High-resolution Numerical Weather Prediction Model Research Articles

Supervised Learning-Based Prediction of Lightning Probability in the Warm Season

The accurate prediction of lightning is crucial for forecasters to respond effectively to its related hazards. The rapid development and confined spatial extent of convective storms, in which lightning frequently occurs, pose considerable challenges for accurately predicting their locations using numerical weather prediction (NWP) models. Lightning occurrence is often prognosed using thermodynamic parameters, convective available potential energy (CAPE), the severe weather threat index (SWEAT), the lifted index (LI), etc. A high-resolution NWP model provides a prediction of these thermodynamic parameters at high spatiotemporal resolution with high accuracy for a few hours. However, a complicated algorithm is required to handle all the useful high-resolution variables from the NWP model. The recently emerging machine learning technique can solve this issue by properly handling these “big data” without any model distributional assumption. In this study, we developed a random forest algorithm for nowcasting and very short-range forecasting (useful for ~6 h), named LightningRF. LightningRF was trained by using lightning occurrence as a response variable and characteristic parameters from the NWP as predictors. It was also applied to analysis and forecast fields, showing a high probability of lightning within the observed lightning regions. This highlights the potential of helping forecasters improve their lightning forecasting skills using real-time probabilistic forecasts from a trained model.

Machine learning parameterization of the multi-scale Kain–Fritsch (MSKF) convection scheme and stable simulation coupled in the Weather Research and Forecasting (WRF) model using WRF–ML v1.0

Abstract. Warm-sector heavy rainfall along the south China coast poses significant forecasting challenges due to its localized nature and prolonged duration. To improve the prediction of such high-impact weather events, high-resolution numerical weather prediction (NWP) models are increasingly used to more accurately represent topographic effects. However, as these models' grid spacing approaches the scale of convective processes, they enter a “gray zone”, where the models struggle to fully resolve the turbulent eddies within the atmospheric boundary layer, necessitating partial parameterization. The appropriateness of applying convection parameterization (CP) schemes within this gray zone remains controversial. To address this, scale-aware CP schemes have been developed to improve the representation of convective transport. Among these, the multi-scale Kain–Fritsch (MSKF) scheme enhances the traditional Kain–Fritsch (KF) scheme, incorporating modifications that facilitate its effective application at spatial resolutions as high as 2 km. In recent years, there has been an increase in the application of machine learning (ML) models across various domains of atmospheric sciences, including efforts to replace conventional physical parameterizations with ML models. This work introduces a multi-output bidirectional long short-term memory (Bi-LSTM) model intended to replace the scale-aware MSKF CP scheme. This multi-output Bi-LSTM model is capable of simultaneously predicting the convection trigger while also modeling the associated convective tendencies and precipitation rates with a high performance. Data for training and testing the model are generated using the Weather Research and Forecast (WRF) model over south China at a horizontal resolution of 5 km. Furthermore, this work evaluates the performance of the WRF model coupled with the ML-based CP scheme against simulations with the traditional MSKF scheme. The results demonstrate that the Bi-LSTM model can achieve high accuracy, indicating the promising potential of ML models to substitute the MSKF scheme in the gray zone.

