Browse
Abstract
The Heat Index (HI), based on Steadman's model of thermoregulation, estimates heat stress on the human body from ambient temperature and humidity. It has been used widely both in applications, such as the issuance of heat advisories by the National Weather Service (NWS), and as well for research on possible changes in the future due to climate change. However, temperature/humidity combinations that exceed the applicable range of the model are becoming more common due to climate warming. Recent work by Lu and Romps has produced an Extended Heat Index (EHI) which is valid for values outside the range of the original HI. For these values the HI can underestimate the EHI by a considerable amount. This work utilizes observed data from 15 US weather stations along with bias-adjusted output from a climate model to explore the spatial and temporal aspects of the disparity between the HI and the EHI from the recent past out to the end of the 21st century. The underestimate of human heat stress by the HI is found to be largest for the most extreme cases (∼ 5-10° C), which are also the most impactful. Conditions warranting NWS excessive heat warnings are found to increase dramatically from less than 5% of days historically at most stations to more than 90% in the future at some stations. Although, by design, the scope of this work is limited, it demonstrates the need for the adoption of the EHI for both applications and research.
Abstract
The Heat Index (HI), based on Steadman's model of thermoregulation, estimates heat stress on the human body from ambient temperature and humidity. It has been used widely both in applications, such as the issuance of heat advisories by the National Weather Service (NWS), and as well for research on possible changes in the future due to climate change. However, temperature/humidity combinations that exceed the applicable range of the model are becoming more common due to climate warming. Recent work by Lu and Romps has produced an Extended Heat Index (EHI) which is valid for values outside the range of the original HI. For these values the HI can underestimate the EHI by a considerable amount. This work utilizes observed data from 15 US weather stations along with bias-adjusted output from a climate model to explore the spatial and temporal aspects of the disparity between the HI and the EHI from the recent past out to the end of the 21st century. The underestimate of human heat stress by the HI is found to be largest for the most extreme cases (∼ 5-10° C), which are also the most impactful. Conditions warranting NWS excessive heat warnings are found to increase dramatically from less than 5% of days historically at most stations to more than 90% in the future at some stations. Although, by design, the scope of this work is limited, it demonstrates the need for the adoption of the EHI for both applications and research.
Abstract
Artificial intelligence and machine learning (AI/ML) have attracted a great deal of attention from the atmospheric science community. The explosion of attention on AI/ML development carries implications for the operational community, prompting questions about how novel AI/ML advancements will translate from research into operations. However, the field lacks empirical evidence on how National Weather Service (NWS) forecasters, as key intended users, perceive AI/ML and its use in operational forecasting. This study addresses this crucial gap through structured interviews conducted with 29 NWS forecasters, from October 2021 through July 2023 in which we explored their perceptions of AI/ML in forecasting. We found that forecasters generally prefer the term “machine learning” over “artificial intelligence” and that labeling a product as being AI/ML did not hurt perceptions of the products and made some forecasters more excited about the product. Forecasters also had a wide range of familiarity with AI/ML, and overall they were (tentatively) open to the use of AI/ML in forecasting. We also provide examples of specific areas related to AI/ML that forecasters are excited or hopeful about and that they are concerned or worried about. One concern that was raised in several ways was that AI/ML could replace forecasters or remove them from the forecasting process. However, forecasters expressed a widespread and deep commitment to the best possible forecasts and services to uphold the agency mission using whatever tools or products that are available to assist them. Lastly, we note how forecasters’ perceptions evolved over the course of the study.
Abstract
Artificial intelligence and machine learning (AI/ML) have attracted a great deal of attention from the atmospheric science community. The explosion of attention on AI/ML development carries implications for the operational community, prompting questions about how novel AI/ML advancements will translate from research into operations. However, the field lacks empirical evidence on how National Weather Service (NWS) forecasters, as key intended users, perceive AI/ML and its use in operational forecasting. This study addresses this crucial gap through structured interviews conducted with 29 NWS forecasters, from October 2021 through July 2023 in which we explored their perceptions of AI/ML in forecasting. We found that forecasters generally prefer the term “machine learning” over “artificial intelligence” and that labeling a product as being AI/ML did not hurt perceptions of the products and made some forecasters more excited about the product. Forecasters also had a wide range of familiarity with AI/ML, and overall they were (tentatively) open to the use of AI/ML in forecasting. We also provide examples of specific areas related to AI/ML that forecasters are excited or hopeful about and that they are concerned or worried about. One concern that was raised in several ways was that AI/ML could replace forecasters or remove them from the forecasting process. However, forecasters expressed a widespread and deep commitment to the best possible forecasts and services to uphold the agency mission using whatever tools or products that are available to assist them. Lastly, we note how forecasters’ perceptions evolved over the course of the study.
