Resolution Numerical Weather Prediction Model Research Articles

Improving the Near-Surface Wind and Turbulence at the Edge of the Orographic Drag Gray Zone by Tuning the Roughness Length

Abstract In this paper, we present the implementation and evaluate the impact of the new roughness length configuration in the ALARO canonical model configuration of the ALADIN system at the edge of the orographic gravity wave drag gray zone. As an essential input for turbulence parameterization, the roughness length affects the near-surface turbulent fluxes and the screen-level interpolation of meteorological parameters. We utilize GMTED2010 and ECOCLIMAP-II databases to derive orographic and vegetation components of the effective roughness length and introduce tuning parameters enabling us to optimize predicted near-surface turbulent momentum fluxes and 10-m wind speed. Based on sensitivity tests, we (i) prove the necessity of tuning the roughness length fields, (ii) considerably reduce the RMSE of near-surface turbulent momentum fluxes (6%–7%) and 10-m wind speed for different groups of stations (3%–10%), and (iii) identify the tree height as the most influential input parameter in our computational domain. The RMSE decomposition indicates that the improvement of 10-m wind speed mostly comes from a decrease in the random error and bias of the mean. The variability is slightly underestimated, thus reducing the model accuracy for wind speeds above the 95th percentile but at an acceptable level. We explain that roughness length tuning also compensates for the missing roughness sublayer correction in our system. Finally, we show that, although the impact of the orographic gravity wave drag scheme at a horizontal mesh size of 1.8 km is generally small, it is still beneficial for capturing some finer features observed in atmospheric soundings. Significance Statement Aiming to improve the 10-m wind speed forecast without sacrificing the accuracy of turbulent momentum fluxes in the kilometric resolution numerical weather prediction model, we derived new roughness length fields from high-resolution physiography databases. Therein, we proved the importance of tuning the input orography and vegetation fields and, depending on the time of day and year, reduced the root-mean-square error of 10-m wind speed by 3%–10%. Further, we demonstrated that orographic gravity wave drag parameterization is still needed to predict finer details seen in wind profiles from atmospheric soundings. Finally, we discussed the related simplifications in our model and their implications and proposed steps toward a more consistent and complete treatment of the near-surface turbulence.

Identifying and characterising trapped lee waves using deep learning techniques

Trapped lee waves, and resultant turbulent rotors downstream, present a hazard for aviation and land‐based transport. Though high‐resolution numerical weather prediction models can represent such phenomena, there is currently no simple and reliable automated method for detecting the extent and characteristics of these waves in model output. Spectral transform methods have traditionally been used to detect and characterise regions of wave activity in model and observational data; however, these methods can be slow and have their limitations. Machine‐learning (ML) techniques offer a new and potentially fruitful method of tackling this problem. We demonstrate that a deep‐learning model can be trained to accurately recognise and label coherent regions of lee waves from vertical velocity data on a single level from a high‐resolution numerical weather prediction (NWP) model. Using transfer learning, wave characteristics (wavelength, orientation, and amplitude) can be extracted from the trained segmentation model. The use of synthetic wave fields with prescribed wave characteristics makes this transfer learning possible without the need to characterise real complex wave fields. Addition of noise to the synthetic data makes the models more robust when applied to more complex and noisy NWP data. The collection of trained models produced provides a valuable tool to investigate the prevalence and nature of lee wave activity, as well as a new way for forecasters to detect resolved waves. The deep‐learning model was more capable and quicker at detecting and characterising lee waves than a spectral technique was. This work is just one example of how already established ML techniques can be used to detect and characterise complex weather phenomena from NWP model output and observational data, and how the careful use of synthetic data can reduce the requirements for large volumes of hand‐labelled training data for ML models.