Downscaling of surface wind forecasts using convolutional neural networks

Abstract. Near-surface winds over complex terrain generally feature a large variability at the local scale. Forecasting these winds requires high-resolution numerical weather prediction (NWP) models, which drastically increase the duration of simulations and hinder them in running on a routine basis. Nevertheless, downscaling methods can help in forecasting such wind flows at limited numerical cost. In this study, we present a statistical downscaling of WRF (Weather Research and Forecasting) wind forecasts over southeastern France (including the southwestern part of the Alps) from its original 9 km resolution onto a 1 km resolution grid (1 km NWP model outputs are used to fit our statistical models). Downscaling is performed using convolutional neural networks (CNNs), which are the most powerful machine learning tool for processing images or any kind of gridded data, as demonstrated by recent studies dealing with wind forecast downscaling. The previous studies mostly focused on testing new model architectures. In this study, we aimed to extend these works by exploring different output variables and their associated loss function. We found that there is no one approach that outperforms the others in terms of both the direction and the speed at the same time. Finally, the best overall performance is obtained by combining two CNNs, one dedicated to the direction forecast based on the calculation of the normalized wind components using a customized mean squared error (MSE) loss function and the other dedicated to the speed forecast based on the calculation of the wind components and using another customized MSE loss function. Local-scale, topography-related wind features, which were poorly forecast at 9 km, are now well reproduced, both for speed (e.g., acceleration on the ridge, leeward deceleration, sheltering in valleys) and direction (deflection, valley channeling). There is a general improvement in the forecast, especially during the nighttime stable stratification period, which is the most difficult period to forecast. The result is that, after downscaling, the wind speed bias is reduced from −0.55 to −0.01 m s−1, the wind speed MAE is reduced from 1.02 to 0.69 m s−1 (32 % reduction) and the wind direction MAE is reduced from 25.9 to 15.5∘ (40 % reduction) in comparison with the 9 km resolution forecast.

Regression-Based Ensemble Perturbations for the Zero-Gradient Issue Posed in Lightning-Flash Data Assimilation with an Ensemble Kalman Filter

Abstract Lightning flash observations are closely associated with the development of convective clouds and have a potential for convective-scale data assimilation with high-resolution numerical weather prediction models. A main challenge with the ensemble Kalman filter (EnKF) is that no ensemble members have nonzero lightning flashes in the places where a lightning flash is observed. In this situation, different model states provide all zero lightning, and the EnKF cannot assimilate the nonzero lightning data effectively. This problem is known as the zero-gradient issue. This study addresses the zero-gradient issue by adding regression-based ensemble perturbations derived from a statistical relationship between simulated lightning and atmospheric variables in the whole computational domain. Regression-based ensemble perturbations are applied if the number of ensemble members with nonzero lightning flashes is smaller than a prescribed threshold (Nmin). Observing system simulation experiments for a heavy precipitation event in Japan show that regression-based ensemble perturbations increase the ensemble spread and successfully induce the analysis increments associated with convection even if only a few members have nonzero lightning flashes. Furthermore, applying regression-based ensemble perturbations improves the forecast accuracy of precipitation although the improvement is sensitive to the choice of Nmin. Significance Statement This study develops an effective method to use lightning flash observations for weather prediction. Lightning flash observations include precious information of the inner structure of clouds, but their effective use for weather prediction is not straightforward since a weather prediction model often misses observed lightning flashes. Our new method uses ensemble-generated statistical relationships to compensate for the misses and successfully improves the forecast accuracy of heavy rains in a simulated case. Our future work will test the method with real observation data.

Numerical Simulation of Flow over an Airport During A Low-level Wind Shear Event

In this paper we present the flow features of a severe low-level wind shear event near an airport simulated using a high-resolution numerical weather prediction model. The open-source model Weather Research and Forecasting (WRF) was used for the numerical simulations. Our initial analysis shows the ability of the model in capturing the possibility of the wind shear event.

Estimating Annual Temperate Rain Attenuation Distributions at 20/40 GHz With a High-Resolution Numerical Weather Prediction Model

Earth-space communications are to make use of higher frequencies, at and above 20 GHz, to offer new services and increased data rates. A consequence is a strong attenuation of the carrier waves by the troposphere, particularly during rain events. The statistical characterization of the rain attenuation on high-frequency channels is thus paramount to their link budget. Traditionally this knowledge is obtained via long-term ground measurements of spaceborne beacon signals. Here, simulated results based on numerical weather prediction models are considered as an alternative. A comparison is given based on two years of beacon data at 20 and 40 GHz in Toulouse (France). Four parametrizations of the weather research forecasting model's microphysics are tested: the best performances are achieved with the NSSL-2 moment scheme.

