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Ayumi Fujisaki-Manome
,
Haoguo Hu
,
Jia Wang
,
Joannes J. Westerink
,
Damrongsak Wirasaet
,
Guoming Ling
,
Mindo Choi
,
Saeed Moghimi
,
Edward Myers
,
Ali Abdolali
,
Clint Dawson
, and
Carol Janzen

Abstract

In Alaska’s coastal environment, accurate information of sea ice conditions is desired by operational forecasters, emergency managers, and responders. Complicated interactions among atmosphere, waves, ocean circulation, and sea ice collectively impact the ice conditions, intensity of storm surges, and flooding, making accurate predictions challenging. A collaborative work to build the Alaska Coastal Ocean Forecast System established an integrated storm surge, wave, and sea ice model system for the coasts of Alaska, where the verified model components are linked using the Earth System Modeling Framework and the National Unified Operational Prediction Capability. We present the verification of the sea ice model component based on the Los Alamos Sea Ice Model, version 6. The regional, high-resolution (3 km) configuration of the model was forced by operational atmospheric and ocean model outputs. Extensive numerical experiments were conducted from December 2018 to August 2020 to verify the model’s capability to represent detailed nearshore and offshore sea ice behavior, including landfast ice, ice thickness, and evolution of air–ice drag coefficient. Comparisons of the hindcast simulations with the observations of ice extent presented the model’s comparable performance with the Global Ocean Forecast System 3.1 (GOFS3.1). The model’s skill in reproducing landfast ice area significantly outperformed GOFS3.1. Comparison of the modeled sea ice freeboard with the Ice, Cloud, and Land Elevation Satellite-2 product showed a mean bias of −4.6 cm. Daily 5-day forecast simulations for October 2020–August 2021 presented the model’s promising performance for future implementation in the coupled model system.

Significance Statement

Accurate sea ice information along Alaska’s coasts is desired by the communities for preparedness of hazardous events, such as storm surges and flooding. However, such information, in particular predicted conditions, remains to be a gap. This study presents the verification of the state-of-art sea ice model for Alaska’s coasts for future use in the more comprehensive coupled model system where ocean circulation, wave, and sea ice models are integrated. The model demonstrates comparable performance with the existing operational ocean–ice coupled model product in reproducing overall sea ice extent and significantly outperformed it in reproducing landfast ice cover. Comparison with the novel satellite product presented the model’s ability to capture sea ice freeboard in the stable ice season.

Open access
Catharina Elisabeth Graafland
,
Swen Brands
, and
José Manuel Gutiérrez

Abstract

The different phases of the Coupled Model Intercomparison Project (CMIP) provide ensembles of past, present, and future climate simulations crucial for climate change impact and adaptation activities. These ensembles are produced using multiple global climate models (GCMs) from different modeling centers with some shared building blocks and interdependencies. Applications typically follow the “model democracy” approach which might have significant implications in the resulting products (e.g., large bias and low spread). Thus, quantifying model similarity within ensembles is crucial for interpreting model agreement and multimodel uncertainty in climate change studies. The classical methods used for assessing GCM similarity can be classified into two groups. The a priori approach relies on expert knowledge about the components of these models, while the a posteriori approach seeks similarity in the GCMs’ output variables and is thus data-driven. In this study, we apply probabilistic network models (PNMs), a well-established machine learning technique, as a new a posteriori method to measure intermodel similarities. The proposed methodology is applied to surface temperature fields of the historical experiments from the CMIP5 multimodel ensemble and different reanalysis gridded datasets. PNMs are able to learn the complex spatial dependency structures present in climate data, including teleconnections operating on multiple spatial scales, characteristic of the underlying GCM. A distance metric building on the resulting PNMs is applied to characterize GCM model dependencies. The results of this approach are in line with those obtained with more traditional methods but have further explanatory potential building on probabilistic model querying.

Significance Statement

The present study proposes the use of probabilistic network models (PNMs) to quantify model similarity within ensembles of global climate models (GCMs). This is crucial for interpreting model agreement and multimodel uncertainty in climate change studies. When applied to climate data (gridded global surface temperature in this study), PNMs encode the relevant spatial dependencies (local and remote connections). Similarities among the PNMs resulting from different GCMs can be quantified and are shown to capture similar GCM formulations reported in previous studies. Differently to other machine learning methods previously applied to this problem, PNMs are fully explainable (allowing probabilistic querying) and are applicable to high-dimensional gridded raw data.

