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- Author or Editor: Howard J. Diamond x
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Abstract
The new South Pacific Enhanced Archive for Tropical Cyclones (SPEArTC) dataset provides an opportunity to develop a more complete climatology of tropical cyclones (TCs) in the southwest Pacific. Here, spatial patterns and characteristics of TCs for the 41-yr period beginning with the 1969/70 season are related to phases of the El Niño–Southern Oscillation (ENSO), taking into account the degree of ocean–atmosphere coupling. Twentieth-century reanalysis data and the coupled ENSO index (CEI) were used to investigate TC genesis areas and climate diagnostics in the extratropical transition (ETT) region at and south of 25°S during different CEI ENSO phases. This is the first study looking at CEI-based ENSO phases and the more detailed relationship of TCs to the coupling of the ocean and atmosphere during different ENSO phases. Consistent with previous findings, positive relationships exist among TCs, sea surface temperature, and atmospheric circulation. A statistically significant greater frequency of major TCs was found during the latter half of the study period (1991–2010) compared to the 1970–90 period, again consistent with the findings of other studies. Also found were significant and consistent linkages highlighting the interplay of TCs and sea surface temperature (SSTs) in the southwest Pacific basin west of 170°E and a closer connection to atmospheric circulation east of 170°E. Moreover, this study demonstrates subtle differences between a fully coupled El Niño or La Niña and atmospheric- or ocean-dominated phases, or neutral conditions.
Abstract
The new South Pacific Enhanced Archive for Tropical Cyclones (SPEArTC) dataset provides an opportunity to develop a more complete climatology of tropical cyclones (TCs) in the southwest Pacific. Here, spatial patterns and characteristics of TCs for the 41-yr period beginning with the 1969/70 season are related to phases of the El Niño–Southern Oscillation (ENSO), taking into account the degree of ocean–atmosphere coupling. Twentieth-century reanalysis data and the coupled ENSO index (CEI) were used to investigate TC genesis areas and climate diagnostics in the extratropical transition (ETT) region at and south of 25°S during different CEI ENSO phases. This is the first study looking at CEI-based ENSO phases and the more detailed relationship of TCs to the coupling of the ocean and atmosphere during different ENSO phases. Consistent with previous findings, positive relationships exist among TCs, sea surface temperature, and atmospheric circulation. A statistically significant greater frequency of major TCs was found during the latter half of the study period (1991–2010) compared to the 1970–90 period, again consistent with the findings of other studies. Also found were significant and consistent linkages highlighting the interplay of TCs and sea surface temperature (SSTs) in the southwest Pacific basin west of 170°E and a closer connection to atmospheric circulation east of 170°E. Moreover, this study demonstrates subtle differences between a fully coupled El Niño or La Niña and atmospheric- or ocean-dominated phases, or neutral conditions.
Abstract
The common use of remotely located, buoy-mounted capacitance rain gauges in the tropical oceans for satellite rainfall verification studies provides motivation for an in situ gauge bias assessment. A comparison of the biases in rainfall catchment between Pacific island tipping-bucket rain gauges and capacitance rain gauges mounted on moored buoys in the tropical Pacific is conducted using the relationship between the fractional time in rain and monthly rainfall. This study utilizes the widespread spatial homogeneity of this relationship in the tropics to assess the rain catchment of both types of gauges at given values for the fractional time in rain. The results indicate that the capacitance gauges are not statistically significantly biased relative to the island-based tipping-bucket gauges. In addition, given the relatively small error bounds about the bias estimates any real bias differences among all the tested gauges are likely to be quite small compared to monthly rainfall totals. Underestimates resulting from wind biases, which may be substantial, are not documented in this paper.
Abstract
The common use of remotely located, buoy-mounted capacitance rain gauges in the tropical oceans for satellite rainfall verification studies provides motivation for an in situ gauge bias assessment. A comparison of the biases in rainfall catchment between Pacific island tipping-bucket rain gauges and capacitance rain gauges mounted on moored buoys in the tropical Pacific is conducted using the relationship between the fractional time in rain and monthly rainfall. This study utilizes the widespread spatial homogeneity of this relationship in the tropics to assess the rain catchment of both types of gauges at given values for the fractional time in rain. The results indicate that the capacitance gauges are not statistically significantly biased relative to the island-based tipping-bucket gauges. In addition, given the relatively small error bounds about the bias estimates any real bias differences among all the tested gauges are likely to be quite small compared to monthly rainfall totals. Underestimates resulting from wind biases, which may be substantial, are not documented in this paper.
