Forecast Wind Fields Research Articles

TC-SRNet: Reconstruction and prediction of typhoon high-resolution turbulent fields based on meteorological numerical forecast scale wind fields and deep learning method

Meteorological numerical forecast models can provide a more accurate typhoon wind fields compared to engineering typhoon models due to their incorporate atmospheric multiphysical processes. However, the latest advancements in supercomputing power indicate that the current finest level of real-time weather forecasting typically operates on grid scales ranging from 1 to 4 km, while the convergence of typhoon intensity and turbulent field characteristics occurs at scales as fine as 62–185 m. Therefore, the primary scientific inquiry lies in determining how to achieve high-precision turbulent wind fields while considering the realistic atmospheric multiphysical processes, that means establish a “bridge” of typhoon wind field between kilometer-level and hundred-meter scales. This study investigates the super-resolution reconstruction of wind fields across different horizontal grid scales, utilizing a benchmark wind field at a 62 m horizontal grid scale (ground truth), which is based on a hybrid down-sampling skip connection (DSC)/multi-scale (MS) model. The research findings demonstrate that compared to traditional interpolation methods, the DSC/MS method significantly improves reconstruction accuracy, albeit with some residual high-frequency energy dissipation issues. Additionally, the DSC/MS method currently exhibits better reconstruction performance for 62 m scale wind fields based on kilometer-scale and smaller horizontal grid scales (1 km, 555 m, 185 m), with improved reconstruction as grid scale decreases. However, significant errors are observed in reconstructing fine turbulent fields at 62 m scale based on wind fields at 1.67 km horizontal grid scale. The findings presented in the present study can provide real and high-precision turbulent wind fields for structural wind engineering and wind energy assessment studies, thereby holding significant scientific and engineering application value.

An Ensemble Forecast Wind Field Correction Model with Multiple Factors and Spatio-Temporal Features

Accurate wind speed prediction is significantly important for the full utilization of wind energy resources and the improvement in the economic benefits of wind farms. Because the ensemble forecast takes into account the uncertainty of information about the atmospheric motion, domestic and foreign weather service forecast centers often choose to use the ensemble numerical forecast to achieve the fine forecast of wind speed. However, due to the unavoidable systematic errors of the ensemble numerical forecast model, it is necessary to correct the deviation in the ensemble numerical forecast wind speed. Considering the typical spatio-temporal characteristics of the grid prediction data of the wind field, based on Convolutional Long–Short Term Memory (ConvLSTM) units and attention mechanism, this paper takes the complex and representative North China region as the research area, aiming to reveal the shortcomings of existing deep learning integrated prediction correction models in extracting temporal features of grid prediction data. We propose a new ensemble prediction wind field correction model integrating multi-factor and spatio-temporal characteristics. This model uses reanalyzed land data provided by the European Center for Medium-Range Weather Forecasts as the real data to correct the deviation in the near-surface 10 m wind field data predicted by the regional ensemble numerical prediction model of the China Meteorological Administration. We used the reanalyzed land data provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) as the live data to correct the deviation in the near-surface 10 m wind field data predicted by the regional ensemble numerical forecast model of the China Meteorological Administration (CMA). At the same time, Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) were used as the scoring indicators, and the results of the China Meteorological Administration–Regional Ensemble Prediction System (CMA–REPS) ensemble average, multiple linear regression method correction, Long–Short Term Memory (LSTM) method correction, and U-net (UNET) method correction were compared. Compared with the UNET model method, the experimental results show that when processing the 10 m zonal wind data, 10 m meridional wind data, and 10 m average wind speed data of CMA–REPS 24 h forecasts, the correction results of our model can reduce the RMSE score index by 9.15%, 4.83%, and 7.79%. At the same time, when processing the 48 h and 72 h near-surface 10 m wind field data of the CMA–REPS forecast, our model can improve the prediction accuracy of CMA–REPS near-surface wind forecast data. Therefore, the correction effect of the proposed model in a complex terrain area is evidently better compared to other methods.