Open access
Chen Liu
,
Lei Chen
, and
Stefan Liess

Abstract

The features of large-scale atmospheric circulations, storm tracks, and the mean flow-eddy interaction during winter Pacific-North American (PNA) events are investigated using National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) reanalysis data at subseasonal timescale from 1979 to 2022. The day-to-day variations of storm-track activity and stream function reveal that storm-track activity varies along the evolution of mean flow. To better understand storm track variability with the mean flow-eddy interaction, further exploration is made by analyzing local energy energetics. The changes in horizontal and vertical baroclinic energy conversions from background flow correspond to the storm track anomalies over the North Pacific, indicating that the anomalies in storm tracks are due to the anomalous mean flow associated with PNA patterns impacting energy conversion through mean flow-eddy interaction. Eddy feedback driven by vorticity and heat fluxes is analyzed. This provides a concrete illustration of how eddy feedback serves as a positive factor for the upper-tropospheric circulation anomalies associated with the PNA pattern.

Restricted access
Yingkai Sha
,
Ryan A. Sobash
, and
David John Gagne II

Abstract

An ensemble postprocessing method is developed for the probabilistic prediction of severe weather (tornadoes, hail, and wind gusts) over the conterminous United States (CONUS). The method combines conditional generative adversarial networks (CGANs), a type of deep generative model, with a convolutional neural network (CNN) to postprocess convection-allowing model (CAM) forecasts. The CGANs are designed to create synthetic ensemble members from deterministic CAM forecasts, and their outputs are processed by the CNN to estimate the probability of severe weather. The method is tested using High-Resolution Rapid Refresh (HRRR) 1–24-h forecasts as inputs and Storm Prediction Center (SPC) severe weather reports as targets. The method produced skillful predictions with up to 20% Brier skill score (BSS) increases compared to other neural-network-based reference methods using a testing dataset of HRRR forecasts in 2021. For the evaluation of uncertainty quantification, the method is overconfident but produces meaningful ensemble spreads that can distinguish good and bad forecasts. The quality of CGAN outputs is also evaluated. Results show that the CGAN outputs behave similarly to a numerical ensemble; they preserved the intervariable correlations and the contribution of influential predictors as in the original HRRR forecasts. This work provides a novel approach to postprocess CAM output using neural networks that can be applied to severe weather prediction.

Significance Statement

We use a new machine learning (ML) technique to generate probabilistic forecasts of convective weather hazards, such as tornadoes and hailstorms, with the output from high-resolution numerical weather model forecasts. The new ML system generates an ensemble of synthetic forecast fields from a single forecast, which are then used to train ML models for convective hazard prediction. Using this ML-generated ensemble for training leads to improvements of 10%–20% in severe weather forecast skills compared to using other ML algorithms that use only output from the single forecast. This work is unique in that it explores the use of ML methods for producing synthetic forecasts of convective storm events and using these to train ML systems for high-impact convective weather prediction.

Open access
Zifan Su
,
Yongkun Xie
,
Jianping Huang
,
Guoxiong Wu
,
Yuzhi Liu
, and
Xiaodan Guan

Abstract

The Tibetan Plateau’s (TP) topography has long been recognized for its impact on climate. However, recognition of the influence of the TP on global weather variability remains insufficient. Therefore, this study used numerical simulations to demonstrate the influences of the TP and its mechanical and thermal forcing on global high-frequency temperature variability and eddy kinetic energy (EKE). Despite local influences, the TP influenced the high-frequency temperature variability in far-flung regions like North America. In summer, the TP’s influence on high-frequency temperature variability showed dipole patterns in Eurasia and tripole patterns in North America, which were mainly induced by TP thermal forcing. In winter, the TP’s influence on high-frequency temperature variability was dominated by mechanical forcing and was less significant for remote regions than in summer. Mechanical forcing dominated EKE in both summer and winter. Furthermore, the horizontal temperature advection dominated the TP’s influence on high-frequency temperature variability for both its thermal effect in summer and its mechanical effect in winter, wherein EKE, as the dynamical factor, determined the horizontal temperature advection rather than the thermodynamical factor, the temperature gradient. Our findings suggest that the TP, via its mechanical and thermal forcing, may have an impact on temperature-related weather extremes around the world.