Abstract
The THINICE field campaign, based from Svalbard in August 2022, provided unique observations of summertime Arctic cyclones, their coupling with cloud cover, and interactions with tropopause polar vortices and sea ice conditions. THINICE was motivated by the need to advance our understanding of these processes and to improve coupled models used to forecast weather and sea ice, as well as long-term projections of climate change in the Arctic. Two research aircraft were deployed with complementary instrumentation. The Safire ATR42 aircraft, equipped with the RALI (RAdar-LIdar) remote sensing instrumentation and in-situ cloud microphysics probes, flew in the mid-troposphere to observe the wind and multi-phase cloud structure of Arctic cyclones. The British Antarctic Survey MASIN aircraft flew at low levels measuring sea-ice properties, including surface brightness temperature, albedo and roughness, and the turbulent fluxes that mediate exchange of heat and momentum between the atmosphere and the surface. Long duration instrumented balloons, operated by WindBorne Systems, sampled meteorological conditions within both cyclones and tropospheric polar vortices across the Arctic. Several novel findings are highlighted. Intense, shallow low-level jets along warm fronts were observed within three Arctic cyclones using the Doppler radar and turbulence probes. A detailed depiction of the interweaving layers of ice crystals and supercooled liquid water in mixed-phase clouds is revealed through the synergistic combination of the Doppler radar, the lidar and in-situ microphysical probes. Measurements of near-surface turbulent fluxes combined with remote sensing measurements of sea ice properties are being used to characterize atmosphere-sea ice interactions in the marginal ice zone.
Abstract
The THINICE field campaign, based from Svalbard in August 2022, provided unique observations of summertime Arctic cyclones, their coupling with cloud cover, and interactions with tropopause polar vortices and sea ice conditions. THINICE was motivated by the need to advance our understanding of these processes and to improve coupled models used to forecast weather and sea ice, as well as long-term projections of climate change in the Arctic. Two research aircraft were deployed with complementary instrumentation. The Safire ATR42 aircraft, equipped with the RALI (RAdar-LIdar) remote sensing instrumentation and in-situ cloud microphysics probes, flew in the mid-troposphere to observe the wind and multi-phase cloud structure of Arctic cyclones. The British Antarctic Survey MASIN aircraft flew at low levels measuring sea-ice properties, including surface brightness temperature, albedo and roughness, and the turbulent fluxes that mediate exchange of heat and momentum between the atmosphere and the surface. Long duration instrumented balloons, operated by WindBorne Systems, sampled meteorological conditions within both cyclones and tropospheric polar vortices across the Arctic. Several novel findings are highlighted. Intense, shallow low-level jets along warm fronts were observed within three Arctic cyclones using the Doppler radar and turbulence probes. A detailed depiction of the interweaving layers of ice crystals and supercooled liquid water in mixed-phase clouds is revealed through the synergistic combination of the Doppler radar, the lidar and in-situ microphysical probes. Measurements of near-surface turbulent fluxes combined with remote sensing measurements of sea ice properties are being used to characterize atmosphere-sea ice interactions in the marginal ice zone.
Abstract
Accurate sub-seasonal (2-8 weeks) prediction of monsoon precipitation is crucial for mitigating flood and heatwave disasters caused by intra-seasonal variability (ISV). However, current state-of-the-art sub-seasonal-to-seasonal (S2S) models have limited prediction skills beyond one week when predicting weekly precipitation. Our findings suggest that predictability primarily arises from strong ISV events, and the prediction skills for ISV events depend on the propagation stability of preceding signals, regardless of models. This allows us to identify opportunities and barriers (OBs) within S2S models, clarifying what the models can and cannot achieve in ISV event prediction. Focusing on the complex East Asian summer monsoon (EASM), we discover that stable propagation of Eurasian and tropical atmospheric wave trains towards East Asia serves as an opportunity. This opportunity offers a one-week leading prediction skill of up to 0.85 and skillful prediction up to 13 days ahead for 43% of all ISV events. However, the Tibetan Plateau barrier highlights the limitation of EASM predictability. Identifying these OBs will help us gain confidence in making more accurate sub-seasonal prediction.
