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Jun Li
,
W. Paul Menzel
,
Timothy J. Schmit
, and
Johannes Schmetz

Abstract

A hyperspectral infrared (IR) sounder from geostationary orbit provides nearly continuous measurements of atmospheric thermodynamic and dynamic information within a weather cube, specifically the atmospheric temperature, moisture, and wind information at different pressure levels that are critical for improving high-impact weather (HIW) nowcasting and numerical weather prediction (NWP). Geostationary hyperspectral IR sounders (GeoHIS) have been on board China’s Fengyun-4 series since 2016 and will be on board Europe’s Meteosat Third Generation (MTG) series in the 2024 time frame; the United States and other countries are also planning to include GeoHIS instruments on their next generation of geostationary weather satellites. Although availability of on-orbit GeoHIS data are limited currently, studies have been conducted and progress has been made on developing the applications of high-temporal-resolution GeoHIS observations. These include but are not limited to deriving three-dimensional wind fields for nowcasting and NWP applications, trending atmospheric instability for warning in preconvective environments, conducting impact studies with data from the experimental Geostationary Interferometric Infrared Sounder (GIIRS) on board Fengyun-4A, preparing observing system simulation experiments (OSSEs), and monitoring diurnal variation of atmospheric composition. This paper provides an overview of the current applications of GeoHIS, discusses the data processing challenges, and provides perspectives on future development. The purpose is to provide direction on utilization of the current and assist preparation for the upcoming GeoHIS observations for nowcasting, NWP and other applications.

Free access
Mina Masoud
and
Rich Pawlowicz

Abstract

The Strait of Georgia is a large and deep fjordlike basin on the northeastern Pacific coast whose bottom waters are dramatically renewed by a series of intermittent gravity currents in summer. Here, we analyze a dataset that includes moored observations from 2008 to 2021 and shipborne measurements from a 2018 field program to describe the vertical and cross-channel structure of these gravity currents. We show that the timing of these currents for more than a decade is well predicted by proxy measurements for both tidal mixing strength in the Haro Strait/Boundary Pass region and coastal upwelling on the west coast of Vancouver Island. Renewals occur as an ∼30-m-thick turbid layer extending along the right-hand slope of a broad V-shaped valley that forms the southern end of the strait. Currents are primarily along-isobath at speeds of up to 20 cm s−1 with a small downhill component. A diagnostic analytical model with a depth-dependent eddy viscosity is fitted to the observations and confirms a clockwise rotation of current vectors with height, partly driven by boundary layer dynamics over a scale of a few meters and partly driven by Coriolis forces in the near-bottom linear density gradient. Bottom drag and (small) entrainment parameters are similar to those found in other oceanic situations, and the current is “laminar” with respect to large-scale instabilities (with Froude number ≈1 and Ekman number ≈0.01), although subject to turbulence at small scales (Reynolds number of ∼106). The predictability and reliability of this accessible rotationally modified gravity current suggests that it is an ideal geophysical laboratory for future studies of such features.

Restricted access
Lander Ver Hoef
,
Henry Adams
,
Emily J. King
, and
Imme Ebert-Uphoff

Abstract

Topological data analysis (TDA) is a tool from data science and mathematics that is beginning to make waves in environmental science. In this work, we seek to provide an intuitive and understandable introduction to a tool from TDA that is particularly useful for the analysis of imagery, namely persistent homology. We briefly discuss the theoretical background but focus primarily on understanding the output of this tool and discussing what information it can glean. To this end, we frame our discussion around a guiding example of classifying satellite images from the Sugar, Fish, Flower, and Gravel Dataset produced for the study of mesocale organization of clouds by Rasp et. al. in 2020. We demonstrate how persistent homology and its vectorization, persistence landscapes, can be used in a workflow with a simple machine learning algorithm to obtain good results, and explore in detail how we can explain this behavior in terms of image-level features. One of the core strengths of persistent homology is how interpretable it can be, so throughout this paper we discuss not just the patterns we find, but why those results are to be expected given what we know about the theory of persistent homology. Our goal is that a reader of this paper will leave with a better understanding of TDA and persistent homology, be able to identify problems and datasets of their own for which persistent homology could be helpful, and gain an understanding of results they obtain from applying the included GitHub example code.

