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- Author or Editor: Trent W. Ford x
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Abstract
The warm season in the United States Great Plains (GP) is characterized by frequent nocturnal low-level jets (LLJs). The GPLLJ serves as a major mechanism of atmospheric moisture transport, contributing to severe weather and precipitation in the region. A combination of synoptic and regional forcing modulates GPLLJ frequency and intensity. The GPLLJ has primarily been studied at the diurnal scale. We hypothesize that, due to the memory of the land surface, longer time scale variability associated with surface moisture also modulates GPLLJ intensity. This work identifies GPLLJ days from ECMWF Reanalysis v5 (ERA5) wind data and isolates extremes using a peaks-over-threshold approach. Extreme GPLLJs are classified by geographic region and synoptic state. Composites of daily soil moisture anomalies show a preference for extreme GPLLJs to occur over anomalously dry soil. Critically, antecedent soil moisture anomalies emerge weeks before the extreme jet occurrence. The dry soil moisture signal coexists with clear skies and drying of the surface at the synoptic time scale. A diurnal PBL heat accumulation, which intensifies the buoyancy oscillation, is also present. The identification of a subseasonal dry anomaly suggests that, although the GPLLJ is generated by diurnally varying oscillations and intensified by synoptic-scale processes, the memory of the land surface can modulate the GPLLJ far beyond the diurnal and synoptic scale. Additionally, the location of the antecedent soil moisture anomalies corresponds with the eventual GPLLJ. The spatiotemporal characteristic of these antecedent anomalies suggests the potential for improved prediction of the GPLLJ activity.
Abstract
The warm season in the United States Great Plains (GP) is characterized by frequent nocturnal low-level jets (LLJs). The GPLLJ serves as a major mechanism of atmospheric moisture transport, contributing to severe weather and precipitation in the region. A combination of synoptic and regional forcing modulates GPLLJ frequency and intensity. The GPLLJ has primarily been studied at the diurnal scale. We hypothesize that, due to the memory of the land surface, longer time scale variability associated with surface moisture also modulates GPLLJ intensity. This work identifies GPLLJ days from ECMWF Reanalysis v5 (ERA5) wind data and isolates extremes using a peaks-over-threshold approach. Extreme GPLLJs are classified by geographic region and synoptic state. Composites of daily soil moisture anomalies show a preference for extreme GPLLJs to occur over anomalously dry soil. Critically, antecedent soil moisture anomalies emerge weeks before the extreme jet occurrence. The dry soil moisture signal coexists with clear skies and drying of the surface at the synoptic time scale. A diurnal PBL heat accumulation, which intensifies the buoyancy oscillation, is also present. The identification of a subseasonal dry anomaly suggests that, although the GPLLJ is generated by diurnally varying oscillations and intensified by synoptic-scale processes, the memory of the land surface can modulate the GPLLJ far beyond the diurnal and synoptic scale. Additionally, the location of the antecedent soil moisture anomalies corresponds with the eventual GPLLJ. The spatiotemporal characteristic of these antecedent anomalies suggests the potential for improved prediction of the GPLLJ activity.
Abstract
The North American Soil Moisture Database (NASMD) was initiated in 2011 to provide support for developing climate forecasting tools, calibrating land surface models, and validating satellite-derived soil moisture algorithms. The NASMD has collected data from over 30 soil moisture observation networks providing millions of in situ soil moisture observations in all 50 states, as well as Canada and Mexico. It is recognized that the quality of measured soil moisture in NASMD is highly variable because of the diversity of climatological conditions, land cover, soil texture, and topographies of the stations, and differences in measurement devices (e.g., sensors) and installation. It is also recognized that error, inaccuracy, and imprecision in the data can have significant impacts on practical operations and scientific studies. Therefore, developing an appropriate quality control procedure is essential to ensure that the data are of the best quality. In this study, an automated quality control approach is developed using the North American Land Data Assimilation System, phase 2 (NLDAS-2), Noah soil porosity, soil temperature, and fraction of liquid and total soil moisture to flag erroneous and/or spurious measurements. Overall results show that this approach is able to flag unreasonable values when the soil is partially frozen. A validation example using NLDAS-2 multiple model soil moisture products at the 20-cm soil layer showed that the quality control procedure had a significant positive impact in Alabama, North Carolina, and west Texas. It had a greater impact in colder regions, particularly during spring and autumn. Over 433 NASMD stations have been quality controlled using the methodology proposed in this study, and the algorithm will be implemented to control data quality from the other ~1200 NASMD stations in the near future.
