Browse
Abstract
Did the strong 2023–24 El Niño live up to the hype? While climate prediction is inherently probabilistic, many users compare El Niño events against a deterministic map of expected impacts (e.g., wetter or drier regions). Here, using this event as a guide, we show that no El Niño perfectly matches the ideal image and that observed anomalies will only partially match what was anticipated. In fact, the degree to which the climate anomalies match the expected ENSO impacts tends to scale with the strength of the event. The 2023–24 event generally matched well with ENSO expectations around the United States. However, this will not always be the case, as the analysis shows larger deviations from the historical ENSO pattern of impacts are commonplace, with some climate variables more prone to inconsistencies (e.g., temperature) than others (e.g., precipitation). Users should incorporate this inherent uncertainty in their risk and decision-making analysis.
Abstract
Did the strong 2023–24 El Niño live up to the hype? While climate prediction is inherently probabilistic, many users compare El Niño events against a deterministic map of expected impacts (e.g., wetter or drier regions). Here, using this event as a guide, we show that no El Niño perfectly matches the ideal image and that observed anomalies will only partially match what was anticipated. In fact, the degree to which the climate anomalies match the expected ENSO impacts tends to scale with the strength of the event. The 2023–24 event generally matched well with ENSO expectations around the United States. However, this will not always be the case, as the analysis shows larger deviations from the historical ENSO pattern of impacts are commonplace, with some climate variables more prone to inconsistencies (e.g., temperature) than others (e.g., precipitation). Users should incorporate this inherent uncertainty in their risk and decision-making analysis.
Abstract
In summer 2021, microphysical properties and climate impact of high- and midlatitude ice clouds over Europe and the North Atlantic were studied during the Cirrus High Latitude (CIRRUS-HL) airborne field campaign. The related forecasting and flight planning tasks provided a testbed for interactive 3D visual analysis. Operational analyses and forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) were visualized with the open-source software “Met.3D.” A combination of traditional 2D displays with innovative 3D views in the interactive visualization framework facilitated rapid and comprehensive exploration of the NWP data. By this means, the benefit of interactive 3D visual forecast products in the routine flight planning procedure was evaluated. Here, we describe the use of 3D tropopause and cloud visualizations during a convective event over the Alps, which became one of the CIRRUS-HL observation targets. For the planning of the research flight on 8 July 2021, our analysis revealed that simulated strong convective updrafts locally disturb the tropopause and inject ice water across the dynamical tropopause into the lower stratosphere. The presented example provides a novel 3D perspective of convective overshooting in a global NWP model and its impact on the tropopause and lower stratosphere. The case study shall encourage the atmospheric science community to further evaluate the use of modern 3D visualization capabilities for NWP analysis.
Abstract
In summer 2021, microphysical properties and climate impact of high- and midlatitude ice clouds over Europe and the North Atlantic were studied during the Cirrus High Latitude (CIRRUS-HL) airborne field campaign. The related forecasting and flight planning tasks provided a testbed for interactive 3D visual analysis. Operational analyses and forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) were visualized with the open-source software “Met.3D.” A combination of traditional 2D displays with innovative 3D views in the interactive visualization framework facilitated rapid and comprehensive exploration of the NWP data. By this means, the benefit of interactive 3D visual forecast products in the routine flight planning procedure was evaluated. Here, we describe the use of 3D tropopause and cloud visualizations during a convective event over the Alps, which became one of the CIRRUS-HL observation targets. For the planning of the research flight on 8 July 2021, our analysis revealed that simulated strong convective updrafts locally disturb the tropopause and inject ice water across the dynamical tropopause into the lower stratosphere. The presented example provides a novel 3D perspective of convective overshooting in a global NWP model and its impact on the tropopause and lower stratosphere. The case study shall encourage the atmospheric science community to further evaluate the use of modern 3D visualization capabilities for NWP analysis.
Abstract
During the week of Christmas 2021, winter storms pummeled the Pacific Northwest and broke daily temperature and snowfall records in scores, especially west of the Cascades and notably in Oregon. With La Niña ruling the tropical Pacific, the record-setting, disruptive snowfall during Christmas week raised questions about its origin, especially as the seasonal outlook was for below-normal precipitation. We show that Pacific–North American (PNA) teleconnection—a well-documented subseasonal variability pattern during winter—reigned over the region in its negative phase; it was the strongest 7-day PNA episode in December in more than 50 years. It led to robust northwesterly onshore flow, whose interaction with the Coastal, Cascade, and Sierra ranges led to blockbuster snowfall and precipitation. Note that one seldom encounters circulation anomalies consisting of just one winter teleconnection pattern. Also worth noting is the tremendous power of subseasonal variability in recharging Western water resources in the context of the seasonal gloom from a La Niña–intensified West Coast drought.
