We present a parsimonious deep learning weather prediction model on the
Hierarchical Equal Area isoLatitude Pixelization (HEALPix) to forecast seven
atmospheric variables for arbitrarily long lead times on a global approximately
110 km mesh at 3h time resolution. In comparison to state-of-the-art machine
learning weather forecast models, such as Pangu-Weather and GraphCast, our
DLWP-HPX model uses coarser resolution and far fewer prognostic variables. Yet,
at one-week lead times its skill is only about one day behind the
state-of-the-art numerical weather prediction model from the European Centre
for Medium-Range Weather Forecasts. We report successive forecast improvements
resulting from model design and data-related decisions, such as switching from
the cubed sphere to the HEALPix mesh, inverting the channel depth of the U-Net,
and introducing gated recurrent units (GRU) on each level of the U-Net
hierarchy. The consistent east-west orientation of all cells on the HEALPix
mesh facilitates the development of location-invariant convolution kernels that
are successfully applied to propagate global weather patterns across our
planet. Without any loss of spectral power after two days, the model can be
unrolled autoregressively for hundreds of steps into the future to generate
stable and realistic states of the atmosphere that respect seasonal trends, as
showcased in one-year simulations. Our parsimonious DLWP-HPX model is
research-friendly and potentially well-suited for sub-seasonal and seasonal
forecasting.

我们提出了一种简约的深度学习天气预测模型，使用 Hierarchical Equal Area isoLatitude Pixelization（HEALPix）在全球近似 110 公里网格上，以 3 小时时间分辨率预测任意长时间的七个大气变量。与现有的机器学习天气预测模型（如 Pangu-Weather 和 GraphCast）相比，我们的 DLWP-HPX 模型使用更粗的分辨率和更少的预报变量。然而，在一周的提前时间内，其技能仅比欧洲中期天气预报中心最先进的数值天气预报模型晚了一天。我们报告了模型设计和与数据相关的决策带来的连续预测改进，例如从立方球切换到 HEALPix 网格，反演 U-Net 的通道深度，并在 U-Net 层次结构的每个级别引入门控循环单元（GRU）。HEALPix 网格上所有单元格的一致东西方向有助于开发位置不变的卷积核，成功地将全球气象模式传播到地球上。该模型在两天后没有任何频谱损失，可以自回归地展开数百步，生成稳定且逼真的大气状态，尊重季节趋势，在一年模拟中展示了这一点。我们简约的 DLWP-HPX 模型具有很好的研究性质，可能非常适合亚季节和季节性的预测。

采用 HEALPix 网格推进简约深度学习天气预测

Advancing Parsimonious Deep Learning Weather Prediction using the  HEALPix Mesh

We present an ensemble prediction system using a Deep Learning Weather
Prediction (DLWP) model that recursively predicts key atmospheric variables
with six-hour time resolution. This model uses convolutional neural networks
(CNNs) on a cubed sphere grid to produce global forecasts. The approach is
computationally efficient, requiring just three minutes on a single GPU to
produce a 320-member set of six-week forecasts at 1.4{\deg} resolution.
Ensemble spread is primarily produced by randomizing the CNN training process
to create a set of 32 DLWP models with slightly different learned weights.
Although our DLWP model does not forecast precipitation, it does forecast total
column water vapor, and it gives a reasonable 4.5-day deterministic forecast of
Hurricane Irma. In addition to simulating mid-latitude weather systems, it
spontaneously generates tropical cyclones in a one-year free-running
simulation. Averaged globally and over a two-year test set, the ensemble mean
RMSE retains skill relative to climatology beyond two-weeks, with anomaly
correlation coefficients remaining above 0.6 through six days. Our primary
application is to subseasonal-to-seasonal (S2S) forecasting at lead times from
two to six weeks. Current forecast systems have low skill in predicting one- or
2-week-average weather patterns at S2S time scales. The continuous ranked
probability score (CRPS) and the ranked probability skill score (RPSS) show
that the DLWP ensemble is only modestly inferior in performance to the European
Centre for Medium Range Weather Forecasts (ECMWF) S2S ensemble over land at
lead times of 4 and 5-6 weeks. At shorter lead times, the ECMWF ensemble
performs better than DLWP.

使用深度学习气象预测模型的集成预测系统通过 CNNs 来预测关键的大气变量，并产生出全球 FORECASTS, 该方法需要最小的计算资源来产生预测，DLWP 集合在 4-6 周的前导时间内表现良好，但在更短的前导时间内表现稍差与 ECMWF 相比。