Abstract
Accurate sub-seasonal (2-8 weeks) prediction of monsoon precipitation is crucial for mitigating flood and heatwave disasters caused by intra-seasonal variability (ISV). However, current state-of-the-art sub-seasonal-to-seasonal (S2S) models have limited prediction skills beyond one week when predicting weekly precipitation. Our findings suggest that predictability primarily arises from strong ISV events, and the prediction skills for ISV events depend on the propagation stability of preceding signals, regardless of models. This allows us to identify opportunities and barriers (OBs) within S2S models, clarifying what the models can and cannot achieve in ISV event prediction. Focusing on the complex East Asian summer monsoon (EASM), we discover that stable propagation of Eurasian and tropical atmospheric wave trains towards East Asia serves as an opportunity. This opportunity offers a one-week leading prediction skill of up to 0.85 and skillful prediction up to 13 days ahead for 43% of all ISV events. However, the Tibetan Plateau barrier highlights the limitation of EASM predictability. Identifying these OBs will help us gain confidence in making more accurate sub-seasonal prediction.
Abstract
Quasi-linear convective systems (QLCSs) are responsible for approximately a quarter of all tornado events in the United States, but no field campaigns have focused specifically on collecting data to understand QLCS tornadogenesis. The Propagation, Evolution, and Rotation in Linear Storms (PERiLS) project was the first observational study of tornadoes associated with QLCSs ever undertaken. Participants were drawn from more than 10 universities, laboratories, and institutes, with over 100 students participating in field activities. The PERiLS field phases spanned 2 years, late winters and early springs of 2022 and 2023, to increase the probability of intercepting significant tornadic QLCS events in a range of large-scale and local environments. The field phases of PERiLS collected data in nine tornadic and nontornadic QLCSs with unprecedented detail and diversity of measurements. The design and execution of the PERiLS field phase and preliminary data and ongoing analyses are shown.
Abstract
Quasi-linear convective systems (QLCSs) are responsible for approximately a quarter of all tornado events in the United States, but no field campaigns have focused specifically on collecting data to understand QLCS tornadogenesis. The Propagation, Evolution, and Rotation in Linear Storms (PERiLS) project was the first observational study of tornadoes associated with QLCSs ever undertaken. Participants were drawn from more than 10 universities, laboratories, and institutes, with over 100 students participating in field activities. The PERiLS field phases spanned 2 years, late winters and early springs of 2022 and 2023, to increase the probability of intercepting significant tornadic QLCS events in a range of large-scale and local environments. The field phases of PERiLS collected data in nine tornadic and nontornadic QLCSs with unprecedented detail and diversity of measurements. The design and execution of the PERiLS field phase and preliminary data and ongoing analyses are shown.
Abstract
The World Meteorological Organization (WMO) is a specialized agency of the United Nations (UN) system, with an intergovernmental mandate for coordinating the generation and exchange of weather, climate and water information across its Members. WMO has played a vital role in coordinating production and dissemination of weather forecasts from short to medium range whereby global weather forecasts from large operational centers are made available to all WMO Members to serve needs of stakeholders at the local level. In recent decades, there has also been an increasing demand for similar forecasts on longer lead times that include prediction on sub-seasonal, seasonal, and annual to decadal leads. To address the increasing requirements for forecast services by Members, WMO has been actively accrediting and coordinating the essential forecast infrastructure that includes provision of forecasts from WMO designated global producing centers and collection of forecasts by lead centers to facilitate the dissemination of information and products to WMO Members and relevant non-governmental organizations. Although the basic ingredients of the infrastructure are now in place, the uptake of the forecast information has been sub-optimal. To engage the community in developing solutions to enhance the utilization of available information, this paper summarizes the WMO infrastructure for long-range forecasts, particularly for seasonal timescale, and follows with a discussion of current issues that are hindering their uptake. Finally, a set of proposals to advance the utilization of the available information from the WMO long-lead forecast infrastructure are discussed.