Abstract
During the week of Christmas 2021, winter storms pummeled the Pacific Northwest and broke daily temperature and snowfall records in scores, especially west of the Cascades and notably in Oregon. With La Niña ruling the tropical Pacific, the record-setting, disruptive snowfall during Christmas week raised questions about its origin, especially as the seasonal outlook was for below-normal precipitation. We show that Pacific–North American (PNA) teleconnection—a well-documented subseasonal variability pattern during winter—reigned over the region in its negative phase; it was the strongest 7-day PNA episode in December in more than 50 years. It led to robust northwesterly onshore flow, whose interaction with the Coastal, Cascade, and Sierra ranges led to blockbuster snowfall and precipitation. Note that one seldom encounters circulation anomalies consisting of just one winter teleconnection pattern. Also worth noting is the tremendous power of subseasonal variability in recharging Western water resources in the context of the seasonal gloom from a La Niña–intensified West Coast drought.
Abstract
On January 2021, the heaviest snowfall in five decades hit central Spain, especially affecting Madrid. The city’s Barajas International Airport closed, along with a number of roads, and all trains to and from Madrid were cancelled. This storm was named Filomena by the Spanish Meteorological Agency (AEMET), and produced continuous snowfall in Spain on 7–10 January. The observed snow depth was around 50 cm in 24 h in Madrid, and even higher in other areas of Spain. However, the measured accumulation of national precipitation gauges was not consistent with the observed accumulated snow on the ground and with the modeled weather forecast. The undercatch of solid precipitation was the primary reason for this inconsistency. This undercatch was quantified using transfer functions developed from the World Meteorological Organization (WMO) Solid Precipitation Intercomparison Experiment (SPICE). Results show that an underestimation of 20%–30% of solid precipitation in large areas of Spain was observed, with some areas experiencing even larger differences. Without adjustments, it was impossible to accurately validate the model forecast. The adjusted precipitation was also more realistically distributed, and it was more consistent with all the damage that occurred. The same methods can be applied to other snowfall events occurring anywhere in the world, and also using different precipitation gauges and/or models. This an example of the type of extreme events that modelers, forecasters, and climatologists should be aware of to avoid misinterpreting differences between modeled precipitation, observed precipitation, and nowcasting.
Abstract
On January 2021, the heaviest snowfall in five decades hit central Spain, especially affecting Madrid. The city’s Barajas International Airport closed, along with a number of roads, and all trains to and from Madrid were cancelled. This storm was named Filomena by the Spanish Meteorological Agency (AEMET), and produced continuous snowfall in Spain on 7–10 January. The observed snow depth was around 50 cm in 24 h in Madrid, and even higher in other areas of Spain. However, the measured accumulation of national precipitation gauges was not consistent with the observed accumulated snow on the ground and with the modeled weather forecast. The undercatch of solid precipitation was the primary reason for this inconsistency. This undercatch was quantified using transfer functions developed from the World Meteorological Organization (WMO) Solid Precipitation Intercomparison Experiment (SPICE). Results show that an underestimation of 20%–30% of solid precipitation in large areas of Spain was observed, with some areas experiencing even larger differences. Without adjustments, it was impossible to accurately validate the model forecast. The adjusted precipitation was also more realistically distributed, and it was more consistent with all the damage that occurred. The same methods can be applied to other snowfall events occurring anywhere in the world, and also using different precipitation gauges and/or models. This an example of the type of extreme events that modelers, forecasters, and climatologists should be aware of to avoid misinterpreting differences between modeled precipitation, observed precipitation, and nowcasting.
Abstract
The Joint Typhoon Warning Center (JTWC) utilized new space-based environmental monitoring (SBEM) data alongside traditional data to adjust JTWC tropical cyclone (TC) intensity and structure estimates during production of the official 2019 Best Track dataset. Intensity estimates from multiple platforms such as Advanced Microwave Scanning Radiometer-2 (AMSR2), the Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) radiometers, and synthetic aperture radar (SAR), along with objective Dvorak and satellite consensus algorithms, not only aided the poststorm best track (BT) process, but also provided robust data that supported real-time analysis and forecasting. This summary attempts to communicate with the TC community the extent to which these new data affected the 2019 official BT data, how JTWC utilized these new data in the poststorm BT process, and provide examples of how these data influenced forecaster decision-making in real time. This paper makes no attempt to validate the accuracy of the wind speed estimates from these methods (SAR, SMAP/SMOS, or AMSR2) and does not outline the entirety of the JTWC process for determining TC intensity, but it does outline, briefly, the impact of these new datasets on the final JTWC BT intensity estimates and on real-time analysis. These methodologies are valuable sources of cyclone intensity estimates in an otherwise data-sparse area of responsibility, and in many cases provide critical data not captured by traditional methods alone, which are detailed further in this summary.