Abstract
Extreme heat is one of the most pressing climate risks in the United States and is exacerbated by a warming climate and aging population. Much work in heat health has focused only on temperature-based metrics, which do not fully measure the physiological impact of heat stress on the human body. The U.S. Climate Reference Network (USCRN) consists of 139 sites across the United States and includes meteorological parameters that fully encompass human tolerance to heat, including relative humidity, wind, and solar radiation. Hourly and 5-min observations from USCRN are used to develop heat exposure products, including heat index (HI), apparent temperature (AT), and wet-bulb globe temperature (WBGT). Validation of this product is conducted with nearby airport and mesonet stations, with reanalysis data used to fill in data gaps. Using these derived heat products, two separate analyses are conducted. The first is based on standardized anomalies, which place current heat state in the context of a long-term climate record. In the second study, heat events are classified by time spent at various levels of severity of conditions. There is no consensus as to what defines a heat event, so a comparison of absolute thresholds (i.e., ≥30.0°, 35.0°, and 40.0°C) and relative thresholds (≥90th, 95th, and 98th percentile) will be examined. The efficacy of the product set will be studied using an extreme heat case study in the southeastern United States. While no heat exposure metric is deemed superior, each has their own advantages and caveats, especially in the context of public communication.
Abstract
Extreme heat is one of the most pressing climate risks in the United States and is exacerbated by a warming climate and aging population. Much work in heat health has focused only on temperature-based metrics, which do not fully measure the physiological impact of heat stress on the human body. The U.S. Climate Reference Network (USCRN) consists of 139 sites across the United States and includes meteorological parameters that fully encompass human tolerance to heat, including relative humidity, wind, and solar radiation. Hourly and 5-min observations from USCRN are used to develop heat exposure products, including heat index (HI), apparent temperature (AT), and wet-bulb globe temperature (WBGT). Validation of this product is conducted with nearby airport and mesonet stations, with reanalysis data used to fill in data gaps. Using these derived heat products, two separate analyses are conducted. The first is based on standardized anomalies, which place current heat state in the context of a long-term climate record. In the second study, heat events are classified by time spent at various levels of severity of conditions. There is no consensus as to what defines a heat event, so a comparison of absolute thresholds (i.e., ≥30.0°, 35.0°, and 40.0°C) and relative thresholds (≥90th, 95th, and 98th percentile) will be examined. The efficacy of the product set will be studied using an extreme heat case study in the southeastern United States. While no heat exposure metric is deemed superior, each has their own advantages and caveats, especially in the context of public communication.
The International Best Track Archive for Climate Stewardship (IBTrACS)
Unifying Tropical Cyclone Data
The goal of the International Best Track Archive for Climate Stewardship (IBTrACS) project is to collect the historical tropical cyclone best-track data from all available Regional Specialized Meteorological Centers (RSMCs) and other agencies, combine the disparate datasets into one product, and disseminate in formats used by the tropical cyclone community. Each RSMC forecasts and monitors storms for a specific region and annually archives best-track data, which consist of information on a storm's position, intensity, and other related parameters. IBTrACS is a new dataset based on the best-track data from numerous sources. Moreover, rather than preferentially selecting one track and intensity for each storm, the mean position, the original intensities from the agencies, and summary statistics are provided. This article discusses the dataset construction, explores the tropical cyclone climatology from IBTrACS, and concludes with an analysis of uncertainty in the tropical cyclone intensity record.
