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This is a large-scale dataset of weather events, collected from 2,071 airport-based weather stations across the contiguous United States from 2016 to 2022. The current version contains about 8.6 million weather events.
5 days ago · Figure 1: Aurora is a 1.3 billion parameter foundation model for high-resolution forecasting of weather and atmospheric processes. Aurora is a flexible 3D Swin Transformer with 3D Perceiver-based encoders and decoders. At pretraining time, Aurora is optimized to minimize a loss on multiple heterogeneous datasets with different resolutions ...
Numerical Weather Prediction (NWP) data are the most familiar form of weather model data. NWP computer models process current weather observations to forecast future weather.
- The Libraries
- The Dataset/Dataset Exploration
- Preprocessing, Advanced Visualization, Stationarity
- Machine Learning Algorithms
- Forecasting
- Conclusions
The libraries that have been used are the most famous ones for data analysis, plot and mathematical operations (pandas, matplotlib, numpy). Then there are some of them for advanced data visualization (like folium) and some of them are specific libraries for ARIMA models (like statsmodels). Here is the code for the import:
The Dataset is open source and can be found here. If you want to know the cities in your dataset, select them by using this line of pandas: If we want to plot these cities in a world map, we need to slightly change the latitude and longitude. In order to do that, let’s use these few lines of code: And display the cities:
I’ve chosen to isolate Chicago and consider the data of that city to be my dataset. There are no special reasons to do that… I just like Chicago :) . Of course you can use your own city and follow the next steps with your own dataset. With this line we identify the NaN valuesand display them with a pie chart: As they are not a consistent part of th...
Let’s consider the 1992–2013 decade and plot it: Performing the train/test split: Plotting the split: The Machine Learning algorithms are the ARIMA models.These are based on an optimization procedure that adopts the Maximum Likelihood function. The zero-differentiated ARIMA models are considered and evaluated using the AIC. While the first-differen...
Let’s plot the results of the forecasting operation: And now let’s consider the specific predicted zone with the correspondent Uncertainty (the one given by the dataset) and the confidence interval (given by the algorithm): Finally, let’s consider a more readable version of the plot:
These methods are extremely easy to adopt as they don’t require any specific computational power like Deep Learning methods (RNN, CNN … ). Nonetheless, predictions perfectly fit in the error range designed by the dataset itself. It is important to consider that we only have examined monthly average values while it may be interesting to consider dai...
Aug 19, 2020 · Key Points. Benchmarks with strong baselines are a key ingredient for rapid progress on a problem. Here, we define a benchmark for data-driven global, medium-range weather prediction. The data are processed for convenient use in machine learning models, and a quickstart guide is provided.
- Stephan Rasp, Peter D. Dueben, Sebastian Scher, Jonathan A. Weyn, Soukayna Mouatadid, Nils Thuerey
- 157
- 2020
- 19 August 2020
2 days ago · Accurate and rapid weather forecasting and climate modeling are universal goals in human development. While Numerical Weather Prediction (NWP) remains the gold standard, it faces challenges like inherent atmospheric uncertainties and computational costs, especially in the post-Moore era. With the advent of deep learning, the field has been revolutionized through data-driven models. This paper ...
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What is numerical weather prediction (NWP) data?
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What is a weather model based on?
mental principles of numerical weather prediction, including the numerical framework of models, numerical methods, physical parameterization, and data assimilation.
