Search Results
Abstract
In the case of a nadir-looking spaceborne or aircraft radar in the presence of rain the return power corresponding to secondary surface scattering may provide information on the properties of the surface and the precipitation. The object of the study is to evaluate a method for determining simultaneously the rainfall rate and the back-scattering coefficient of the surface, σ0. The method is based upon the mirror-reflected power, Pm , which corresponds to the portion of the incident power scattered from the surface to the precipitation, intercepted by the precipitation, and again returned to the surface where it is scattered a final time back to the antenna. Two approximations are obtained for Pm depending on whether the held of view at the surface is either much greater or much less than the height of the reflection layer. Since the dependence of Pm on the backscattering coefficient of the surface differs in the two cases, two algorithms are given by which the path-averaged rain rate and σ0 can be deduced. We also discuss the delectability of Pm , the relative strength of other contributions to the return power arriving simultaneously with Pm , and the validity of the approximations used in deriving Pm .
Abstract
In the case of a nadir-looking spaceborne or aircraft radar in the presence of rain the return power corresponding to secondary surface scattering may provide information on the properties of the surface and the precipitation. The object of the study is to evaluate a method for determining simultaneously the rainfall rate and the back-scattering coefficient of the surface, σ0. The method is based upon the mirror-reflected power, Pm , which corresponds to the portion of the incident power scattered from the surface to the precipitation, intercepted by the precipitation, and again returned to the surface where it is scattered a final time back to the antenna. Two approximations are obtained for Pm depending on whether the held of view at the surface is either much greater or much less than the height of the reflection layer. Since the dependence of Pm on the backscattering coefficient of the surface differs in the two cases, two algorithms are given by which the path-averaged rain rate and σ0 can be deduced. We also discuss the delectability of Pm , the relative strength of other contributions to the return power arriving simultaneously with Pm , and the validity of the approximations used in deriving Pm .
Variational Analysis of Simulated Ocean Surface Winds from the Cyclone Global Navigation Satellite System (CYGNSS) and Evaluation Using a Regional OSSEAbstract
A positive impact of adding directional information to observations from the Cyclone Global Navigation Satellite System (CYNGSS) constellation of microsatellites is observed in simulation using a high-resolution nature run of an Atlantic hurricane for a 4-day period. Directional information is added using a two-dimensional variational analysis method (VAM) for near-surface vector winds that blends simulated CYGNSS wind speeds with an a priori background vector wind field at 6-h analysis times. The resulting wind vectors at CYGNSS data locations are more geophysically self-consistent when using high-resolution 6-h forecast backgrounds from a Hurricane Weather Research and Forecast Model (HWRF) control observing system simulation experiment (OSSE) compared to low-resolution 6-h forecasts from an associated Global Forecast System (GFS) model control OSSE. An important contributing factor is the large displacement error in the center of circulation in the GFS background wind fields that produces asymmetric circulations in the associated VAM analyses. Results of a limited OSSE indicate that CYGNSS winds reduce forecast error in hurricane intensity in 0–48-h forecasts compared to using no CYGNSS data. Assimilation of VAM-CYGNSS vector winds reduces maximum wind speed error by 2–5 kt (1 kt = 0.51 m s−1) and reduces minimum central pressure error by 2–5 hPa. The improvement in forecast intensity is notably larger and more consistent than the reduction in track error. The assimilation of VAM-CYGNSS wind vectors constrains analyses of surface wind field structures during OSSE more effectively than wind speeds alone. Because of incomplete sampling and the limitations of the data assimilation system used, CYGNSS scalar winds produce unwanted wind/pressure imbalances and asymmetries more often than the assimilation of VAM-CYGNSS data.