Free access
Yongkang Xue
,
Ismaila Diallo
,
Aaron A. Boone
,
Tandong Yao
,
Yang Zhang
,
Xubin Zeng
,
J. David Neelin
,
William K. M. Lau
,
Yan Pan
,
Ye Liu
,
Xiaoduo Pan
,
Qi Tang
,
Peter J. van Oevelen
,
Tomonori Sato
,
Myung-Seo Koo
,
Stefano Materia
,
Chunxiang Shi
,
Jing Yang
,
Constantin Ardilouze
,
Zhaohui Lin
,
Xin Qi
,
Tetsu Nakamura
,
Subodh K. Saha
,
Retish Senan
,
Yuhei Takaya
,
Hailan Wang
,
Hongliang Zhang
,
Mei Zhao
,
Hara Prasad Nayak
,
Qiuyu Chen
,
Jinming Feng
,
Michael A. Brunke
,
Tianyi Fan
,
Songyou Hong
,
Paulo Nobre
,
Daniele Peano
,
Yi Qin
,
Frederic Vitart
,
Shaocheng Xie
,
Yanling Zhan
,
Daniel Klocke
,
Ruby Leung
,
Xin Li
,
Michael Ek
,
Weidong Guo
,
Gianpaolo Balsamo
,
Qing Bao
,
Sin Chan Chou
,
Patricia de Rosnay
,
Yanluan Lin
,
Yuejian Zhu
,
Yun Qian
,
Ping Zhao
,
Jianping Tang
,
Xin-Zhong Liang
,
Jinkyu Hong
,
Duoying Ji
,
Zhenming Ji
,
Yuan Qiu
,
Shiori Sugimoto
,
Weicai Wang
,
Kun Yang
, and
Miao Yu

Abstract

Subseasonal-to-seasonal (S2S) precipitation prediction in boreal spring and summer months, which contains a significant number of high-signal events, is scientifically challenging and prediction skill has remained poor for years. Tibetan Plateau (TP) spring observed surface ­temperatures show a lag correlation with summer precipitation in several remote regions, but current global land–atmosphere coupled models are unable to represent this behavior due to significant errors in producing observed TP surface temperatures. To address these issues, the Global Energy and Water Exchanges (GEWEX) program launched the “Impact of Initialized Land Temperature and Snowpack on Subseasonal-to-Seasonal Prediction” (LS4P) initiative as a community effort to test the impact of land temperature in high-mountain regions on S2S prediction by climate models: more than 40 institutions worldwide are participating in this project. After using an innovative new land state initialization approach based on observed surface 2-m temperature over the TP in the LS4P experiment, results from a multimodel ensemble provide evidence for a causal relationship in the observed association between the Plateau spring land temperature and summer precipitation over several regions across the world through teleconnections. The influence is underscored by an out-of-phase oscillation between the TP and Rocky Mountain surface temperatures. This study reveals for the first time that high-mountain land temperature could be a substantial source of S2S precipitation predictability, and its effect is probably as large as ocean surface temperature over global “hotspot” regions identified here; the ensemble means in some “hotspots” produce more than 40% of the observed anomalies. This LS4P approach should stimulate more follow-on explorations.

Free access
Zhengyu Liu
,
Peng Gu
, and
Thomas L. Delworth

Abstract

The role of ocean forcing on Atlantic multidecadal variability (AMV) is assessed from the (downward) heat flux–SST relation in the framework of a new stochastic climate theory forced by red noise ocean forcing. Previous studies suggested that atmospheric forcing drives SST variability from monthly to interannual time scales, with a positive heat flux–SST correlation, while heat flux induced by ocean processes can drive SST variability at decadal and longer time scales, with a negative heat flux–SST correlation. Here, first, we develop a theory to show how the sign of heat flux–SST correlation is affected by atmospheric and oceanic forcing with time scale. In particular, a red noise ocean forcing is necessary for the sign reversal of heat flux–SST correlation. Furthermore, this sign reversal can be detected equivalently in three approaches: the low-pass correlation at lag zero, the unfiltered correlation at long (heat flux) lead, and the real part of the heat flux–SST coherence. Second, we develop a new scheme in combination with the theory to assess the magnitude and time scale of the red noise ocean forcing for AMV in the GFDL SPEAR model (Seamless System for Prediction and Earth System Research) and observations. In both the model and observations, the ocean forcing on AMV is in general comparable with the atmospheric forcing, with a 90% probability greater than the atmospheric forcing in observations. In contrast to the white noise atmospheric forcing, the ocean forcing has a persistence time comparable or longer than a year, much longer than the SST persistence of ∼3 months. This slow ocean forcing is associated implicitly with slow subsurface ocean dynamics.

Significance Statement

A new theoretical framework is developed to estimate the ocean forcing on Atlantic multidecadal variability form heat flux–SST relations in climate models and observation. Our estimation shows the ocean forcing is comparable with the atmospheric forcing and, in particular, has a slow time scale of years.