Systematic diurnal bias of the CMA-MESO model in southern China: Characteristics and correction

Model error is an important source of numerical weather prediction (NWP) errors. Among model errors, the systematic diurnal bias plays an important role in high-resolution numerical weather prediction models. The main purpose of this study is to explore the characteristics of the systematic diurnal bias of a high-resolution NWP model in southern China and reduce the diurnal bias to improve the forecast results, hence providing a better background field for data assimilation. Based on the China Meteorological Administration Meso-scale (CMA-MESO) high-resolution NWP model, a 15-day sequential numerical weather prediction experiment was performed in southern China, and the forecast results were analyzed. A sequential bias correction scheme (SBCS) based on analysis increments was designed to reduce the systematic diurnal bias of the CMA-MESO model, and 15-day sequential comparative experiments were carried out. The analysis results showed that the CMA-MESO model has apparent systematic diurnal biases, and the characteristics differ among variables. A large diurnal bias was mainly found in the lower model layers, and it was concentrated in areas with a complex underlying surface for the horizontal distribution, such as the Qinghai-Tibet Plateau and South China Coast. The results based on the 15-day sequential experiment showed that the sequential bias correction scheme partly reduced the systematic diurnal biases of the CMA-MESO model. The mean biases of meridional wind, zonal wind, potential temperature, and water vapor mixing ratio were reduced by 45%, 35%, 20%, and 10%, respectively, and the root mean square errors (RMSEs) were reduced by approximately 5%. This study revealed the characteristics of the systematic diurnal bias of CMA-MESO model in southern China, which may be caused by the diurnal variation in the thermal and dynamic exchange on underlying surfaces. The effectiveness of the sequential bias correction scheme was also verified, and the results had good prospects for providing more reference information for high-resolution numerical prediction models and data assimilation.

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

An optimal estimation algorithm for the retrieval of fog and low cloud thermodynamic and micro-physical properties

Abstract. A new generation of cloud radars, with the ability to make observations close to the surface, presents the possibility of observing fog properties with better insight than was previously possible. The use of these instruments as part of an operational observation network could improve the prediction of fog events, something which is still a problem for even high-resolution numerical weather prediction models. However, the retrieval of liquid water content (LWC) profiles from radar reflectivity alone is an under-determined problem, something which ground-based microwave radiometer observations can help to constrain. In fact, microwave radiometers are not only sensitive to temperature and humidity profiles but are also known to be instruments of reference for the liquid water path. By providing the thermodynamic state of the atmosphere, to which the formation and evolution of fog events are highly sensitive, in addition to accurate liquid water path, which can be used to constrain the LWC retrieval from the cloud radar alone, combining microwave radiometers with cloud radars seems a natural next step to better understand and forecast fog events. To that end, a newly developed one-dimensional variational (1D-Var) algorithm designed for the retrieval of temperature, specific humidity and liquid water content profiles with both cloud radar and microwave radiometer (MWR) observations is presented in this study. The algorithm was developed to evaluate the capability of cloud radar and MWR to provide accurate LWC profiles in addition to temperature and humidity in view of assimilating the retrieved profiles into a 3D- and 4D-Var operational assimilation system. The algorithm is firstly tested on a synthetic dataset, which allows the evaluation of the developed algorithm in idealised conditions. This dataset was constructed by perturbing a high-resolution forecast dataset of fog and low-cloud cases by its expected errors. The algorithm is then tested with real data from the recent field campaign SOFOG-3D, carried out with the use of LWC measurements made from a tethered balloon platform. As expected, results from the synthetic dataset study were found to contain lower errors than those found from the retrievals on the dataset of real observations. It was found that LWC can be retrieved in idealised conditions with an uncertainty of less than 0.04 g m−3. With real data, as expected, retrievals with a good correlation (0.7) to in situ measurements were found but with a higher uncertainty than the synthetic dataset of around 0.06 g m−3 (41 %). This was reduced to 0.05 g m−3 (35 %) when an accurate droplet number concentration could be prescribed to the algorithm. A sensitivity study was conducted to discuss the impact of different settings used in the 1D-Var algorithm and the forward operator. Additionally, retrievals of LWC from a real fog event observed during the SOFOG-3D field campaign were found to significantly improve the operational background profiles of the AROME (Application of Research to Operations at MEsoscale) model, showing encouraging results for future improvement of the AROME model initial state during fog conditions.