An adversarial learning approach to forecasted wind field correction with an application to oil spill drift prediction

Reanalysis wind fields are obtained by correcting the numerically forecasted wind fields based on observation data (i.e., either remote sensing or in-situ observations, or both). Although they are more accurate than forecasted wind fields, reanalysis wind fields tend to have time latencies because they can only be released after the observations are obtained. In order to produce accurate estimates of wind fields in a more timely manner, we develop an adversarial learning approach to correcting forecasted wind fields to be close to reanalysis wind fields. The adversarial learning approach is conducted by an adversarial ConvLSTM network (ACLN) framework that consists of a corrector and a discriminator. The corrector aims at comprehensively capturing both spatial and temporal characteristics of a sequence of forecasted wind fields and producing a corrected forecast wind field for the final field in the sequence. The discriminator tries to distinguish corrected forecast wind field from its corresponding reanalysis wind field. The training of ACLN is alternate between the corrector and the discriminator in an adversarial fashion. The adversarial training mechanism enhances the corrector’s representational power. Additionally, the corrector exploits a residual learning architecture that effectively learns the differences between forecasted wind fields and the corresponding reanalysis wind fields. In this scenario, the well trained corrector requires neither reanalysis wind fields nor observations such that it can correct forecasted wind fields in a timely manner. Furthermore, corrected forecast wind fields are employed for oil spill drift prediction. Extensive experiments validate the effectiveness of the proposed ACLN framework in forecasted wind field correction along with oil spill drift prediction. Compared with ECMWF numerical forecasts, the ACLN achieves an average reduction of 6.2%, 6.9%, and 10.6% in RMSE, MAE, and MAPE, respectively. Compared with a basic drift prediction method, the ACLN based prediction method reduces the error by about 5000 m in the Sanchi oil spill accident. The source codes are available at https://github.com/liyongqingupc/ACLN-WindFieldCorrection, providing a baseline for correcting forecasted wind fields.

地球同步卫星向日葵8号水汽频道推算高层大气运动向量

本文使用日本地球同步卫星向日葵8号上所载7.0微米水汽频道推导大气运动向量，实验的结果显示：以连续两张间隔10分钟的水汽频道观测影像所推导的大气运动向量，与探空风场数据比对可以发现，当推导过程中设定比较严格的质量检定情形下，卫星推导的大气运动向量与探空风场差值的大小，与区域模式6小时预报风场与探空风场差值十分接近，这显示目前以向日葵8号卫星水汽频道所推导的大气运动向量，具有不错的准确性，有机会于未来将此风场数据放进数值预报数据同化系统中。 In this article the water vapor channel at 7.0 micron on geostationary satellite Himawari-8 has been used to derive atmospheric motion vectors. The atmospheric motion vectors were created by two consecutive water vapor channel images with time interval 10 minutes. In order to estimate the errors of atmospheric motion vectors the atmospheric motion vectors were compare to sounding observation. It can be found that the errors of atmospheric motion vectors are close to the errors of 6 hours numerical weather forecast wind field where a strict quality check was adopted in deriving atmospheric motion vector procedure. These results reveal that the atmospheric motion vectors were accurate and have the potential to be used in data assimilation system in the future.

Multi-model ensemble-based probabilistic prediction of tropical cyclogenesis using TIGGE model forecasts

An extended range tropical cyclogenesis forecast model has been developed using the forecasts of global models available from TIGGE portal. A scheme has been developed to detect the signatures of cyclogenesis in the global model forecast fields [i.e., the mean sea level pressure and surface winds (10 m horizontal winds)]. For this, a wind matching index was determined between the synthetic cyclonic wind fields and the forecast wind fields. The thresholds of 0.4 for wind matching index and 1005 hpa for pressure were determined to detect the cyclonic systems. These detected cyclonic systems in the study region are classified into different cyclone categories based on their intensity (maximum wind speed). The forecasts of up to 15 days from three global models viz., ECMWF, NCEP and UKMO have been used to predict cyclogenesis based on multi-model ensemble approach. The occurrence of cyclonic events of different categories in all the forecast steps in the grided region (10 × 10 km2) was used to estimate the probability of the formation of cyclogenesis. The probability of cyclogenesis was estimated by computing the grid score using the wind matching index by each model and at each forecast step and convolving it with Gaussian filter. The proposed method is used to predict the cyclogenesis of five named tropical cyclones formed during the year 2013 in the north Indian Ocean. The 6–8 days advance cyclogenesis of theses systems were predicted using the above approach. The mean lead prediction time for the cyclogenesis event of the proposed model has been found as 7 days.