Restricted access
Hairu Ding
,
Li Dong
,
Kaijun Liu
,
Ting Lin
,
Zhiang Xie
,
Bo Zhang
, and
Xiaoxue Wang

Abstract

As the only remaining ice sheet in the Northern Hemisphere, the Greenland ice sheet (GrIS) plays a crucial role in influencing atmospheric circulations, particularly with its rapid melting under global warming. In this paper, the influences of GrIS topography and surface thermal conditions are investigated by a series of aquaplanet experiments. The results show that the GrIS topography induces stationary waves and favors more blocking events through the generation of negative potential vorticity (PV) anomalies, while it tends to suppress local storm activities through the induced stationary waves. The surface cooling center of the GrIS is found to strengthen the jet streams by enhancing the meridional temperature gradient and thermal wind, while it causes the PV and static stability to increase during near-Greenland blocking days, thereby disfavoring blocking onset. Altogether, the topography and surface thermal effects of GrIS appear to compete with each other so that the net effect would determine the final response. Nevertheless, nonlinearity is found in both GrIS-topography alone and GrIS-surface temperature alone experiments, where nonlinear responses of atmospheric circulation are detected when the GrIS topography height or surface temperature exceeds their critical values, respectively. Hence, through this study, the response of the blocking in the vicinity of Greenland to the combined effects of topography and surface thermal conditions may shed light on comprehending the underlying mechanism of blocking aleration in a changing climate.

Restricted access
Matthias Zech
and
Lueder von Bremen

Abstract

Dynamical numerical weather prediction has remarkably improved over the last decades. Yet, postprocessing techniques are needed to calibrate forecasts which are based on statistical and Machine Learning techniques. With recent advances in the derivation of year-round, large-scale atmospheric circulations, or weather regimes, the question arises of whether this information can be valuable within forecast postprocessing methods. This paper investigates this by proposing a bias correction scheme to integrate the atmospheric circulation state derived from empirical orthogonal functions, referred to as weather patterns, for deterministic short-term, near-surface temperature forecasts based on LASSO regression. We propose a computational study which first evaluates different weather pattern definitions (spatial domain) to improve temperature forecasts in Europe. As a bias could be associated with the weather pattern at the model initialization time or at the realization time of the forecast, both variants are tested in this study. We show that forecasted weather patterns with the identical spatial domain as the forecast show best skill reaching Mean Squared Error Skill improvements of up to 3% (day-ahead) or 1% respectively (week ahead). Only considering land surface improvements in Europe, improvements of 4-6% for day-ahead and 1 to 5% for week-ahead forecasts are observable. We believe that this study not only introduces a simple yet effective tool to reduce bias in temperature forecasts but also contributes to the active discussion of how valuable weather patterns are and how to use them within forecast calibration techniques.

Restricted access
M. Timofeyeva-Livezey
,
Jenna Meyers
,
Stephen Baxter
,
Margaret Hurwitz
,
James Zdrojewski
,
Keith White
,
David Ross
,
Barbara Mayes Boustead
,
Viviane Silva
,
Christopher Stachelski
,
Audra Bruschi
,
Victor Murphy
,
Andrea Bair
,
David DeWitt
,
Richard Thoman
,
Fiona Horsfall
,
Brian Brettschneider
,
Elizabeth Vickery
,
Ray Wolf
, and
Bill Ward

Abstract

The National Oceanic and Atmospheric Administration’s (NOAA) National Weather Service (NWS) has been providing national, regional, and local climate services for more than 20 years. The NWS climate services building blocks consist of service provision infrastructure, partnership and outreach, discovery of user needs and requirements, and service delivery at national, regional, local, and tribal levels. To improve services, the NWS climate services program accelerated user engagement through customer surveys, workshops, and collaborations. Since 2002, the annual Climate Prediction Applications Science Workshop has developed a community of climate information producers and users through sharing of climate science applications, decision support tools, and effective communication practices. Although NWS had been producing operational climate monitoring and prediction products for several decades, the Weather Research and Forecasting Innovation Act of 2017 (U.S. Public Law 115-25) specifically mandated that NWS deliver services at subseasonal to seasonal time scales, including periods from two weeks to two years. Looking ahead, both the Department of Commerce (DOC) and NOAA have included climate services in their new 2022–26 strategic plans, including DOC’s goal to address the climate crisis through mitigation, adaptation, and resilience efforts and NOAA’s initiatives to build a Climate Ready Nation (CRN). The NWS Climate Services Program supports these strategic goals and CRN initiatives through integrating climate information into Impact-based Decision Support Services, the most critical element for implementation of the NWS strategy for a Weather-Ready Nation. This includes application of state-of-the-art climate monitoring and prediction products to the most societally relevant impacts while empowering regional and local climate delivery of enhanced services.

