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- Author or Editor: Trent W. Ford x
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Abstract
Extreme heat events have been connected with antecedent soil moisture in many global regions, such that dry soils increase sensible heat content of the near-surface atmosphere and impede precipitation through boundary layer growth. However, negative soil moisture–temperature feedbacks (dry soils = higher temperatures) are founded on investigations of maximum temperature that neglect the potentially important latent heating component provided by soil moisture. In this study, the association of spring soil moisture and subsequent summer oppressive heat events is quantified, defined by equivalent temperature. The advantage of equivalent temperature over maximum temperature is that it accounts for both the temperature and moisture components of atmospheric heat content. Quantile regression and composite analysis are used to determine the association between spring soil moisture and summer oppressive heat events using a 25-yr station observation record in Illinois. A consistent response of summer oppressive heat events to antecedent 5-cm soil moisture anomalies is found at all four stations. The frequency of oppressive summer equivalent temperature events is significantly increased following spring seasons with wetter-than-normal soils compared with spring seasons with dry soils. This provides evidence of a possible positive soil moisture–temperature feedback. Further, it is found that oppressive heat events correspond with the combination of wetter-than-normal spring soils and persistent summertime upper-level ridging to the northeast of the region, thereby leading to the conclusion that abundant-to-surplus spring soil moisture is necessary but not sufficient for the occurrence of oppressive heat in Illinois.
Abstract
Extreme heat events have been connected with antecedent soil moisture in many global regions, such that dry soils increase sensible heat content of the near-surface atmosphere and impede precipitation through boundary layer growth. However, negative soil moisture–temperature feedbacks (dry soils = higher temperatures) are founded on investigations of maximum temperature that neglect the potentially important latent heating component provided by soil moisture. In this study, the association of spring soil moisture and subsequent summer oppressive heat events is quantified, defined by equivalent temperature. The advantage of equivalent temperature over maximum temperature is that it accounts for both the temperature and moisture components of atmospheric heat content. Quantile regression and composite analysis are used to determine the association between spring soil moisture and summer oppressive heat events using a 25-yr station observation record in Illinois. A consistent response of summer oppressive heat events to antecedent 5-cm soil moisture anomalies is found at all four stations. The frequency of oppressive summer equivalent temperature events is significantly increased following spring seasons with wetter-than-normal soils compared with spring seasons with dry soils. This provides evidence of a possible positive soil moisture–temperature feedback. Further, it is found that oppressive heat events correspond with the combination of wetter-than-normal spring soils and persistent summertime upper-level ridging to the northeast of the region, thereby leading to the conclusion that abundant-to-surplus spring soil moisture is necessary but not sufficient for the occurrence of oppressive heat in Illinois.
Abstract
Soil moisture–vegetation interactions are an important component of land–atmosphere coupling, especially in semiarid regions such as the North American Great Plains. However, many land surface models parameterize vegetation using an interannually invariant leaf area index (LAI). This study quantifies how utilizing a dynamic vegetation parameter in the variability infiltration capacity (VIC) hydrologic model influences model-simulated soil moisture. Accuracy is assessed using in situ soil moisture observations from 20 stations from the Oklahoma Mesonet. Results show that VIC simulations generated with an interannually variant LAI parameter are not consistently more accurate than those generated with the invariant (static) LAI parameter. However, the static LAI parameter tends to overestimate LAI during anomalously dry periods. This has the greatest influence on the accuracy of the soil moisture simulations in the deeper soil layers. Soil moisture drought, as simulated with the static LAI parameter, tends to be more severe and persist for considerably longer than drought simulated using the interannually variant LAI parameter. Dynamic vegetation parameters can represent interannual variations in vegetation health and growing season length. Therefore, simulations with a dynamic LAI parameter better capture the intensity and duration of drought conditions and are recommended for use in drought monitoring.
Abstract
Soil moisture–vegetation interactions are an important component of land–atmosphere coupling, especially in semiarid regions such as the North American Great Plains. However, many land surface models parameterize vegetation using an interannually invariant leaf area index (LAI). This study quantifies how utilizing a dynamic vegetation parameter in the variability infiltration capacity (VIC) hydrologic model influences model-simulated soil moisture. Accuracy is assessed using in situ soil moisture observations from 20 stations from the Oklahoma Mesonet. Results show that VIC simulations generated with an interannually variant LAI parameter are not consistently more accurate than those generated with the invariant (static) LAI parameter. However, the static LAI parameter tends to overestimate LAI during anomalously dry periods. This has the greatest influence on the accuracy of the soil moisture simulations in the deeper soil layers. Soil moisture drought, as simulated with the static LAI parameter, tends to be more severe and persist for considerably longer than drought simulated using the interannually variant LAI parameter. Dynamic vegetation parameters can represent interannual variations in vegetation health and growing season length. Therefore, simulations with a dynamic LAI parameter better capture the intensity and duration of drought conditions and are recommended for use in drought monitoring.
