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- Author or Editor: Robert Atlas x
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Abstract
As operational forecast and data assimilation (DA) systems evolve, observing system simulation experiment (OSSE) systems must evolve in parallel. Expected development of operational systems—especially the use of data that are currently not used or are just beginning to be used, such as all-sky and surface-affected microwave radiances—will greatly challenge our ability to construct realistic OSSE systems. An additional set of challenges will arise when future DA systems strongly couple the different Earth system components. In response, future OSSE systems will require coupled models to simulate nature and coupled observation simulators. The requirements for future evolving OSSE systems and potential solutions to satisfy these requirements are discussed. It is anticipated that in the future the OSSE technique will be applied to diverse and coupled domains with the use of increasingly advanced and sophisticated simulations of nature and observations.
Abstract
As operational forecast and data assimilation (DA) systems evolve, observing system simulation experiment (OSSE) systems must evolve in parallel. Expected development of operational systems—especially the use of data that are currently not used or are just beginning to be used, such as all-sky and surface-affected microwave radiances—will greatly challenge our ability to construct realistic OSSE systems. An additional set of challenges will arise when future DA systems strongly couple the different Earth system components. In response, future OSSE systems will require coupled models to simulate nature and coupled observation simulators. The requirements for future evolving OSSE systems and potential solutions to satisfy these requirements are discussed. It is anticipated that in the future the OSSE technique will be applied to diverse and coupled domains with the use of increasingly advanced and sophisticated simulations of nature and observations.
Abstract
Over 50 years since Lorenz’s 1963 study and a follow-up presentation in 1972, the statement “weather is chaotic” has been well accepted. Such a view turns our attention from regularity associated with Laplace’s view of determinism to irregularity associated with chaos. In contrast to single-type chaotic solutions, recent studies using a generalized Lorenz model (GLM) have focused on the coexistence of chaotic and regular solutions that appear within the same model using the same modeling configurations but different initial conditions. The results, with attractor coexistence, suggest that the entirety of weather possesses a dual nature of chaos and order with distinct predictability. In this study, based on the GLM, we illustrate the following two mechanisms that may enable or modulate two kinds of attractor coexistence and, thus, contribute to distinct predictability: 1) the aggregated negative feedback of small-scale convective processes that can produce stable nontrivial equilibrium points and, thus, enable the appearance of stable steady-state solutions and their coexistence with chaotic or nonlinear oscillatory solutions, referred to as the first and second kinds of attractor coexistence; and 2) the modulation of large-scale time-varying forcing (heating) that can determine (or modulate) the alternative appearance of two kinds of attractor coexistence. Based on our results, we then discuss new opportunities and challenges in predictability research with the aim of improving predictions at extended-range time scales, as well as subseasonal to seasonal time scales.
Abstract
Over 50 years since Lorenz’s 1963 study and a follow-up presentation in 1972, the statement “weather is chaotic” has been well accepted. Such a view turns our attention from regularity associated with Laplace’s view of determinism to irregularity associated with chaos. In contrast to single-type chaotic solutions, recent studies using a generalized Lorenz model (GLM) have focused on the coexistence of chaotic and regular solutions that appear within the same model using the same modeling configurations but different initial conditions. The results, with attractor coexistence, suggest that the entirety of weather possesses a dual nature of chaos and order with distinct predictability. In this study, based on the GLM, we illustrate the following two mechanisms that may enable or modulate two kinds of attractor coexistence and, thus, contribute to distinct predictability: 1) the aggregated negative feedback of small-scale convective processes that can produce stable nontrivial equilibrium points and, thus, enable the appearance of stable steady-state solutions and their coexistence with chaotic or nonlinear oscillatory solutions, referred to as the first and second kinds of attractor coexistence; and 2) the modulation of large-scale time-varying forcing (heating) that can determine (or modulate) the alternative appearance of two kinds of attractor coexistence. Based on our results, we then discuss new opportunities and challenges in predictability research with the aim of improving predictions at extended-range time scales, as well as subseasonal to seasonal time scales.
