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Abstract
The hurricane season of 2006 in the eastern North Pacific basin is summarized, and the individual tropical cyclones are described. Also, the official track and intensity forecasts of these cyclones are verified and evaluated. The 2006 eastern North Pacific season was an active one, in which 18 tropical storms formed. Of these, 10 became hurricanes and 5 became major hurricanes. A total of 2 hurricanes and 1 tropical depression made landfall in Mexico, causing 13 direct deaths in that country along with significant property damage. On average, the official track forecasts in the eastern Pacific for 2006 were quite skillful. No appreciable improvement in mean intensity forecasts was noted, however.
Abstract
The hurricane season of 2006 in the eastern North Pacific basin is summarized, and the individual tropical cyclones are described. Also, the official track and intensity forecasts of these cyclones are verified and evaluated. The 2006 eastern North Pacific season was an active one, in which 18 tropical storms formed. Of these, 10 became hurricanes and 5 became major hurricanes. A total of 2 hurricanes and 1 tropical depression made landfall in Mexico, causing 13 direct deaths in that country along with significant property damage. On average, the official track forecasts in the eastern Pacific for 2006 were quite skillful. No appreciable improvement in mean intensity forecasts was noted, however.
Abstract
This study analyzes spatial and temporal characteristics of multiyear droughts and pluvials over the southwestern United States with a focus on the upper Colorado River basin. The study uses two multiscalar moisture indices: standardized precipitation evapotranspiration index (SPEI) and standardized precipitation index (SPI) on a 36-month scale (SPEI36 and SPI36, respectively). The indices are calculated from monthly average precipitation and maximum and minimum temperatures from the Parameter-Elevation Regressions on Independent Slopes Model dataset for the period 1950–2012. The study examines the relationship between individual climate variables as well as large-scale atmospheric circulation features found in reanalysis output during drought and pluvial periods. The results indicate that SPEI36 and SPI36 show similar temporal and spatial patterns, but that the inclusion of temperatures in SPEI36 leads to more extreme magnitudes in SPEI36 than in SPI36. Analysis of large-scale atmospheric fields indicates an interplay between different fields that yields extremes over the study region. Widespread drought (pluvial) events are associated with enhanced positive (negative) 500-hPa geopotential height anomaly linked to subsidence (ascent) and negative (positive) moisture convergence and precipitable water anomalies. Considering the broader context of the conditions responsible for the occurrence of prolonged hydrologic anomalies provides water resource managers and other decision-makers with valuable understanding of these events. This perspective also offers evaluation opportunities for climate models.
Abstract
This study analyzes spatial and temporal characteristics of multiyear droughts and pluvials over the southwestern United States with a focus on the upper Colorado River basin. The study uses two multiscalar moisture indices: standardized precipitation evapotranspiration index (SPEI) and standardized precipitation index (SPI) on a 36-month scale (SPEI36 and SPI36, respectively). The indices are calculated from monthly average precipitation and maximum and minimum temperatures from the Parameter-Elevation Regressions on Independent Slopes Model dataset for the period 1950–2012. The study examines the relationship between individual climate variables as well as large-scale atmospheric circulation features found in reanalysis output during drought and pluvial periods. The results indicate that SPEI36 and SPI36 show similar temporal and spatial patterns, but that the inclusion of temperatures in SPEI36 leads to more extreme magnitudes in SPEI36 than in SPI36. Analysis of large-scale atmospheric fields indicates an interplay between different fields that yields extremes over the study region. Widespread drought (pluvial) events are associated with enhanced positive (negative) 500-hPa geopotential height anomaly linked to subsidence (ascent) and negative (positive) moisture convergence and precipitable water anomalies. Considering the broader context of the conditions responsible for the occurrence of prolonged hydrologic anomalies provides water resource managers and other decision-makers with valuable understanding of these events. This perspective also offers evaluation opportunities for climate models.