Abstract
A positive impact of adding directional information to observations from the Cyclone Global Navigation Satellite System (CYNGSS) constellation of microsatellites is observed in simulation using a high-resolution nature run of an Atlantic hurricane for a 4-day period. Directional information is added using a two-dimensional variational analysis method (VAM) for near-surface vector winds that blends simulated CYGNSS wind speeds with an a priori background vector wind field at 6-h analysis times. The resulting wind vectors at CYGNSS data locations are more geophysically self-consistent when using high-resolution 6-h forecast backgrounds from a Hurricane Weather Research and Forecast Model (HWRF) control observing system simulation experiment (OSSE) compared to low-resolution 6-h forecasts from an associated Global Forecast System (GFS) model control OSSE. An important contributing factor is the large displacement error in the center of circulation in the GFS background wind fields that produces asymmetric circulations in the associated VAM analyses. Results of a limited OSSE indicate that CYGNSS winds reduce forecast error in hurricane intensity in 0–48-h forecasts compared to using no CYGNSS data. Assimilation of VAM-CYGNSS vector winds reduces maximum wind speed error by 2–5 kt (1 kt = 0.51 m s−1) and reduces minimum central pressure error by 2–5 hPa. The improvement in forecast intensity is notably larger and more consistent than the reduction in track error. The assimilation of VAM-CYGNSS wind vectors constrains analyses of surface wind field structures during OSSE more effectively than wind speeds alone. Because of incomplete sampling and the limitations of the data assimilation system used, CYGNSS scalar winds produce unwanted wind/pressure imbalances and asymmetries more often than the assimilation of VAM-CYGNSS data.
Impact of CYGNSS Ocean Surface Wind Speeds on Numerical Simulations of a Hurricane in Observing System Simulation ExperimentsAbstract
The NASA Cyclone Global Navigation Satellite System (CYGNSS) was launched in late 2016. It will make available frequent ocean surface wind speed observations throughout the life cycle of tropical storms and hurricanes. In this study, the impact of CYGNSS ocean surface winds on numerical simulations of a hurricane case is assessed with a research version of the Hurricane Weather Research and Forecasting Model and a Gridpoint Statistical Interpolation analysis system in a regional observing system simulation experiment framework. Two different methods for reducing the CYGNSS data volume were tested: one in which the winds were thinned and one in which the winds were superobbed.
The results suggest that assimilation of the CYGNSS winds has great potential to improve hurricane track and intensity simulations through improved representations of the surface wind fields, hurricane inner-core structures, and surface fluxes. The assimilation of the superobbed CYGNSS data seems to be more effective in improving hurricane track forecasts than thinning the data.
Abstract
The NASA Cyclone Global Navigation Satellite System (CYGNSS) was launched in late 2016. It will make available frequent ocean surface wind speed observations throughout the life cycle of tropical storms and hurricanes. In this study, the impact of CYGNSS ocean surface winds on numerical simulations of a hurricane case is assessed with a research version of the Hurricane Weather Research and Forecasting Model and a Gridpoint Statistical Interpolation analysis system in a regional observing system simulation experiment framework. Two different methods for reducing the CYGNSS data volume were tested: one in which the winds were thinned and one in which the winds were superobbed.
The results suggest that assimilation of the CYGNSS winds has great potential to improve hurricane track and intensity simulations through improved representations of the surface wind fields, hurricane inner-core structures, and surface fluxes. The assimilation of the superobbed CYGNSS data seems to be more effective in improving hurricane track forecasts than thinning the data.
Abstract
The Desroziers diagnostics (DD) are applied to the cross-calibrated, multiplatform (CCMP) ocean surface wind datasets to estimate wind speed errors of the ECMWF background, the microwave satellite observations, and the resulting CCMP analysis. The DD confirm that the ECMWF operational surface wind speed error standard deviations vary with latitude in the range 0.8–1.3 m s−1 and that the cross-calibrated Remote Sensing Systems (RSS) wind speed retrievals’ standard deviations are in the range 0.5–0.7 m s−1. Further, the estimated CCMP analysis wind speed standard deviations are in the range 0.2–0.3 m s−1. The results suggest the need to revise the parameterization of the errors of the first guess at appropriate time (FGAT) procedure. Errors for wind speeds <16 m s−1 are homogeneous; however, for the relatively rare but critical higher wind speed situations, errors are much larger.
Abstract
The Desroziers diagnostics (DD) are applied to the cross-calibrated, multiplatform (CCMP) ocean surface wind datasets to estimate wind speed errors of the ECMWF background, the microwave satellite observations, and the resulting CCMP analysis. The DD confirm that the ECMWF operational surface wind speed error standard deviations vary with latitude in the range 0.8–1.3 m s−1 and that the cross-calibrated Remote Sensing Systems (RSS) wind speed retrievals’ standard deviations are in the range 0.5–0.7 m s−1. Further, the estimated CCMP analysis wind speed standard deviations are in the range 0.2–0.3 m s−1. The results suggest the need to revise the parameterization of the errors of the first guess at appropriate time (FGAT) procedure. Errors for wind speeds <16 m s−1 are homogeneous; however, for the relatively rare but critical higher wind speed situations, errors are much larger.