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
The ocean surface wind mediates exchanges between the ocean and the atmosphere. These air–sea exchange processes are critical for understanding and predicting atmosphere, ocean, and wave phenomena on many time and space scales. A cross-calibrated multiplatform (CCMP) long-term data record of satellite ocean surface winds is available from 1987 to 2008 with planned extensions through 2012. A variational analysis method (VAM) is used to combine surface wind data derived from conventional and in situ sources and multiple satellites into a consistent nearglobal analysis at 25-km resolution, every 6 h. The input data are cross-calibrated wind speeds derived from the Special Sensor Microwave Imager (SSM/I; F08–F15), the Tropical Rainfall Measuring Mission Microwave Imager (TMI), and the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), and wind vectors from SeaWinds on the NASA Quick Scatterometer (QuikSCAT) and on the second Japanese Advanced Earth Observing Satellite (ADEOS- 2; i.e., the Midori-2 satellite). These are combined with ECMWF reanalyses and operational analyses by the VAM. VAM analyses and derived data are currently available for interested investigators through the Jet Propulsion Laboratory (JPL) Physical Oceanography Distributed Active Archive Center (PO.DAAC). This paper describes the methodology used to assimilate the input data along with the validation and evaluation of the derived CCMP products.
A supplement to this article is available online:
Abstract
The ocean surface wind mediates exchanges between the ocean and the atmosphere. These air–sea exchange processes are critical for understanding and predicting atmosphere, ocean, and wave phenomena on many time and space scales. A cross-calibrated multiplatform (CCMP) long-term data record of satellite ocean surface winds is available from 1987 to 2008 with planned extensions through 2012. A variational analysis method (VAM) is used to combine surface wind data derived from conventional and in situ sources and multiple satellites into a consistent nearglobal analysis at 25-km resolution, every 6 h. The input data are cross-calibrated wind speeds derived from the Special Sensor Microwave Imager (SSM/I; F08–F15), the Tropical Rainfall Measuring Mission Microwave Imager (TMI), and the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), and wind vectors from SeaWinds on the NASA Quick Scatterometer (QuikSCAT) and on the second Japanese Advanced Earth Observing Satellite (ADEOS- 2; i.e., the Midori-2 satellite). These are combined with ECMWF reanalyses and operational analyses by the VAM. VAM analyses and derived data are currently available for interested investigators through the Jet Propulsion Laboratory (JPL) Physical Oceanography Distributed Active Archive Center (PO.DAAC). This paper describes the methodology used to assimilate the input data along with the validation and evaluation of the derived CCMP products.
A supplement to this article is available online:
A multiscale modeling framework (MMF), which replaces the conventional cloud parameterizations with a cloud-resolving model (CRM) in each grid column of a GCM, constitutes a new and promising approach for climate modeling. The MMF can provide for global coverage and two-way interactions between the CRMs and their parent GCM. The CRM allows for explicit simulation of cloud processes and their interactions with radiation and surface processes, and the GCM allows for global coverage.
A new MMF has been developed that is based on the NASA Goddard Space Flight Center (GSFC) finite-volume GCM (fvGCM) and the Goddard Cumulus Ensemble (GCE) model. This Goddard MMF produces many features that are similar to another MMF that was developed at Colorado State University (CSU), such as an improved surface precipitation pattern, better cloudiness, improved diurnal variability over both oceans and continents, and a stronger propagating Madden-Julian oscillation (MJO) compared to their parent GCMs using traditional cloud parameterizations. Both MMFs also produce a large and positive precipitation bias in the Indian Ocean and western Pacific during the Northern Hemisphere summer. However, there are also notable differences between the two MMFs. For example, the CSU MMF simulates less rainfall over land than its parent GCM. This is why the CSU MMF simulated less overall global rainfall than its parent GCM. The Goddard MMF simulates more global rainfall than its parent GCM because of the high contribution from the oceanic component. A number of critical issues (i.e., the CRM's physical processes and its configuration) involving the Goddard MMF are discussed in this paper.
A multiscale modeling framework (MMF), which replaces the conventional cloud parameterizations with a cloud-resolving model (CRM) in each grid column of a GCM, constitutes a new and promising approach for climate modeling. The MMF can provide for global coverage and two-way interactions between the CRMs and their parent GCM. The CRM allows for explicit simulation of cloud processes and their interactions with radiation and surface processes, and the GCM allows for global coverage.
A new MMF has been developed that is based on the NASA Goddard Space Flight Center (GSFC) finite-volume GCM (fvGCM) and the Goddard Cumulus Ensemble (GCE) model. This Goddard MMF produces many features that are similar to another MMF that was developed at Colorado State University (CSU), such as an improved surface precipitation pattern, better cloudiness, improved diurnal variability over both oceans and continents, and a stronger propagating Madden-Julian oscillation (MJO) compared to their parent GCMs using traditional cloud parameterizations. Both MMFs also produce a large and positive precipitation bias in the Indian Ocean and western Pacific during the Northern Hemisphere summer. However, there are also notable differences between the two MMFs. For example, the CSU MMF simulates less rainfall over land than its parent GCM. This is why the CSU MMF simulated less overall global rainfall than its parent GCM. The Goddard MMF simulates more global rainfall than its parent GCM because of the high contribution from the oceanic component. A number of critical issues (i.e., the CRM's physical processes and its configuration) involving the Goddard MMF are discussed in this paper.