Three-dimensional visualization of ensemble weather forecasts – Part 2: Forecasting warm conveyor belt situations for aircraft-based field campaigns

Abstract. We present the application of interactive three-dimensional (3-D) visualization of ensemble weather predictions to forecasting warm conveyor belt situations during aircraft-based atmospheric research campaigns. Motivated by forecast requirements of the T-NAWDEX-Falcon 2012 (THORPEX – North Atlantic Waveguide and Downstream Impact Experiment) campaign, a method to predict 3-D probabilities of the spatial occurrence of warm conveyor belts (WCBs) has been developed. Probabilities are derived from Lagrangian particle trajectories computed on the forecast wind fields of the European Centre for Medium Range Weather Forecasts (ECMWF) ensemble prediction system. Integration of the method into the 3-D ensemble visualization tool Met.3D, introduced in the first part of this study, facilitates interactive visualization of WCB features and derived probabilities in the context of the ECMWF ensemble forecast. We investigate the sensitivity of the method with respect to trajectory seeding and grid spacing of the forecast wind field. Furthermore, we propose a visual analysis method to quantitatively analyse the contribution of ensemble members to a probability region and, thus, to assist the forecaster in interpreting the obtained probabilities. A case study, revisiting a forecast case from T-NAWDEX-Falcon, illustrates the practical application of Met.3D and demonstrates the use of 3-D and uncertainty visualization for weather forecasting and for planning flight routes in the medium forecast range (3 to 7 days before take-off).

Temporal, probabilistic mapping of ash clouds using wind field stochastic variability and uncertain eruption source parameters: Example of the 14 April 2010 Eyjafjallajökull eruption

AbstractUncertainty in predictions from a model of volcanic ash transport in the atmosphere arises from uncertainty in both eruption source parameters and the model wind field. In a previous contribution, we analyzed the probability of ash cloud presence using weighted samples of volcanic ash transport and dispersal model runs and a reanalysis wind field to propagate uncertainty in eruption source parameters alone. In this contribution, the probabilistic modeling is extended by using ensemble forecast wind fields as well as uncertain source parameters. The impact on ash transport of variability in wind fields due to unresolved scales of motion as well as model physics uncertainty is also explored. We have therefore generated a weighted, probabilistic forecast of volcanic ash transport with only a priori information, exploring uncertainty in both the wind field and the volcanic source.

Skill assessment of a real-time forecast system utilizing a coupled hydrologic and coastal hydrodynamic model during Hurricane Irene (2011)

Due to the devastating effects of recent hurricanes in the Gulf of Mexico (e.g., Katrina, Rita, Ike and Gustav), the development of a high-resolution, real-time, total water level prototype system has been accelerated. The fully coupled model system that includes hydrology is an extension of the ADCIRC Surge Guidance System (ASGS), and will henceforth be referred to as ASGS-STORM (Scalable, Terrestrial, Ocean, River, Meteorological) to emphasize the major processes that are represented by the system.The ASGS-STORM system incorporates tides, waves, winds, rivers and surge to produce a total water level, which provides a holistic representation of coastal flooding. ASGS-STORM was rigorously tested during Hurricane Irene, which made landfall in late August 2011 in North Carolina. All results from ASGS-STORM for the advisories were produced in real-time, forced by forecast wind and pressure fields computed using a parametric tropical cyclone model, and made available via the web. Herein, a skill assessment, analyzing wind speed and direction, significant wave heights, and total water levels, is used to evaluate ASGS-STORM's performance during Irene for three advisories and the best track from the National Hurricane Center (NHC). ASGS-STORM showed slight over-prediction for two advisories (Advisory 23 and 25) due to the over-estimation of the storm intensity. However, ASGS-STORM shows notable skill in capturing total water levels, wind speed and direction, and significant wave heights in North Carolina when utilizing Advisory 28, which had a slight shift in the track but provided a more accurate estimation of the storm intensity, along with the best track from the NHC. Results from ASGS-STORM have shown that as the forecast of the advisories improves, so does the accuracy of the models used in the study; therefore, accurate input from the weather forecast is a necessary, but not sufficient, condition to ensure the accuracy of the guidance provided by the system. While Irene provided a real-time test of the viability of a total water level system, the relatively insignificant freshwater discharges precludes definitive conclusions about the role of freshwater discharges on total water levels in estuarine zones. Now that the system has been developed, on-going work will examine storms (e.g., Floyd) for which the freshwater discharge played a more meaningful role.

