Numerical Weather Prediction Data Research Articles

Day-ahead Numerical Weather Prediction solar irradiance correction using a clustering method based on weather conditions

Accurate solar irradiance forecasts can make solar power forecasts more reliable, which can help the power grid dispatch reasonably. In practice, Numerical Weather Prediction (NWP) is widely applied in solar irradiance forecasts. In this paper, the statistical NWP Global Horizontal Irradiance (GHI) error analysis shows that the characteristics of NWP GHI error vary obviously under different weather conditions. However, existing correction methods are not designed contrapuntally for different weather conditions, resulting in poor correction performance. To solve this problem, a hybrid method is proposed to get day-ahead correction results for NWP GHI. Specifically, the hybrid method consists of three key parts, including Deep Clustering (DC), Variational Mode Decomposition (VMD), and an Encoder–Decoder based Correction model (EDC). DC is used to categorize the historical samples into three clusters, the input of which is the multi-dimensional information series containing observed data and NWP data. After a feature selection by VMD, the correction models are trained on each cluster respectively. The performance of the proposed model is evaluated with a public dataset and an actual field dataset, and the results demonstrate that the accuracy has been effectively improved by the proposed method compared with other models. In addition, we find that adopting VMD is effective in improving the correction accuracy, and the root mean square error is reduced by 5.70% and 9.32% compared with models without it.

Method for wind power forecasting based on support vector machines optimized and weighted composite gray relational analysis

This study proposes a weighted composite approach for grey relational analysis (GRA) that utilizes a numerical weather prediction (NWP) and support vector machine (SVM). The approach is optimized using an improved grey wolf optimization (IGWO) algorithm. Initially, the dimension of NWP data is decreased by t-distributed stochastic neighbor embedding (t-SNE), then the weight of sample coefficients is calculated by entropy-weight method (EWM), and the weighted grey relational of data points is calculated for different weather numerical time series data. At the same time, a new weighted composite grey relational degree is formed by combining the weighted cosine similarity of NWP values of the historical day and to be measured day. The SVM’s regression power prediction model is constructed by the time series data. To improve the accuracy of the system’s predictions, the grey relational time series data is chosen as the input variable for the SVM, and the influence parameters of the ideal SVM are discovered using the IGWO technique. According to the simulated prediction and analysis based on NWP, it can be observed that the proposed method in this study significantly improves the prediction accuracy of the data. Specifically, evaluation metrics such as root mean squared error (RMSE), regression correlation coefficient (r2), mean absolute error (MAE) and mean absolute percent error (MAPE) all show corresponding enhancements, while the computational burden remains relatively low.

Maritime free space optical communications field test and link budget statistics

During sub-optimal weather, a free-space optical (FSO) link range degrades depending on attenuation (atmospheric extinction) and turbulence effects. The ability to predict the system level performance can be exceedingly challenging as the atmospheric variability in a maritime link can be large and difficult to model. Link budget estimation for FSO systems often takes a nominal view of atmospheric conditions; here, we use statistical atmospheric predictions specific to a geographic area of interest to enable performance trades to be evaluated through link budget analysis. We compare these models to field-collected data to show the utility of the statistical atmospheric analysis in predicting FSO link performance for specific parts of the world. We have performed shore-to-ship FSO communications field tests at 10 Gb/s with links reaching out to a horizon limit over 40 km away in times of moderate extinction to clear weather. We provide further analysis by describing the expected performance of the link using statistical probabilities via cumulative distribution functions of both extinction and turbulence. The atmospheric variability can be determined for nearly any region of interest through the implementation of numerical weather prediction data to calculate the atmospheric performance drivers. These conditions are specifically evaluated for the 2017 Trident Warrior field test off the coast of San Diego, California, USA.

