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Abstract
Harsh winters and hazards such as blizzards are synonymous with the northern Great Plains of the United States. Studying these events is difficult; the juxtaposition of cold temperatures and high winds makes microphysical observations of both blowing and falling snow challenging. Historically, these observations have been provided by costly hydrometeor imagers that have been deployed for field campaigns or at select observation sites. This has slowed the development and validation of microphysics parameterizations and remote-sensing retrievals of various properties. If cheaper, more mobile instrumentation can be developed, this progress can be accelerated. Further, lowering price barriers can make deployment of instrumentation feasible for education and outreach purposes.
The Blowing Snow Observations at the University of North Dakota: Education through Research (BLOWN-UNDER) Campaign took place during the winter of 2019-2020 to investigate strategies for obtaining microphysical measurements in the harsh North Dakota winter. Student led, the project blended education, outreach, and scientific objectives. While a variety of in-situ and remote-sensing instruments were deployed for the campaign, the most novel aspect of the project was the development and deployment of OSCRE, the Open Snowflake Camera for Research and Education. Images from this instrument were combined with winter weather educational modules to describe properties of snow to the public, K-12 students, and members of indigenous communities through a tribal outreach program. Along with an educational deployment of a Doppler on Wheels mobile radar, nearly 1000 individuals were reached during the project.
Abstract
Harsh winters and hazards such as blizzards are synonymous with the northern Great Plains of the United States. Studying these events is difficult; the juxtaposition of cold temperatures and high winds makes microphysical observations of both blowing and falling snow challenging. Historically, these observations have been provided by costly hydrometeor imagers that have been deployed for field campaigns or at select observation sites. This has slowed the development and validation of microphysics parameterizations and remote-sensing retrievals of various properties. If cheaper, more mobile instrumentation can be developed, this progress can be accelerated. Further, lowering price barriers can make deployment of instrumentation feasible for education and outreach purposes.
The Blowing Snow Observations at the University of North Dakota: Education through Research (BLOWN-UNDER) Campaign took place during the winter of 2019-2020 to investigate strategies for obtaining microphysical measurements in the harsh North Dakota winter. Student led, the project blended education, outreach, and scientific objectives. While a variety of in-situ and remote-sensing instruments were deployed for the campaign, the most novel aspect of the project was the development and deployment of OSCRE, the Open Snowflake Camera for Research and Education. Images from this instrument were combined with winter weather educational modules to describe properties of snow to the public, K-12 students, and members of indigenous communities through a tribal outreach program. Along with an educational deployment of a Doppler on Wheels mobile radar, nearly 1000 individuals were reached during the project.
Abstract
For the first time in over 50 years, the United States has, at the direction of Congress, restructured the way in which Federal departments and agencies coordinate to advance meteorological services. The new framework, known as the Interagency Council for Advancing Meteorological Services (ICAMS), encompasses activities spanning local weather to global climate using an Earth system approach. Compared to the previous structure, ICAMS provides a simplified, streamlined framework for coordination across all stakeholders in implementing policies and practices associated with the broad set of services needed by the United States now and into the future. ICAMS also provides improved pathways for research and services integration, as well as mechanisms to more effectively engage the broader community, including academia, industry, nonprofit organizations, and particularly the next generation of educators, researchers, and operational practitioners.
Abstract
For the first time in over 50 years, the United States has, at the direction of Congress, restructured the way in which Federal departments and agencies coordinate to advance meteorological services. The new framework, known as the Interagency Council for Advancing Meteorological Services (ICAMS), encompasses activities spanning local weather to global climate using an Earth system approach. Compared to the previous structure, ICAMS provides a simplified, streamlined framework for coordination across all stakeholders in implementing policies and practices associated with the broad set of services needed by the United States now and into the future. ICAMS also provides improved pathways for research and services integration, as well as mechanisms to more effectively engage the broader community, including academia, industry, nonprofit organizations, and particularly the next generation of educators, researchers, and operational practitioners.
Abstract
Inclusion of unmanned aircraft systems (UAS) into the weather surveillance network has the potential to improve short-term (< 1 day) weather forecasts through direct integration of UAS-collected data into the forecast process and assimilation into numerical weather prediction models. However, one of the primary means by which the value of any new sensing platform can be assessed is through consultation with principal stakeholders. National Weather Service (NWS) forecasters are principal stakeholders responsible for the issuance of short-term forecasts. The purpose of the work presented here is to use results from a survey of 630 NWS forecasters to assess critical data gaps that impact short-term forecast accuracy, and explore the potential role of UAS in filling these gaps.