The Use of Sentinel-3 Altimetry Data to Assess Wind Speed from the Weather Research and Forecasting (WRF) Model: Application over the Gulf of Cadiz

This work presents the quality performance and the capabilities of altimetry derived wind speed (WS) retrievals from the altimeters on-board Copernicus satellites Sentinel-3A/B (S3A/B) for the spatial assessment of WS outputs from the weather research and forecasting (WRF) model over the complex area of the Gulf of Cádiz (GoC), Spain. In order to assess the applicability of the altimetry data for this purpose, comparisons between three different WS data sources over the area were evaluated: in situ measurements, S3A/B 20 Hz altimetry data, and WRF model outputs. Sentinel-3A/B WS data were compared against two different moored buoys to guarantee the quality of the data over the GoC, resulting in satisfying scores (average results: RMSE = 1.21 m/s, r = 0.93 for S3A and RMSE = 1.36 m/s, r = 0.89 for S3B). Second, the WRF model was validated with in situ data from four different stations to ensure the correct performance over the area. Finally, the spatial variability of the WS derived from the WRF model was compared with the along-track altimetry-derived WS. The analysis was carried out under different wind synoptic conditions. Qualitative and quantitative results (average RMSE < 1.0 m/s) show agreement between both data sets under low/high wind regimes, proving that the spatial coverage of satellite altimetry enables the spatial assessment of high-resolution numerical weather prediction models in complex water-covered zones.

A High-Resolution Quantitative Precipitation Estimate over Alaska through Kriging-Based Merging of Rain Gauges and Short-Range Regional Precipitation Forecasts

Abstract A method is presented to generate quantitative precipitation estimates over Alaska using kriging to merge sparse, unevenly distributed rain gauge observations with quantitative precipitation forecasts from a three-member ensemble of high-resolution numerical weather prediction models. The estimated error variance of the analysis is computed by starting with the estimated error variance from kriging and then refining the variance in k-fold cross validation by an empirically derived inflation factor. The method combines dynamical model forecast information with observational data to deliver a best linear unbiased estimate of precipitation, along with an analysis uncertainty estimate, that provides a much-needed precipitation analysis in a region where sparse in situ observations, poor coverage by remote sensing platforms, and complex terrain introduce large uncertainties that need to be quantified. For 6-hourly accumulation estimates produced four times daily from 1 August 2019 to 31 July 2020, three analysis configurations are tested to measure the value added by including model forecast data and how those data are best utilized in the analysis. Several directions for further improvement and validation of the analysis product are provided.

Stratospheric Gravity Waves Excited by a Propagating Rossby Wave Train—A DEEPWAVE Case Study

Abstract Stratospheric gravity waves observed during the DEEPWAVE research flight RF25 over the Southern Ocean are analyzed and compared with numerical weather prediction (NWP) model results. The quantitative agreement of the NWP model output and the tropospheric and lower-stratospheric observations is remarkable. The high-resolution NWP models are even able to reproduce qualitatively the observed upper-stratospheric gravity waves detected by an airborne Rayleigh lidar. The usage of high-resolution ERA5 data—partially capturing the long internal gravity waves—enabled a thorough interpretation of the particular event. Here, the observed and modeled gravity waves are excited by the stratospheric flow past a deep tropopause depression belonging to an eastward-propagating Rossby wave train. In the reference frame of the propagating Rossby wave, vertically propagating hydrostatic gravity waves appear stationary; in reality, of course, they are transient and propagate horizontally at the phase speed of the Rossby wave. The subsequent refraction of these transient gravity waves into the polar night jet explains their observed and modeled patchy stratospheric occurrence near 60°S. The combination of both unique airborne observations and high-resolution NWP output provides evidence for the one case investigated in this paper. As the excitation of such gravity waves persists during the quasi-linear propagation phase of the Rossby wave’s life cycle, a hypothesis is formulated that parts of the stratospheric gravity wave belt over the Southern Ocean might be generated by such Rossby wave trains propagating along the midlatitude waveguide.

