context of epidemic asthma events. Here, we evaluate diagnostics of thunderstorm environments from historical simulations of weather conditions in the vicinity of Melbourne, Australia, in relation to the identification of epidemic asthma cases based on hospital data from a set of controls. Skillful identification of epidemic asthma cases is achieved using a thunderstorm diagnostic that describes near-surface water vapor mixing ratio. This diagnostic is then used to gain insights on the variability of meteorological environments related to epidemic asthma in this region, including diurnal variations, long-term trends, and the relationship with large-scale climate drivers. Results suggest that there has been a long-term increase in days with high water vapor mixing ratio during the grass pollen season, with large-scale climate drivers having a limited influence on these conditions. Significance Statement We investigate the atmospheric conditions associated with epidemic thunderstorm asthma events in Melbourne, Australia, using historical model simulations of the weather. Conditions appear to be associated with high atmospheric moisture content, which relates to environments favorable for severe thunderstorms, but also potentially pollen rupturing as suggested by previous studies. These conditions are shown to be just as important as the concentration of grass pollen for a set of epidemic thunderstorm asthma events in this region. This means that weather model simulations of thunderstorm conditions can be incorporated into the forecasting process for epidemic asthma in Melbourne, Australia. We also investigate long-term variability in atmospheric conditions associated with severe thunderstorms, including relationships with the large-scale climate and long-term trends.

Clear Air Turbulence Observed Across a Tropopause Fold Over the Drake Passage—A Case Study

AbstractAn aircraft turbulence encounter over the Drake Passage is investigated by combining unique high‐frequency flight level data, vertical profiles of a near‐simultaneous radiosonde profile and numerical results from global and regional numerical weather prediction (NWP) models. Meteorological analysis reveals an intense polar low propagating from the Bellinghausen Sea toward the Drake Passage. A small and deep stratospheric intrusion formed a tropopause fold that promoted strong upper‐level frontogenesis and enhanced shear and horizontal deformation of the upper tropospheric and lower stratospheric (UTLS) airflow. In this region, the Basic HALO Measurement and Sensor System (BAHAMAS) aboard the HALO research aircraft flying at FL450 detected large peak‐to‐peak variations in all meteorological parameters. The computed Energy dissipation rate (EDR) values (cubic root of the eddy dissipation rate) indicate moderate to severe turbulence. The location of this turbulence encounter was well‐predicted by the clear air turbulence (CAT) indices derived from the NWP results. The enhanced CAT indices emphasize the large shear and horizontal deformation of the airflow as the cause of the turbulence. Horizontal and vertical energy spectra calculated from the 10 Hz BAHAMAS data show a well‐defined energy cascade toward small scales with Kolmogorov scaling. Maximum EDR values of about 0.35 derived both from the spectra and structure functions for the wind speed agree quantitatively very well. In addition, the structure functions support the detection of turbulent atmospheric conditions with signatures of flow anisotropy generated by enhanced thermal stratification in the UTLS. The scales involved are between the buoyancy length scale LB ≈ 1,500 m and the Ozmidov scale LO ≈ 111 m.

A high spatiotemporal resolution framework for urban temperature prediction using IoT data

Accurate weather prediction, particularly accurate temperature prediction, is critical in decision-making for energy consumption, health risks, and economics. Regional numerical weather prediction (NWP) models produce operational-level temperature forecasts based on local atmospheric circulation conditions. They suffer from data- and computational intensity, resulting in low availability of high-spatiotemporal resolution. We embed a data fusion technique into the proposed framework to address this shortcoming, integrating measurements from the Internet of Things (IoT) with a high spatiotemporal resolution with weather stations observations. The framework utilizes a Long Short-Term Memory (LSTM) network to predict surface temperature from the fusion dataset for four major cities in the U.S. (Los Angeles, New York City, Atlanta, and Chicago). The predictive framework achieves an average RMSE of 1.72 °C and an average R2 of 0.97 using the past 24 h to predict the future 12 h for Los Angeles (RMSE of 2.13 °C, 2.05 °C, 2.59 °C, and R2 of 0.98, 0.98, 0.97 for New York City, Atlanta, and Chicago). The transfer learning is adopted to leverage the pre-trained model from regions with a higher number of observation stations to predict regions with fewer stations. The transferable model improved the predicting MAE for regions with data scarcity up to 26%.

Model inter-comparison for short-range forecasts over the southern African domain

Numerical weather prediction (NWP) models have been increasing in skill and their capability to simulate weather systems and provide valuable information at convective scales has improved in recent years. Much effort has been put into developing NWP models across the globe. Representation of physical processes is one of the critical issues in NWP, and it differs from one model to another. We investigated the performance of three regional NWP models used by the South African Weather Service over southern Africa, to identify the model that produces the best deterministic forecasts for the study domain. The three models – Unified Model (UM), Consortium for Small-scale Modelling (COSMO) and Weather Research and Forecasting (WRF) – were run at a horizontal grid spacing of about 4.4 km. Model forecasts for precipitation, 2-m temperature, and wind speed were verified against different observations. Snow was evaluated against reported snow records. Both the temporal and spatial verification of the model forecasts showed that the three models are comparable, with slight variations. Temperature and wind speed forecasts were similar for the three different models. Accumulated precipitation was mostly similar, except where WRF captured small rainfall amounts from a coastal low, while it over-estimated rainfall over the ocean. The UM showed a bubble-like shape towards the tropics, while COSMO cut-off part of the rainfall band that extended from the tropics to the sub-tropics. The COSMO and WRF models simulated a larger spatial coverage of precipitation than UM and snow-report records.Significance: Extreme weather events, such as tornadoes, floods, strong winds and heat waves, have significant impacts on society, the economy, infrastructure, agriculture and many other sectors. These impacts may be mitigated or even prevented through early warning systems which depend on the use of weather forecasts and information from NWP models. As South Africa depends on models from developed countries, these models may have shortcomings in capturing extreme weather events over the southern African region.