Super high resolution numerical weather prediction model simulation of tiny anticyclones observed at the Hong Kong International Airport

As a follow-up study of the observation of tiny anticyclones by short range LIDAR at the Hong Kong International Airport, super high model numerical weather prediction model simulation of the airflow is performed at a spatial resolution of 50 m. The simulation successfully captures the tiny anticyclonic flow, suggesting that such flow could partly arise from terrain effect on the airflow at the airport. The variability of the winds as observed by an anemometer and as forecast by the model compare well, suggesting that the simulations are realistic. Two case studies are considered. The results could be useful for reference by weather forecasters at the other airports in monitoring and studying terrain-disrupted airflow.

Evaluation of a Prototype Broadband Water-Vapour Profiling Differential Absorption Lidar at Cardington, UK

For a one-month period in summer 2020, a prototype Vaisala broadband differential absorption lidar (BB-DIAL) was deployed at a Met Office research site. It was compared with in-situ observations of humidity (93 radiosonde ascents and 27 of uncrewed aerial vehicle flights) and the Met Office 1.5 km resolution numerical weather prediction (NWP) model: UK Variable resolution model (UKV). The BB-DIAL was able to collect data up to the cloud base, in all-weather situations including rain, when it was possible to reach 3 km. The average maximum height was 1300 m, with 75% of the data reaching 1000 m and 35% extending to 1500 m. Compared with radiosondes, the standard deviation for the water vapour is between 5% and 10%. The comparison with the UKV is very encouraging, with a correlation of 0.90. The error against the radiosonde is smaller than against the UKV, which is encouraging for assimilation the BB-DIAL data in UKV. Some data quality issues, such as an increase in error and variable bias in the region of overlap between the far field and close field, spurious oscillations and an unrealistic dry layer above fog are identified. Despite these issues, the overall results from this assessment are promising in terms of potential benefit, instrument reliability and capturing significant humidity changes in the boundary layer.

Objective Calibration of Numerical Weather Prediction Model: Application on Fine Resolution COSMO Model over Switzerland

The objective calibration method originally performed on regional climate models is applied to a fine horizontal resolution Numerical Weather Prediction (NWP) model over a mainly continental domain covering the Alpine Arc. The method was implemented on the MeteoSwiss COSMO (consortium for a small-scale modeling) model with a resolution of 0.01° (approximately 1 km). For the model calibration, five tuning parameters of the parameterization schemes affecting turbulence, soil-surface exchange and radiation were chosen. A full year was simulated, with the history of the soil included (hindcast) to find the optimal parameter value. A different year has been used to give an independent assessment of the impact of the optimization process. Although the operational MeteoSwiss model is already a well-tuned configuration, the results showed that a slight model performance gain is obtained by using the Calibration of COSMO (CALMO) methodology.

Quantitative Precipitation Forecasting Using an Improved Probability-Matching Method and Its Application to a Typhoon Event

This present study aims to explore how forecasters can quickly make accurate predictions by using various high-resolution model forecasts. Based on three high temporal-spatial resolution (3 km, hourly) numerical weather prediction models (CMA-MESO, CMA-GD, CMA-SH3) from the China Meteorological Administration (CMA), the hourly precipitation characteristics of three model within 24 h from March to September 2020 are discussed and integrated into a single, hourly, deterministic quantitative precipitation forecast (QPF) by making use of an improved weighted moving average probability-matching method (WPM). The results are as follows: (1) In non-rainstorm forecasts, CMA-MESO and CMA-GD have similar forecast abilities. However, in rainstorm forecasts, CMA-MESO has a notable advantage over the other two models. Thus, CMA-MESO is selected as a critical factor when participating in sensitivity experiments. (2) Compared with the traditional equal-weight probability-matching method (PM), the WPM improves the different grade QPF because it can effectively reduce rainfall pattern bias by making use of the weighted moving average (WMA). Additionally, the WPM threat score in rainstorm forecast similarly improved from 0.051 to 0.056, with a 9.8% increase relative to the PM. (3) The sensitivity experiments show that an optimal rainfall intensity score (WPM-best) can further improve the QPF and overcome all single models in both rainstorm and non-rainstorm forecasts, and the WPM-best has a rainstorm threat score skill of 0.062, with an increase of 21.6% compared with the PM. The performance of the WPM-best will be better if the precipitation intensity is stronger and the valid forecast periods is longer. It should be noted that there is no need to select models before using the WPM-best method, because WPM-best can give a very low weight to the less-skillful model in a more objective way. (4) The improved WPM method is also applied to investigate the heavy-rainfall case induced by typhoon Mekkhala (2020), where the improved WPM technique significantly improves rainstorm forecasting ability compared with a single model.