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

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

A new implementation of FLEXPART with Enviro-HIRLAM meteorological input, and a case study during a heavy air pollution event

ABSTRACT We integrated Enviro-HIRLAM (Environment-High Resolution Limited Area Model) meteorological output into FLEXPART (FLEXible PARTicle dispersion model). A FLEXPART simulation requires meteorological input from a numerical weather prediction (NWP) model. The publicly available version of FLEXPART can utilize either ECMWF (European Centre for Medium-range Weather Forecasts) Integrated Forecast System (IFS) forecast or reanalysis NWP data, or NCEP (U.S. National Center for Environmental Prediction) Global Forecast System (GFS) forecast or reanalysis NWP data. The primary benefits of using Enviro-HIRLAM are that it runs at a higher resolution and accounts for aerosol effects in meteorological fields. We compared backward trajectories generated with FLEXPART using Enviro-HIRLAM (both with and without aerosol effects) to trajectories generated using NCEP GFS and ECMWF IFS meteorological inputs, for a case study of a heavy haze event which occurred in Beijing, China in November 2018. We found that results from FLEXPART were considerably different when using different meteorological inputs. When aerosol effects were included in the NWP, there was a small but noticeable difference in calculated trajectories. Moreover, when looking at potential emission sensitivity instead of simply expressing trajectories as lines, additional information, which may have been missed when looking only at trajectories as lines, can be inferred.

TFEformer: A new temporal frequency ensemble transformer for day-ahead photovoltaic power prediction

The accurate prediction of day-ahead Photovoltaic (PV) power can provide technical support for complex solar management systems. This problem involves forecasting a long time series, and although several models have been proposed, the current state of stability and precision leaves much room for improvement. In this study, we introduce a novel Temporal Frequency Ensemble Transformer (TEFformer) designed for day-ahead PV power prediction, which integrates four crucial components: temporal attention, frequency attention, Fourier attention, and weather embedding. Initially, temporal attention is employed to directly model the original PV power series, providing fundamental insights. Fourier attention is devised to analyze the trend series derived from the original data in the frequency domain. Furthermore, frequency attention dissects seasonal variations into real and imaginary components for independent study, offering practical insights. Finally, numerical weather prediction data serves as future covariates, augmenting environmental information. Our experiments demonstrate that the TFEformer outperforms 9 advanced baseline models, showcasing its superior accuracy and robustness. Furthermore, in contrast to mainstream hybrid models that involve decomposition and frequency analysis in preprocessing, our proposed model exhibits higher efficiency.

Developing a statistical approach of evaluating daily maximum and minimum temperature observations from third‐party automatic weather stations in Australia

AbstractThird‐party automatic weather stations (TPAWS) provide a compelling data source for scientists and practitioners to observe and estimate more accurate fine‐scale atmospheric conditions, including daily maximum and minimum temperature (denoted as Tmax and Tmin, respectively), than the current primary weather observation network can offer. Several uncertainties and errors arise in data from TPAWS as the quality control applied to these stations may be inadequate or ad hoc. In this study, we develop a statistical approach to evaluate the quality of daily Tmax and Tmin observations collected from TPAWS in Australia. Our approach compares a target observation with multiple types of reliable reference data, including neighbouring primary weather observations from the official Bureau of Meteorology of Australia stations, Australian Gridded Climate Data, and numerical weather prediction data. Guided by the operational requirements in terms of automation, interpretability, and simplicity as well as expandability, a separate test is formed for each type of reference data and then all the individual tests are merged to generate a single result based on a Gaussian mixture model that is used to provide the final overall assessment for each TPAWS observation. The overall assessment is made in the form of a p‐value‐based confidence score that measures the difference between the target observation and trusted reference data. Our method is validated by synthetic datasets based on high‐quality observations and is also applied to daily Tmax and Tmin observations from 184 TPAWS owned by the Department of Primary Industries and Regional Development of Western Australia. The framework can be readily applied to different regions with different reliable or trusted data sources.