Comparative performance of HWRF model coupled with POM and HYCOM for tropical cyclones over North Indian Ocean

The demonstration of air-sea interaction in the high-resolution numerical weather prediction (NWP) model is the necessary condition to predict the extreme event like tropical cyclone. It is already established that the coupled mesoscale models are capable of forecasting the track and intensity of tropical cyclones with reasonable skill over North Indian Ocean (NIO) basin and over other ocean basins as well. The Hurricane Weather Research and Forecasting (HWRF) model is consequently operational by India Meteorological Department (IMD) for the forecasting tropical cyclones like many other National Meteorological and Hydrological Services. Two different coupled versions of HWRF modeling systems with feature based Princeton Ocean Model (POM) and eddy-resolving Hybrid Coordinate Ocean Model (HYCOM) are employed for the real-time forecasting of tropical cyclones over NIO. In this study, the comparison of forecast skills between two coupled variants in predicting track and intensity for all eight cyclones during 2019 has been carried out. The results show that the track prediction of all cyclones by HWRF model coupled with HYCOM is comparatively better than that with POM. But the intensity prediction skills of both coupled models do not portray any obvious superiority for all forecast hours. The absolute intensity errors of HWRF model with POM coupling are large compared to HYCOM during initial phase up to 60 hours of forecast except for Vayu cyclone. But, the errors in intensity exhibited by POM are a bit smaller compared to HYCOM for longer lead period. However, the standard deviation is very large for both models and numbers of forecasts verified are also less. In case of two cyclones of 2020, the ability of coupled variant with HYCOM is superior to POM in forecasting the tracks of cyclones. Considering the skills of both the models for intensity prediction, it is clear that the use of coupled HCOM model for short-lived weaker storms is advantageous but the performance of the model is not consistently maintained with longer lead time for intense and long-lived cyclone. A brief assessment of surface fluxes and Sea Surface Temperature reveals that HYCOM portray more realistic ocean condition in turn surface fluxes compared to POM and therefore the coupled variant of HWRF with HYCOM is superior to the variant with POM while forecasting cyclones over deep Ocean.

The smoother the better? A comparison of six post-processing methods to improve short-term offshore wind power forecasts in the Baltic Sea

Abstract. With a rapidly increasing capacity of electricity generation from wind power, the demand for accurate power production forecasts is growing. To date, most wind power installations have been onshore and thus most studies on production forecasts have focused on onshore conditions. However, as offshore wind power is becoming increasingly popular it is also important to assess forecast quality in offshore locations. In this study, forecasts from the high-resolution numerical weather prediction model AROME was used to analyze power production forecast performance for an offshore site in the Baltic Sea. To improve the AROME forecasts, six post-processing methods were investigated and their individual performance analyzed in general as well as for different wind speed ranges, boundary layer stratifications, synoptic situations and in low-level jet conditions. In general, AROME performed well in forecasting the power production, but applying smoothing or using a random forest algorithm increased forecast skill. Smoothing the forecast improved the performance at all wind speeds, all stratifications and for all synoptic weather classes, and the random forest method increased the forecast skill during low-level jets. To achieve the best performance, we recommend selecting which method to use based on the forecasted weather conditions. Combining forecasts from neighboring grid points, combining the recent forecast with the forecast from yesterday or applying linear regression to correct the forecast based on earlier performance were not fruitful methods to increase the overall forecast quality.

W-band radar observations for fog forecast improvement: an analysis of model and forward operator errors

Abstract. The development of ground-based cloud radars offers a new capability to continuously monitor fog structure. Retrievals of fog microphysics are key for future process studies, data assimilation, or model evaluation and can be performed using a variational method. Both the one-dimensional variational retrieval method (1D-Var) or direct 3D/4D-Var data assimilation techniques rely on the combination of cloud radar measurements and a background profile weighted by their corresponding uncertainties to obtain the optimal solution for the atmospheric state. In order to prepare for the use of ground-based cloud radar measurements for future applications based on variational approaches, the different sources of uncertainty due to instrumental, background, and forward operator errors need to be properly treated and accounted for. This paper aims at preparing 1D-Var retrievals by analysing the errors associated with a background profile and a forward operator during fog conditions. For this, the background was provided by a high-resolution numerical weather prediction model and the forward operator by a radar simulator. Firstly, an instrumental dataset was taken from the SIRTA observatory near Paris, France, for winter 2018–2019 during which 31 fog events were observed. Statistics were calculated comparing cloud radar observations to those simulated. It was found that the accuracy of simulations could be drastically improved by correcting for significant spatio-temporal background errors. This was achieved by implementing a most resembling profile method in which an optimal model background profile is selected from a domain and time window around the observation location and time. After selecting the background profiles with the best agreement with the observations, the standard deviation of innovations (observations–simulations) was found to decrease significantly. Moreover, innovation statistics were found to satisfy the conditions needed for future 1D-Var retrievals (un-biased and normally distributed).

