24 research outputs found
TRU-NET: A Deep Learning Approach to High Resolution Prediction of Rainfall
Climate models (CM) are used to evaluate the impact of climate change on the
risk of floods and strong precipitation events. However, these numerical
simulators have difficulties representing precipitation events accurately,
mainly due to limited spatial resolution when simulating multi-scale dynamics
in the atmosphere. To improve the prediction of high resolution precipitation
we apply a Deep Learning (DL) approach using an input of CM simulations of the
model fields (weather variables) that are more predictable than local
precipitation. To this end, we present TRU-NET (Temporal Recurrent U-Net), an
encoder-decoder model featuring a novel 2D cross attention mechanism between
contiguous convolutional-recurrent layers to effectively model multi-scale
spatio-temporal weather processes. We use a conditional-continuous loss
function to capture the zero-skewed %extreme event patterns of rainfall.
Experiments show that our model consistently attains lower RMSE and MAE scores
than a DL model prevalent in short term precipitation prediction and improves
upon the rainfall predictions of a state-of-the-art dynamical weather model.
Moreover, by evaluating the performance of our model under various, training
and testing, data formulation strategies, we show that there is enough data for
our deep learning approach to output robust, high-quality results across
seasons and varying regions
Deep learning for quality control of surface physiographic fields using satellite Earth observations
A purposely built deep learning algorithm for the Verification of
Earth-System ParametERisation (VESPER) is used to assess recent upgrades of the
global physiographic datasets underpinning the quality of the Integrated
Forecasting System (IFS) of the European Centre for Medium-Range Weather
Forecasts (ECMWF), which is used both in numerical weather prediction and
climate reanalyses. A neural network regression model is trained to learn the
mapping between the surface physiographic dataset plus the meteorology from
ERA5, and the MODIS satellite skin temperature observations. Once trained, this
tool is applied to rapidly assess the quality of upgrades of the land-surface
scheme. Upgrades which improve the prediction accuracy of the machine learning
tool indicate a reduction of the errors in the surface fields used as input to
the surface parametrisation schemes. Conversely, incorrect specifications of
the surface fields decrease the accuracy with which VESPER can make
predictions. We apply VESPER to assess the accuracy of recent upgrades of the
permanent lake and glaciers covers as well as planned upgrades to represent
seasonally varying water bodies (i.e. ephemeral lakes). We show that for
grid-cells where the lake fields have been updated, the prediction accuracy in
the land surface temperature (i.e mean absolute error difference between
updated and original physiographic datasets) improves by 0.37 K on average,
whilst for the subset of points where the lakes have been exchanged for bare
ground (or vice versa) the improvement is 0.83 K. We also show that updates to
the glacier cover improve the prediction accuracy by 0.22 K. We highlight how
neural networks such as VESPER can assist the research and development of
surface parameterizations and their input physiography to better represent
Earth's surface couples processes in weather and climate models.Comment: 26 pages, 16 figures. Submitted to Hydrology and Earth System
Sciences (HESS
TRU-NET : a deep learning approach to high resolution prediction of rainfall
Climate models (CM) are used to evaluate the impact of climate change on the risk of floods and heavy precipitation events. However, these numerical simulators produce outputs with low spatial resolution that exhibit difficulties representing precipitation events accurately. This is mainly due to computational limitations on the spatial resolution used when simulating multi-scale weather dynamics in the atmosphere. To improve the prediction of high resolution precipitation we apply a Deep Learning (DL) approach using input data from a reanalysis product, that is comparable to a climate modelâs output, but can be directly related to precipitation observations at a given time and location. Further, our input excludes local precipitation, but includes model fields (weather variables) that are more predictable and generalizable than local precipitation. To this end, we present TRU-NET (Temporal Recurrent U-Net), an encoderdecoder model featuring a novel 2D cross attention mechanism between contiguous convolutional-recurrent layers to effectively model multi-scale spatio-temporal weather processes. We also propose a non-stochastic variant of the conditionalcontinuous (CC) loss function to capture the zero-skewed patterns of rainfall. Experiments show that our models, trained with our CC loss, consistently attain lower RMSE and MAE scores than a DL model prevalent in precipitation downscaling and outperform a state-of-the-art dynamical weather model. Moreover, by evaluating the performance of our model under various data formulation strategies, for the training and test sets, we show that there is enough data for our deep learning approach to output robust, high-quality results across seasons and varying regions
A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts
Despite continuous improvements, precipitation forecasts are still not as
accurate and reliable as those of other meteorological variables. A major
contributing factor to this is that several key processes affecting
precipitation distribution and intensity occur below the resolved scale of
global weather models. Generative adversarial networks (GANs) have been
demonstrated by the computer vision community to be successful at
super-resolution problems, i.e., learning to add fine-scale structure to coarse
images. Leinonen et al. (2020) previously applied a GAN to produce ensembles of
reconstructed high-resolution atmospheric fields, given coarsened input data.