Abstract
The NOAA Science Advisory Board appointed a task force to prepare a white paper on the use of observing system simulation experiments (OSSEs). Considering the importance and timeliness of this topic and based on this white paper, here we briefly review the use of OSSEs in the United States, discuss their values and limitations, and develop five recommendations for moving forward: national coordination of relevant research efforts, acceleration of OSSE development for Earth system models, consideration of the potential impact on OSSEs of deficiencies in the current data assimilation and prediction system, innovative and new applications of OSSEs, and extension of OSSEs to societal impacts. OSSEs can be complemented by calculations of forecast sensitivity to observations, which simultaneously evaluate the impact of different observation types in a forecast model system.
Abstract
The NOAA Science Advisory Board appointed a task force to prepare a white paper on the use of observing system simulation experiments (OSSEs). Considering the importance and timeliness of this topic and based on this white paper, here we briefly review the use of OSSEs in the United States, discuss their values and limitations, and develop five recommendations for moving forward: national coordination of relevant research efforts, acceleration of OSSE development for Earth system models, consideration of the potential impact on OSSEs of deficiencies in the current data assimilation and prediction system, innovative and new applications of OSSEs, and extension of OSSEs to societal impacts. OSSEs can be complemented by calculations of forecast sensitivity to observations, which simultaneously evaluate the impact of different observation types in a forecast model system.
Abstract
Over a two-year period beginning in 2015, a panel of subject matter experts, the Space Platform Requirements Working Group (SPRWG), carried out an analysis and prioritization of different space-based observations supporting the National Oceanic and Atmospheric Administration (NOAA)’s operational services in the areas of weather, oceans, and space weather. NOAA leadership used the SPRWG analysis of space-based observational priorities in different mission areas, among other inputs, to inform the Multi-Attribute Utility Theory (MAUT)-based value model and the NOAA Satellite Observing Systems Architecture (NSOSA) study. The goal of the NSOSA study is to develop candidate satellite architectures for the era beginning in approximately 2030. The SPRWG analysis included a prioritized list of observational objectives together with the quantitative attributes of each objective at three levels of performance: a threshold level of minimal utility, an intermediate level that the community expects by 2030, and a maximum effective level, a level for which further improvements would not be cost effective. This process is believed to be unprecedented in the analysis of long-range plans for providing observations from space. This paper describes the process for developing the prioritized objectives and their attributes and how they were combined in the Environmental Data Record (EDR) Value Model (EVM). The EVM helped inform NOAA’s assessment of many potential architectures for its future observing system within the NSOSA study. However, neither the SPRWG nor its report represents official NOAA policy positions or decisions, and the responsibility for selecting and implementing the final architecture rests solely with NOAA senior leadership.
Abstract
Over a two-year period beginning in 2015, a panel of subject matter experts, the Space Platform Requirements Working Group (SPRWG), carried out an analysis and prioritization of different space-based observations supporting the National Oceanic and Atmospheric Administration (NOAA)’s operational services in the areas of weather, oceans, and space weather. NOAA leadership used the SPRWG analysis of space-based observational priorities in different mission areas, among other inputs, to inform the Multi-Attribute Utility Theory (MAUT)-based value model and the NOAA Satellite Observing Systems Architecture (NSOSA) study. The goal of the NSOSA study is to develop candidate satellite architectures for the era beginning in approximately 2030. The SPRWG analysis included a prioritized list of observational objectives together with the quantitative attributes of each objective at three levels of performance: a threshold level of minimal utility, an intermediate level that the community expects by 2030, and a maximum effective level, a level for which further improvements would not be cost effective. This process is believed to be unprecedented in the analysis of long-range plans for providing observations from space. This paper describes the process for developing the prioritized objectives and their attributes and how they were combined in the Environmental Data Record (EDR) Value Model (EVM). The EVM helped inform NOAA’s assessment of many potential architectures for its future observing system within the NSOSA study. However, neither the SPRWG nor its report represents official NOAA policy positions or decisions, and the responsibility for selecting and implementing the final architecture rests solely with NOAA senior leadership.