NWS forecasters view winter precipitation, icing, flood, lake-effect/enhanced snow, turbulence, and waves as the phenomena principally impacted by data gaps. Of the ten high-priority weather-related characteristics that need to be observed to fill critical data gaps, seven are either measures of precipitation or related to precipitation-producing phenomena. The three most important UAS capabilities/characteristics required for useful data for weather forecasting are real- or near-real-time data, the ability to integrate UAS data with additional data gathered by other systems, and UASs equipped with cameras to verify forecasts and monitor weather. Of the three operation modes offered for forecasters to consider, targeted surveillance is considered to be the most important compared to fixed site profiling or transects between fixed sites.
Abstract
Inclusion of unmanned aircraft systems (UAS) into the weather surveillance network has the potential to improve short-term (< 1 day) weather forecasts through direct integration of UAS-collected data into the forecast process and assimilation into numerical weather prediction models. However, one of the primary means by which the value of any new sensing platform can be assessed is through consultation with principal stakeholders. National Weather Service (NWS) forecasters are principal stakeholders responsible for the issuance of short-term forecasts. The purpose of the work presented here is to use results from a survey of 630 NWS forecasters to assess critical data gaps that impact short-term forecast accuracy, and explore the potential role of UAS in filling these gaps.
NWS forecasters view winter precipitation, icing, flood, lake-effect/enhanced snow, turbulence, and waves as the phenomena principally impacted by data gaps. Of the ten high-priority weather-related characteristics that need to be observed to fill critical data gaps, seven are either measures of precipitation or related to precipitation-producing phenomena. The three most important UAS capabilities/characteristics required for useful data for weather forecasting are real- or near-real-time data, the ability to integrate UAS data with additional data gathered by other systems, and UASs equipped with cameras to verify forecasts and monitor weather. Of the three operation modes offered for forecasters to consider, targeted surveillance is considered to be the most important compared to fixed site profiling or transects between fixed sites.
Abstract
With their extensive coverage, marine low clouds greatly impact global climate. Presently, marine low clouds are poorly represented in global climate models, and the response of marine low clouds to changes in atmospheric greenhouse gases and aerosols remains the major source of uncertainty in climate simulations. The Eastern North Atlantic (ENA) is a region of persistent but diverse subtropical marine boundary layer clouds, whose albedo and precipitation are highly susceptible to perturbations in aerosol properties. In addition, the ENA is periodically impacted by continental aerosols, making it an excellent location to study the cloud condensation nuclei (CCN) budget in a remote marine region periodically perturbed by anthropogenic emissions, and to investigate the impacts of long-range transport of aerosols on remote marine clouds. The Aerosol and Cloud Experiments in Eastern North Atlantic (ACE-ENA) campaign was motivated by the need of comprehensive in-situ measurements for improving the understanding of marine boundary layer CCN budget, cloud and drizzle microphysics, and the impact of aerosol on marine low cloud and precipitation. The airborne deployments took place from June 21 to July 20, 2017 and January 15 to February 18, 2018 in the Azores. The flights were designed to maximize the synergy between in-situ airborne measurements and ongoing long-term observations at a ground site. Here we present measurements, observation strategy, meteorological conditions during the campaign, and preliminary findings. Finally, we discuss future analyses and modeling studies that improve the understanding and representation of marine boundary layer aerosols, clouds, precipitation, and the interactions among them.
Abstract
With their extensive coverage, marine low clouds greatly impact global climate. Presently, marine low clouds are poorly represented in global climate models, and the response of marine low clouds to changes in atmospheric greenhouse gases and aerosols remains the major source of uncertainty in climate simulations. The Eastern North Atlantic (ENA) is a region of persistent but diverse subtropical marine boundary layer clouds, whose albedo and precipitation are highly susceptible to perturbations in aerosol properties. In addition, the ENA is periodically impacted by continental aerosols, making it an excellent location to study the cloud condensation nuclei (CCN) budget in a remote marine region periodically perturbed by anthropogenic emissions, and to investigate the impacts of long-range transport of aerosols on remote marine clouds. The Aerosol and Cloud Experiments in Eastern North Atlantic (ACE-ENA) campaign was motivated by the need of comprehensive in-situ measurements for improving the understanding of marine boundary layer CCN budget, cloud and drizzle microphysics, and the impact of aerosol on marine low cloud and precipitation. The airborne deployments took place from June 21 to July 20, 2017 and January 15 to February 18, 2018 in the Azores. The flights were designed to maximize the synergy between in-situ airborne measurements and ongoing long-term observations at a ground site. Here we present measurements, observation strategy, meteorological conditions during the campaign, and preliminary findings. Finally, we discuss future analyses and modeling studies that improve the understanding and representation of marine boundary layer aerosols, clouds, precipitation, and the interactions among them.