Abstract
Seamless prediction means bridging discrete short-term weather forecasts valid at a specific time and time-averaged forecasts at longer periods. Subseasonal predictions span this time range and must contend with this transition. Seamless forecasts and seamless validation methods go hand-in-hand. Time-averaged forecasts often feature a verification window that widens in time with growing forecast leads. Ideally, a smooth transition across daily to monthly time scales would provide true seamlessness—a generalized approach is presented here to accomplish this. We discuss prior attempts to achieve this transition with individual weighting functions before presenting the two-parameter Hill equation as a general weighting function to blend discrete and time-averaged forecasts, achieving seamlessness. The Hill equation can be tuned to specify the lead time at which the discrete forecast loses dominance to time-averaged forecasts, as well as the swiftness of the transition with lead time. For this application, discrete forecasts are defined at any lead time using a Kronecker delta weighting, and any time-averaged weighting approach can be used at longer leads. Time-averaged weighting functions whose averaging window widens with lead time are used. Example applications are shown for deterministic and ensemble forecasts and validation and a variety of validation metrics, along with sensitivities to parameter choices and a discussion of caveats. This technique aims to counterbalance the natural increase in uncertainty with forecast lead. It is not meant to construct forecasts with the highest skill, but to construct forecasts with the highest utility across time scales from weather to subseasonal in a single seamless product.
Abstract
Seamless prediction means bridging discrete short-term weather forecasts valid at a specific time and time-averaged forecasts at longer periods. Subseasonal predictions span this time range and must contend with this transition. Seamless forecasts and seamless validation methods go hand-in-hand. Time-averaged forecasts often feature a verification window that widens in time with growing forecast leads. Ideally, a smooth transition across daily to monthly time scales would provide true seamlessness—a generalized approach is presented here to accomplish this. We discuss prior attempts to achieve this transition with individual weighting functions before presenting the two-parameter Hill equation as a general weighting function to blend discrete and time-averaged forecasts, achieving seamlessness. The Hill equation can be tuned to specify the lead time at which the discrete forecast loses dominance to time-averaged forecasts, as well as the swiftness of the transition with lead time. For this application, discrete forecasts are defined at any lead time using a Kronecker delta weighting, and any time-averaged weighting approach can be used at longer leads. Time-averaged weighting functions whose averaging window widens with lead time are used. Example applications are shown for deterministic and ensemble forecasts and validation and a variety of validation metrics, along with sensitivities to parameter choices and a discussion of caveats. This technique aims to counterbalance the natural increase in uncertainty with forecast lead. It is not meant to construct forecasts with the highest skill, but to construct forecasts with the highest utility across time scales from weather to subseasonal in a single seamless product.
Abstract
Flash droughts are noted by their unusually rapid rate of onset or intensification, which makes it difficult to anticipate and prepare for them, thus resulting in severe impacts. Although the development of flash drought can be associated with certain atmospheric conditions, vegetation also plays a role in propagating flash drought. This study examines the climatology of warm season (March–September) flash drought occurrence in the United States between 1979 and 2014, and quantifies the possible impacts of vegetation on flash drought based on a set of sensitivity experiments using the Community Earth System Model, version 2 (CESM2). With atmospheric nudging, CESM2 well captures historical flash drought. Compared with NASA’s Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), and National Climate Assessment–Land Data Assimilation System (NCA-LDAS), CESM2 shows agreement on the high flash drought frequency in the Great Plains and southeastern United States, but overestimates flash drought occurrence in the Midwest. The vegetation sensitivity experiments suggest that vegetation greening can significantly increase the flash drought frequency in the Great Plains and the western United States during the warm seasons through enhanced evapotranspiration. However, flash drought occurrence is not significantly affected by vegetation phenology in the eastern United States and Midwest due to weak land–atmosphere coupling. In response to vegetation greening, the extent of flash drought also increases, but the duration of flash drought is not sensitive to greening. This study highlights the importance of vegetation in flash drought development, and provides insights for improving flash drought monitoring and early warning.