Abstract
Clouds pose many operational hazards to the aviation community in terms of ceilings and visibility, turbulence, and aircraft icing. Realistic descriptions of the three-dimensional (3D) distribution and temporal evolution of clouds in numerical weather prediction models used for flight planning and routing are therefore of central importance. The introduction of satellite-based cloud radar (CloudSat) and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) sensors to the National Aeronautics and Space Administration A-Train is timely in light of these needs but requires a new paradigm of model-evaluation tools that are capable of exploiting the vertical-profile information. Early results from the National Center for Atmospheric Research Model Evaluation Toolkit (MET), augmented to work with the emergent satellite-based active sensor observations, are presented here. Existing horizontal-plane statistical evaluation techniques have been adapted to operate on observations in the vertical plane and have been extended to 3D object evaluations, leveraging blended datasets from the active and passive A-Train sensors. Case studies of organized synoptic-scale and mesoscale distributed cloud systems are presented to illustrate the multiscale utility of the MET tools. Definition of objects on the basis of radar-reflectivity thresholds was found to be strongly dependent on the model’s ability to resolve details of the cloud’s internal hydrometeor distribution. Contoured-frequency-by-altitude diagrams provide a useful mechanism for evaluating the simulated and observed 3D distributions for regional domains. The expanded MET provides a new dimension to model evaluation and positions the community to better exploit active-sensor satellite observing systems that are slated for launch in the near future.
Abstract
Clouds pose many operational hazards to the aviation community in terms of ceilings and visibility, turbulence, and aircraft icing. Realistic descriptions of the three-dimensional (3D) distribution and temporal evolution of clouds in numerical weather prediction models used for flight planning and routing are therefore of central importance. The introduction of satellite-based cloud radar (CloudSat) and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) sensors to the National Aeronautics and Space Administration A-Train is timely in light of these needs but requires a new paradigm of model-evaluation tools that are capable of exploiting the vertical-profile information. Early results from the National Center for Atmospheric Research Model Evaluation Toolkit (MET), augmented to work with the emergent satellite-based active sensor observations, are presented here. Existing horizontal-plane statistical evaluation techniques have been adapted to operate on observations in the vertical plane and have been extended to 3D object evaluations, leveraging blended datasets from the active and passive A-Train sensors. Case studies of organized synoptic-scale and mesoscale distributed cloud systems are presented to illustrate the multiscale utility of the MET tools. Definition of objects on the basis of radar-reflectivity thresholds was found to be strongly dependent on the model’s ability to resolve details of the cloud’s internal hydrometeor distribution. Contoured-frequency-by-altitude diagrams provide a useful mechanism for evaluating the simulated and observed 3D distributions for regional domains. The expanded MET provides a new dimension to model evaluation and positions the community to better exploit active-sensor satellite observing systems that are slated for launch in the near future.
Abstract
The upper-air sounding network for Dynamics of the Madden–Julian Oscillation (DYNAMO) has provided an unprecedented set of observations for studying the MJO over the Indian Ocean, where coupling of this oscillation with deep convection first occurs. With 72 rawinsonde sites and dropsonde data from 13 aircraft missions, the sounding network covers the tropics from eastern Africa to the western Pacific. In total nearly 26 000 soundings were collected from this network during the experiment’s 6-month extended observing period (from October 2011 to March 2012). Slightly more than half of the soundings, collected from 33 sites, are at high vertical resolution. Rigorous post–field phase processing of the sonde data included several levels of quality checks and a variety of corrections that address a number of issues (e.g., daytime dry bias, baseline surface data errors, ship deck heating effects, and artificial dry spikes in slow-ascent soundings).
Because of the importance of an accurate description of the moisture field in meeting the scientific goals of the experiment, particular attention is given to humidity correction and its validation. The humidity corrections, though small relative to some previous field campaigns, produced high-fidelity moisture analyses in which sonde precipitable water compared well with independent estimates. An assessment of operational model moisture analyses using corrected sonde data shows an overall good agreement with the exception at upper levels, where model moisture and clouds are more abundant than the sonde data would indicate.