Abstract
The Joint Typhoon Warning Center (JTWC) utilized new space-based environmental monitoring (SBEM) data alongside traditional data to adjust JTWC tropical cyclone (TC) intensity and structure estimates during production of the official 2019 Best Track dataset. Intensity estimates from multiple platforms such as Advanced Microwave Scanning Radiometer-2 (AMSR2), the Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) radiometers, and synthetic aperture radar (SAR), along with objective Dvorak and satellite consensus algorithms, not only aided the poststorm best track (BT) process, but also provided robust data that supported real-time analysis and forecasting. This summary attempts to communicate with the TC community the extent to which these new data affected the 2019 official BT data, how JTWC utilized these new data in the poststorm BT process, and provide examples of how these data influenced forecaster decision-making in real time. This paper makes no attempt to validate the accuracy of the wind speed estimates from these methods (SAR, SMAP/SMOS, or AMSR2) and does not outline the entirety of the JTWC process for determining TC intensity, but it does outline, briefly, the impact of these new datasets on the final JTWC BT intensity estimates and on real-time analysis. These methodologies are valuable sources of cyclone intensity estimates in an otherwise data-sparse area of responsibility, and in many cases provide critical data not captured by traditional methods alone, which are detailed further in this summary.
Abstract
The Multi-Radar Multi-Sensor (MRMS) system is an operational, state-of-the-science hydrometeorological data analysis and nowcasting framework that combines data from multiple radar networks, satellites, surface observational systems, and numerical weather prediction models to produce a suite of real-time, decision-support products every 2 min over the contiguous United States and southern Canada. The Flooded Locations and Simulated Hydrograph (FLASH) component of the MRMS system was designed for the monitoring and prediction of flash floods across small time and spatial scales required for urban areas given their rapid hydrologic response to precipitation. Developed at the National Severe Storms Laboratory in collaboration with the Cooperative Institute for Mesoscale Meteorological Studies (CIMMS) and other research entities, the objective for MRMS and FLASH is to be the world’s most advanced system for severe weather and storm-scale hydrometeorology, leveraging the latest science and observation systems to produce the most accurate and reliable hydrometeorological and severe weather analyses. NWS forecasters, the public, and the private sector utilize a variety of products from the MRMS and FLASH systems for hydrometeorological situational awareness and to provide warnings to the public and other users about potential impacts from flash flooding. This article will examine the performance of hydrometeorological products from MRMS and FLASH and provide perspectives on how NWS forecasters use these products in the prediction of flash flood events with an emphasis on the urban environment.
Abstract
The Multi-Radar Multi-Sensor (MRMS) system is an operational, state-of-the-science hydrometeorological data analysis and nowcasting framework that combines data from multiple radar networks, satellites, surface observational systems, and numerical weather prediction models to produce a suite of real-time, decision-support products every 2 min over the contiguous United States and southern Canada. The Flooded Locations and Simulated Hydrograph (FLASH) component of the MRMS system was designed for the monitoring and prediction of flash floods across small time and spatial scales required for urban areas given their rapid hydrologic response to precipitation. Developed at the National Severe Storms Laboratory in collaboration with the Cooperative Institute for Mesoscale Meteorological Studies (CIMMS) and other research entities, the objective for MRMS and FLASH is to be the world’s most advanced system for severe weather and storm-scale hydrometeorology, leveraging the latest science and observation systems to produce the most accurate and reliable hydrometeorological and severe weather analyses. NWS forecasters, the public, and the private sector utilize a variety of products from the MRMS and FLASH systems for hydrometeorological situational awareness and to provide warnings to the public and other users about potential impacts from flash flooding. This article will examine the performance of hydrometeorological products from MRMS and FLASH and provide perspectives on how NWS forecasters use these products in the prediction of flash flood events with an emphasis on the urban environment.
ABSTRACT
Wind gusts are common to everyday life and affect a wide range of interests including wind energy, structural design, forestry, and fire danger. Strong gusts are a common environmental hazard that can damage buildings, bridges, aircraft, and trains, and interrupt electric power distribution, air traffic, waterways transport, and port operations. Despite representing the component of wind most likely to be associated with serious and costly hazards, reliable forecasts of peak wind gusts have remained elusive. A project at the University of Wisconsin–Milwaukee is addressing the need for improved peak gust forecasts with the development of the meteorologically stratified gust factor (MSGF) model. The MSGF model combines gust factors (the ratio of peak wind gust to average wind speed) with wind speed and direction forecasts to predict hourly peak wind gusts. The MSGF method thus represents a simple, viable option for the operational prediction of peak wind gusts. Here we describe the results of a project designed to provide the site-specific gust factors necessary for operational use of the MSGF model at a large number of locations across the United States. Gust web diagrams depicting the wind speed– and wind direction–stratified gust factors, as well as peak gust climatologies, are presented for all sites analyzed.