OSSE Assessment of Underwater Glider Arrays to Improve Ocean Model Initialization for Tropical Cyclone PredictionAbstract
Credible tropical cyclone (TC) intensity prediction by coupled models requires accurate forecasts of enthalpy flux from ocean to atmosphere, which in turn requires accurate forecasts of sea surface temperature cooling beneath storms. Initial ocean fields must accurately represent ocean mesoscale features and the associated thermal and density structure. Observing system simulation experiments (OSSEs) are performed to quantitatively assess the impact of assimilating profiles collected from multiple underwater gliders deployed over the western North Atlantic Ocean TC region, emphasizing advantages gained by profiling from moving versus stationary platforms. Assimilating ocean profiles collected repeatedly at fixed locations produces large root-mean-square error reduction only within ~50 km of each profiler for two primary reasons. First, corrections performed during individual update cycles tend to introduce unphysical eddy structure resulting from smoothing properties of the background error covariance matrix and the tapering of innovations by a localization radius function. Second, advection produces rapid nonlinear error growth at larger distances from profiler locations. The ability of each individual moving glider to cross gradients and map mesoscale structure in its vicinity substantially reduces this nonlinear error growth. Glider arrays can be deployed with horizontal separation distances that are 50%–100% larger than those of fixed-location profilers to achieve similar mesoscale error reduction. By contrast, substantial larger-scale bias reduction in upper-ocean heat content can be achieved by deploying profiler arrays with separation distances up to several hundred kilometers, with moving gliders providing only modest additional improvement. Expected sensitivity of results to study region and data assimilation method is discussed.
Abstract
Credible tropical cyclone (TC) intensity prediction by coupled models requires accurate forecasts of enthalpy flux from ocean to atmosphere, which in turn requires accurate forecasts of sea surface temperature cooling beneath storms. Initial ocean fields must accurately represent ocean mesoscale features and the associated thermal and density structure. Observing system simulation experiments (OSSEs) are performed to quantitatively assess the impact of assimilating profiles collected from multiple underwater gliders deployed over the western North Atlantic Ocean TC region, emphasizing advantages gained by profiling from moving versus stationary platforms. Assimilating ocean profiles collected repeatedly at fixed locations produces large root-mean-square error reduction only within ~50 km of each profiler for two primary reasons. First, corrections performed during individual update cycles tend to introduce unphysical eddy structure resulting from smoothing properties of the background error covariance matrix and the tapering of innovations by a localization radius function. Second, advection produces rapid nonlinear error growth at larger distances from profiler locations. The ability of each individual moving glider to cross gradients and map mesoscale structure in its vicinity substantially reduces this nonlinear error growth. Glider arrays can be deployed with horizontal separation distances that are 50%–100% larger than those of fixed-location profilers to achieve similar mesoscale error reduction. By contrast, substantial larger-scale bias reduction in upper-ocean heat content can be achieved by deploying profiler arrays with separation distances up to several hundred kilometers, with moving gliders providing only modest additional improvement. Expected sensitivity of results to study region and data assimilation method is discussed.
Community Global Observing System Simulation Experiment (OSSE) Package (CGOP): Assessment and Validation of the OSSE System Using an OSSE–OSE Intercomparison of Summary Assessment MetricsAbstract
Observing system simulation experiments (OSSEs) are used to simulate and assess the impacts of new observing systems planned for the future or the impacts of adopting new techniques for exploiting data or for forecasting. This study focuses on the impacts of satellite data on global numerical weather prediction (NWP) systems. Since OSSEs are based on simulations of nature and observations, reliable results require that the OSSE system be validated. This validation involves cycles of assessment and calibration of the individual system components, as well as the complete system, with the end goal of reproducing the behavior of real-data observing system experiments (OSEs). This study investigates the accuracy of the calibration of an OSSE system—here, the Community Global OSSE Package (CGOP) system—before any explicit tuning has been performed by performing an intercomparison of the OSSE summary assessment metrics (SAMs) with those obtained from parallel real-data OSEs. The main conclusion reached in this study is that, based on the SAMs, the CGOP is able to reproduce aspects of the analysis and forecast performance of parallel OSEs despite the simplifications employed in the OSSEs. This conclusion holds even when the SAMs are stratified by various subsets (the tropics only, temperature only, etc.).