Evaluating the use of Aeolus satellite observations in the regional numerical weather prediction (NWP) model Harmonie–Arome

Abstract. The impact of using wind observations from the Aeolus satellite in a limited-area numerical weather prediction (NWP) system is being investigated using the limited-area NWP model Harmonie–Arome over the Nordic region. We assimilate the horizontal line-of-sight (HLOS) winds observed by Aeolus using 3D-Var data assimilation for two different periods, one in September–October 2018 when the satellite was recently launched and a later period in April–May 2020 to investigate the updated data processing of the HLOS winds. We find that the quality of the Aeolus observations has degraded between the first and second experiment period over our domain. However, observations from Aeolus, in particular the Mie winds, have a clear impact on the analysis of the NWP model for both periods, whereas the forecast impact is neutral when compared against radiosondes. Results from evaluation of observation minus background and observation minus analysis departures based on Desroziers diagnostics show that the observation error should be increased for Aeolus data in our experiments, but the impact of doing so is small. We also see that there is potential improvement in using 4D-Var data assimilation, which generates flow-dependent analysis increments, with the Aeolus data.

Validation of wind measurements of two mesosphere–stratosphere–troposphere radars in northern Sweden and in Antarctica

Abstract. Two atmospheric VHF radars: ESRAD (Esrange MST radar) located near Kiruna in the Swedish Arctic and MARA (Moveable Atmospheric Radar for Antarctica) at the Indian research station Maitri in Antarctica perform wind measurements in the troposphere and lower stratosphere on a regular basis. We compared horizontal winds at altitudes between about 0.5 and 14 km derived from the radar data using the full correlation analysis (FCA) technique with radiosonde observations and models. The comparison with 28 radiosondes launched from January 2017 to August 2019 showed that ESRAD underestimates the zonal and meridional winds by about 8 % and 25 %, respectively. This is likely caused by the receiver group arrangement used for the FCA together with a high level of non-white noise. A similar result was found when comparing with the regional numerical weather prediction model HARMONIE-AROME (Bengtsson et al., 2017) for the period September 2018–May 2019. The MARA winds were compared with winds from radiosondes for the period February–October 2014 (291 occasions). In contrast to ESRAD, there is no indication that MARA underestimates the winds compared to the sondes. The mean difference between the radar and radiosonde winds is close to zero for both zonal and meridional components. The comparison of MARA with the ECMWF ERA5 reanalysis for January–December 2019 reveals good agreement with the mean difference between 0.1 and −0.5 m/s depending on the component and season. The random errors in the wind components (standard deviations over all estimates in 1 h averages) are typically 2–3 m/s for both radars. Standard deviation of the differences between radars and sondes are 3–5 m/s.

The behavior of high-CAPE (convective available potential energy) summer convection in large-domain large-eddy simulations with ICON

Abstract. Current state-of-the-art regional numerical weather prediction (NWP) models employ kilometer-scale horizontal grid resolutions, thereby simulating convection within the grey zone. Increasing resolution leads to resolving the 3D motion field and has been shown to improve the representation of clouds and precipitation. Using a hectometer-scale model in forecasting mode on a large domain therefore offers a chance to study processes that require the simulation of the 3D motion field at small horizontal scales, such as deep summertime moist convection, a notorious problem in NWP. We use the ICOsahedral Nonhydrostatic weather and climate model in large-eddy simulation mode (ICON-LEM) to simulate deep moist convection and distinguish between scattered, large-scale dynamically forced, and frontal convection. We use different ground- and satellite-based observational data sets, which supply information on ice water content and path, ice cloud cover, and cloud-top height on a similar scale as the simulations, in order to evaluate and constrain our model simulations. We find that the timing and geometric extent of the convectively generated cloud shield agree well with observations, while the lifetime of the convective anvil was, at least in one case, significantly overestimated. Given the large uncertainties of individual ice water path observations, we use a suite of observations in order to better constrain the simulations. ICON-LEM simulates a cloud ice water path that lies between the different observational data sets, but simulations appear to be biased towards a large frozen water path (all frozen hydrometeors). Modifications of parameters within the microphysical scheme have little effect on the bias in the frozen water path and the longevity of the anvil. In particular, one of our convective days appeared to be very sensitive to the initial and boundary conditions, which had a large impact on the convective triggering but little impact on the high frozen water path and long anvil lifetime bias. Based on this limited set of sensitivity experiments, the evolution of locally forced convection appears to depend more on the uncertainty of the large-scale dynamical state based on data assimilation than of microphysical parameters. Overall, we judge ICON-LEM simulations of deep moist convection to be very close to observations regarding the timing, geometrical structure, and cloud ice water path of the convective anvil, but other frozen hydrometeors, in particular graupel, are likely overestimated. Therefore, ICON-LEM supplies important information for weather forecasting and forms a good basis for parameterization development based on physical processes or machine learning.