Improving WRF-Chem meteorological analyses and forecasts over aerosol polluted regions by incorporating NAAPS aerosol analyses

AbstractWhen unaccounted for in numerical weather prediction (NWP) models, heavy aerosol events can cause significant unrealized biases in forecasted meteorological parameters such as surface temperature. To improve near-surface forecasting accuracies during heavy aerosol loadings, we demonstrate the feasibility of incorporating aerosol fields from a global chemical transport model as initial and boundary conditions into a higher resolution NWP model with aerosol-meteorological coupling. This concept is tested for a major biomass burning smoke event over the Northern Great Plains region of the United States that occurred during summer of 2015. Aerosol analyses from the global Navy Aerosol Analysis and Prediction System (NAAPS) are used as initial and boundary conditions for Weather Research and Forecasting with Chemistry (WRF-Chem) simulations. Through incorporating more realistic aerosol direct effects into the WRF-Chem simulations, errors in WRF-Chem simulated surface downward shortwave radiative fluxes and near-surface temperature are reduced compared with surface-based observations. This study confirms the ability to decrease biases induced by the aerosol direct effect for regional NWP forecasts during high-impact aerosol episodes through the incorporation of analyses and forecasts from a global aerosol transport model.

Evaluating Wind Fields for Use in Basin‐Scale Distributed Snow Models

AbstractMountain winds are the driving force behind snow accumulation patterns in mountainous catchments, making accurate wind fields a prerequisite to accurate simulations of snow depth for ecological or water resource applications. In this study, we examine the effect that wind fields derived from different coarse data sets and downscaling schemes has on simulations of modeled snow depth at resolutions suitable for basin‐scale modeling (>50 m). Simulations are run over the Tuolumne River Basin, CA for the accumulation season of Water Year 2017 using the distributed snow model, SnowModel. We derived wind fields using observations from either sensor networks, the North American Land Data Assimilation System (12.5 km resolution), or High‐Resolution Rapid Refresh (HRRR, 3 km resolution) data, and downscaled using terrain‐based multipliers (MicroMet), a mass‐conserving flow model (WindNinja), or bilinear interpolation. Two wind fields derived from 3 km HRRR data and downscaled with respect to terrain produced snow depth maps that best matched observations of snow depth from airborne LiDAR. We find that modeling these wind fields at 100 and 50 m resolutions do not produce improvements in simulated snow depth when compared to wind fields modeled at a 150 m resolution due to their inability to represent wind dynamics at these scales. For input to distributed snow models at the basin‐scale, we recommend deriving wind fields from high resolution numerical weather prediction model output and downscaling with respect to terrain. Future studies should compare suspension schemes used in blowing snow models and investigate wind downscaling schemes of complexity between statistical and fluid dynamic models.

Bridging Disciplines: The Application of Forensic Science to Meteorological Evidence