Evaluating Convective Initiation in High-Resolution Numerical Weather Prediction Models Using GOES-16 Infrared Brightness Temperatures

AbstractThe evolution of model-based cloud-top brightness temperatures (BT) associated with convective initiation (CI) is assessed for three bulk cloud microphysics schemes in the Weather Research and Forecasting Model. Using a composite-based analysis, cloud objects derived from high-resolution (500 m) model simulations are compared to 5-min GOES-16 imagery for a case study day located near the Alabama–Mississippi border. Observed and simulated cloud characteristics for clouds reaching CI are examined by utilizing infrared BTs commonly used in satellite-based CI nowcasting methods. The results demonstrate the ability of object-based verification methods with satellite observations to evaluate the evolution of model cloud characteristics, and the BT comparison provides insight into a known issue of model simulations producing too many convective cells reaching CI. The timing of CI from the different microphysical schemes is dependent on the production of ice in the upper levels of the cloud, which typically occurs near the time of maximum cloud growth. In particular, large differences in precipitation formation drive differences in the amount of cloud water able to reach upper layers of the cloud, which impacts cloud-top glaciation. Larger cloud mixing ratios are found in clouds with sustained growth leading to more cloud water lofted to the upper levels of the cloud and the formation of ice. Clouds unable to sustain growth lack the necessary cloud water needed to form ice and grow into cumulonimbus. Clouds with slower growth rates display similar BT trends as clouds exhibiting growth, which suggests that forecasting CI using geostationary satellites might require additional information beyond those derived at cloud top.

In Situ Measurements of Wind and Turbulence by a Motor Glider in the Andes

AbstractA Stemme S10-VT motor glider was equipped with a newly developed sensor suite consisting of a five-hole probe, an inertial navigation and global navigation satellite system, two temperature sensors, and a humidity sensor. By design, the system provides three-dimensional wind vector data that enable the analysis of atmospheric motion scales up to a temporal resolution of 10 Hz. We give a description of components and installation of the system, its calibration, and its performance. The accuracy for the measurement of the wind vector is estimated to be on the order of 0.5 m s−1. As part of the Southern Hemisphere Transport, Dynamics, and Chemistry (SouthTRAC) field campaign, 30 research flights were performed from September 2019 to January 2020. We present statistical analysis of the observations, discriminating pure motor flights from soaring flights in the lee waves of the Andes. We present histograms of flight altitude, airspeed, wind speed and direction, temperature, and relative humidity to document the atmospheric conditions. Probability density functions of vertical air velocity, turbulence kinetic energy (TKE), and dissipation rate complete the statistical analysis. Altogether, 41% of the flights are in weak, 14% in moderate, and 0.4% in strong mountain wave conditions according to thresholds for the measured vertical air velocity. As an exemplary case study, we compare measurements on 11 September 2019 to a high-resolution numerical weather prediction model. The case study provides a meaningful example of how data from soaring flights might be utilized for model validation on the mesoscale and within the troposphere.