Assessment of Urban Wind Potential and the Stakeholders Involved in Energy Decision-Making

Urban wind energy has emerged as an attractive source of distributed generation in cities to achieve sustainable development goals. The advancement in technologies for the use of urban wind energy has offered an alternative for the decarbonization of cities and the energy transition. The objectives of this work are (1) to identify the potential of wind energy through numerical weather prediction (NWP) data tools and (2) to identify the roles and responsibilities of the stakeholders involved in the decision-making process. A methodology was developed in two phases and applied to a case study in the Dominican Republic. The first phase consisted of estimating the wind energy potential for the 32 provinces at a height of 10 m using open access NWP tools provided by NASA. In the second phase, 28 stakeholders were identified through snowball sampling. The Responsible, Accountable, Consulted, and Informed (RACI) matrix tool was applied to identify the roles of the 28 institutions addressed at the country level as relevant in the decision-making process for the energy sector. The annual average wind speed and energy potential for each province were determined. It was found that 24 provinces have poor potentials, below <4.5 m/s. In the northwest and east is where there is the greatest potential, between 4.83 and 6.63 m/s. The population density was established, and it was observed that the provinces with greater potential are less densely populated. Through 59 interviews, 28 institutions were identified and evaluated due to their relevance in decision making for the implementation of energy projects. According to the RACI matrix, the Ministry of Energy and Mines has been categorized as “A”, electricity distribution companies as “R”, energy associations and universities as “C”, and educational and justice institutions as “I”.

A Wind Field Reconstruction from Numerical Weather Prediction Data Based on a Meteo Particle Model

In the present work, a methodology for wind field reconstruction based on the Meteo Particle model (MPM) from numerical weather prediction (NWP) data is presented. The development of specific wind forecast services is a challenging research topic, in particular for what concerns the availability of accurate local weather forecasts in highly populated areas. Currently, even if NWP limited area models (LAMs) are run at a spatial resolution of about 1 km, this level of information is not sufficient for many applications; for example, to support drone operation in urban contexts. The coupling of the MPM with the NWP limited area model COSMO has been implemented in such a way that the MPM reads the NWP output over a selected area and provides wind values for the generic point considered for the investigation. The numerical results obtained reveal the good behavior of the method in reproducing the general trend of the wind speed, as also confirmed by the power spectra analysis. The MPM is able to step over the intrinsic limitations of the NWP model in terms of the spatial and temporal resolution, even if the MPM inherits the bias that inevitably affects the COSMO output.

Hourly Photovoltaic Production Prediction Using Numerical Weather Data and Neural Networks for Solar Energy Decision Support

The day-ahead photovoltaic electricity forecast is increasingly necessary for grid operators and for energy communities. In the present work, the hourly PV production is estimated using two models based on feedforward neural networks (FFNNs). Most existing models use solar radiation as an input. Instead, the models proposed here use numerical weather prediction (NWP) data: ambient temperature, relative humidity, and wind speed, which are easily accessible to anyone. The first proposed model uses multiple inputs, while the second one uses only the necessary information. A sensitivity analysis allows for the identification of the variables that are most influential on the estimation accuracy. This study concludes that the hourly temperature trend is the most important variable for prediction. The models’ accuracy was tested using experimental and NWP data, with the second model having almost the same accuracy as the first despite using fewer input data. The results obtained using experimental data as inputs show a coefficient of determination (R2) of 0.95 for the hourly PV energy produced. The RMSE is about 6.4% of the panel peak power. When NWP data are used as inputs, R2 is 0.879 and the RMSE is 10.5%. These models can have a significant impact by enabling individual energy communities to make their forecasts, resulting in energy savings and increased self-consumed energy.