Forensic science relies on the application of biological, chemical and physical science, individually or collectively to adjudicate evidence presented in criminal and civil proceedings. The origins of meteorology and the science of forensic can be traced back to Aristotelian philosophies following similar trajectories to their ultimate use in the courtroom. This paper presents a framework for the implementation of both utilizing actual examples to illustrate how the two disciplines can be bridged successfully in a court of law. The certified meteorologist applies an understanding of fundamental meteorology, a broad knowledge of the discipline, and experience to provide critical insight into a forensic investigation. An attorney will seek to leverage this knowledge to bolster their position relying on the expert meteorologist to provide key insight into the role of the local weather conditions pertinent to a criminal or civil case. The demand for an expert meteorologist to reconstruct the local weather for use in civil litigation began more than two decades ago and continues today. The ready appeal is the field of meteorology can be applied directly in a court of law or indirectly through its application to other scientific disciplines such as entomology and engineering. We present several case studies to illustrate the role of meteorology in previously adjudicated casework. Improved sampling of the atmosphere, higher resolution numerical weather prediction models and the information gathered from these efforts are used to improve our knowledge of future weather and climate, and as effective tools applied judiciously in court by the forensic meteorologist.

Low level windshear associated with atmospheric boundary layer jets – case studies

Jet streams in the atmospheric boundary layer may lead to hazardous weather over southern China. In this paper, the jet-related low level windshear to be encountered by the aircraft is documented. Two typical cases under the northeast monsoon regime are considered, namely, easterly jet disrupted by the mountains to the south of Hong Kong International Airport, and outbreak of monsoon surge that produces a low level northeasterly jet. The Doppler Light Detection And Ranging Systems (LIDARs) are found to capture the corresponding windshear features every well, e.g. consistent with pilot reports and flight data. They are useful in providing timely alert to the aircraft. In particular, the LIDAR captures a double jet structure in the atmospheric boundary layer for the easterly wind case, which has not been reported in the literature before. The physical mechanism for the occurrence of the double jet is yet to be revealed. Moreover, the performance of a high spatial resolution (200 m) numerical weather prediction (NWP) model in predicting the jet and the associated low level windshear is studied. The model is found to provide reasonable prediction of the windshear features at a few hours ahead, and, for the cases studied, shows skills in providing timely alerts to the aircraft.

Low-level windshear associated with atmospheric boundary layer jets – Case studies

Jet streams in the atmospheric boundary layer may lead to hazardous weather over southern China. In this paper, the jet-related low-level windshear to be encountered by an aircraft is documented. Two typical cases under the northeast monsoon regime are considered, namely, easterly jet disrupted by the mountains to the south of Hong Kong International Airport, and outbreak of monsoon surge that produces a low-level northeasterly jet. The Doppler Light Detection and Ranging (LIDAR) systems are found to capture the corresponding windshear features very well, e.g., consistent with pilot reports and flight data. They are useful in providing timely alert to the aircraft. In particular, the LIDAR captures a double jet structure in the atmospheric boundary layer for the easterly wind case, which has not been reported in the literature before. The physical mechanism for the occurrence of the double jet is yet to be revealed. Moreover, the performance of a high spatial resolution (200 m) numerical weather prediction (NWP) model in predicting the jet and the associated low-level windshear is studied. The model is found to provide reasonable prediction of the windshear features at a few hours ahead, and, for the cases studied, shows skills in providing timely alerts to the aircraft.

An Atmospheric Correction Using High Resolution Numerical Weather Prediction Models for Satellite-Borne Single-Channel Mid-Wavelength and Thermal Infrared Imaging Sensors