In this paper, we demonstrate this approach can be extended to the more
challenging problem of increasing the accuracy and resolution of comparatively
low-resolution input from a weather forecasting model, using high-resolution
radar measurements as a "ground truth". The neural network must learn to add
resolution and structure whilst accounting for non-negligible forecast error.
We show that GANs and VAE-GANs can match the statistical properties of
state-of-the-art pointwise post-processing methods whilst creating
high-resolution, spatially coherent precipitation maps. Our model compares
favourably to the best existing downscaling methods in both pixel-wise and
pooled CRPS scores, power spectrum information and rank histograms (used to
assess calibration). We test our models and show that they perform in a range
of scenarios, including heavy rainfall.Comment: Submitted to JAMES 4/4/2
Improving medium-range ensemble weather forecasts with hierarchical ensemble transformers
Statistical post-processing of global ensemble weather forecasts is revisited
by leveraging recent developments in machine learning. Verification of past
forecasts is exploited to learn systematic deficiencies of numerical weather
predictions in order to boost post-processed forecast performance. Here, we
introduce PoET, a post-processing approach based on hierarchical transformers.
PoET has 2 major characteristics: 1) the post-processing is applied directly to
the ensemble members rather than to a predictive distribution or a functional
of it, and 2) the method is ensemble-size agnostic in the sense that the number
of ensemble members in training and inference mode can differ. The PoET output
is a set of calibrated members that has the same size as the original ensemble
but with improved reliability. Performance assessments show that PoET can bring
up to 20% improvement in skill globally for 2m temperature and 2% for
precipitation forecasts and outperforms the simpler statistical
member-by-member method, used here as a competitive benchmark. PoET is also
applied to the ENS10 benchmark dataset for ensemble post-processing and
provides better results when compared to other deep learning solutions that are
evaluated for most parameters. Furthermore, because each ensemble member is
calibrated separately, downstream applications should directly benefit from the
improvement made on the ensemble forecast with post-processing
Machine learning emulation of 3D cloud radiative effects
Abstract: The treatment of cloud structure in numerical weather and climate models is often greatly simplified to make them computationally affordable. Here we propose to correct the European Centre for MediumâRange Weather Forecasts 1D radiation scheme ecRad for 3D cloud effects using computationally cheap neural networks. 3D cloud effects are learned as the difference between ecRad's fast 1D Tripleclouds solver that neglects them and its 3D SPARTACUS (SPeedy Algorithm for Radiative TrAnsfer through CloUd Sides) solver that includes them but is about five times more computationally expensive. With typical errors between 20% and 30% of the 3D signal, neural networks improve Tripleclouds' accuracy for about 1% increase in runtime. Thus, rather than emulating the whole of SPARTACUS, we keep Tripleclouds unchanged for cloudâfree parts of the atmosphere and 3Dâcorrect it elsewhere. The focus on the comparably small 3D correction instead of the entire signal allows us to improve predictions significantly if we assume a similar signalâtoânoise ratio for both
Bridging observations, theory and numerical simulation of the ocean using machine learning
Progress within physical oceanography has been concurrent with the increasing sophistication of tools available for its study. The incorporation of machine learning (ML) techniques offers exciting possibilities for advancing the capacity and speed of established methods and for making substantial and serendipitous discoveries. Beyond vast amounts of complex data ubiquitous in many modern scientific fields, the study of the ocean poses a combination of unique challenges that ML can help address. The observational data available is largely spatially sparse, limited to the surface, and with few time series spanning more than a handful of decades. Important timescales span seconds to millennia, with strong scale interactions and numerical modelling efforts complicated by details such as coastlines. This review covers the current scientific insight offered by applying ML and points to where there is imminent potential. We cover the main three branches of the field: observations, theory, and numerical modelling. Highlighting both challenges and opportunities, we discuss both the historical context and salient ML tools. We focus on the use of ML in situ sampling and satellite observations, and the extent to which ML applications can advance theoretical oceanographic exploration, as well as aid numerical simulations. Applications that are also covered include model error and bias correction and current and potential use within data assimilation. While not without risk, there is great interest in the potential benefits of oceanographic ML applications; this review caters to this interest within the research community
WeatherBench 2: A benchmark for the next generation of data-driven global weather models
WeatherBench 2 is an update to the global, medium-range (1-14 day) weather
forecasting benchmark proposed by Rasp et al. (2020), designed with the aim to
accelerate progress in data-driven weather modeling. WeatherBench 2 consists of
an open-source evaluation framework, publicly available training, ground truth
and baseline data as well as a continuously updated website with the latest
metrics and state-of-the-art models:
https://sites.research.google/weatherbench. This paper describes the design
principles of the evaluation framework and presents results for current
state-of-the-art physical and data-driven weather models. The metrics are based
on established practices for evaluating weather forecasts at leading
operational weather centers. We define a set of headline scores to provide an
overview of model performance. In addition, we also discuss caveats in the
current evaluation setup and challenges for the future of data-driven weather
forecasting