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- Author or Editor: Luke E. Madaus x
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Abstract
Idealized ensemble simulations of isolated convective initiation (CI) are analyzed to identify storm-scale features in surface weather fields that precede initiation in a variety of background environments and the observations that would be needed to resolve these features. Precipitating storms are identified with an object-based method and composites of surface anomalies are generated for the variables of interest surrounding times and locations of initiation. Correlation length scales and anomaly magnitudes throughout the CI process are examined in detail with the latter comparing favorably to anomaly estimates obtained from previous observational and modeling studies. Negative temperature anomalies due to cloud shadowing are found to be the most prominent storm-scale feature prior to initiation. Significant spatial correlations are shown to extend from the surface throughout the boundary layer and even into the cloud-bearing layer once deep convective clouds become established. The findings are discussed in the context of data assimilation, particularly with respect to current assumptions about surface observation error. It is shown that, to resolve the storm-scale anomalies in these simulations, the minimum necessary temperature and wind observation densities would likely be limited by spatial correlation length scale while moisture and pressure observations are more limited by observation error.
Abstract
Idealized ensemble simulations of isolated convective initiation (CI) are analyzed to identify storm-scale features in surface weather fields that precede initiation in a variety of background environments and the observations that would be needed to resolve these features. Precipitating storms are identified with an object-based method and composites of surface anomalies are generated for the variables of interest surrounding times and locations of initiation. Correlation length scales and anomaly magnitudes throughout the CI process are examined in detail with the latter comparing favorably to anomaly estimates obtained from previous observational and modeling studies. Negative temperature anomalies due to cloud shadowing are found to be the most prominent storm-scale feature prior to initiation. Significant spatial correlations are shown to extend from the surface throughout the boundary layer and even into the cloud-bearing layer once deep convective clouds become established. The findings are discussed in the context of data assimilation, particularly with respect to current assumptions about surface observation error. It is shown that, to resolve the storm-scale anomalies in these simulations, the minimum necessary temperature and wind observation densities would likely be limited by spatial correlation length scale while moisture and pressure observations are more limited by observation error.
Abstract
Smartphone pressure observations have the potential to greatly increase surface observation density on convection-resolving scales. Currently available smartphone pressure observations are tested through assimilation in a mesoscale ensemble for a 3-day, convectively active period in the eastern United States. Both raw pressure (altimeter) observations and 1-h pressure (altimeter) tendency observations are considered. The available observation density closely follows population density, but observations are also available in rural areas. The smartphone observations are found to contain significant noise, which can limit their effectiveness. The assimilated smartphone observations contribute to small improvements in 1-h forecasts of surface pressure and 10-m wind, but produce larger errors in 2-m temperature forecasts. Short-term (0–4 h) precipitation forecasts are improved when smartphone pressure and pressure tendency observations are assimilated as compared with an ensemble that assimilates no observations. However, these improvements are limited to broad, mesoscale features with minimal skill provided at convective scales using the current smartphone observation density. A specific mesoscale convective system (MCS) is examined in detail, and smartphone pressure observations captured the expected dynamic structures associated with this feature. Possibilities for further development of smartphone observations are discussed.
Abstract
Smartphone pressure observations have the potential to greatly increase surface observation density on convection-resolving scales. Currently available smartphone pressure observations are tested through assimilation in a mesoscale ensemble for a 3-day, convectively active period in the eastern United States. Both raw pressure (altimeter) observations and 1-h pressure (altimeter) tendency observations are considered. The available observation density closely follows population density, but observations are also available in rural areas. The smartphone observations are found to contain significant noise, which can limit their effectiveness. The assimilated smartphone observations contribute to small improvements in 1-h forecasts of surface pressure and 10-m wind, but produce larger errors in 2-m temperature forecasts. Short-term (0–4 h) precipitation forecasts are improved when smartphone pressure and pressure tendency observations are assimilated as compared with an ensemble that assimilates no observations. However, these improvements are limited to broad, mesoscale features with minimal skill provided at convective scales using the current smartphone observation density. A specific mesoscale convective system (MCS) is examined in detail, and smartphone pressure observations captured the expected dynamic structures associated with this feature. Possibilities for further development of smartphone observations are discussed.
Abstract
Predicting when and where individual convective storms will develop remains an elusive challenge. Previous studies have suggested that surface observations can capture convective-scale features relevant to the convective initiation (CI) process, and new surface observing platforms such as crowdsourcing could significantly increase surface observation density in the near future. Here, a series of observing system simulation experiments (OSSEs) are performed to determine the required density of surface observations necessary to constrain storm-scale forecasts of CI. Ensemble simulations of an environment where CI occurs are cycled hourly using the CM1 model while assimilating synthetic surface observations at varying densities. Skillful and reliable storm-scale forecasts of CI are produced when surface observations of at least 4-km—and particularly with 1-km—density are assimilated, but only for forecasts initiated within 1 h of CI. Time scales of forecast improvement in surface variables suggest that hourly cycling is at the upper limit for CI forecast improvement. In addition, the structure of the assimilation increments, ensemble calibration in these experiments, and challenges of convective-scale assimilation are discussed.
