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Tsuyoshi Thomas Sekiyama
,
Syugo Hayashi
,
Ryo Kaneko
, and
Ken-ichi Fukui

Abstract

Surrogate modeling is one of the most promising applications of deep learning techniques in meteorology. The purpose of this study was to downscale surface wind fields in a gridded format at a much lower computational load. We employed a superresolution convolutional neural network (SRCNN) as a surrogate model and created a 20-member ensemble by training the same SRCNN model with different random seeds. The downscaling accuracy of the ensemble mean remained stable throughout a year and was consistently better than that of the input wind fields. It was confirmed that 1) the ensemble spread was efficiently created and that 2) the ensemble mean was superior to individual ensemble members and 3) robust to the presence of outlier members. Training, validation, and test data for 10 years were computed via our nested mesoscale weather forecast models not derived from public analysis datasets or real observations. The predictands were 1-km gridded surface zonal and meridional winds, of which the domain was defined as a 180 km × 180 km area around Tokyo, Japan. The predictors included 5-km gridded surface zonal and meridional winds, temperature, humidity, vertical gradient of the potential temperature, elevation, and land-to-water ratio as well as 1-km gridded elevation and land-to-water ratio. Although a perfect surrogate of the weather forecast model could not be achieved, the SRCNN downscaling accuracy could likely enable us to apply this approach in high-resolution advection simulations, considering its overwhelmingly high prediction speed.

Open access
Cameron C. Lee
,
Scott C. Sheridan
,
Gregory P. Dusek
, and
Douglas E. Pirhalla

Abstract

With climate change causing rising sea levels around the globe, multiple recent efforts in the United States have focused on the prediction of various meteorological factors that can lead to periods of anomalously high tides despite seemingly benign atmospheric conditions. As part of these efforts, this research explores monthly scale relationships between sea level variability and atmospheric circulation patterns and demonstrates two options for subseasonal to seasonal (S2S) predictions of anomalous sea levels using these patterns as inputs to artificial neural network (ANN) models. Results on the monthly scale are similar to previous research on the daily scale, with above-average sea levels and an increased risk of high-water events on days with anomalously low atmospheric pressure patterns and wind patterns leading to onshore or downwelling-producing wind stress. Some wind patterns show risks of high-water events to be over 6 times higher than baseline risk and exhibit an average water level anomaly of +94 mm above normal. In terms of forecasting, nonlinear autoregressive ANN models with exogenous input (NARX models) and pattern-based lagged ANN (PLANN) models show skill over postprocessed numerical forecast model output, and simple climatology. Damped-persistence forecasts and PLANN models show nearly the same skill in terms of predicting anomalous sea levels out to 9 months of lead time, with a slight edge to PLANN models, especially with regard to error statistics. This perspective on forecasting—using predefined circulation patterns along with ANN models—should aid in the real-time prediction of coastal flooding events, among other applications.

Open access
Oksana A. Chkrebtii
and
Frederick M. Bingham

Abstract

We explore the use of ocean near-surface salinity (NSS), that is, salinity at 1-m depth, as a rainfall occurrence detector for hourly precipitation using data from the Salinity Processes in the Upper-Ocean Regional Studies–2 (SPURS-2) mooring at 10°N, 125°W. Our proposed unsupervised learning algorithm consists of two stages. First, an empirical quantile-based identification of dips in NSS enables us to capture most events with hourly averaged rainfall rate of >5 mm h−1. Overestimation of precipitation duration is then corrected locally by fitting a parametric model based on the salinity balance equation. We propose a local precipitation model composed of a small number of calibration parameters representing individual rainfall events and their location in time. We show that unsupervised rainfall detection can be formulated as a statistical problem of predicting these variables from NSS data. We present our results and provide a validation technique based on data collected at the SPURS-2 mooring.

Significance Statement

Continuous monitoring of precipitation in the ocean is challenging when a physical rain gauge is not available in the region of interest. Indirect detection of precipitation using available data, such as changes in ocean near-surface salinity (NSS) can be used to construct a virtual rainfall detector. We propose to combine data-based and model-based methods to detect rainfall without the use of a physical rain gauge. We use NSS and precipitation data from a mooring in the eastern tropical Pacific Ocean to develop and test the method.