Abstract
Flash droughts are noted by their unusually rapid rate of onset or intensification, which makes it difficult to anticipate and prepare for them, thus resulting in severe impacts. Although the development of flash drought can be associated with certain atmospheric conditions, vegetation also plays a role in propagating flash drought. This study examines the climatology of warm season (March–September) flash drought occurrence in the United States between 1979 and 2014, and quantifies the possible impacts of vegetation on flash drought based on a set of sensitivity experiments using the Community Earth System Model, version 2 (CESM2). With atmospheric nudging, CESM2 well captures historical flash drought. Compared with NASA’s Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), and National Climate Assessment–Land Data Assimilation System (NCA-LDAS), CESM2 shows agreement on the high flash drought frequency in the Great Plains and southeastern United States, but overestimates flash drought occurrence in the Midwest. The vegetation sensitivity experiments suggest that vegetation greening can significantly increase the flash drought frequency in the Great Plains and the western United States during the warm seasons through enhanced evapotranspiration. However, flash drought occurrence is not significantly affected by vegetation phenology in the eastern United States and Midwest due to weak land–atmosphere coupling. In response to vegetation greening, the extent of flash drought also increases, but the duration of flash drought is not sensitive to greening. This study highlights the importance of vegetation in flash drought development, and provides insights for improving flash drought monitoring and early warning.
Abstract
In this study, the robustness of a previously developed classification system that categorizes convective thunderstorm events initiated during various synoptic and dynamic conditions is analyzed. This classification system was used to distinguish between organized and unorganized convection and then used to determine whether unorganized convection occurs preferentially over wet or dry soils. The focus is on 12 events that occurred in synoptically benign (SB) environments where the Great Plains low-level jet was not present (noLLJ), and whether these events were accurately classified as unorganized convection is evaluated. Although there is a small sample size, the results show that the classification system fails to differentiate between local unorganized convection and large-scale organized convection under SB–noLLJ conditions. The authors conclude that past studies that have used this classification to study how soil moisture influences unorganized convection should be revisited. Additional variables and/or alternative precipitation datasets should be employed to enhance the robustness of the classification system.
Abstract
In this study, the robustness of a previously developed classification system that categorizes convective thunderstorm events initiated during various synoptic and dynamic conditions is analyzed. This classification system was used to distinguish between organized and unorganized convection and then used to determine whether unorganized convection occurs preferentially over wet or dry soils. The focus is on 12 events that occurred in synoptically benign (SB) environments where the Great Plains low-level jet was not present (noLLJ), and whether these events were accurately classified as unorganized convection is evaluated. Although there is a small sample size, the results show that the classification system fails to differentiate between local unorganized convection and large-scale organized convection under SB–noLLJ conditions. The authors conclude that past studies that have used this classification to study how soil moisture influences unorganized convection should be revisited. Additional variables and/or alternative precipitation datasets should be employed to enhance the robustness of the classification system.
Abstract
The ability to use in situ soil moisture for large-scale soil moisture monitoring, model and satellite validation, and climate investigations is contingent on properly standardizing soil moisture observations. Percentiles are a useful method for homogenizing in situ soil moisture. However, very few stations have been continuously monitoring in situ soil moisture for 20 years or more. Therefore, one challenge in evaluating soil moisture is determining whether the period of record is sufficient to produce a stable distribution from which to generate percentiles. In this study daily in situ soil moisture observations, measured at three separate depths in the soil column at 15 stations in the United States and Canada, are used to determine the record length that is necessary to generate a stable soil moisture distribution. The Anderson–Darling test is implemented, both with and without a Bonferroni adjustment, to quantify the necessary record length. The authors evaluate how the necessary record length varies by location, measurement depth, and month. They find that between 3 and 15 years of data are required to produce stable distributions, with the majority of stations requiring only 3–6 years of data. Not surprisingly, more years of data are required to obtain stable estimates of the 5th and 95th percentiles than of the first, second, and third quartiles of the soil moisture distribution. Overall these results suggest that 6 years of continuous, daily in situ soil moisture data will be sufficient in most conditions to create stable and robust percentiles.