Abstract
The North American Soil Moisture Database (NASMD) was initiated in 2011 to provide support for developing climate forecasting tools, calibrating land surface models, and validating satellite-derived soil moisture algorithms. The NASMD has collected data from over 30 soil moisture observation networks providing millions of in situ soil moisture observations in all 50 states, as well as Canada and Mexico. It is recognized that the quality of measured soil moisture in NASMD is highly variable because of the diversity of climatological conditions, land cover, soil texture, and topographies of the stations, and differences in measurement devices (e.g., sensors) and installation. It is also recognized that error, inaccuracy, and imprecision in the data can have significant impacts on practical operations and scientific studies. Therefore, developing an appropriate quality control procedure is essential to ensure that the data are of the best quality. In this study, an automated quality control approach is developed using the North American Land Data Assimilation System, phase 2 (NLDAS-2), Noah soil porosity, soil temperature, and fraction of liquid and total soil moisture to flag erroneous and/or spurious measurements. Overall results show that this approach is able to flag unreasonable values when the soil is partially frozen. A validation example using NLDAS-2 multiple model soil moisture products at the 20-cm soil layer showed that the quality control procedure had a significant positive impact in Alabama, North Carolina, and west Texas. It had a greater impact in colder regions, particularly during spring and autumn. Over 433 NASMD stations have been quality controlled using the methodology proposed in this study, and the algorithm will be implemented to control data quality from the other ~1200 NASMD stations in the near future.
Abstract
Soil moisture is an important variable for numerous scientific disciplines, and therefore provision of accurate and timely soil moisture information is critical. Recent initiatives, such as the National Soil Moisture Network effort, have increased the spatial coverage and quality of soil moisture monitoring infrastructure across the contiguous United States. As a result, the foundation has been laid for a high-resolution, real-time gridded soil moisture product that leverages data from in situ networks, satellite platforms, and land surface models. An important precursor to this development is a comprehensive, national-scale assessment of in situ soil moisture data fidelity. Additionally, evaluation of the United States’s current in situ soil moisture monitoring infrastructure can provide a means toward more informed satellite and model calibration and validation. This study employs a triple collocation approach to evaluate the fidelity of in situ soil moisture observations from over 1200 stations across the contiguous United States. The primary goal of the study is to determine the monitoring stations that are best suited for 1) inclusion in national-scale soil moisture datasets, 2) deriving in situ–informed gridded soil moisture products, and 3) validating and benchmarking satellite and model soil moisture data. We find that 90% of the 1233 stations evaluated exhibit high spatial consistency with satellite remote sensing and land surface model soil moisture datasets. In situ error did not significantly vary by climate, soil type, or sensor technology, but instead was a function of station-specific properties such as land cover and station siting.
Abstract
Soil moisture is an important variable for numerous scientific disciplines, and therefore provision of accurate and timely soil moisture information is critical. Recent initiatives, such as the National Soil Moisture Network effort, have increased the spatial coverage and quality of soil moisture monitoring infrastructure across the contiguous United States. As a result, the foundation has been laid for a high-resolution, real-time gridded soil moisture product that leverages data from in situ networks, satellite platforms, and land surface models. An important precursor to this development is a comprehensive, national-scale assessment of in situ soil moisture data fidelity. Additionally, evaluation of the United States’s current in situ soil moisture monitoring infrastructure can provide a means toward more informed satellite and model calibration and validation. This study employs a triple collocation approach to evaluate the fidelity of in situ soil moisture observations from over 1200 stations across the contiguous United States. The primary goal of the study is to determine the monitoring stations that are best suited for 1) inclusion in national-scale soil moisture datasets, 2) deriving in situ–informed gridded soil moisture products, and 3) validating and benchmarking satellite and model soil moisture data. We find that 90% of the 1233 stations evaluated exhibit high spatial consistency with satellite remote sensing and land surface model soil moisture datasets. In situ error did not significantly vary by climate, soil type, or sensor technology, but instead was a function of station-specific properties such as land cover and station siting.