Abstract
Observing system simulation experiments (OSSEs) are used to simulate and assess the impacts of new observing systems planned for the future or the impacts of adopting new techniques for exploiting data or for forecasting. This study focuses on the impacts of satellite data on global numerical weather prediction (NWP) systems. Since OSSEs are based on simulations of nature and observations, reliable results require that the OSSE system be validated. This validation involves cycles of assessment and calibration of the individual system components, as well as the complete system, with the end goal of reproducing the behavior of real-data observing system experiments (OSEs). This study investigates the accuracy of the calibration of an OSSE system—here, the Community Global OSSE Package (CGOP) system—before any explicit tuning has been performed by performing an intercomparison of the OSSE summary assessment metrics (SAMs) with those obtained from parallel real-data OSEs. The main conclusion reached in this study is that, based on the SAMs, the CGOP is able to reproduce aspects of the analysis and forecast performance of parallel OSEs despite the simplifications employed in the OSSEs. This conclusion holds even when the SAMs are stratified by various subsets (the tropics only, temperature only, etc.).
Community Global Observing System Simulation Experiment (OSSE) Package (CGOP): Perfect Observations Simulation ValidationAbstract
The simulation of observations—a critical Community Global Observing System Simulation Experiment (OSSE) Package (CGOP) component—is validated first by a comparison of error-free simulated observations for the first 24 h at the start of the nature run (NR) to the real observations for those sensors that operated during that period. Sample results of this validation are presented here for existing low-Earth-orbiting (LEO) infrared (IR) and microwave (MW) brightness temperature (BT) observations, for radio occultation (RO) bending angle observations, and for various types of conventional observations. For sensors not operating at the start of the NR, a qualitative validation is obtained by comparing geographic and statistical characteristics of observations over the initial day for such a sensor and an existing similar sensor. The comparisons agree, with no significant unexplained bias, and to within the uncertainties caused by real observation errors, time and space collocation differences, radiative transfer uncertainties, and differences between the NR and reality. To validate channels of a proposed future MW sensor with no equivalent existing spaceborne sensor channel, multiple linear regression is used to relate these channels to existing similar channels. The validation then compares observations simulated from the NR to observations predicted by the regression relationship applied to actual real observations of the existing channels. Overall, the CGOP simulations of error-free observations from conventional and satellite platforms that make up the global observing system are found to be reasonably accurate and suitable as a starting point for creating realistic simulated observations for OSSEs. These findings complete a critical step in the CGOP validation, thereby reducing the caveats required when interpreting the OSSE results.
Abstract
The simulation of observations—a critical Community Global Observing System Simulation Experiment (OSSE) Package (CGOP) component—is validated first by a comparison of error-free simulated observations for the first 24 h at the start of the nature run (NR) to the real observations for those sensors that operated during that period. Sample results of this validation are presented here for existing low-Earth-orbiting (LEO) infrared (IR) and microwave (MW) brightness temperature (BT) observations, for radio occultation (RO) bending angle observations, and for various types of conventional observations. For sensors not operating at the start of the NR, a qualitative validation is obtained by comparing geographic and statistical characteristics of observations over the initial day for such a sensor and an existing similar sensor. The comparisons agree, with no significant unexplained bias, and to within the uncertainties caused by real observation errors, time and space collocation differences, radiative transfer uncertainties, and differences between the NR and reality. To validate channels of a proposed future MW sensor with no equivalent existing spaceborne sensor channel, multiple linear regression is used to relate these channels to existing similar channels. The validation then compares observations simulated from the NR to observations predicted by the regression relationship applied to actual real observations of the existing channels. Overall, the CGOP simulations of error-free observations from conventional and satellite platforms that make up the global observing system are found to be reasonably accurate and suitable as a starting point for creating realistic simulated observations for OSSEs. These findings complete a critical step in the CGOP validation, thereby reducing the caveats required when interpreting the OSSE results.