Abstract
The upper-air sounding network for Dynamics of the Madden–Julian Oscillation (DYNAMO) has provided an unprecedented set of observations for studying the MJO over the Indian Ocean, where coupling of this oscillation with deep convection first occurs. With 72 rawinsonde sites and dropsonde data from 13 aircraft missions, the sounding network covers the tropics from eastern Africa to the western Pacific. In total nearly 26 000 soundings were collected from this network during the experiment’s 6-month extended observing period (from October 2011 to March 2012). Slightly more than half of the soundings, collected from 33 sites, are at high vertical resolution. Rigorous post–field phase processing of the sonde data included several levels of quality checks and a variety of corrections that address a number of issues (e.g., daytime dry bias, baseline surface data errors, ship deck heating effects, and artificial dry spikes in slow-ascent soundings).
Because of the importance of an accurate description of the moisture field in meeting the scientific goals of the experiment, particular attention is given to humidity correction and its validation. The humidity corrections, though small relative to some previous field campaigns, produced high-fidelity moisture analyses in which sonde precipitable water compared well with independent estimates. An assessment of operational model moisture analyses using corrected sonde data shows an overall good agreement with the exception at upper levels, where model moisture and clouds are more abundant than the sonde data would indicate.
Abstract
The Rapid Update Cycle (RUC), an operational regional analysis–forecast system among the suite of models at the National Centers for Environmental Prediction (NCEP), is distinctive in two primary aspects: its hourly assimilation cycle and its use of a hybrid isentropic–sigma vertical coordinate. The use of a quasi-isentropic coordinate for the analysis increment allows the influence of observations to be adaptively shaped by the potential temperature structure around the observation, while the hourly update cycle allows for a very current analysis and short-range forecast. Herein, the RUC analysis framework in the hybrid coordinate is described, and some considerations for high-frequency cycling are discussed.
A 20-km 50-level hourly version of the RUC was implemented into operations at NCEP in April 2002. This followed an initial implementation with 60-km horizontal grid spacing and a 3-h cycle in 1994 and a major upgrade including 40-km horizontal grid spacing in 1998. Verification of forecasts from the latest 20-km version is presented using rawinsonde and surface observations. These verification statistics show that the hourly RUC assimilation cycle improves short-range forecasts (compared to longer-range forecasts valid at the same time) even down to the 1-h projection.
Abstract
The Rapid Update Cycle (RUC), an operational regional analysis–forecast system among the suite of models at the National Centers for Environmental Prediction (NCEP), is distinctive in two primary aspects: its hourly assimilation cycle and its use of a hybrid isentropic–sigma vertical coordinate. The use of a quasi-isentropic coordinate for the analysis increment allows the influence of observations to be adaptively shaped by the potential temperature structure around the observation, while the hourly update cycle allows for a very current analysis and short-range forecast. Herein, the RUC analysis framework in the hybrid coordinate is described, and some considerations for high-frequency cycling are discussed.
A 20-km 50-level hourly version of the RUC was implemented into operations at NCEP in April 2002. This followed an initial implementation with 60-km horizontal grid spacing and a 3-h cycle in 1994 and a major upgrade including 40-km horizontal grid spacing in 1998. Verification of forecasts from the latest 20-km version is presented using rawinsonde and surface observations. These verification statistics show that the hourly RUC assimilation cycle improves short-range forecasts (compared to longer-range forecasts valid at the same time) even down to the 1-h projection.