The goal of the International Best Track Archive for Climate Stewardship (IBTrACS) project is to collect the historical tropical cyclone best-track data from all available Regional Specialized Meteorological Centers (RSMCs) and other agencies, combine the disparate datasets into one product, and disseminate in formats used by the tropical cyclone community. Each RSMC forecasts and monitors storms for a specific region and annually archives best-track data, which consist of information on a storm's position, intensity, and other related parameters. IBTrACS is a new dataset based on the best-track data from numerous sources. Moreover, rather than preferentially selecting one track and intensity for each storm, the mean position, the original intensities from the agencies, and summary statistics are provided. This article discusses the dataset construction, explores the tropical cyclone climatology from IBTrACS, and concludes with an analysis of uncertainty in the tropical cyclone intensity record.
Abstract
The ability of high-resolution mesoscale models to simulate near-surface and subsurface meteorological processes is critical for representing land–atmosphere feedback processes. The High-Resolution Rapid Refresh (HRRR) model is a 3-km numerical weather prediction model that has been used operationally since 2014. In this study, we evaluated the HRRR over the contiguous United States from 1 January 2021 to 31 December 2021. We compared the 1-, 3-, 6-, 12-, 18-, 24-, 30-, and 48-h forecasts against observations of air and surface temperature, shortwave radiation, and soil temperature and moisture from the 114 stations of the U.S. Climate Reference Network (USCRN) and evaluated the HRRR’s performance for different geographic regions and land cover types. We found that the HRRR well simulated air and surface temperatures, but underestimated soil temperatures when temperatures were subfreezing. The HRRR had the largest overestimates in shortwave radiation under cloudy skies, and there was a positive relationship between the shortwave radiation mean bias error (MBE) and air temperature MBE that was stronger in summer than winter. Additionally, the HRRR underestimated soil moisture when the values exceeded about 0.2 m3 m−3, but overestimated soil moisture when measurements were below this value. Consequently, the HRRR exhibited a positive soil moisture MBE over the drier areas of the western United States and a negative MBE over the eastern United States. Although caution is needed when applying conclusions regarding HRRR’s biases to locations with subgrid-scale land cover variations, general knowledge of HRRR’s biases will help guide improvements to land surface models used in high-resolution weather forecasting models.
Significance Statement
Weather forecasters rely upon output from many different models. However, the models’ ability to represent processes happening near the land surface over short time scales is critical for producing accurate weather forecasts. In this study, we evaluated the High-Resolution Rapid Refresh (HRRR) model using observations from the U.S. Climate Reference Network, which currently includes 114 reference climate observing stations in the contiguous United States. These stations provide highly accurate measurements of air temperature, precipitation, soil temperature, and soil moisture. Our findings helped illustrate conditions when the HRRR performs well, but also conditions in which the HRRR can be improved, which we expect will motivate ongoing improvements to the HRRR and other weather forecasting models.
Abstract
The ability of high-resolution mesoscale models to simulate near-surface and subsurface meteorological processes is critical for representing land–atmosphere feedback processes. The High-Resolution Rapid Refresh (HRRR) model is a 3-km numerical weather prediction model that has been used operationally since 2014. In this study, we evaluated the HRRR over the contiguous United States from 1 January 2021 to 31 December 2021. We compared the 1-, 3-, 6-, 12-, 18-, 24-, 30-, and 48-h forecasts against observations of air and surface temperature, shortwave radiation, and soil temperature and moisture from the 114 stations of the U.S. Climate Reference Network (USCRN) and evaluated the HRRR’s performance for different geographic regions and land cover types. We found that the HRRR well simulated air and surface temperatures, but underestimated soil temperatures when temperatures were subfreezing. The HRRR had the largest overestimates in shortwave radiation under cloudy skies, and there was a positive relationship between the shortwave radiation mean bias error (MBE) and air temperature MBE that was stronger in summer than winter. Additionally, the HRRR underestimated soil moisture when the values exceeded about 0.2 m3 m−3, but overestimated soil moisture when measurements were below this value. Consequently, the HRRR exhibited a positive soil moisture MBE over the drier areas of the western United States and a negative MBE over the eastern United States. Although caution is needed when applying conclusions regarding HRRR’s biases to locations with subgrid-scale land cover variations, general knowledge of HRRR’s biases will help guide improvements to land surface models used in high-resolution weather forecasting models.