This paper presents a single-channel atmospheric correction method for remotely sensed infrared (wavelength of 3–15 μm) images with various observation angles. The method is based on basic radiative transfer equations with a simple absorption-focused regression model to calculate the optical thickness of each atmospheric layer. By employing a simple regression model and re-organization of atmospheric profiles by considering viewing geometry, the proposed method conducts atmospheric correction at every pixel of a numerical weather prediction model in a single step calculation. The Visible Infrared Imaging Radiometer Suite (VIIRS) imaging channel (375 m) I4 (3.55~3.93 μm) and I5 (10.50~12.40 μm) bands were used as mid-wavelength and thermal infrared images to demonstrate the effectiveness of the proposed single-channel atmospheric correction method. The estimated sea surface temperatures (SSTs) obtained by the proposed method with high resolution numerical weather prediction models were compared with sea-truth temperature data from ocean buoys, multichannel-based SST products from VIIRS/MODIS, and results from MODerate resolution atmospheric TRANsmission 5 (MODTRAN 5), for validation. High resolution (1.5 km and 12 km) numerical weather prediction (NWP) models distributed by the Korea Meteorological Administration (KMA) were employed as input atmospheric data. Nighttime SST estimations with the I4 band showed a root mean squared error (RMSE) of 0.95 °C, similar to that of the VIIRS product (RMSE: 0.92 °C) and lower than that of the MODIS product (RMSE: 1.74 °C), while estimations with the I5 band showed an RMSE of 1.81 °C. RMSEs from MODTRAN simulations were similar (within 0.2 °C) to those of the proposed method (I4: 0.81 °C, I5: 1.67 °C). These results demonstrated the competitive performance of a regression-based method using high-resolution numerical weather prediction (NWP) models for atmospheric correction of single-channel infrared imaging sensors.

Observations and Models of Turbulence During Heat Events in Complex Urban Coastal Regions Using Multiple Doppler Wind Lidars

Multiple Doppler Lidars have been co-operated in the NYC region during the summer of 2018 to provide detailed observations of the turbulent atmosphere especially during heat wave events. The co-operating Doppler Lidar observations allow for mean flows to be distinguished from complex flows so that a better understanding of the transport of air masses can be provided to investigate the fidelity of high resolution numerical weather prediction models that are being developed to interpret and model turbulence during such events.

Improving wintertime low level cloud forecasts in a high resolution numerical weather prediction model

Improving wintertime low level cloud forecasts in a high resolution numerical weather prediction model

Data compression in the presence of observational error correlations

Numerical weather prediction (NWP) models are moving towards km-scale (and smaller) resolutions in order to forecast high-impact weather. As the resolution of NWP models increase the need for high-resolution observations to constrain these models also increases. A major hurdle to the assimilation of dense observations in NWP is the presence of non-negligible observation error correlations (OECs). Despite the difficulty in estimating these error correlations, progress is being made, with centres around the world now explicitly accounting for OECs in a variety of observation types. This paper explores how to make efficient use of this potentially dramatic increase in the amount of data available for assimilation. In an idealised framework it is illustrated that as the length-scales of the OECs increase the scales that the analysis is most sensitive to the observations become smaller. This implies that a denser network of observations is more beneficial with increasing OEC length-scales. However, the computational and storage burden associated with such a dense network may not be feasible. To reduce the amount of data, a compression technique based on retaining the maximum information content of the observations can be used. When the OEC length-scales are large (in comparison to the prior error correlations), the data compression will select observations of the smaller scales for assimilation whilst throwing out the larger scale information. In this case it is shown that there is a discrepancy between the observations with the maximum information and those that minimise the analysis error variances. Experiments are performed using the Ensemble Kalman Filter and the Lorenz-1996 model, comparing different forms of data reduction. It is found that as the OEC length-scales increase the assimilation becomes more sensitive to the choice of data reduction technique.

Performance of super high resolution numerical weather prediction model in forecasting terrain‐disrupted airflow at the Hong Kong International Airport: case studies

ABSTRACTHong Kong International Airport (HKIA) is situated in an area of complex terrain. Mountains near the airport can lead to terrain‐disrupted airflow disturbances, which may bring about hazardous weather phenomena for aviation, such as low‐level windshear and turbulence. Timely forecasting of such airflow disturbances would be useful in providing early warnings to the pilots. The Hong Kong Observatory has been running a super high resolution numerical weather prediction model suite, the aviation model (AVM), since late 2013, with spatial resolution up to 200 m immediately around HKIA. After testing and tuning over the past year, the performance of the AVM in forecasting terrain‐disrupted airflow at the airport area is investigated in this study. Two typical cases of such complex airflow are considered, Foehn wind and mountain wave. The AVM is found to capture successfully some salient features of the airflow as observed around HKIA through comparison with light detection and ranging (LIDAR) and weather buoy observations. The model also helps in shedding new light on the airflow from the vertical cross sections of the winds across the mountains. Limitations of the AVM are also discussed.