Abstract
Accurate cloud and precipitation forecasts are a fundamental component of short-range data assimilation/model prediction systems such as the NOAA 3-km High-Resolution Rapid Refresh (HRRR) or the 13-km Rapid Refresh (RAP). To reduce cloud and precipitation spinup problems, a nonvariational assimilation technique for stratiform clouds was developed within the Gridpoint Statistical Interpolation (GSI) data assimilation system. One goal of this technique is retention of observed stratiform cloudy and clear 3D volumes into the subsequent model forecast. The cloud observations used include cloud-top data from satellite brightness temperatures, surface-based ceilometer data, and surface visibility. Quality control, expansion into spatial information content, and forward operators are described for each observation type. The projection of data from these observation types into an observation-based cloud-information 3D gridded field is accomplished via identification of cloudy, clear, and cloud-unknown 3D volumes. Updating of forecast background fields is accomplished through clearing and building of cloud water and cloud ice with associated modifications to water vapor and temperature. Impact of the cloud assimilation on short-range forecasts is assessed with a set of retrospective experiments in warm and cold seasons using the RAPv5 model. Short-range (1–9 h) forecast skill is improved in both seasons for cloud ceiling and visibility and for 2-m temperature in daytime and with mixed results for other measures. Two modifications were introduced and tested with success: use of prognostic subgrid-scale cloud fraction to condition cloud building (in response to a high bias) and removal of a WRF-based rebalancing.
Abstract
Accurate cloud and precipitation forecasts are a fundamental component of short-range data assimilation/model prediction systems such as the NOAA 3-km High-Resolution Rapid Refresh (HRRR) or the 13-km Rapid Refresh (RAP). To reduce cloud and precipitation spinup problems, a nonvariational assimilation technique for stratiform clouds was developed within the Gridpoint Statistical Interpolation (GSI) data assimilation system. One goal of this technique is retention of observed stratiform cloudy and clear 3D volumes into the subsequent model forecast. The cloud observations used include cloud-top data from satellite brightness temperatures, surface-based ceilometer data, and surface visibility. Quality control, expansion into spatial information content, and forward operators are described for each observation type. The projection of data from these observation types into an observation-based cloud-information 3D gridded field is accomplished via identification of cloudy, clear, and cloud-unknown 3D volumes. Updating of forecast background fields is accomplished through clearing and building of cloud water and cloud ice with associated modifications to water vapor and temperature. Impact of the cloud assimilation on short-range forecasts is assessed with a set of retrospective experiments in warm and cold seasons using the RAPv5 model. Short-range (1–9 h) forecast skill is improved in both seasons for cloud ceiling and visibility and for 2-m temperature in daytime and with mixed results for other measures. Two modifications were introduced and tested with success: use of prognostic subgrid-scale cloud fraction to condition cloud building (in response to a high bias) and removal of a WRF-based rebalancing.
The deployment of a space-based Doppler lidar would provide information that is fundamental to advancing the understanding and prediction of weather and climate.
This paper reviews the concepts of wind measurement by Doppler lidar, highlights the results of some observing system simulation experiments with lidar winds, and discusses the important advances in earth system science anticipated with lidar winds.
Observing system simulation experiments, conducted using two different general circulation models, have shown 1) that there is a significant improvement in the forecast accuracy over the Southern Hemisphere and tropical oceans resulting from the assimilation of simulated satellite wind data, and 2) that wind data are significantly more effective than temperature or moisture data in controlling analysis error. Because accurate wind observations are currently almost entirely unavailable for the vast majority of tropical cyclones worldwide, lidar winds have the potential to substantially improve tropical cyclone forecasts. Similarly, to improve water vapor flux divergence calculations, a direct measure of the ageostrophic wind is needed since the present level of uncertainty cannot be reduced with better temperature and moisture soundings alone.
The deployment of a space-based Doppler lidar would provide information that is fundamental to advancing the understanding and prediction of weather and climate.
This paper reviews the concepts of wind measurement by Doppler lidar, highlights the results of some observing system simulation experiments with lidar winds, and discusses the important advances in earth system science anticipated with lidar winds.