Abstract
The ability to use in situ soil moisture for large-scale soil moisture monitoring, model and satellite validation, and climate investigations is contingent on properly standardizing soil moisture observations. Percentiles are a useful method for homogenizing in situ soil moisture. However, very few stations have been continuously monitoring in situ soil moisture for 20 years or more. Therefore, one challenge in evaluating soil moisture is determining whether the period of record is sufficient to produce a stable distribution from which to generate percentiles. In this study daily in situ soil moisture observations, measured at three separate depths in the soil column at 15 stations in the United States and Canada, are used to determine the record length that is necessary to generate a stable soil moisture distribution. The Anderson–Darling test is implemented, both with and without a Bonferroni adjustment, to quantify the necessary record length. The authors evaluate how the necessary record length varies by location, measurement depth, and month. They find that between 3 and 15 years of data are required to produce stable distributions, with the majority of stations requiring only 3–6 years of data. Not surprisingly, more years of data are required to obtain stable estimates of the 5th and 95th percentiles than of the first, second, and third quartiles of the soil moisture distribution. Overall these results suggest that 6 years of continuous, daily in situ soil moisture data will be sufficient in most conditions to create stable and robust percentiles.
Abstract
Monthly to seasonal precipitation extremes, both flood and drought, are important components of regional climates worldwide, and are the subjects of numerous investigations. However, variability in and transition between precipitation extremes, and associated impacts are the subject of far fewer studies. Recent such events in the Midwest region of the United States, such as the 2011–12 flood to drought transition in the upper Mississippi River basin and the flood to drought transition experienced in parts of Kentucky, Ohio, Indiana, and Illinois in 2019, have sparked concerns of increased variability and rapid transitions between precipitation extremes and compounded economic and environmental impacts. In response to these concerns, this study focuses on characterizing variability and change in Midwest precipitation extremes and transitions between extremes over the last 70 years. Overall we find that the Midwest as a region has gotten wetter over the last seven decades, and that in general the annual maximum and median wetness, defined using the standardized precipitation index (SPI), have increased at a larger magnitude than the annual minimum. We find large areas of the southern Midwest have experienced a significant increase in the annual SPI range and associated magnitude of transition between annual maximum and minimum SPI. We additionally find wet to dry transitions between extremes have largely increased in speed (i.e., less time between extremes), while long-term changes in transition frequency are more regional within the Midwest.
Abstract
Monthly to seasonal precipitation extremes, both flood and drought, are important components of regional climates worldwide, and are the subjects of numerous investigations. However, variability in and transition between precipitation extremes, and associated impacts are the subject of far fewer studies. Recent such events in the Midwest region of the United States, such as the 2011–12 flood to drought transition in the upper Mississippi River basin and the flood to drought transition experienced in parts of Kentucky, Ohio, Indiana, and Illinois in 2019, have sparked concerns of increased variability and rapid transitions between precipitation extremes and compounded economic and environmental impacts. In response to these concerns, this study focuses on characterizing variability and change in Midwest precipitation extremes and transitions between extremes over the last 70 years. Overall we find that the Midwest as a region has gotten wetter over the last seven decades, and that in general the annual maximum and median wetness, defined using the standardized precipitation index (SPI), have increased at a larger magnitude than the annual minimum. We find large areas of the southern Midwest have experienced a significant increase in the annual SPI range and associated magnitude of transition between annual maximum and minimum SPI. We additionally find wet to dry transitions between extremes have largely increased in speed (i.e., less time between extremes), while long-term changes in transition frequency are more regional within the Midwest.
Abstract
Soil moisture is an integral part of the climate system and can drive land–atmosphere interactions through the partitioning of latent and sensible heat. Soil moisture feedback to precipitation has been documented in several regions of the world, most notably in the southern Great Plains. However, the impact of soil moisture on precipitation, particularly at short (subdaily) time scales, has not been resolved. Here, in situ soil moisture observations and satellite-based precipitation estimates are used to examine if afternoon precipitation falls preferentially over wet or dry soils in Oklahoma. Afternoon precipitation events during the warm season (May–September) in Oklahoma from 2003 and 2012 are categorized by how favorable atmospheric conditions are for convection, as well as the presence or absence of the Great Plains low-level jet. The results show afternoon precipitation falls preferentially over wet soils when the Great Plains low-level jet is absent. In contrast, precipitation falls preferentially over dry soils when the low-level jet is present. Humidity (temperature) is increased (decreased) as soil moisture increases for all conditions, and convective available potential energy prior to convection is strongest when atmospheric humidity is above normal. The results do not demonstrate a causal link between soil moisture and precipitation, but they do suggest that soil moisture feedback to precipitation could potentially manifest itself over wetter- and drier-than-normal soils, depending on the overall synoptic and dynamic conditions.