Abstract
Characteristics and predictability of drought in the midwestern United States, spanning the from the Great Plains to the Ohio Valley, at local and regional scales are examined during 1916–2015. Given vast differences in hydroclimatic variability across the Midwest, drought is evaluated in four regions identified using a hierarchical clustering algorithm applied to an integrated drought index based on soil moisture, snow water equivalent, and 3-month runoff from land surface models forced by observed analyses. Highlighting the regions containing the Ohio Valley (OV) and Northern Great Plains (NGP), the OV demonstrates a preference for subannual droughts, the timing of which can lead to prevalent dry epochs, while the NGP demonstrates a preference for annual-to-multiannual droughts. Regional drought variations are closely related to precipitation, resulting in a higher likelihood of drought onset or demise during wet seasons: March–November in the NGP and all year in the OV, with a preference for March–May and September–November. Due to the distinct dry season in the NGP, there is a higher likelihood of longer drought persistence, as the NGP is 4 times more likely to experience drought lasting at least one year compared to the OV. While drought variability in all regions and seasons is related to atmospheric wave trains spanning the Pacific–North American sector, longer-lead predictability is limited to the OV in December–February because it is the only region/season related to slow-varying sea surface temperatures consistent with El Niño–Southern Oscillation. The wave trains in all other regions appear to be generated in the atmosphere, highlighting the importance of internal atmospheric variability in shaping Midwest drought.
Abstract
Characteristics and predictability of drought in the midwestern United States, spanning the from the Great Plains to the Ohio Valley, at local and regional scales are examined during 1916–2015. Given vast differences in hydroclimatic variability across the Midwest, drought is evaluated in four regions identified using a hierarchical clustering algorithm applied to an integrated drought index based on soil moisture, snow water equivalent, and 3-month runoff from land surface models forced by observed analyses. Highlighting the regions containing the Ohio Valley (OV) and Northern Great Plains (NGP), the OV demonstrates a preference for subannual droughts, the timing of which can lead to prevalent dry epochs, while the NGP demonstrates a preference for annual-to-multiannual droughts. Regional drought variations are closely related to precipitation, resulting in a higher likelihood of drought onset or demise during wet seasons: March–November in the NGP and all year in the OV, with a preference for March–May and September–November. Due to the distinct dry season in the NGP, there is a higher likelihood of longer drought persistence, as the NGP is 4 times more likely to experience drought lasting at least one year compared to the OV. While drought variability in all regions and seasons is related to atmospheric wave trains spanning the Pacific–North American sector, longer-lead predictability is limited to the OV in December–February because it is the only region/season related to slow-varying sea surface temperatures consistent with El Niño–Southern Oscillation. The wave trains in all other regions appear to be generated in the atmosphere, highlighting the importance of internal atmospheric variability in shaping Midwest drought.
Abstract
Soil moisture is an important variable in the climate system that integrates the combined influence of the atmosphere, land surface, and soil. Soil moisture is frequently used for drought monitoring and climate forecasting. However, in situ soil moisture observations are not systematically archived and there are relatively few national soil moisture networks. The lack of observed soil moisture data makes it difficult to characterize long-term soil moisture variability and trends. The North American Soil Moisture Database (NASMD) is a new high-quality observational soil moisture database. It includes over 1,800 monitoring stations in the United States, Canada, and Mexico, making it the largest collections of in situ soil moisture observations in North America. Data are collected from multiple sources, quality controlled, and integrated into an online database (soilmoisture.tamu.edu). Here we describe the development of the database, including quality control/quality assurance, standardization, and collection of metadata. The utility of the NASMD is demonstrated through an analysis of the inter- and intraannual variability of soil moisture from multiple networks. The NASMD is a useful tool for drought monitoring and forecasting, calibrating/validating satellites and land surface models, and documenting how soil moisture influences the climate system on seasonal to interannual time scales.