Abstract
The Cyclone Global Navigation Satellite System (CYGNSS) is a new NASA earth science mission scheduled to be launched in 2016 that focuses on tropical cyclones (TCs) and tropical convection. The mission’s two primary objectives are the measurement of ocean surface wind speed with sufficient temporal resolution to resolve short-time-scale processes such as the rapid intensification phase of TC development and the ability of the surface measurements to penetrate through the extremely high precipitation rates typically encountered in the TC inner core. The mission’s goal is to support significant improvements in our ability to forecast TC track, intensity, and storm surge through better observations and, ultimately, better understanding of inner-core processes. CYGNSS meets its temporal sampling objective by deploying a constellation of eight satellites. Its ability to see through heavy precipitation is enabled by its operation as a bistatic radar using low-frequency GPS signals. The mission will deploy an eight-spacecraft constellation in a low-inclination (35°) circular orbit to maximize coverage and sampling in the tropics. Each CYGNSS spacecraft carries a four-channel radar receiver that measures GPS navigation signals scattered by the ocean surface. The mission will measure inner-core surface winds with high temporal resolution and spatial coverage, under all precipitating conditions, and over the full dynamic range of TC wind speeds.
Abstract
The Cyclone Global Navigation Satellite System (CYGNSS) is a new NASA earth science mission scheduled to be launched in 2016 that focuses on tropical cyclones (TCs) and tropical convection. The mission’s two primary objectives are the measurement of ocean surface wind speed with sufficient temporal resolution to resolve short-time-scale processes such as the rapid intensification phase of TC development and the ability of the surface measurements to penetrate through the extremely high precipitation rates typically encountered in the TC inner core. The mission’s goal is to support significant improvements in our ability to forecast TC track, intensity, and storm surge through better observations and, ultimately, better understanding of inner-core processes. CYGNSS meets its temporal sampling objective by deploying a constellation of eight satellites. Its ability to see through heavy precipitation is enabled by its operation as a bistatic radar using low-frequency GPS signals. The mission will deploy an eight-spacecraft constellation in a low-inclination (35°) circular orbit to maximize coverage and sampling in the tropics. Each CYGNSS spacecraft carries a four-channel radar receiver that measures GPS navigation signals scattered by the ocean surface. The mission will measure inner-core surface winds with high temporal resolution and spatial coverage, under all precipitating conditions, and over the full dynamic range of TC wind speeds.
Abstract
The prediction of tropical cyclone rapid intensification is one of the most pressing unsolved problems in hurricane forecasting. The signatures of gravity waves launched by strong convective updrafts are often clearly seen in airglow and carbon dioxide thermal emission spectra under favorable atmospheric conditions. By continuously monitoring the Atlantic hurricane belt from the main development region to the vulnerable sections of the continental United States at high cadence, it will be possible to investigate the utility of storm-induced gravity wave observations for the diagnosis of impending storm intensification. Such a capability would also enable significant improvements in our ability to characterize the 3D transient behavior of upper-atmospheric gravity waves and point the way to future observing strategies that could mitigate the risk to human life caused by severe storms. This paper describes a new mission concept involving a midinfrared imager hosted aboard a geostationary satellite positioned at approximately 80°W longitude. The sensor’s 3-km pixel size ensures that the gravity wave horizontal structure is adequately resolved, while a 30-s refresh rate enables improved definition of the dynamic intensification process. In this way the transient development of gravity wave perturbations caused by both convective and cyclonic storms may be discerned in near–real time.
Abstract
The prediction of tropical cyclone rapid intensification is one of the most pressing unsolved problems in hurricane forecasting. The signatures of gravity waves launched by strong convective updrafts are often clearly seen in airglow and carbon dioxide thermal emission spectra under favorable atmospheric conditions. By continuously monitoring the Atlantic hurricane belt from the main development region to the vulnerable sections of the continental United States at high cadence, it will be possible to investigate the utility of storm-induced gravity wave observations for the diagnosis of impending storm intensification. Such a capability would also enable significant improvements in our ability to characterize the 3D transient behavior of upper-atmospheric gravity waves and point the way to future observing strategies that could mitigate the risk to human life caused by severe storms. This paper describes a new mission concept involving a midinfrared imager hosted aboard a geostationary satellite positioned at approximately 80°W longitude. The sensor’s 3-km pixel size ensures that the gravity wave horizontal structure is adequately resolved, while a 30-s refresh rate enables improved definition of the dynamic intensification process. In this way the transient development of gravity wave perturbations caused by both convective and cyclonic storms may be discerned in near–real time.