A Hybrid Model for Long-Term Wind Power Forecasting Utilizing NWP Subsequence Correction and Multi-Scale Deep Learning Regression Methods

The accuracy of long-term wind power forecasting (WPF) is crucial for the efficient operation of grid systems. However, wind power generation is highly stochastic and intermittent due to the influence of weather, which makes long-term WPF less effective. Numerical weather prediction (NWP) data contains valuable weather forecast information, which can mitigate the negative effects of stochastic weather fluctuations on WPF. However, the accuracy of NWP data decreases over time, and multiple NWP data can have redundancy and errors that make it challenging to extract valid information. Reducing the variety and errors in NWP data and using more effective information extraction methods are essential for improving long-term WPF performance. In this article, we propose a novel long-term WPF hybrid model that corrects NWP wind speed and uses multi-scale deep learning regression prediction to exclude excessive NWP data. We use only the corrected NWP wind speed data to establish a nonlinear mapping relationship with actual power data. The validation case study shows that our proposed model reduces the mean squared error (MSE) and mean absolute error (MAE) by 65.0% and 43.8%, respectively, compared to the current state-of-the-art time series forecasting model in a seven-day forecasting scenario.

Evaluation of wind resource uncertainty on energy production estimates for offshore wind farms

Wind farm design generally relies on the use of historical data and analytical wake models to predict farm quantities, such as annual energy production (AEP). Uncertainty in input wind data that drive these predictions can translate to significant uncertainty in output quantities. We examine two sources of uncertainty stemming from the level of description of the relevant meteorological variables and the source of the data. The former comes from a standard practice of simplifying the representation of the wind conditions in wake models, such as AEP estimates based on averaged turbulence intensity (TI), as opposed to instantaneous. Uncertainty from the data source arises from practical considerations related to the high cost of in situ measurements, especially for offshore wind farms. Instead, numerical weather prediction (NWP) modeling can be used to characterize the more exact location of the proposed site, with the trade-off of an imperfect model form. In the present work, both sources of input uncertainty are analyzed through a study of the site of the future Vineyard Wind 1 offshore wind farm. This site is analyzed using wind data from LiDAR measurements located 25 km from the farm and NWP data located within the farm. Error and uncertainty from the TI and data sources are quantified through forward analysis using an analytical wake model. We find that the impact of TI error on AEP predictions is negligible, while data source uncertainty results in 0.4%–3.7% uncertainty over feasible candidate hub heights for offshore wind farms, which can exceed interannual variability.

An improved typhoon monitoring model based on precipitable water vapor and pressure

The potential of monitoring the movement of typhoons using the precipitable water vapor (PWV) has been confirmed. However, monitoring the movement of typhoon is focused on PWV, making it difficult to describe the movement of a typhoon in detail minutely and resulting in insufficient accuracy. Hence, based on PWV and meteorological data, we propose an improved typhoon monitoring mode. First, the European Centre for Medium-Range Weather Forecasts Reanalysis 5-derived PWV (ERA5-PWV) and the Global Navigation Satellite System-derived PWV (GNSS-PWV) were compared with the reference radiosonde PWV (RS-PWV). Then, using the PWV and atmospheric parameters derived from ERA5, we discussed the anomalous variations of PWV, pressure (P), precipitation, and wind speed during different typhoons. Finally, we compiled a list of critical factors related to typhoon movement, PWV and P. We developed an improved multi-factor typhoon monitoring mode (IMTM) with different models (i.e., IMTM-I and IMTM-II) in different cases with a higher density of GNSS observation or only Numerical Weather Prediction (NWP) data. The IMTM was evaluated through the reference movement speeds of HATO and Mangkhut from the China Meteorological Observatory Typhoon Network (CMOTN). The results show that the root mean square (RMS) of the IMTM-I is 1.26 km/h based on ERA5-P and ERA5-PWV, and the absolute bias values are mostly within 2 km/h. Compared with the models considering the single factor ERA5-P/ERA5-PWV, the RMS of the IMTM-I is improved by 26.3% and 38.5%, respectively. The IMTM-II model manifests a residual of only 0.35 km/h. Compared with the single-factor model based on GNSS-PWV/P, the residual of the IMTM-II model is reduced by 90.8% and 84.1%, respectively. These results propose that the typhoon movement monitoring approach combining PWV and P has evident advantages over the single-factor model and is expected to supplement traditional typhoon monitoring.