Significance Statement
Weather forecasters rely upon output from many different models. However, the models’ ability to represent processes happening near the land surface over short time scales is critical for producing accurate weather forecasts. In this study, we evaluated the High-Resolution Rapid Refresh (HRRR) model using observations from the U.S. Climate Reference Network, which currently includes 114 reference climate observing stations in the contiguous United States. These stations provide highly accurate measurements of air temperature, precipitation, soil temperature, and soil moisture. Our findings helped illustrate conditions when the HRRR performs well, but also conditions in which the HRRR can be improved, which we expect will motivate ongoing improvements to the HRRR and other weather forecasting models.
While the global upper-air observing network has provided useful observations for operational weather forecasting for decades, its measurements lack the accuracy and long-term continuity needed for understanding climate change. Consequently, the scientific community faces uncertainty on key climate issues, such as the nature of temperature trends in the troposphere and stratosphere; the climatology, radiative effects, and hydrological role of water vapor in the upper troposphere and stratosphere; and the vertical profile of changes in atmospheric ozone, aerosols, and other trace constituents. Radiosonde data provide adequate vertical resolution to address these issues, but they have questionable accuracy and time-varying biases due to changing instrumentation and techniques. Although satellite systems provide global coverage, their vertical resolution is sometimes inadequate and they require independent reference observations for sensor and data product validation, and for merging observations from different platforms into homogeneous climate records. To address these shortcomings, and to ensure that future climate records will be more useful than the records to date, the Global Climate Observing System (GCOS) program is initiating a GCOS Reference Upper-Air Network (GRUAN) to provide high-quality observations using specialized radiosondes and complementary remote sensing profiling instrumentation that can be used for validation. This paper outlines the scientific rationale for GRUAN, its role in the Global Earth Observation System of Systems, network requirements and likely instrumentation, management structure, current status, and future plans. It also illustrates the value of prototype reference upper-air observations in constructing climate records and their potential contribution to the Global Space-Based Inter-Calibration System. We invite constructive feedback on the GRUAN concept and the engagement of the scientific community.
While the global upper-air observing network has provided useful observations for operational weather forecasting for decades, its measurements lack the accuracy and long-term continuity needed for understanding climate change. Consequently, the scientific community faces uncertainty on key climate issues, such as the nature of temperature trends in the troposphere and stratosphere; the climatology, radiative effects, and hydrological role of water vapor in the upper troposphere and stratosphere; and the vertical profile of changes in atmospheric ozone, aerosols, and other trace constituents. Radiosonde data provide adequate vertical resolution to address these issues, but they have questionable accuracy and time-varying biases due to changing instrumentation and techniques. Although satellite systems provide global coverage, their vertical resolution is sometimes inadequate and they require independent reference observations for sensor and data product validation, and for merging observations from different platforms into homogeneous climate records. To address these shortcomings, and to ensure that future climate records will be more useful than the records to date, the Global Climate Observing System (GCOS) program is initiating a GCOS Reference Upper-Air Network (GRUAN) to provide high-quality observations using specialized radiosondes and complementary remote sensing profiling instrumentation that can be used for validation. This paper outlines the scientific rationale for GRUAN, its role in the Global Earth Observation System of Systems, network requirements and likely instrumentation, management structure, current status, and future plans. It also illustrates the value of prototype reference upper-air observations in constructing climate records and their potential contribution to the Global Space-Based Inter-Calibration System. We invite constructive feedback on the GRUAN concept and the engagement of the scientific community.
Abstract
The U.S. Climate Reference Network (USCRN) is a network of climate-monitoring stations maintained and operated by the National Oceanic and Atmospheric Administration (NOAA) to provide climate-science-quality measurements of air temperature and precipitation. The stations in the network were designed to be extensible to other missions, and the National Integrated Drought Information System program determined that the USCRN could be augmented to provide observations that are more drought relevant. To increase the network’s capability of monitoring soil processes and drought, soil observations were added to USCRN instrumentation. In 2011, the USCRN team completed at each USCRN station in the conterminous United States the installation of triplicate-configuration soil moisture and soil temperature probes at five standards depths (5, 10, 20, 50, and 100 cm) as prescribed by the World Meteorological Organization; in addition, the project included the installation of a relative humidity sensor at each of the stations. Work is also under way to eventually install soil sensors at the expanding USCRN stations in Alaska. USCRN data are stewarded by the NOAA National Climatic Data Center, and instrument engineering and performance studies, installation, and maintenance are performed by the NOAA Atmospheric Turbulence and Diffusion Division. This article provides a technical description of the USCRN soil observations in the context of U.S. soil-climate–measurement efforts and discusses the advantage of the triple-redundancy approach applied by the USCRN.