Numerical Simulation and Water Tank Experiment on Atmospheric Wind Field over a Mountain under Neutral Stability Condition

Analysis and forecast atmospheric wind velocity profiles have the important roles in research the dispersion of the pollutants and its effects to surrounding environment. A three-dimensional numerical modeling on atmospheric wind flow over a mountain under neutral condition was processed to attain the velocity contours of atmospheric wind field on complex terrains. Meteorological conditions and geographical conditions including four downwind velocities and two underlying surface roughness characteristics were considered in the present study. Water tank experiment and Particle Image Velocimetry (PIV) technology were used to gain typical wind flow field and validate the simulation results. The numerical results agree well with the experimental results, which prove the numerical methods used in this paper could be the alternative good tool to research and forecast atmospheric wind field.

A study on height reassignment for the AMV products of the FY-2C satellite

A method for using height reassignment to improve the quality of satellite-derived atmospheric motion vectors (AMVs) is presented. The rationale underlying height reassignment is explored, and the technical details are studied by applying three height reassignment schemes that use NCEP reanalysis winds. The quality of the AMVs is generally improved following reassignment, although the magnitude of the improvement differs according to the scheme applied. Scheme 3 provides the best quality and stability, followed by Scheme 1 and Scheme 2. The negative biases in the zonal components of the AMVs decrease from [−5, −4] m s−1 to <−1 m s−1 following reassignment. The meridional components also improve. The AMVs derived from the infrared and water vapor channels improve by 58.7% and 25%, respectively. The feasibility of using Scheme 3 in the operational derivation of AMVs is studied by incorporating the forecast wind field predicted by a T511 medium-range numerical weather prediction (NWP) system. Incorporating the 12-h forecast reduces the negative biases in zonal winds and positive biases in meridional winds retrieved from the water vapor channel, improving the overall quality of the AMVs by 26.7%. Extending the validity period of the forecast field linearly reduces the improvement in retrieved AMVs, but the magnitude of this reduction is small. Incorporating the 120-h forecast field still results in a 13% improvement, although it may eliminate a larger number of AMVs of good quality.

A significant wind shear event leading to aircraft diversion at the Hong Kong international airport

AbstractA significant wind shear event occurred in the early morning of 27 December 2009 at the Hong Kong International Airport (HKIA), leading to the diversion of three aircraft to the airport of Shenzhen (about 39 km to the north‐northwest of HKIA). This paper documents the meteorological observations and predictions for the event. The significant wind shear arose from disruption of the prevailing easterly to southeasterly flow by the complex terrain to the south of the airport. It was well captured by the Doppler Light Detection and Ranging (LIDAR) systems at HKIA and, as such, the aircraft were given sufficient warning about the occurrence of significant wind shear. The headwind changes along the glide paths as measured by the LIDAR were consistent with the aircraft measurements. The observations of the remote‐sensing instruments during the event, including the LIDAR, are described in the paper. Moreover, this paper discusses the possibility of including wind shear forecast information for flight planning purpose for this particular case. The forecast wind field near HKIA based on a high‐resolution numerical weather prediction (NWP) model is described. For the present wind shear event, it appears that the occurrence of terrain‐disrupted airflow near HKIA may be forecast about 6 h ahead. The timing of significant wind shear is, however, not reproduced well in the NWP forecast. Copyright © 2011 Royal Meteorological Society

Numerical simulation of fog using non-hydrostatic NWP model with third-order turbulence closure

Numerical simulation of a fog episode is conducted using the Hong Kong Observatory’s Non-Hydrostatic Model (NHM) to study various physical processes in the formation of a shallow saturated layer. It is found that a third-order turbulence closure model and partial condensation scheme implemented in NHM can improve the parametrization of turbulent mixing and condensation processes. Physically, the formation of fog or low-level clouds over the coastal waters is primarily due to advection of warm and humid maritime air mass over a cooler sea surface. The numerical experiments show that the radiative cooling above the low-level cloud top further enhances the mixing process during nighttime and results in condensation of cloud liquid water near the surface. Differences in the forecast low-level clouds, wind and moisture fields within the boundary layer using the original first-order turbulence closure scheme will also be illustrated.

Predicting wind field in the Bay of Bengal from scatterometer observations using genetic algorithm

A technique based on genetic algorithm (GA) is applied for predicting wind field in the Bay of Bengal (BOB) using satellite scatterometer observations. Empirical orthogonal function (EOF) analysis is used for compressing the spatial variability into a set of eigenmodes. The time series of each principal component (PC) is subjected to singular spectrum analysis (SSA) and GA is applied to the resulting filtered time series. The forecast PCs are weighted by the spatial eigenmodes for computing forecast wind fields. Predictions made up to 5 days in advance are found to be superior to forecast by persistence method.