Open access
Sem Vijverberg
,
Raed Hamed
, and
Dim Coumou

Abstract

Soy harvest failure events can severely impact farmers, insurance companies, and raise global prices. Reliable seasonal forecasts of misharvests would allow stakeholders to prepare and take appropriate early action. However, especially for farmers, the reliability and lead time of current prediction systems provide insufficient information to justify within-season adaptation measures. Recent innovations increased our ability to generate reliable statistical seasonal forecasts. Here, we combine these innovations to predict the 1–3 poor soy harvest years in the eastern United States. We first use a clustering algorithm to spatially aggregate crop producing regions within the eastern United States that are particularly sensitive to hot–dry weather conditions. Next, we use observational climate variables [sea surface temperature (SST) and soil moisture] to extract precursor time series at multiple lags. This allows the machine learning model to learn the low-frequency evolution, which carries important information for predictability. A selection based on causal inference allows for physically interpretable precursors. We show that the robust selected predictors are associated with the evolution of the horseshoe Pacific SST pattern, in line with previous research. We use the state of the horseshoe Pacific to identify years with enhanced predictability. We achieve high forecast skill of poor harvests events, even 3 months prior to sowing, using a strict one-step-ahead train-test splitting. Over the last 25 years, when the horseshoe Pacific SST pattern was anomalously strong, 67% of the poor harvests predicted in February were correct. When operational, this forecast would enable farmers to make informed decisions on adaption measures, for example, selecting more drought-resistant cultivars or change planting management.

Significance Statement

If soy farmers would know that the upcoming growing season will be hot and dry, they could decide to take anticipatory action to reduce losses, that is, buy more drought resistant soy cultivars or change planting management. To make such decisions, farmers would need information even prior to sowing. On these very long lead times, a predictable signal can emerge from low-frequency processes of the climate system that can affect surface weather via teleconnections. However, traditional forecast systems are unable to make reliable predictions at these lead times. In this work, we used machine learning techniques to train a forecast model based on these low-frequency components. This allowed us to make reliable predictions of poor harvest years even 3 months prior to sowing.

Open access
Lukas Hubert Leufen
,
Felix Kleinert
, and
Martin G. Schultz

Abstract

With the impact of tropospheric ozone pollution on humankind, there is a compelling need for robust air quality forecasts. Here, we introduce a novel deep learning (DL) forecasting system called O3ResNet that produces a 4-day forecast for ground-level ozone. O3ResNet is based on a convolutional neural network with residual blocks. The model has been trained on 22 yr of ozone and nitrogen oxides in situ measurements and ERA5 reanalysis data from 2000 to 2021 at 328 stations in central Europe located in rural and suburban environments. Our model outperforms the state-of-the-art Copernicus Atmosphere Monitoring Service regional forecast model ensemble for ground-level ozone with respect to the mean-square error and mean absolute error of the daily maximum 8-h running-average ozone, thus marking a major milestone for DL-based ozone prediction. O3ResNet has a very small bias without requiring additional postprocessing, and it generalizes well so that new stations can be added with no need to retrain the neural network. Because the model works on hourly data, it can be easily adapted to output other air quality metrics. We conclude that O3ResNet is sufficiently advanced and robust to become a test application for operational air quality forecasting with DL.

Significance Statement

In this paper, we introduce a novel deep learning approach to forecast ground-level ozone for rural and suburban environments on a local scale. Our model is able to outperform the state-of-the-art Copernicus Atmosphere Monitoring Service regional ensemble forecast and is a major milestone toward a more reliable ozone prediction. This is important because local-scale ozone forecasts using conventional methods show significant bias or require site-dependent postprocessing. The findings suggest that the model presented in this article can become an important tool for air quality prediction.