A Structured Graph Neural Network for Improving the Numerical Weather Prediction of Rainfall

AbstractThe current challenges of numerical weather prediction (NWP) of rainfall mainly stem from the complex and multiscale nature of rainfall. In recent years, as observation capability improved worldwide, there has been an increased feasibility to use data‐driven models to enhance forecasting performance with rainfall observation. Compared to traditional statistical and machine learning models, deep‐learning models show considerable promise in capturing the spatial‐temporal features of weather processes from multiple predictors, but the convolution‐based feature extractor is suboptimal due to the linear nature of convolution kernels. In this study, a multilevel forecasting model is proposed to forecast each rainfall level, in which each submodel adopts a graph neural network for feature extraction. Spatial and temporal propagation functions based on grid structure are designed to explicitly represent feature fusion and propagation of multiple predictors across multiple scales. On model training, a weight setting strategy that balances the impact of samples with different rainfall values on the total training loss is proposed, and a soft classification label is designed to convert observed rainfall into the probability of rainfall above each threshold. The proposed model was trained and validated on NWP data provided by European Center for Medium‐Range Weather Forecast, and results show significant improvement over the NWP in terms of threat score (TS) and Heidke Skill Score (HSS) scores. Analysis of the forecast results for two typical rainfall processes also illustrates that the proposed method can predict rainfall with more reasonable location and intensity.

Thunderstorm prediction during pre-tactical air-traffic-flow management using convolutional neural networks

Thunderstorms can be a large source of disruption for European air-traffic management causing a chaotic state of operation within the airspace system. In current practice, air-traffic managers are provided with imprecise forecasts which limit their ability to plan strategically. As a result, weather mitigation is performed using tactical measures with a time horizon of three hours. Increasing the lead time of thunderstorm predictions to the day before operations could help air-traffic managers plan around weather and improve the efficiency of air-traffic-management operations. Emerging techniques based on machine learning have provided promising results, partly attributed to reduced human bias and improved capacity in predicting thunderstorms purely from numerical weather prediction data. In this paper, we expand on our previous work on thunderstorm forecasting, by applying convolutional neural networks (CNNs) to exploit the spatial characteristics embedded in the weather data. The learning task of predicting convection is formulated as a binary-classification problem based on satellite data. The performance of multiple CNN-based architectures, including a fully-convolutional neural network (FCN), a CNN-based encoder–decoder, a U-Net, and a pyramid-scene parsing network (PSPNet) are compared against a multi-layer-perceptron (MLP) network. Our work indicates that CNN-based architectures improve the performance of point-prediction models, with a fully-convolutional neural-network architecture having the best performance. Results show that CNN-based architectures can be used to increase the prediction lead time of thunderstorms. Lastly, a case study illustrating the applications of convection-prediction models in an air-traffic-management setting is presented.

Development and assessment of artificial neural network models for direct normal solar irradiance forecasting using operational numerical weather prediction data

Accurate operational solar irradiance forecasts are crucial for better decision making by solar energy system operators due to the variability of resource and energy demand. Although numerical weather prediction (NWP) models can forecast solar radiation variables, they often have significant errors, particularly in the direct normal irradiance (DNI), which is especially affected by the type and concentration of aerosols and clouds. This paper presents a method based on artificial neural networks (ANN) for generating operational DNI forecasts using weather and aerosol forecasts from the European Center for Medium-range Weather Forecasts (ECMWF) and the Copernicus Atmospheric Monitoring Service (CAMS), respectively. Two ANN models were designed: one uses as input the predicted weather and aerosol variables for a given instant, while the other uses a period of the improved DNI forecasts before the forecasted instant. The models were developed using observations for the location of Évora, Portugal, resulting in 10 min DNI forecasts that for day 1 of forecast horizon showed an improvement over the downscaled original forecasts regarding R2, MAE and RMSE of 0.0646, 21.1 W/m2 and 27.9 W/m2, respectively. The model was also evaluated for different timesteps and locations in southern Portugal, providing good agreement with experimental data.

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.