Open access
Xin Ma
and
Aihui Wang

Abstract

The land surface model is extensively used to simulate turbulence fluxes and hydrological and momentum variables at the land–atmosphere interface. In this study, the Community Land Model, version 5 (CLM5), driven by the 0.1° × 0.1° Chinese Meteorological Forcing Dataset (CMFD) and the field-surveyed soil parameters, is used to simulate land surface processes during 1979–2018. Various high-quality land surface datasets are adopted to assess the model simulations. In general, the CLM5 well captures the monthly variations of 0–10-cm soil moisture in subregions, particularly in the Tibetan Plateau, with an anomaly correlation coefficient between 0.56 and 0.88. However, the simulated soil moisture shows overall wet biases in the whole country, resulting from several reasons. The model simulation is skillful in replicating both the magnitude and spatial pattern when they are compared with the MODIS snow cover dataset. Compared with in situ measured soil temperature in multiple soil layers within 320-cm soil depth from 1980 to 2018, the simulations accurately capture spatial patterns, vertical profiles, and long-term warming trends. For land surface energy components, the simulations have a highly temporal correlation with the observation of Chinese Flux Observation and Research Network (ChinaFLUX) cropland and grassland sites, except for four forest sites, where biases exist in both atmospheric forcing variables and surface vegetation phenology in the model default input dataset. In summary, this study reveals the overall capability of CLM5 in reproducing land surface energy fluxes and hydrological variables over conterminous China, and the validation results may also provide some references for future model improvement and application.

Significance Statement

The offline Community Land Model, version 5 (CLM5), driven by a 0.1° × 0.1° (∼10 km) horizontal resolution atmospheric forcing dataset and a set of field-surveyed soil parameters, are used to simulate the land surface hydrological and heat fluxes in continental China for 1980–2018. The simulated hydrological variables and energy fluxes are validated with various sources of high-quality observation-based datasets. From our systematic evaluations, the current CLM5 high–resolution simulation accurately captures the spatial patterns and temporal variations in most of the water and energy balance components, although biases exist in some simulated variables. Overall, this study reveals the capability of the offline CLM5 simulation in conterminous China and provides the reference for future model improvement and application.

Restricted access
C. Bruce Baker
,
Michael Cosh
,
John Bolten
,
Mark Brusberg
,
Todd Caldwell
,
Stephanie Connolly
,
Iliyana Dobreva
,
Nathan Edwards
,
Peter E. Goble
,
Tyson E. Ochsner
,
Steven M. Quiring
,
Michael Robotham
,
Marina Skumanich
,
Mark Svoboda
,
W. Alex White
, and
Molly Woloszyn

Abstract

Soil moisture is a critical land surface variable, impacting the water, energy, and carbon cycles. While in situ soil moisture monitoring networks are still developing, there is no cohesive strategy or framework to coordinate, integrate, or disseminate these diverse data sources in a synergistic way that can improve our ability to understand climate variability at the national, state, and local levels. Thus, a national strategy is needed to guide network deployment, sustainable network operation, data integration and dissemination, and user-focused product development. The National Coordinated Soil Moisture Monitoring Network (NCSMMN) is a federally led, multi-institution effort that aims to address these needs by capitalizing on existing wide-ranging soil moisture monitoring activities, increasing the utility of observational data, and supporting their strategic application to the full range of decision-making needs. The goals of the NCSMMN are to 1) establish a national “network of networks” that effectively demonstrates data integration and operational coordination of diverse in situ networks; 2) build a community of practice around soil moisture measurement, interpretation, and application—a “network of people” that links data providers, researchers, and the public; and 3) support research and development (R&D) on techniques to merge in situ soil moisture data with remotely sensed and modeled hydrologic data to create user-friendly soil moisture maps and associated tools. The overarching mission of the NCSMMN is to provide coordinated high-quality, nationwide soil moisture information for the public good by supporting applications like drought and flood monitoring, water resource management, agricultural and forestry planning, and fire danger ratings.

Free access
Vincent-Henri Peuch
,
Richard Engelen
,
Michel Rixen
,
Dick Dee
,
Johannes Flemming
,
Martin Suttie
,
Melanie Ades
,
Anna Agustí-Panareda
,
Cristina Ananasso
,
Erik Andersson
,
David Armstrong
,
Jérôme Barré
,
Nicolas Bousserez
,
Juan Jose Dominguez
,
Sébastien Garrigues
,
Antje Inness
,
Luke Jones
,
Zak Kipling
,
Julie Letertre-Danczak
,
Mark Parrington
,
Miha Razinger
,
Roberto Ribas
,
Stijn Vermoote
,
Xiaobo Yang
,
Adrian Simmons
,
Juan Garcés de Marcilla
, and
Jean-Noël Thépaut