Open access
Amy McGovern
,
Randy J. Chase
,
Montgomery Flora
,
David J. Gagne II
,
Ryan Lagerquist
,
Corey K. Potvin
,
Nathan Snook
, and
Eric Loken

Abstract

We present an overview of recent work on using artificial intelligence (AI)/machine learning (ML) techniques for forecasting convective weather and its associated hazards, including tornadoes, hail, wind, and lightning. These high-impact phenomena globally cause both massive property damage and loss of life, yet they are very challenging to forecast. Given the recent explosion in developing ML techniques across the weather spectrum and the fact that the skillful prediction of convective weather has immediate societal benefits, we present a thorough review of the current state of the art in AI and ML techniques for convective hazards. Our review includes both traditional approaches, including support vector machines and decision trees, as well as deep learning approaches. We highlight the challenges in developing ML approaches to forecast these phenomena across a variety of spatial and temporal scales. We end with a discussion of promising areas of future work for ML for convective weather, including a discussion of the need to create trustworthy AI forecasts that can be used for forecasters in real time and the need for active cross-sector collaboration on testbeds to validate ML methods in operational situations.

Significance Statement

We provide an overview of recent machine learning research in predicting hazards from thunderstorms, specifically looking at lightning, wind, hail, and tornadoes. These hazards kill people worldwide and also destroy property and livestock. Improving the prediction of these events in both the local space as well as globally can save lives and property. By providing this review, we aim to spur additional research into developing machine learning approaches for convective hazard prediction.

Open access
Chiem van Straaten
,
Kirien Whan
,
Dim Coumou
,
Bart van den Hurk
, and
Maurice Schmeits

Abstract

Subseasonal forecasts are challenging for numerical weather prediction (NWP) and machine learning models alike. Forecasting 2-m temperature (t2m) with a lead time of 2 or more weeks requires a forward model to integrate multiple complex interactions, like oceanic and land surface conditions leading to predictable weather patterns. NWP models represent these interactions imperfectly, meaning that in certain conditions, errors accumulate and model predictability deviates from real predictability, often for poorly understood reasons. To advance that understanding, this paper corrects conditional errors in NWP forecasts with an artificial neural network (ANN). The ANN postprocesses ECMWF extended-range summer temperature forecasts by learning to correct the ECMWF-predicted probability that monthly t2m in western and central Europe exceeds the climatological median. Predictors are objectively selected from ECMWF forecasts themselves, and from states at initialization, i.e., the ERA5 reanalysis. The latter allows the ANN to account for sources of predictability that are biased in the NWP model itself. We attribute ANN corrections with two explainable artificial intelligence (AI) tools. This reveals that certain erroneous forecasts relate to tropical western Pacific Ocean sea surface temperatures at initialization. We conjecture that the atmospheric teleconnection following this source of predictability is imperfectly represented by the ECMWF model. Correcting the associated conditional errors with the ANN improves forecast skill.

Significance Statement

We want to understand occasions in which a numerical weather prediction (NWP) model fails to forecast a predictable event existing in the real world. For forecasts of European summer weather more than 2 weeks in advance, real predictable events are rare. When misrepresented by the model, predicted future states become needlessly biased. We diagnose these missed opportunities with an explainable neural network. The neural network is aware of the initial state and learns to correct the NWP forecast on occasions when it misrepresents a teleconnection from the western tropical Pacific Ocean to Europe. The explainable architecture can be useful for other applications in which conditional model errors need to be understood and corrected.

Open access
Mark S. Veillette
,
James M. Kurdzo
,
Phillip M. Stepanian
,
Joseph McDonald
,
Siddharth Samsi
, and
John Y. N. Cho