The London Model: forecasting fog at 333 m resolution

A very high resolution numerical weather prediction model is nested inside the Met Office's main United Kingdom forecast model to investigate whether further enhancements to resolution provide any benefit for fog forecasting. The London Model shows similar performance to its lower resolution equivalent at short lead times, but improved performance at longer lead times and an improved frequency bias of forecast fog events. Differences in the model cloud parametrization are the key reason for the differing behaviour, leading to systematically less cloud, colder night time minimum temperatures and therefore more fog in the London Model. Benefit of the enhanced resolution is also found, via an improved representation of how orographic variability enhances turbulence in the stable boundary layer.

Comparison of numerical weather prediction based deterministic and probabilistic wind resource assessment methods

Numerical weather prediction (NWP) models have been widely used for wind resource assessment. Model runs with higher spatial resolution are generally more accurate, yet extremely computational expensive. An alternative approach is to use data generated by a low resolution NWP model, in conjunction with statistical methods. In order to analyze the accuracy and computational efficiency of different types of NWP-based wind resource assessment methods, this paper performs a comparison of three deterministic and probabilistic NWP-based wind resource assessment methodologies: (i) a coarse resolution (0.5°×0.67°) global reanalysis data set, the Modern-Era Retrospective Analysis for Research and Applications (MERRA); (ii) an analog ensemble methodology based on the MERRA, which provides both deterministic and probabilistic predictions; and (iii) a fine resolution (2-km) NWP data set, the Wind Integration National Dataset (WIND) Toolkit, based on the Weather Research and Forecasting model. Results show that: (i) as expected, the analog ensemble and WIND Toolkit perform significantly better than MERRA confirming their ability to downscale coarse estimates; (ii) the analog ensemble provides the best estimate of the multi-year wind distribution at seven of the nine sites, while the WIND Toolkit is the best at one site; (iii) the WIND Toolkit is more accurate in estimating the distribution of hourly wind speed differences, which characterizes the wind variability, at five of the available sites, with the analog ensemble being best at the remaining four locations; and (iv) the analog ensemble computational cost is negligible, whereas the WIND Toolkit requires large computational resources. Future efforts could focus on the combination of the analog ensemble with intermediate resolution (e.g., 10–15km) NWP estimates, to considerably reduce the computational burden, while providing accurate deterministic estimates and reliable probabilistic assessments.

Verification of Forecasts from High Resolution Numerical Weather Prediction Model

Assessment of forecast quality is a critical component for weather model development as well as evaluating the impact on weather sensitive business applications such as renewable energy forecasting, agriculture, insurance etc. This study presents forecast quality results of a high resolution numerical weather model deployed for the country of Brunei at Universiti Brunei Darussalam. We present the monthly accuracy and probability of detection scores for precipitation as well as accuracy scores for Relative Humidity (RH) and Dew Point Temperature (DPT) for the year 2013.

A new snow and ice load map for mechanical design of power lines in Great Britain

A new high resolution map of extreme snow and ice loads has been produced. Wet snow accumulations on overhead power lines are estimated using observations from a network of surface synoptic weather stations, while areas of severe in-cloud icing (rime icing) are identified from high resolution numerical weather prediction model simulations. Ice loads at 50year return period were estimated using the “Peaks-Over-Threshold” (POT) method and interpolated to a high resolution map using regression kriging. The results indicate a significant variation with elevation and latitude, with the highest loads expected in the wettest parts of the Scottish Highlands. In-cloud icing occurs mainly above 300–400m and is the dominant icing type above 400–600m depending on the location. The final icing map demonstrates how state of the art atmospheric icing models can be successfully utilized to estimate the icing climate even when only very limited measurements of actual icing are available.