Modeling and predicting complex space–time structures and patterns of coastal wind fields

AbstractA statistical technique is developed for wind field mapping that can be used to improve either the assimilation of surface wind observations into a model initial field or the accuracy of post‐processing algorithms run on meteorological model output. The observed wind field at any particular location is treated as a function of the true (but unknown) wind and measurement error. The wind field from numerical weather prediction models is treated as a function of a linear and multiplicative bias and a term which represents random deviations with respect to the true wind process. A Bayesian approach is taken to provide information about the true underlying wind field, which is modeled as a stochastic process with a non‐stationary and non‐separable covariance. The method is applied to forecast wind fields from a widely used mesoscale numerical weather prediction (NWP) model (MM5). The statistical model tests are carried out for the wind speed over the Chesapeake Bay and the surrounding region for 21 July 2002. Coastal wind observations that have not been used in the MM5 initial conditions or forecasts are used in conjunction with the MM5 forecast wind field (valid at the same time that the observations were available) in a post‐processing technique that combined these two sources of information to predict the true wind field. Based on the mean square error, this procedure provides a substantial correction to the MM5 wind field forecast over the Chesapeake Bay region. Copyright © 2005 John Wiley &amp; Sons, Ltd.

Forecasting and Diagnostic Analysis of Plume Transport around a Power Plant

A nonreactive Lagrangian atmospheric diffusion model is used for the simulation of SO 2 concentration around the As Pontes 1400-MW power plant located in northwestern Spain. This diffusion model has two kinds of input: 1) diagnostic wind fields from real measurements and 2) forecast wind fields from a 24-h mesoscale prediction. This model-based system is applied for a particular day around the As Pontes 1400-MW power plant, which is a coal-fired power plant. The shape of estimated and forecast plumes are compared, and the meteorological prediction results are analyzed.

The accuracy of plume trajectories forecast using the U.K. Meteorological Office operational forecasting models and their sensitivity to calculation schemes

Studies into the various errors associated with long-range 950 mb trajectories computed using the Meteorological Office numerical weather prediction suite are presented. In the absence of observational data, trajectory error is measured using a verification computed from the model analysed and short-period forecast wind fields. While it is safest to assume that errors determined in this way are the minimum to be expected in relation to “true” trajectories, they nonetheless provide a useful diagnostic of model performance under different synoptic conditions and for trajectory releases at different stages of the forecast. Absolute and directional errors are analysed and the effect of altering the trajectory calculation schemes considered. The possible implications of the results for acid precipitation control strategies are discussed in some detail.

Certain aspects on the realization of primitive equations Forecasting model in the low latitudes

A number of numerical experiments are carried out with a barotropic primitive equations (PE) model for forecasting the tropical systems. A systematic approach has been made to solve some of the important problems arising in the process of integrating such a simple PE model in the low latitudes. Emphasis has been given to the initial data problem in the tropics. In order to study the influence of dynamical initialization and filtering techniques on the forecasting model and to study their smoothing characteristics, the spectral decomposition of kinetic energy of initial, initialized, forecast and actual wind fields, is carried out. Further, the improvement in the efficiency of the model by incorporating these techniques is demonstrated. Finally, this model is applied to forecast the movement of tropical cyclones and a monsoon depression in the Bay of Bengal and inspite of the constraints of the model, the results are quite encouraging.&#x0D; ]

SASS Wind Ambiguity Removal by Direct Minimization

Abstract A gridded surface wind analysis is obtained by minimizing an objective function, the magnitude of which measures the error made by the gridded analysis in fitting the SASS wind data, the conventional surface wind observations and the forecast surface wind field. The ambiguity of the SASS winds is then removed by choosing the alias closest to the analyzed wind. Because minimizing the objective function is a problem of nonlinear least-squares, the minimizing gridded analysis is not unique and a good first guess is necessary to assure convergence to a reasonable solution. A good first guess may be generated by performing the analysis in stages. Illustrative results are shown for a limited region in the North Atlantic containing the QE II storm and for a limited amount (∼12 min) of data observed near the synoptic time 1200 GMT 10 September 1978. Within the SASS data swath the resulting gridded analysis is a reasonable representation of the surface wind and is not sensitive to the forecast surface win...

TESTS OF THE EFFECT OF GRID RESOLUTION IN A GLOBAL PREDICTION MODEL

Angular grid spacing resolution near poles effect tests in global prediction model for geophysical fluid dynamics, investigating forecast height and wind fields