Abstract
The World Meteorological Organization (WMO) is a specialized agency of the United Nations (UN) system, with an intergovernmental mandate for coordinating the generation and exchange of weather, climate and water information across its Members. WMO has played a vital role in coordinating production and dissemination of weather forecasts from short to medium range whereby global weather forecasts from large operational centers are made available to all WMO Members to serve needs of stakeholders at the local level. In recent decades, there has also been an increasing demand for similar forecasts on longer lead times that include prediction on sub-seasonal, seasonal, and annual to decadal leads. To address the increasing requirements for forecast services by Members, WMO has been actively accrediting and coordinating the essential forecast infrastructure that includes provision of forecasts from WMO designated global producing centers and collection of forecasts by lead centers to facilitate the dissemination of information and products to WMO Members and relevant non-governmental organizations. Although the basic ingredients of the infrastructure are now in place, the uptake of the forecast information has been sub-optimal. To engage the community in developing solutions to enhance the utilization of available information, this paper summarizes the WMO infrastructure for long-range forecasts, particularly for seasonal timescale, and follows with a discussion of current issues that are hindering their uptake. Finally, a set of proposals to advance the utilization of the available information from the WMO long-lead forecast infrastructure are discussed.
Abstract
The World Meteorological Organization’s Lead Centre for Annual-to-Decadal Climate prediction issues operational forecasts annually as guidance for regional climate centers, climate outlook forums and national meteorological and hydrological services. The occurrence of a large volcanic eruption such as that of Mount Pinatubo in 1991, however, would invalidate these forecasts and prompt producers to modify their predictions. To assist and prepare decadal prediction centers for this eventuality, the Volcanic Response activities under the World Climate Research Programme’s Stratosphere-troposphere Processes And their Role in Climate (SPARC) and the Decadal Climate Prediction Project (DCPP) organized a community exercise to respond to a hypothetical large eruption occurring in April 2022. As part of this exercise, the Easy Volcanic Aerosol forcing generator was used to provide stratospheric sulfate aerosol optical properties customized to the configurations of individual decadal prediction models. Participating centers then reran forecasts for 2022-2026 from their original initialization dates, and in most cases also from just before the eruption at the beginning of April 2022, according to two candidate response protocols. This article describes various aspects of this SPARC/DCPP Volcanic Response Readiness Exercise (VolRes-RE), including the hypothesized volcanic event, the modified forecasts under the two protocols from the eight contributing centers, the lessons learned during the coordination and execution of this exercise, and the recommendations to the decadal prediction community for the response to an actual eruption.
Abstract
The World Meteorological Organization’s Lead Centre for Annual-to-Decadal Climate prediction issues operational forecasts annually as guidance for regional climate centers, climate outlook forums and national meteorological and hydrological services. The occurrence of a large volcanic eruption such as that of Mount Pinatubo in 1991, however, would invalidate these forecasts and prompt producers to modify their predictions. To assist and prepare decadal prediction centers for this eventuality, the Volcanic Response activities under the World Climate Research Programme’s Stratosphere-troposphere Processes And their Role in Climate (SPARC) and the Decadal Climate Prediction Project (DCPP) organized a community exercise to respond to a hypothetical large eruption occurring in April 2022. As part of this exercise, the Easy Volcanic Aerosol forcing generator was used to provide stratospheric sulfate aerosol optical properties customized to the configurations of individual decadal prediction models. Participating centers then reran forecasts for 2022-2026 from their original initialization dates, and in most cases also from just before the eruption at the beginning of April 2022, according to two candidate response protocols. This article describes various aspects of this SPARC/DCPP Volcanic Response Readiness Exercise (VolRes-RE), including the hypothesized volcanic event, the modified forecasts under the two protocols from the eight contributing centers, the lessons learned during the coordination and execution of this exercise, and the recommendations to the decadal prediction community for the response to an actual eruption.
Abstract
The National Oceanic and Atmospheric Administration (NOAA) Global Surface Temperature (NOAAGlobalTemp) dataset is widely used for scientific research, operational monitoring, and climate assessment activities. Aligning with NOAA’s mission values, NOAAGlobalTemp has been updated to version 6 (i.e., NGTv6), which includes two enhancements over its predecessor (NGTv5). The first enhancement is the expansion of the spatial coverage to encompass the entire globe and the extension of temporal coverage back to 1850 (an interim version of NOAAGlobalTemp with these features was released in February 2023). The expansion of spatial coverage is accomplished by utilizing surface air temperatures over the Arctic Ocean and by eliminating the data reconstruction mask used in NGTv5 that had suppressed interpolation in data-sparse regions. This change has important implications for global temperature trends since the Arctic region has been warming at a much faster pace, more than four times the global average, in the twenty-first century to date. The second enhancement is the implementation of a methodology based on artificial intelligence (AI) for reconstructing surface air temperature over the global land surface and the Arctic Ocean. The AI model employs an artificial neural network to fill data gaps and is demonstrated to be more robust, stable, and accurate than the previous gap-filling method, particularly in observation-sparse areas such as the polar regions. The model outperforms the previous approach across all evaluated statistical metrics, and the output reaches a stable state more quickly as observations are received, which facilitates climate monitoring.