Abstract
Soil moisture is an important variable in the climate system that integrates the combined influence of the atmosphere, land surface, and soil. Soil moisture is frequently used for drought monitoring and climate forecasting. However, in situ soil moisture observations are not systematically archived and there are relatively few national soil moisture networks. The lack of observed soil moisture data makes it difficult to characterize long-term soil moisture variability and trends. The North American Soil Moisture Database (NASMD) is a new high-quality observational soil moisture database. It includes over 1,800 monitoring stations in the United States, Canada, and Mexico, making it the largest collections of in situ soil moisture observations in North America. Data are collected from multiple sources, quality controlled, and integrated into an online database (soilmoisture.tamu.edu). Here we describe the development of the database, including quality control/quality assurance, standardization, and collection of metadata. The utility of the NASMD is demonstrated through an analysis of the inter- and intraannual variability of soil moisture from multiple networks. The NASMD is a useful tool for drought monitoring and forecasting, calibrating/validating satellites and land surface models, and documenting how soil moisture influences the climate system on seasonal to interannual time scales.
Abstract
Given the increasing use of the term “flash drought” by the media and scientific community, it is prudent to develop a consistent definition that can be used to identify these events and to understand their salient characteristics. It is generally accepted that flash droughts occur more often during the summer owing to increased evaporative demand; however, two distinct approaches have been used to identify them. The first approach focuses on their rate of intensification, whereas the second approach implicitly focuses on their duration. These conflicting notions for what constitutes a flash drought (i.e., unusually fast intensification vs short duration) introduce ambiguity that affects our ability to detect their onset, monitor their development, and understand the mechanisms that control their evolution. Here, we propose that the definition for “flash drought” should explicitly focus on its rate of intensification rather than its duration, with droughts that develop much more rapidly than normal identified as flash droughts. There are two primary reasons for favoring the intensification approach over the duration approach. First, longevity and impact are fundamental characteristics of drought. Thus, short-term events lasting only a few days and having minimal impacts are inconsistent with the general understanding of drought and therefore should not be considered flash droughts. Second, by focusing on their rapid rate of intensification, the proposed “flash drought” definition highlights the unique challenges faced by vulnerable stakeholders who have less time to prepare for its adverse effects.
Abstract
Given the increasing use of the term “flash drought” by the media and scientific community, it is prudent to develop a consistent definition that can be used to identify these events and to understand their salient characteristics. It is generally accepted that flash droughts occur more often during the summer owing to increased evaporative demand; however, two distinct approaches have been used to identify them. The first approach focuses on their rate of intensification, whereas the second approach implicitly focuses on their duration. These conflicting notions for what constitutes a flash drought (i.e., unusually fast intensification vs short duration) introduce ambiguity that affects our ability to detect their onset, monitor their development, and understand the mechanisms that control their evolution. Here, we propose that the definition for “flash drought” should explicitly focus on its rate of intensification rather than its duration, with droughts that develop much more rapidly than normal identified as flash droughts. There are two primary reasons for favoring the intensification approach over the duration approach. First, longevity and impact are fundamental characteristics of drought. Thus, short-term events lasting only a few days and having minimal impacts are inconsistent with the general understanding of drought and therefore should not be considered flash droughts. Second, by focusing on their rapid rate of intensification, the proposed “flash drought” definition highlights the unique challenges faced by vulnerable stakeholders who have less time to prepare for its adverse effects.
Abstract
The complex interactions between soil moisture and precipitation are difficult to observe, and consequently there is a lack of consensus as to the sign, strength, and location of these interactions. Inconsistency between soil moisture–precipitation interaction studies can be attributed to a multitude of factors, including the difficulty of demonstrating causal relationships, dataset differences, and precipitation autocorrelation. The purpose of this study is to explore these potential confounding factors and determine which are most important for consideration when assessing statistical coupling between soil moisture and precipitation. Soil moisture is assessed via three remote sensing datasets: the Advanced Microwave Scanning Radiometer for Earth Observing System, the Tropical Rainfall Measuring Mission Microwave Imager, and the Essential Climate Variable Soil Moisture. Estimates of soil moisture are coupled with afternoon thunderstorm events identified by the Thunderstorm Observation by Radar (ThOR) algorithm, and dry soil or wet soil preferences for convection initiation are determined for over 16 000 thunderstorm events between 2005 and 2007. Differences in soil moisture datasets were found to have the largest impact with regard to determining wet or dry soil preferences. Precipitation autocorrelation is prevalent in the data; however, precipitation autocorrelation did not influence the results with regard to dry or wet soil preferences. Consideration of the convective environment (i.e., weakly or synoptically forced) did result in significant differences in wet/dry soil preference, but only for certain soil moisture datasets. The results suggest that observation-driven soil moisture–precipitation interaction studies should both consider the convective environment and implement multiple soil moisture datasets to assure robust results.