Abstract
Soil moisture is an integral part of the climate system and can drive land–atmosphere interactions through the partitioning of latent and sensible heat. Soil moisture feedback to precipitation has been documented in several regions of the world, most notably in the southern Great Plains. However, the impact of soil moisture on precipitation, particularly at short (subdaily) time scales, has not been resolved. Here, in situ soil moisture observations and satellite-based precipitation estimates are used to examine if afternoon precipitation falls preferentially over wet or dry soils in Oklahoma. Afternoon precipitation events during the warm season (May–September) in Oklahoma from 2003 and 2012 are categorized by how favorable atmospheric conditions are for convection, as well as the presence or absence of the Great Plains low-level jet. The results show afternoon precipitation falls preferentially over wet soils when the Great Plains low-level jet is absent. In contrast, precipitation falls preferentially over dry soils when the low-level jet is present. Humidity (temperature) is increased (decreased) as soil moisture increases for all conditions, and convective available potential energy prior to convection is strongest when atmospheric humidity is above normal. The results do not demonstrate a causal link between soil moisture and precipitation, but they do suggest that soil moisture feedback to precipitation could potentially manifest itself over wetter- and drier-than-normal soils, depending on the overall synoptic and dynamic conditions.
Abstract
Soil moisture can be obtained from in situ measurements, satellite observations, and model simulations. This study evaluates the importance of in situ observations in soil moisture blending, and compares different weighting and sampling methods for combining model, satellite, and in situ soil moisture data to generate an accurate and spatially continuous soil moisture product at 4-km resolution. Four different datasets are used: the antecedent precipitation index (API); KAPI, which incorporates in situ soil moisture observations with the API using regression kriging; SMOS L3 soil moisture; and model-simulated soil moisture from the Noah model as part of the North American Land Data Assimilation System (NLDAS). Triple collocation, least squares weighting, and equal weighting are used to generate blended soil moisture products. An enumerated weighting scheme is designed to investigate the impact of different weighting schemes. The sensitivity of the blended soil moisture products to sampling schemes, station density, and data formats (absolute, anomalies, and percentiles) are also investigated. The results reveal that KAPI outperforms API. This indicates that incorporating in situ soil moisture improves the accuracy of the blended soil moisture products. There are no statistically significant (p > 0.05) differences between blended soil moisture using triple collocation and equal weighting approaches, and both methods provide suboptimal weighting. Optimal weighting is achieved by assigning larger weights to KAPI and smaller weights to SMOS. Using multiple sources of soil moisture is helpful for reducing uncertainty and improving accuracy, especially when the sampling density is low, or the sampling stations are less representative. These results are consistent regardless of how soil moisture is represented (absolute, anomalies, or percentiles).
Abstract
Soil moisture can be obtained from in situ measurements, satellite observations, and model simulations. This study evaluates the importance of in situ observations in soil moisture blending, and compares different weighting and sampling methods for combining model, satellite, and in situ soil moisture data to generate an accurate and spatially continuous soil moisture product at 4-km resolution. Four different datasets are used: the antecedent precipitation index (API); KAPI, which incorporates in situ soil moisture observations with the API using regression kriging; SMOS L3 soil moisture; and model-simulated soil moisture from the Noah model as part of the North American Land Data Assimilation System (NLDAS). Triple collocation, least squares weighting, and equal weighting are used to generate blended soil moisture products. An enumerated weighting scheme is designed to investigate the impact of different weighting schemes. The sensitivity of the blended soil moisture products to sampling schemes, station density, and data formats (absolute, anomalies, and percentiles) are also investigated. The results reveal that KAPI outperforms API. This indicates that incorporating in situ soil moisture improves the accuracy of the blended soil moisture products. There are no statistically significant (p > 0.05) differences between blended soil moisture using triple collocation and equal weighting approaches, and both methods provide suboptimal weighting. Optimal weighting is achieved by assigning larger weights to KAPI and smaller weights to SMOS. Using multiple sources of soil moisture is helpful for reducing uncertainty and improving accuracy, especially when the sampling density is low, or the sampling stations are less representative. These results are consistent regardless of how soil moisture is represented (absolute, anomalies, or percentiles).
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.