Abstract
The National Oceanic and Atmospheric Administration (NOAA) Global Surface Temperature (NOAAGlobalTemp) dataset is widely used for scientific research, operational monitoring, and climate assessment activities. Aligning with NOAA’s mission values, NOAAGlobalTemp has been updated to version 6 (i.e., NGTv6), which includes two enhancements over its predecessor (NGTv5). The first enhancement is the expansion of the spatial coverage to encompass the entire globe and the extension of temporal coverage back to 1850 (an interim version of NOAAGlobalTemp with these features was released in February 2023). The expansion of spatial coverage is accomplished by utilizing surface air temperatures over the Arctic Ocean and by eliminating the data reconstruction mask used in NGTv5 that had suppressed interpolation in data-sparse regions. This change has important implications for global temperature trends since the Arctic region has been warming at a much faster pace, more than four times the global average, in the twenty-first century to date. The second enhancement is the implementation of a methodology based on artificial intelligence (AI) for reconstructing surface air temperature over the global land surface and the Arctic Ocean. The AI model employs an artificial neural network to fill data gaps and is demonstrated to be more robust, stable, and accurate than the previous gap-filling method, particularly in observation-sparse areas such as the polar regions. The model outperforms the previous approach across all evaluated statistical metrics, and the output reaches a stable state more quickly as observations are received, which facilitates climate monitoring.
Abstract
This article provides a brief, technical narrative of the WoFS journey to a cloud-based high performance computing (HPC) system, including some of the technological challenges encountered and solutions found. Also discussed are a few new components that are in development for cloud-based WoFS (cb-WoFS), such as the cloud infrastructure project for managing resources, as well as a new web application that manages cb-WoFS runs.
An important initial step in our cloud journey is containerizing all of the compiled applications, such as WRF, GSI, EnKF and their dependencies like NetCDF and MPI. With these applications compiled within a Apptainer container, WoFS can run on any local or cloud-based HPC cluster that supports MPI. Furthermore, an additional software layer was developed that creates and manages cloud vendor resources. This layer, which is referred to as the WoFS Framework, contains the workflow required to run cb-WoFS, as well as management for other aspects of cb-WoFS (including but not limited to creation of HPC pools in the end-to-end workflow, runtime notifications and database management). This additional layer was developed to separate the WoFS business logic from vendor-specific API calls. The WoFS Framework exposes features through its service library, which is then referenced by the newly developed cb-WoFS web application and other cloud applications. This makes WoFS a complete end-to-end cloud-based application, where an administrator can launch a model run, manage resources and view output all within a single web app.
Abstract
This article provides a brief, technical narrative of the WoFS journey to a cloud-based high performance computing (HPC) system, including some of the technological challenges encountered and solutions found. Also discussed are a few new components that are in development for cloud-based WoFS (cb-WoFS), such as the cloud infrastructure project for managing resources, as well as a new web application that manages cb-WoFS runs.
An important initial step in our cloud journey is containerizing all of the compiled applications, such as WRF, GSI, EnKF and their dependencies like NetCDF and MPI. With these applications compiled within a Apptainer container, WoFS can run on any local or cloud-based HPC cluster that supports MPI. Furthermore, an additional software layer was developed that creates and manages cloud vendor resources. This layer, which is referred to as the WoFS Framework, contains the workflow required to run cb-WoFS, as well as management for other aspects of cb-WoFS (including but not limited to creation of HPC pools in the end-to-end workflow, runtime notifications and database management). This additional layer was developed to separate the WoFS business logic from vendor-specific API calls. The WoFS Framework exposes features through its service library, which is then referenced by the newly developed cb-WoFS web application and other cloud applications. This makes WoFS a complete end-to-end cloud-based application, where an administrator can launch a model run, manage resources and view output all within a single web app.