Estimating Individual Radio Occultation Uncertainties Using the Observations and Environmental Parameters

Abstract Estimation of uncertainties (random error statistics) of radio occultation (RO) observations is important for their effective assimilation in numerical weather prediction (NWP) models. Average uncertainties can be estimated for large samples of RO observations and these statistics may be used for specifying the observation errors in NWP data assimilation. However, the uncertainties of individual RO observations vary, and so using average uncertainty estimates will overestimate the uncertainties of some observations and underestimate those of others, reducing their overall effectiveness in the assimilation. Several parameters associated with RO observations or their atmospheric environments have been proposed to estimate individual RO errors. These include the standard deviation of bending angle (BA) departures from either climatology in the upper stratosphere and lower mesosphere (STDV) or the sample mean between 40 and 60 km (STD4060), the local spectral width (LSW), and the magnitude of the horizontal gradient of refractivity (|∇HN|). In this paper we show how the uncertainties of two RO datasets, COSMIC-2 and Spire BA, as well as their combination, vary with these parameters. We find that the uncertainties are highly correlated with STDV and STD4060 in the stratosphere, and with LSW and |∇HN| in the lower troposphere. These results suggest a hybrid error model for individual BA observations that uses an average statistical model of RO errors modified by STDV or STD4060 above 30 km, and LSW or |∇HN| below 8 km. Significance Statement These results contribute to the understanding of the sources of uncertainties in radio occultation observations. They could be used to improve the effectiveness of these observations in their assimilation into numerical weather prediction and reanalysis models by improving the estimation of their observational errors.

Transferable wind power probabilistic forecasting based on multi-domain adversarial networks

Due to the limited availability of historical data, forecasting the wind power of newly-built wind farms poses a significant challenge. Transfer learning methods offer a potential solution by transferring knowledge from the source domain to the target domain, thereby reducing the data requirements for wind power forecasting. However, the difference in dataset distribution between the source and target domains causes domain shift. To address this issue, we propose a multi-domain adversarial network (MDAN). MDAN uses multi-domain datasets for adversarial learning, which maps numerical weather prediction (NWP) data to an adaptive latent space, thereby reducing domain shift. Additionally, we propose a data fusion-based incremental learning method to mitigate catastrophic forgetting. Through comprehensive case studies, MDAN provides accurate short-term wind power probabilistic forecasts in zero-shot learning. The incremental learning method enhances forecast accuracy in few-shot learning. Moreover, visualization analysis using t-stochastic neighbor embedding (t-SNE) shows that MDAN successfully reduces the domain shift between the source and target domains.

Numerical model generation of test frames for pre-launch studies of EarthCARE's retrieval algorithms and data management system

Abstract. The Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) satellite consists of active and passive sensors whose observations will be acted on by an array of retrieval algorithms. EarthCARE's retrieval algorithms have undergone pre-launch verifications within a virtual observing system that consists of 3D atmosphere–surface data produced by the Global Environmental Multiscale (GEM) numerical weather prediction (NWP) model, as well as instrument simulators that when applied to NWP data yield synthetic observations for EarthCARE's four sensors. Retrieval algorithms operate on the synthetic observations, and their estimates go into radiative transfer models that produce top-of-atmosphere solar and thermal broadband radiative quantities, which are compared to synthetic broadband measurements, thus mimicking EarthCARE's radiative closure assessment. Three high-resolution test frames were simulated; each measures ∼6200 km along-track by 200 km across-track. Horizontal grid spacing is 250 m, and there are 57 atmospheric layers up to 10 mbar. The frames span wide ranges of conditions and extend over (i) Greenland to the Caribbean, crossing a cold front off Nova Scotia; (ii) Nunavut to Baja California, crossing over Colorado's Rocky Mountains; and (iii) the central equatorial Pacific Ocean, which includes a mesoscale convective system. This report discusses how the test frames were produced and presents their key geophysical features. All data are publicly available and, owing to their high-resolution, could be used to simulate observations for other measurement systems.