Abstract

The Copernicus Atmosphere Monitoring Service (CAMS), part of the European Union’s Earth observation program Copernicus, entered operations in July 2015. Implemented by the European Centre for Medium-Range Weather Forecasts (ECMWF) as a truly European effort with over 23,500 direct data users and well over 200 million end users worldwide as of March 2022, CAMS delivers numerous global and regional information products about air quality, inventory-based emissions and observation-based surface fluxes of greenhouse gases and from biomass burning, solar energy, ozone and UV radiation, and climate forcings. Access to CAMS products is open and free of charge via the Atmosphere Data Store. The CAMS global atmospheric composition analyses, forecasts, and reanalyses build on ECMWF’s Integrated Forecasting System (IFS) and exploit over 90 different satellite data streams. The global products are complemented by coherent higher-resolution regional air quality products over Europe derived from multisystem analyses and forecasts. CAMS information products also include policy support such as quantitative impact assessment of short- and long-term pollutant-emission mitigation scenarios, source apportionment information, and annual European air quality assessment reports. Relevant CAMS products are cited and used for instance in IPCC Assessment Reports. Providing dedicated support for users operating smartphone applications, websites, or TV bulletins in Europe and worldwide is also integral to the service. This paper presents key achievements of the CAMS initial phase (2014–21) and outlines some of its new components for the second phase (2021–28), e.g., the new Copernicus anthropogenic CO2 emissions Monitoring and Verification Support capacity that will monitor global anthropogenic emissions of key greenhouse gases.

Free access
Carlo Buontempo
,
Samantha N. Burgess
,
Dick Dee
,
Bernard Pinty
,
Jean-Noël Thépaut
,
Michel Rixen
,
Samuel Almond
,
David Armstrong
,
Anca Brookshaw
,
Angel Lopez Alos
,
Bill Bell
,
Cedric Bergeron
,
Chiara Cagnazzo
,
Edward Comyn-Platt
,
Eduardo Damasio-Da-Costa
,
Anabelle Guillory
,
Hans Hersbach
,
András Horányi
,
Julien Nicolas
,
Andre Obregon
,
Eduardo Penabad Ramos
,
Baudouin Raoult
,
Joaquín Muñoz-Sabater
,
Adrian Simmons
,
Cornel Soci
,
Martin Suttie
,
Freja Vamborg
,
James Varndell
,
Stijn Vermoote
,
Xiaobo Yang
, and
Juan Garcés de Marcilla

Abstract

The Copernicus Climate Change Service (C3S) provides open and free access to state-of-the-art climate data and tools for use by governments, public authorities, and private entities around the world. It is fully funded by the European Union and implemented by the European Centre for Medium-Range Weather Forecasts (ECMWF) together with public and private entities in Europe and elsewhere. With over 120,000 registered users worldwide, C3S has rapidly become an authoritative climate service in Europe and beyond, delivering quality-assured climate data and information based on the latest science. Established in 2014, C3S became fully operational in 2018 with the launch of its Climate Data Store, a powerful cloud-based infrastructure providing access to a vast range of global and regional information, including climate data records derived from observations, the latest ECMWF reanalyses, seasonal forecast data from multiple providers, and a large collection of climate projections. The system has been designed to be accessible to nonspecialists, offering a uniform interface to all data and documentation as well as a Python-based toolbox that can be used to process and use the data online. C3S publishes European State of the Climate reports annually for policy-makers, as well as monthly and annual summaries that are widely disseminated in the international press. Together with users, C3S develops customized indicators of climate impacts in economic sectors such as energy, water management, agriculture, insurance, health, and urban planning. C3S works closely with national climate service providers, satellite agencies, and other stakeholders on the improvement of its data and services.

Free access
Cory L. Armstrong
and
Anna Grace Usery

Abstract

When a tornado hits, there is little time to think through mental checklists for needed items. This study attempted to understand what information sources those in the path of tornados utilized for preparation and how those sources influence people to act. Results from the study indicate that television and radio are the top two information sources, and that some visual graphics—gauged via heat maps to understand higher levels of severe weather preparation—were reported as useful. Contrary to meteorological intentions, results showed that participants were less likely to prepare for impending weather when radar displayed tornado locations and intensity. In addition, those who identified as having more interest in weather-related information in the study were significantly more likely to prepare, along with those who fear future tornadoes. Each variable explored is underpinned by the theory of planned behavior and the risk information seeking and processing (RISP) model to better understand behavioral intentions and actions. This study offers two new concepts of general weather that have not previously been explored: interest and general versus specific storm preparation.

Significance Statement

The purpose of this work is to learn more about how individuals gather information and obtain weather warnings, primarily during tornado events. In particular, the study seeks to understand how individuals view and interpret visual graphics with information about the location and details about the event. Further, results suggest some differences between those who generally prepare for storm season versus those who only prepare for a specific event. Researchers may also be interested to know how weather enthusiasts may differ in their preparatory activities in comparison with nonweather enthusiasts. All of this information will help meteorologists and media professionals to better target their messages during severe weather.

Restricted access