Abstract
The U.S. Climate Reference Network (USCRN) is a network of climate-monitoring stations maintained and operated by the National Oceanic and Atmospheric Administration (NOAA) to provide climate-science-quality measurements of air temperature and precipitation. The stations in the network were designed to be extensible to other missions, and the National Integrated Drought Information System program determined that the USCRN could be augmented to provide observations that are more drought relevant. To increase the network’s capability of monitoring soil processes and drought, soil observations were added to USCRN instrumentation. In 2011, the USCRN team completed at each USCRN station in the conterminous United States the installation of triplicate-configuration soil moisture and soil temperature probes at five standards depths (5, 10, 20, 50, and 100 cm) as prescribed by the World Meteorological Organization; in addition, the project included the installation of a relative humidity sensor at each of the stations. Work is also under way to eventually install soil sensors at the expanding USCRN stations in Alaska. USCRN data are stewarded by the NOAA National Climatic Data Center, and instrument engineering and performance studies, installation, and maintenance are performed by the NOAA Atmospheric Turbulence and Diffusion Division. This article provides a technical description of the USCRN soil observations in the context of U.S. soil-climate–measurement efforts and discusses the advantage of the triple-redundancy approach applied by the USCRN.
The year 2012 marks a decade of observations undertaken by the U.S. Climate Reference Network (USCRN) under the auspices of NOAA's National Climatic Data Center and Atmospheric Turbulence and Diffusion Division. The network consists of 114 sites across the conterminous 48 states, with additional sites in Alaska and Hawaii. Stations are installed in open (where possible), rural sites very likely to have stable land-cover/use conditions for several decades to come. At each site a suite of meteorological parameters are monitored, including triple redundancy for the primary air temperature and precipitation variables and for soil moisture/temperature. Instrumentation is regularly calibrated to National Institute for Standards and Technology (NIST) standards and maintained by a staff of expert engineers. This attention to detail in USCRN is intended to ensure the creation of an unimpeachable record of changes in surface climate over the United States for decades to come. Data are made available without restriction for all public, private, and government use. This article describes the rationale for the USCRN, its implementation, and some of the highlights of the first decade of operations. One critical use of these observations is as an independent data source to verify the existing U.S. temperature record derived from networks corrected for nonhomogenous histories. Future directions for the network are also discussed, including the applicability of USCRN approaches for networks monitoring climate at scales from regional to global. Constructive feedback from end users will allow for continued improvement of USCRN in the future and ensure that it continues to meet stakeholder requirements for precise climate measurements.
The year 2012 marks a decade of observations undertaken by the U.S. Climate Reference Network (USCRN) under the auspices of NOAA's National Climatic Data Center and Atmospheric Turbulence and Diffusion Division. The network consists of 114 sites across the conterminous 48 states, with additional sites in Alaska and Hawaii. Stations are installed in open (where possible), rural sites very likely to have stable land-cover/use conditions for several decades to come. At each site a suite of meteorological parameters are monitored, including triple redundancy for the primary air temperature and precipitation variables and for soil moisture/temperature. Instrumentation is regularly calibrated to National Institute for Standards and Technology (NIST) standards and maintained by a staff of expert engineers. This attention to detail in USCRN is intended to ensure the creation of an unimpeachable record of changes in surface climate over the United States for decades to come. Data are made available without restriction for all public, private, and government use. This article describes the rationale for the USCRN, its implementation, and some of the highlights of the first decade of operations. One critical use of these observations is as an independent data source to verify the existing U.S. temperature record derived from networks corrected for nonhomogenous histories. Future directions for the network are also discussed, including the applicability of USCRN approaches for networks monitoring climate at scales from regional to global. Constructive feedback from end users will allow for continued improvement of USCRN in the future and ensure that it continues to meet stakeholder requirements for precise climate measurements.