Abstract

Radial velocity estimates provided by Doppler weather radar are critical measurements used by operational forecasters for the detection and monitoring of life-impacting storms. The sampling methods used to produce these measurements are inherently susceptible to aliasing, which produces ambiguous velocity values in regions with high winds and needs to be corrected using a velocity dealiasing algorithm (VDA). In the United States, the Weather Surveillance Radar-1988 Doppler (WSR-88D) Open Radar Product Generator (ORPG) is a processing environment that provides a world-class VDA; however, this algorithm is complex and can be difficult to port to other radar systems outside the WSR-88D network. In this work, a deep neural network (DNN) is used to emulate the two-dimensional WSR-88D ORPG dealiasing algorithm. It is shown that a DNN, specifically a customized U-Net, is highly effective for building VDAs that are accurate, fast, and portable to multiple radar types. To train the DNN model, a large dataset is generated containing aligned samples of folded and dealiased velocity pairs. This dataset contains samples collected from WSR-88D Level-II and Level-III archives and uses the ORPG dealiasing algorithm output as a source of truth. Using this dataset, a U-Net is trained to produce the number of folds at each point of a velocity image. Several performance metrics are presented using WSR-88D data. The algorithm is also applied to other non-WSR-88D radar systems to demonstrate portability to other hardware/software interfaces. A discussion of the broad applicability of this method is presented, including how other Level-III algorithms may benefit from this approach.

Significance Statement

Accurate and timely estimates of wind within storms are critically important for a number of applications, including severe storm nowcasting, maritime operational planning, aviation forecasting, and public safety coordination. Velocity aliasing is a common artifact that requires data quality control. While velocity dealiasing algorithms (VDAs) have been developed for decades, they remain a computationally complex and challenging problem. This paper presents an application of deep neural networks (DNNs) to increase the computational efficiency and portability of VDAs. A DNN is trained to emulate an operational algorithm, and performance is quantified over a large dataset. This work gives a convincing example of the benefits that deep learning can provide for radar algorithms, and future work highlighting these opportunities is discussed.

Open access
Daniel Galea
,
Julian Kunkel
, and
Bryan N. Lawrence

Abstract

Tropical cyclones are high-impact weather events that have large human and economic effects, so it is important to be able to understand how their location, frequency, and structure might change in a future climate. Here, a lightweight deep learning model is presented that is intended for detecting the presence or absence of tropical cyclones during the execution of numerical simulations for use in an online data reduction method. This will help to avoid saving vast amounts of data for analysis after the simulation is complete. With run-time detection, it might be possible to reduce the need for some of the high-frequency high-resolution output that would otherwise be required. The model was trained on ERA-Interim reanalysis data from 1979 to 2017, and the training was concentrated on delivering the highest possible recall rate (successful detection of cyclones) while rejecting enough data to make a difference in outputs. When tested using data from the two subsequent years, the recall or probability of detection rate was 92%. The precision rate or success ratio obtained was that of 36%. For the desired data reduction application, if the desired target included all tropical cyclone events, even those that did not obtain hurricane-strength status, the effective precision was 85%. The recall rate and the area under curve for the precision–recall (AUC-PR) compare favorably with other methods of cyclone identification while using the smallest number of parameters for both training and inference.

Open access
Andrew D. Justin
,
Colin Willingham
,
Amy McGovern
, and
John T. Allen

Abstract

We present and evaluate a deep learning first-guess front-identification system that identifies cold, warm, stationary, and occluded fronts. Frontal boundaries play a key role in the daily weather around the world. Human-drawn fronts provided by the National Weather Service’s Weather Prediction Center, Ocean Prediction Center, Tropical Analysis and Forecast Branch, and Honolulu Forecast Office are treated as ground-truth labels for training the deep learning models. The models are trained using ERA5 data with variables known to be important for distinguishing frontal boundaries, including temperature, equivalent potential temperature, and wind velocity and direction at multiple heights. Using a 250-km neighborhood over the contiguous U.S. domain, our best models achieve critical success index scores of 0.60 for cold fronts, 0.43 for warm fronts, 0.48 for stationary fronts, 0.45 for occluded fronts, and 0.71 using a binary classification system (front/no front), whereas scores over the full unified surface analysis domain were lower. For cold and warm fronts and binary classification, these scores significantly outperform prior baseline methods that utilize 250-km neighborhoods. These first-guess deep learning algorithms can be used by forecasters to locate frontal boundaries more effectively and expedite the frontal analysis process.

Significance Statement

Fronts are boundaries that affect the weather that people experience daily. Currently, forecasters must identify these boundaries through manual analysis. We have developed an automated machine learning method for detecting cold, warm, stationary, and occluded fronts. Our automated method provides forecasters with an additional tool to expedite the frontal analysis process.

Open access