Abstract
Teleconnections are sources of predictability for regional weather and climate but the relative contributions of different teleconnections to regional anomalies are usually not understood. While physical knowledge about the involved mechanisms is often available, how to quantify a particular causal pathway from data is usually unclear. Here we argue for adopting a causal inference-based framework in the statistical analysis of teleconnections to overcome this challenge. A causal approach requires explicitly including expert knowledge in the statistical analysis, which allows one to draw quantitative conclusions. We illustrate some of the key concepts of this theory with concrete examples of well-known atmospheric teleconnections. We further discuss the particular challenges and advantages these imply for climate science and argue that a systematic causal approach to statistical inference should become standard practice in the study of teleconnections.
Abstract
Teleconnections are sources of predictability for regional weather and climate but the relative contributions of different teleconnections to regional anomalies are usually not understood. While physical knowledge about the involved mechanisms is often available, how to quantify a particular causal pathway from data is usually unclear. Here we argue for adopting a causal inference-based framework in the statistical analysis of teleconnections to overcome this challenge. A causal approach requires explicitly including expert knowledge in the statistical analysis, which allows one to draw quantitative conclusions. We illustrate some of the key concepts of this theory with concrete examples of well-known atmospheric teleconnections. We further discuss the particular challenges and advantages these imply for climate science and argue that a systematic causal approach to statistical inference should become standard practice in the study of teleconnections.
Capsule
The field program has contributed to improvements in tropical cyclone intensity forecasts through aircraft observations. Future strategies will broaden these efforts by focusing on forecasts of storm hazards.
Capsule
The field program has contributed to improvements in tropical cyclone intensity forecasts through aircraft observations. Future strategies will broaden these efforts by focusing on forecasts of storm hazards.
Abstract
Better understanding and quantification of river floods for very local and flashy events calls for modeling capability at fine spatial and temporal scales. However, long-term discharge records with a global coverage suitable for extreme events analysis are still lacking. Here, grounded on recent breakthroughs in global runoff hydrology, river modeling, high resolution hydrography, and climate reanalysis, we developed a 3-hourly river discharge record globally for 2.94 million river reaches during the 40-year period of 1980-2019. The underlying modeling chain consists of the VIC land surface model (0.05°, 3-hourly) that is well calibrated and bias corrected and the RAPID routing model (2.94 million river and catchment vectors), with precipitation input from MSWEP and other meteorological fields downscaled from ERA5. Flood events (above 2-year return) and their characteristics (number, spatial distribution, and seasonality) were extracted and studied. Validations against 3-hourly flow records from 6,000+ gauges in CONUS and daily records from 14,000+ gauges globally show good modeling performance across all flow ranges, good skills in reconstructing flood events (high extremes), and the benefit of (and need for) sub-daily modeling. This data record, referred as Global Reach-level Flood Reanalysis (GRFR), is publicly available at https://www.reachhydro.org/home/records/grfr.
Abstract
Better understanding and quantification of river floods for very local and flashy events calls for modeling capability at fine spatial and temporal scales. However, long-term discharge records with a global coverage suitable for extreme events analysis are still lacking. Here, grounded on recent breakthroughs in global runoff hydrology, river modeling, high resolution hydrography, and climate reanalysis, we developed a 3-hourly river discharge record globally for 2.94 million river reaches during the 40-year period of 1980-2019. The underlying modeling chain consists of the VIC land surface model (0.05°, 3-hourly) that is well calibrated and bias corrected and the RAPID routing model (2.94 million river and catchment vectors), with precipitation input from MSWEP and other meteorological fields downscaled from ERA5. Flood events (above 2-year return) and their characteristics (number, spatial distribution, and seasonality) were extracted and studied. Validations against 3-hourly flow records from 6,000+ gauges in CONUS and daily records from 14,000+ gauges globally show good modeling performance across all flow ranges, good skills in reconstructing flood events (high extremes), and the benefit of (and need for) sub-daily modeling. This data record, referred as Global Reach-level Flood Reanalysis (GRFR), is publicly available at https://www.reachhydro.org/home/records/grfr.
Capsule
Small weather-sensing Uncrewed Aircraft Systems are becoming reliable and accurate enough to be considered as a cost-effective solution for filling observational gaps that could enhance National Meteorological and Hydrological Services around the world.
Capsule
Small weather-sensing Uncrewed Aircraft Systems are becoming reliable and accurate enough to be considered as a cost-effective solution for filling observational gaps that could enhance National Meteorological and Hydrological Services around the world.