Impact of the assimilation of DWR-derived precipitation rates through latent heat nudging on simulation of rainfall events over Indian region using NCUM-R

Radar estimate of precipitation is an important observation which can be used to initialize the high-resolution numerical weather prediction models for improved forecast of high impact weather. Impact of Indian Doppler Weather Radar (DWR) derived precipitation rates on short-range weather forecasts is studied here. The present work is one of the first-of-its-kind studies to assimilate the DWR derived rain rate into a high resolution model over Indian region. Chennai DWR derived surface precipitation rates are included in the high resolution regional version of NCMRWF Unified Model (NCUM-R) through latent heat nudging to study its impact on the simulations of two convective rainfall events that occurred over east-peninsular India. The high resolution initial condition for NCUM-R is prepared using 4D-VAR data assimilation system in which various conventional and satellite observations are used. In this study, two sets of numerical experiments are carried out, one experiment named as control experiment (CNTL) uses only the analysis created by 4D-VAR and in the second (LHN), latent heat nudging is also performed with DWR derived precipitation rates in addition to the use of the 4D-VAR analysis.The results of the study show that the assimilation of DWR derived precipitation rates has a positive impact on the simulation of convective rainfall events. The root mean square errors (RMSE) of the analysed meteorological fields, mainly the mass fields are improved in the LHN compared to CNTL in both cases. The error of Convective Available Potential Energy (CAPE) in the LHN analyses is considerably reduced compared to the CNTL analyses for both cases. The stability indices also suggested that the condition for convection is properly reproduced in the LHN analysis in both cases. When evaluated against rainfall observations, the amount and orientation of the main rainfall pattern showed noteworthy improvements in the LHN relative to CNTL. The rainfall forecasts are further verified based on the object oriented Contiguous Rain Area (CRA) technique. The vector displacement errors and total mean square error are reduced considerably in LHN simulations compared to CNTL. The vertical structure and time evolution of moisture flux and moisture convergence are also well simulated in the LHN. This preliminary study revealed encouraging results which shows that the assimilation of Indian DWR derived rain rates in the high resolution regional NWP system can improve the forecast of convective rainfall events over Indian region.

Objective identification of thunderstorm gust fronts in numerical weather prediction models for fire weather forecasting

Abrupt changes in wind direction and speed can dramatically impact wildfire development and spread, endangering firefighters. A frequent cause of such wind shifts is outflow from thunderstorms and organised convective systems; thus, their identification and prediction present critical challenges for fire weather forecasters. Here, we develop a methodology and implement it in a software tool that can identify and depict convective outflow boundaries in high-resolution numerical weather prediction (NWP) models to provide guidance for fire weather forecasting. The tool can process model output, objectively identify gust fronts, and graphically display detected gust fronts and similar boundaries in NWP model forecasts. The tool is demonstrated with output from the Weather Research and Forecasting (WRF) model from the operational High-Resolution Rapid Refresh (HRRR) forecasting system and from a WRF ensemble run at the National Center for Atmospheric Research that can provide probabilistic information about model-predicted gust fronts. The tool can identify outflow boundaries in model forecasts of convective events occurring in both simple and complex terrain, both with and without concurrent wildfire activity. With accurate underlying model forecast output, the tool can reliably reveal areas of potential gust front activity and thus provide valuable guidance to incident meteorologists and command personnel.

Study of the Vertical Structure of the Coastal Boundary Layer Integrating Surface Measurements and Ground-Based Remote Sensing.

The understanding of the atmospheric processes in coastal areas requires the availability of quality datasets describing the vertical and horizontal spatial structure of the Atmospheric Boundary Layer (ABL) on either side of the coastline. High-resolution Numerical Weather Prediction (NWP) models can provide this information and the main ingredients for good simulations are: an accurate description of the coastline and a correct subgrid process parametrization permitting coastline discontinuities to be caught. To provide an as comprehensive as possible dataset on Mediterranean coastal area, an intensive experimental campaign was realized at a near-shore Italian site, using optical and acoustic ground-based remote sensing and surface instruments, under different weather characteristic and stability conditions; the campaign is also fully simulated by a NWP model. Integrating information from instruments responding to different atmospheric properties allowed for an explanation of the development of various patterns in the vertical structure of the atmosphere. Wind LiDAR measurements provided information of the internal boundary layer from the value of maximum height reached by the wind profile; a height between 80 and 130 m is often detected as an interface between two different layers. The NWP model was able to simulate the vertical wind profiles and the eight of the ABL.