Abstract
Predicting when and where individual convective storms will develop remains an elusive challenge. Previous studies have suggested that surface observations can capture convective-scale features relevant to the convective initiation (CI) process, and new surface observing platforms such as crowdsourcing could significantly increase surface observation density in the near future. Here, a series of observing system simulation experiments (OSSEs) are performed to determine the required density of surface observations necessary to constrain storm-scale forecasts of CI. Ensemble simulations of an environment where CI occurs are cycled hourly using the CM1 model while assimilating synthetic surface observations at varying densities. Skillful and reliable storm-scale forecasts of CI are produced when surface observations of at least 4-km—and particularly with 1-km—density are assimilated, but only for forecasts initiated within 1 h of CI. Time scales of forecast improvement in surface variables suggest that hourly cycling is at the upper limit for CI forecast improvement. In addition, the structure of the assimilation increments, ensemble calibration in these experiments, and challenges of convective-scale assimilation are discussed.
Millions of smartphones possess relatively accurate pressure sensors and the expectation is that these numbers will grow into the hundreds of millions globally during the next few years. The availability of millions of pressure observations each hour from smartphones has major implications for high-resolution numerical weather prediction. This paper reviews smartphone pressure-sensor technology, describes commercial efforts to collect the data in real time, examines the implications for mesoscale weather prediction, and provides an example of assimilating smartphone pressure observations for a strong convective event over eastern Washington State.
Millions of smartphones possess relatively accurate pressure sensors and the expectation is that these numbers will grow into the hundreds of millions globally during the next few years. The availability of millions of pressure observations each hour from smartphones has major implications for high-resolution numerical weather prediction. This paper reviews smartphone pressure-sensor technology, describes commercial efforts to collect the data in real time, examines the implications for mesoscale weather prediction, and provides an example of assimilating smartphone pressure observations for a strong convective event over eastern Washington State.
Abstract
The use of dense pressure observations is investigated for creating mesoscale ensemble analyses and improving short-term mesoscale forecasts. By exploiting additional observation platforms, the number of pressure observations over the Pacific Northwest region is increased by an order of magnitude over standard airport observations. Quality control and bias correction methods for these observations are discussed, including the use of pressure tendency as an alternative observation type with fewer bias concerns. The enhanced station density provided by these observations contributes to localized adjustments for a variety of mesoscale phenomena. These adjusted analyses yield improved forecasts, including more accurate forecasts of frontal passages and convective bands. Assimilating dense 3-h pressure tendency observations also reduces the error in some forecast surface fields similarly to raw pressure observations, suggesting further investigation into pressure tendency as a mesoscale observation type.
Abstract
The use of dense pressure observations is investigated for creating mesoscale ensemble analyses and improving short-term mesoscale forecasts. By exploiting additional observation platforms, the number of pressure observations over the Pacific Northwest region is increased by an order of magnitude over standard airport observations. Quality control and bias correction methods for these observations are discussed, including the use of pressure tendency as an alternative observation type with fewer bias concerns. The enhanced station density provided by these observations contributes to localized adjustments for a variety of mesoscale phenomena. These adjusted analyses yield improved forecasts, including more accurate forecasts of frontal passages and convective bands. Assimilating dense 3-h pressure tendency observations also reduces the error in some forecast surface fields similarly to raw pressure observations, suggesting further investigation into pressure tendency as a mesoscale observation type.
Abstract
The Olympic Mountains Experiment (OLYMPEX) took place during the 2015/16 fall–winter season in the vicinity of the mountainous Olympic Peninsula of Washington State. The goals of OLYMPEX were to provide physical and hydrologic ground validation for the U.S.–Japan Global Precipitation Measurement (GPM) satellite mission and, more specifically, to study how precipitation in Pacific frontal systems is modified by passage over coastal mountains. Four transportable scanning dual-polarization Doppler radars of various wavelengths were installed. Surface stations were placed at various altitudes to measure precipitation rates, particle size distributions, and fall velocities. Autonomous recording cameras monitored and recorded snow accumulation. Four research aircraft supplied by NASA investigated precipitation processes and snow cover, and supplemental rawinsondes and dropsondes were deployed during precipitation events. Numerous Pacific frontal systems were sampled, including several reaching “atmospheric river” status, warm- and cold-frontal systems, and postfrontal convection.
Abstract
The Olympic Mountains Experiment (OLYMPEX) took place during the 2015/16 fall–winter season in the vicinity of the mountainous Olympic Peninsula of Washington State. The goals of OLYMPEX were to provide physical and hydrologic ground validation for the U.S.–Japan Global Precipitation Measurement (GPM) satellite mission and, more specifically, to study how precipitation in Pacific frontal systems is modified by passage over coastal mountains. Four transportable scanning dual-polarization Doppler radars of various wavelengths were installed. Surface stations were placed at various altitudes to measure precipitation rates, particle size distributions, and fall velocities. Autonomous recording cameras monitored and recorded snow accumulation. Four research aircraft supplied by NASA investigated precipitation processes and snow cover, and supplemental rawinsondes and dropsondes were deployed during precipitation events. Numerous Pacific frontal systems were sampled, including several reaching “atmospheric river” status, warm- and cold-frontal systems, and postfrontal convection.