Open access
Temple R. Lee
,
Sandip Pal
,
Ronald D. Leeper
,
Tim Wilson
,
Howard J. Diamond
,
Tilden P. Meyers
, and
David D. Turner

Abstract

The scientific literature has many studies evaluating numerical weather prediction (NWP) models. However, many of those studies averaged across a myriad of different atmospheric conditions and surface forcings that can obfuscate the atmospheric conditions when NWP models perform well versus when they perform inadequately. To help isolate these different weather conditions, we used observations from the U.S. Climate Reference Network (USCRN) obtained between 1 January and 31 December 2021 to distinguish among different near-surface atmospheric conditions [i.e., different near-surface heating rates ( d T / d t ), incoming shortwave radiation (SW d ) regimes, and 5-cm soil moisture (SM05)] to evaluate the High-Resolution Rapid Refresh (HRRR) Model, which is a 3-km model used for operational weather forecasting in the United States. On days with small (large) d T / d t , we found afternoon T biases of about 2°C (−1°C) and afternoon SW d biases of up to 170 W m−2 (100 W m−2), but negligible impacts on SM05 biases. On days with small (large) SW d , we found daytime temperature biases of about 3°C (−2.5°C) and daytime SW d biases of up to 190 W m−2 (80 W m−2). Whereas different SM05 had little impact on T and SW d biases, dry (wet) conditions had positive (negative) SM05 biases. We argue that the proper evaluation of weather forecasting models requires careful consideration of different near-surface atmospheric conditions and is critical to better identify model deficiencies in order to support improvements to the parameterization schemes used therein. A similar, regime-specific verification approach may also be used to help evaluate other geophysical models.

Significance Statement

Improving weather forecasting models requires careful evaluations against high-quality observations. We used observations from the U.S. Climate Reference Network (USCRN) and found that the performance of the High-Resolution Rapid Refresh (HRRR) Model varies as a function of differences in near-surface heating and solar radiation. This finding indicates that model evaluations need to be conducted under varying near-surface weather conditions rather than averaging across multiple weather types. This new approach will allow for model developers to better identify model deficiencies and is a useful step to helping improve weather forecasts.

Open access
Martin Schön
,
Vasileios Savvakis
,
Maria Kezoudi
,
Andreas Platis
, and
Jens Bange

Abstract

Atmospheric aerosols affect human health and influence atmospheric and biological processes. Dust can be transported long distances in the atmosphere, and the mechanisms that influence dust transport are not fully understood. To improve the database for numerical models that simulate dust transport, measurements are needed that cover both the vertical distribution of the dust and its size distribution. In addition to measurements with crewed aircraft, uncrewed aircraft systems (UASs) provide a particularly suitable platform for this purpose. In this paper, we present a payload for the small fixed-wing UAS of the type Multiple-Purpose Airborne Sensor Carrier 3 (MASC-3) for aerosol particle measurements that is based on the optical particle counter (OPC) OPC-N3 (Alphasense, United Kingdom), modified by the addition of a dryer and a passive aspiration system (OPC-Pod). Based on field tests with a reference instrument in Mannheim, Germany, wind tunnel tests, and a comparison measurement with the UAS-mounted aerosol particle measurement Universal Cloud and Aerosol Sounding System (UCASS) during a dust event over Cyprus, we show that the OPC-Pod can measure particle number concentrations in the range of 0.66–31 μm as well as particle size distributions. The agreement of the OPC-Pod with UCASS is good. Both instruments resolve a vertical profile of the Saharan dust event, with a prominent dust layer between 1500 and 2800 m MSL, with particle number concentrations up to 35 cm−3 for particles between 0.66 and 31 μm.

Restricted access