Observing system simulation experiments, conducted using two different general circulation models, have shown 1) that there is a significant improvement in the forecast accuracy over the Southern Hemisphere and tropical oceans resulting from the assimilation of simulated satellite wind data, and 2) that wind data are significantly more effective than temperature or moisture data in controlling analysis error. Because accurate wind observations are currently almost entirely unavailable for the vast majority of tropical cyclones worldwide, lidar winds have the potential to substantially improve tropical cyclone forecasts. Similarly, to improve water vapor flux divergence calculations, a direct measure of the ageostrophic wind is needed since the present level of uncertainty cannot be reduced with better temperature and moisture soundings alone.
Abstract
Numerical simulations of flow over steep terrain using 11 different nonhydrostatic numerical models are compared and analyzed. A basic benchmark and five other test cases are simulated in a two-dimensional framework using the same initial state, which is based on conditions during Intensive Observation Period (IOP) 6 of the Terrain-Induced Rotor Experiment (T-REX), in which intense mountain-wave activity was observed. All of the models use an identical horizontal resolution of 1 km and the same vertical resolution. The six simulated test cases use various terrain heights: a 100-m bell-shaped hill, a 1000-m idealized ridge that is steeper on the lee slope, a 2500-m ridge with the same terrain shape, and a cross-Sierra terrain profile. The models are tested with both free-slip and no-slip lower boundary conditions.
The results indicate a surprisingly diverse spectrum of simulated mountain-wave characteristics including lee waves, hydraulic-like jump features, and gravity wave breaking. The vertical velocity standard deviation is twice as large in the free-slip experiments relative to the no-slip simulations. Nevertheless, the no-slip simulations also exhibit considerable variations in the wave characteristics. The results imply relatively low predictability of key characteristics of topographically forced flows such as the strength of downslope winds and stratospheric wave breaking. The vertical flux of horizontal momentum, which is a domain-integrated quantity, exhibits considerable spread among the models, particularly for the experiments with the 2500-m ridge and Sierra terrain. The differences among the various model simulations, all initialized with identical initial states, suggest that model dynamical cores may be an important component of diversity for the design of mesoscale ensemble systems for topographically forced flows. The intermodel differences are significantly larger than sensitivity experiments within a single modeling system.
Abstract
Numerical simulations of flow over steep terrain using 11 different nonhydrostatic numerical models are compared and analyzed. A basic benchmark and five other test cases are simulated in a two-dimensional framework using the same initial state, which is based on conditions during Intensive Observation Period (IOP) 6 of the Terrain-Induced Rotor Experiment (T-REX), in which intense mountain-wave activity was observed. All of the models use an identical horizontal resolution of 1 km and the same vertical resolution. The six simulated test cases use various terrain heights: a 100-m bell-shaped hill, a 1000-m idealized ridge that is steeper on the lee slope, a 2500-m ridge with the same terrain shape, and a cross-Sierra terrain profile. The models are tested with both free-slip and no-slip lower boundary conditions.
The results indicate a surprisingly diverse spectrum of simulated mountain-wave characteristics including lee waves, hydraulic-like jump features, and gravity wave breaking. The vertical velocity standard deviation is twice as large in the free-slip experiments relative to the no-slip simulations. Nevertheless, the no-slip simulations also exhibit considerable variations in the wave characteristics. The results imply relatively low predictability of key characteristics of topographically forced flows such as the strength of downslope winds and stratospheric wave breaking. The vertical flux of horizontal momentum, which is a domain-integrated quantity, exhibits considerable spread among the models, particularly for the experiments with the 2500-m ridge and Sierra terrain. The differences among the various model simulations, all initialized with identical initial states, suggest that model dynamical cores may be an important component of diversity for the design of mesoscale ensemble systems for topographically forced flows. The intermodel differences are significantly larger than sensitivity experiments within a single modeling system.