Abstract
The complex interactions between soil moisture and precipitation are difficult to observe, and consequently there is a lack of consensus as to the sign, strength, and location of these interactions. Inconsistency between soil moisture–precipitation interaction studies can be attributed to a multitude of factors, including the difficulty of demonstrating causal relationships, dataset differences, and precipitation autocorrelation. The purpose of this study is to explore these potential confounding factors and determine which are most important for consideration when assessing statistical coupling between soil moisture and precipitation. Soil moisture is assessed via three remote sensing datasets: the Advanced Microwave Scanning Radiometer for Earth Observing System, the Tropical Rainfall Measuring Mission Microwave Imager, and the Essential Climate Variable Soil Moisture. Estimates of soil moisture are coupled with afternoon thunderstorm events identified by the Thunderstorm Observation by Radar (ThOR) algorithm, and dry soil or wet soil preferences for convection initiation are determined for over 16 000 thunderstorm events between 2005 and 2007. Differences in soil moisture datasets were found to have the largest impact with regard to determining wet or dry soil preferences. Precipitation autocorrelation is prevalent in the data; however, precipitation autocorrelation did not influence the results with regard to dry or wet soil preferences. Consideration of the convective environment (i.e., weakly or synoptically forced) did result in significant differences in wet/dry soil preference, but only for certain soil moisture datasets. The results suggest that observation-driven soil moisture–precipitation interaction studies should both consider the convective environment and implement multiple soil moisture datasets to assure robust results.
Abstract
Increased flash drought awareness in recent years has motivated the development of numerous indicators for monitoring, early warning, and assessment. The flash drought indicators can act as a complementary set of tools by which to inform flash drought response and management. However, the limitations of each indicator much be measured and communicated between research and practitioners to ensure effectiveness. The limitations of any flash drought indicator are better understood and overcome through assessment of indicator sensitivity and consistency; however, such assessment cannot assume any single indicator properly represents the flash drought “truth.” To better understand the current state of flash drought monitoring, this study presents an intercomparison of nine, widely used flash drought indicators. The indicators represent perspectives and processes that are known to drive flash drought, including evapotranspiration and evaporative demand, precipitation, and soil moisture. We find no single flash drought indicator consistently outperforms all others across the contiguous United States. We do find the evaporative demand- and evapotranspiration-driven indicators tend to lead precipitation- and soil moisture-based indicators in flash drought onset, but also tend to produce more flash drought events collectively. Overall, the regional and definition-specific variability in results supports the argument for a multi-indicator approach for flash drought monitoring, as advocated by recent studies. Furthermore, flash drought research—especially evaluation of historical and potential future changes in flash drought characteristics—should test multiple indicators, datasets, and methods for representing flash drought, and ideally employ a multi-indicator analysis framework over use of a single indicator from which to infer all flash drought information.
Significance Statement
Rapid onset or “flash” drought has been an increasing concern globally, with quickly intensifying impacts to agriculture, ecosystems, and water resources. Many tools and indicators have been developed to monitor and provide early warning for flash drought, ideally resulting in more time for effective mitigation and reduced impacts. However, there remains no widely accepted single method for defining, monitoring, and measuring flash drought, which means most indicators that are developed are compared with other individual indicators or conditions and impacts in one or two flash drought events. In this study, we measure the state of flash drought monitoring through an intercomparison of nine, widely used flash drought indicators that represent different aspects of flash drought. We find that no single flash drought indicator outperformed all others and suggest that a comprehensive flash drought monitor should leverage multiple, complementary indicators, datasets, and methods. Furthermore, we suggest flash drought research—especially that which reflects on historical or projected changes in flash drought characteristics—should seek multiple indicators, datasets, and methods for analyses, thereby reducing the potentially confounding effects of sensitivity to a single indicator.