ABSTRACT
Wind gusts are common to everyday life and affect a wide range of interests including wind energy, structural design, forestry, and fire danger. Strong gusts are a common environmental hazard that can damage buildings, bridges, aircraft, and trains, and interrupt electric power distribution, air traffic, waterways transport, and port operations. Despite representing the component of wind most likely to be associated with serious and costly hazards, reliable forecasts of peak wind gusts have remained elusive. A project at the University of Wisconsin–Milwaukee is addressing the need for improved peak gust forecasts with the development of the meteorologically stratified gust factor (MSGF) model. The MSGF model combines gust factors (the ratio of peak wind gust to average wind speed) with wind speed and direction forecasts to predict hourly peak wind gusts. The MSGF method thus represents a simple, viable option for the operational prediction of peak wind gusts. Here we describe the results of a project designed to provide the site-specific gust factors necessary for operational use of the MSGF model at a large number of locations across the United States. Gust web diagrams depicting the wind speed– and wind direction–stratified gust factors, as well as peak gust climatologies, are presented for all sites analyzed.
Abstract
Atmospheric rivers (ARs) are responsible for some of the hydroclimatic extremes around the world. Their mechanisms and contribution to flooding in the Middle East are relatively poorly understood. This study shows that the record floods during March 2019 across the Middle East were caused by a powerful AR, originated from the North Atlantic Ocean. Iran, in particular, was substantially affected by the floods. The nearly 9,000-km-long AR propagated across North Africa and the Middle East, and was fed by additional moisture from several other sources on its pathway. Simultaneous presence of a midlatitude system and a subtropical jet facilitated the moisture supply. The AR, as passing over the Zagros Mountains, produced record rainfall induced by the orographic forcing. The resulting floods caused widespread damage to infrastructures and left a death toll of at least 76 in Iran.
Abstract
Atmospheric rivers (ARs) are responsible for some of the hydroclimatic extremes around the world. Their mechanisms and contribution to flooding in the Middle East are relatively poorly understood. This study shows that the record floods during March 2019 across the Middle East were caused by a powerful AR, originated from the North Atlantic Ocean. Iran, in particular, was substantially affected by the floods. The nearly 9,000-km-long AR propagated across North Africa and the Middle East, and was fed by additional moisture from several other sources on its pathway. Simultaneous presence of a midlatitude system and a subtropical jet facilitated the moisture supply. The AR, as passing over the Zagros Mountains, produced record rainfall induced by the orographic forcing. The resulting floods caused widespread damage to infrastructures and left a death toll of at least 76 in Iran.
Abstract
On 23 July 2018, Attica, Greece, was impacted by a major wildfire that took place in a wildland–urban interface area and exhibited extreme fire behavior, characterized by a very high rate of spread. One-hundred civilian fatalities were registered, establishing this wildfire as the second-deadliest weather-related natural disaster in Greece, following the heat wave of July 1987. On the day of the deadly wildfire, a very strong westerly flow was blowing for more than 10 h over Attica. Wind gusts up to 30–34 m s−1 occurred over the mountainous areas of Attica, with 20–25 m s−1 in the city of Athens and surrounding suburban areas. This strong westerly flow interacted with the local topography and acted as downslope flow over the eastern part of Attica, with temperatures rising up to 39°C and relative humidity dropping to 19% prior to the onset of the wildfire. These weather elements are widely acknowledged as the major contributing factors to extreme fire behavior. WRF-SFIRE correctly predicted the spatiotemporal distribution of the fire spread and demonstrated its utility for fire spread warning purposes.
Abstract
On 23 July 2018, Attica, Greece, was impacted by a major wildfire that took place in a wildland–urban interface area and exhibited extreme fire behavior, characterized by a very high rate of spread. One-hundred civilian fatalities were registered, establishing this wildfire as the second-deadliest weather-related natural disaster in Greece, following the heat wave of July 1987. On the day of the deadly wildfire, a very strong westerly flow was blowing for more than 10 h over Attica. Wind gusts up to 30–34 m s−1 occurred over the mountainous areas of Attica, with 20–25 m s−1 in the city of Athens and surrounding suburban areas. This strong westerly flow interacted with the local topography and acted as downslope flow over the eastern part of Attica, with temperatures rising up to 39°C and relative humidity dropping to 19% prior to the onset of the wildfire. These weather elements are widely acknowledged as the major contributing factors to extreme fire behavior. WRF-SFIRE correctly predicted the spatiotemporal distribution of the fire spread and demonstrated its utility for fire spread warning purposes.