Accurate quantitative precipitation estimates (QPE) and very short term quantitative precipitation forecasts (VSTQPF) are critical to accurate monitoring and prediction of water-related hazards and water resources. While tremendous progress has been made in the last quarter-century in many areas of QPE and VSTQPF, significant gaps continue to exist in both knowledge and capabilities that are necessary to produce accurate high-resolution precipitation estimates at the national scale for a wide spectrum of users. Toward this goal, a national next-generation QPE and VSTQPF (Q2) workshop was held in Norman, Oklahoma, on 28–30 June 2005. Scientists, operational forecasters, water managers, and stakeholders from public and private sectors, including academia, presented and discussed a broad range of precipitation and forecasting topics and issues, and developed a list of science focus areas. To meet the nation's needs for the precipitation information effectively, the authors herein propose a community-wide integrated approach for precipitation information that fully capitalizes on recent advances in science and technology, and leverages the wide range of expertise and experience that exists in the research and operational communities. The concepts and recommendations from the workshop form the Q2 science plan and a suggested path to operations. Implementation of these concepts is expected to improve river forecasts and flood and flash flood watches and warnings, and to enhance various hydrologic and hydrometeorological services for a wide range of users and customers. In support of this initiative, the National Mosaic and Q2 (NMQ) system is being developed at the National Severe Storms Laboratory to serve as a community test bed for QPE and VSTQPF research and to facilitate the transition to operations of research applications. The NMQ system provides a real-time, around-the-clock data infusion and applications development and evaluation environment, and thus offers a community-wide platform for development and testing of advances in the focus areas.
Accurate quantitative precipitation estimates (QPE) and very short term quantitative precipitation forecasts (VSTQPF) are critical to accurate monitoring and prediction of water-related hazards and water resources. While tremendous progress has been made in the last quarter-century in many areas of QPE and VSTQPF, significant gaps continue to exist in both knowledge and capabilities that are necessary to produce accurate high-resolution precipitation estimates at the national scale for a wide spectrum of users. Toward this goal, a national next-generation QPE and VSTQPF (Q2) workshop was held in Norman, Oklahoma, on 28–30 June 2005. Scientists, operational forecasters, water managers, and stakeholders from public and private sectors, including academia, presented and discussed a broad range of precipitation and forecasting topics and issues, and developed a list of science focus areas. To meet the nation's needs for the precipitation information effectively, the authors herein propose a community-wide integrated approach for precipitation information that fully capitalizes on recent advances in science and technology, and leverages the wide range of expertise and experience that exists in the research and operational communities. The concepts and recommendations from the workshop form the Q2 science plan and a suggested path to operations. Implementation of these concepts is expected to improve river forecasts and flood and flash flood watches and warnings, and to enhance various hydrologic and hydrometeorological services for a wide range of users and customers. In support of this initiative, the National Mosaic and Q2 (NMQ) system is being developed at the National Severe Storms Laboratory to serve as a community test bed for QPE and VSTQPF research and to facilitate the transition to operations of research applications. The NMQ system provides a real-time, around-the-clock data infusion and applications development and evaluation environment, and thus offers a community-wide platform for development and testing of advances in the focus areas.
Abstract
A hydrostatic global weather prediction model based on an icosahedral horizontal grid and a hybrid terrain-following/isentropic vertical coordinate is described. The model is an extension to three spatial dimensions of a previously developed, icosahedral, shallow-water model featuring user-selectable horizontal resolution and employing indirect addressing techniques. The vertical grid is adaptive to maximize the portion of the atmosphere mapped into the isentropic coordinate subdomain. The model, best described as a stacked shallow-water model, is being tested extensively on real-time medium-range forecasts to ready it for possible inclusion in operational multimodel ensembles for medium-range to seasonal prediction.
Abstract
A hydrostatic global weather prediction model based on an icosahedral horizontal grid and a hybrid terrain-following/isentropic vertical coordinate is described. The model is an extension to three spatial dimensions of a previously developed, icosahedral, shallow-water model featuring user-selectable horizontal resolution and employing indirect addressing techniques. The vertical grid is adaptive to maximize the portion of the atmosphere mapped into the isentropic coordinate subdomain. The model, best described as a stacked shallow-water model, is being tested extensively on real-time medium-range forecasts to ready it for possible inclusion in operational multimodel ensembles for medium-range to seasonal prediction.