Abstract
Increased flash drought awareness in recent years has motivated the development of numerous indicators for monitoring, early warning, and assessment. The flash drought indicators can act as a complementary set of tools by which to inform flash drought response and management. However, the limitations of each indicator much be measured and communicated between research and practitioners to ensure effectiveness. The limitations of any flash drought indicator are better understood and overcome through assessment of indicator sensitivity and consistency; however, such assessment cannot assume any single indicator properly represents the flash drought “truth.” To better understand the current state of flash drought monitoring, this study presents an intercomparison of nine, widely used flash drought indicators. The indicators represent perspectives and processes that are known to drive flash drought, including evapotranspiration and evaporative demand, precipitation, and soil moisture. We find no single flash drought indicator consistently outperforms all others across the contiguous United States. We do find the evaporative demand- and evapotranspiration-driven indicators tend to lead precipitation- and soil moisture-based indicators in flash drought onset, but also tend to produce more flash drought events collectively. Overall, the regional and definition-specific variability in results supports the argument for a multi-indicator approach for flash drought monitoring, as advocated by recent studies. Furthermore, flash drought research—especially evaluation of historical and potential future changes in flash drought characteristics—should test multiple indicators, datasets, and methods for representing flash drought, and ideally employ a multi-indicator analysis framework over use of a single indicator from which to infer all flash drought information.
Significance Statement
Rapid onset or “flash” drought has been an increasing concern globally, with quickly intensifying impacts to agriculture, ecosystems, and water resources. Many tools and indicators have been developed to monitor and provide early warning for flash drought, ideally resulting in more time for effective mitigation and reduced impacts. However, there remains no widely accepted single method for defining, monitoring, and measuring flash drought, which means most indicators that are developed are compared with other individual indicators or conditions and impacts in one or two flash drought events. In this study, we measure the state of flash drought monitoring through an intercomparison of nine, widely used flash drought indicators that represent different aspects of flash drought. We find that no single flash drought indicator outperformed all others and suggest that a comprehensive flash drought monitor should leverage multiple, complementary indicators, datasets, and methods. Furthermore, we suggest flash drought research—especially that which reflects on historical or projected changes in flash drought characteristics—should seek multiple indicators, datasets, and methods for analyses, thereby reducing the potentially confounding effects of sensitivity to a single indicator.
Abstract
The catastrophic derecho that occurred on 10 August 2020 across the midwestern United States caused billions of dollars of damage to both urban and rural infrastructure as well as agricultural crops, most notably across the state of Iowa. This paper documents the complex evolution of the derecho through the use of low-Earth-orbit passive-microwave imager and GOES-16 satellite-derived products complemented by products derived from NEXRAD weather radar observations. Additional satellite sensors including optical imagers and synthetic aperture radar (SAR) were used to observe impacts to the power grid and agriculture in Iowa. SAR improved the identification and quantification of damaged corn and soybeans, as compared to true-color composites and normalized difference vegetation index (NDVI). A statistical approach to identify damaged corn and soybean crops from SAR was created with estimates of 1.97 million acres of damaged corn and 1.40 million acres of damaged soybeans in the state of Iowa. The damage estimates generated by this study were comparable to estimates produced by others after the derecho, including two commercial agricultural companies.
Abstract
The catastrophic derecho that occurred on 10 August 2020 across the midwestern United States caused billions of dollars of damage to both urban and rural infrastructure as well as agricultural crops, most notably across the state of Iowa. This paper documents the complex evolution of the derecho through the use of low-Earth-orbit passive-microwave imager and GOES-16 satellite-derived products complemented by products derived from NEXRAD weather radar observations. Additional satellite sensors including optical imagers and synthetic aperture radar (SAR) were used to observe impacts to the power grid and agriculture in Iowa. SAR improved the identification and quantification of damaged corn and soybeans, as compared to true-color composites and normalized difference vegetation index (NDVI). A statistical approach to identify damaged corn and soybean crops from SAR was created with estimates of 1.97 million acres of damaged corn and 1.40 million acres of damaged soybeans in the state of Iowa. The damage estimates generated by this study were comparable to estimates produced by others after the derecho, including two commercial agricultural companies.