Abstract
The potential impact of Doppler wind lidar (DWL) observations from a proposed optical autocovariance wind lidar (OAWL) instrument is quantified in observing system simulation experiments (OSSEs). The OAWL design would provide profiles of useful wind vectors along a ground track to the left of the International Space Station (ISS), which is in a 51.6° inclination low-Earth orbit (LEO). These observations are simulated realistically, accounting for cloud and aerosol distributions inferred from the OSSE nature runs (NRs), and measurement and sampling error sources. The impact of the simulated observations is determined in both global and regional OSSE frameworks. The global OSSE uses the ECMWF T511 NR and the NCEP operational Global Data Assimilation System at T382 resolution. The regional OSSE uses an embedded hurricane NR and the NCEP operational HWRF data assimilation system with outer and inner domains of 9- and 3-km resolution, respectively.
The global OSSE results show improved analyses and forecasts of tropical winds and extratropical geopotential heights. The tropical wind RMSEs are significantly reduced in the analyses and in short-term forecasts. The tropical wind improvement decays as the forecasts lengthen. The regional OSSEs are limited but show some improvements in hurricane track and intensity forecasts.
Abstract
The potential impact of Doppler wind lidar (DWL) observations from a proposed optical autocovariance wind lidar (OAWL) instrument is quantified in observing system simulation experiments (OSSEs). The OAWL design would provide profiles of useful wind vectors along a ground track to the left of the International Space Station (ISS), which is in a 51.6° inclination low-Earth orbit (LEO). These observations are simulated realistically, accounting for cloud and aerosol distributions inferred from the OSSE nature runs (NRs), and measurement and sampling error sources. The impact of the simulated observations is determined in both global and regional OSSE frameworks. The global OSSE uses the ECMWF T511 NR and the NCEP operational Global Data Assimilation System at T382 resolution. The regional OSSE uses an embedded hurricane NR and the NCEP operational HWRF data assimilation system with outer and inner domains of 9- and 3-km resolution, respectively.
The global OSSE results show improved analyses and forecasts of tropical winds and extratropical geopotential heights. The tropical wind RMSEs are significantly reduced in the analyses and in short-term forecasts. The tropical wind improvement decays as the forecasts lengthen. The regional OSSEs are limited but show some improvements in hurricane track and intensity forecasts.
Community Global Observing System Simulation Experiment (OSSE) Package (CGOP): Description and UsageAbstract
A modular extensible framework for conducting observing system simulation experiments (OSSEs) has been developed with the goals of 1) supporting decision-makers with quantitative assessments of proposed observing systems investments, 2) supporting readiness for new sensors, 3) enhancing collaboration across the community by making the most up-to-date OSSE components accessible, and 4) advancing the theory and practical application of OSSEs. This first implementation, the Community Global OSSE Package (CGOP), is for short- to medium-range global numerical weather prediction applications. The CGOP is based on a new mesoscale global nature run produced by NASA using the 7-km cubed sphere version of the Goddard Earth Observing System, version 5 (GEOS-5), atmospheric general circulation model and the January 2015 operational version of the NOAA global data assimilation (DA) system. CGOP includes procedures to simulate the full suite of observing systems used operationally in the global DA system, including conventional in situ, satellite-based radiance, and radio occultation observations. The methodology of adding a new proposed observation type is documented and illustrated with examples of current interest. The CGOP is designed to evolve, both to improve its realism and to keep pace with the advance of operational systems.
Abstract
A modular extensible framework for conducting observing system simulation experiments (OSSEs) has been developed with the goals of 1) supporting decision-makers with quantitative assessments of proposed observing systems investments, 2) supporting readiness for new sensors, 3) enhancing collaboration across the community by making the most up-to-date OSSE components accessible, and 4) advancing the theory and practical application of OSSEs. This first implementation, the Community Global OSSE Package (CGOP), is for short- to medium-range global numerical weather prediction applications. The CGOP is based on a new mesoscale global nature run produced by NASA using the 7-km cubed sphere version of the Goddard Earth Observing System, version 5 (GEOS-5), atmospheric general circulation model and the January 2015 operational version of the NOAA global data assimilation (DA) system. CGOP includes procedures to simulate the full suite of observing systems used operationally in the global DA system, including conventional in situ, satellite-based radiance, and radio occultation observations. The methodology of adding a new proposed observation type is documented and illustrated with examples of current interest. The CGOP is designed to evolve, both to improve its realism and to keep pace with the advance of operational systems.
