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Abstract
A cloud detection algorithm for a low power micropulse lidar is presented that attempts to identify all of the significant power returns from the vertical column above the lidar at all times. The main feature of the algorithm is construction of lidar power return profiles during periods of clear sky against which cloudy-sky power returns are compared. This algorithm supplements algorithms designed to detect cloud-base height in that the tops of optically thin clouds are identified and it provides an alternative approach to algorithms that identify significant power returns by analysis of changes in the slope of the backscattered powers with height. The cloud-base heights produced by the current algorithm during nonprecipitating periods are comparable with the results of a cloud-base height algorithm applied to the same data. Although an objective validation of algorithm performance on high, thin cirrus is lacking because of no truth data, the current algorithm produces few false positive and false negative classifications as determined through manual comparison of the original photoelectron count data to the final cloud mask image.
Abstract
A cloud detection algorithm for a low power micropulse lidar is presented that attempts to identify all of the significant power returns from the vertical column above the lidar at all times. The main feature of the algorithm is construction of lidar power return profiles during periods of clear sky against which cloudy-sky power returns are compared. This algorithm supplements algorithms designed to detect cloud-base height in that the tops of optically thin clouds are identified and it provides an alternative approach to algorithms that identify significant power returns by analysis of changes in the slope of the backscattered powers with height. The cloud-base heights produced by the current algorithm during nonprecipitating periods are comparable with the results of a cloud-base height algorithm applied to the same data. Although an objective validation of algorithm performance on high, thin cirrus is lacking because of no truth data, the current algorithm produces few false positive and false negative classifications as determined through manual comparison of the original photoelectron count data to the final cloud mask image.
Abstract
We define and examine extreme frost events at three station locations across southern Australia. A synoptic assessment of the events shows that they are generally characterized by passage of a front or trough followed by a developing blocking high. Frost typically occurs at the leading edge of the block. The very cold air pool leading to the frost event is the result of descent of cold, dry midtropospheric air parcels from regions poleward of the station. The air is exceptionally cold because it is advected across the strong meridional temperature gradients in the storm track. The air is dry because this equatorward meridional pathway requires descent and so must have origins well above the surface in the dryer midtroposphere. The position of the block and location of the dry descent are dynamically determined by large-scale waveguide modes in the polar jet waveguide. The role of the waveguide modes is deduced from composites of midtropospheric flow anomalies over the days preceding and after the frost events. These show organized wavenumber 3 or 4 wave trains, with the block associated with the frost formed as a node of the wave train. The wave trains resemble known waveguide modes such as the Pacific–South America mode, and the frost event projects clearly onto these modes during their life cycle. The strong interannual and decadal variability of extreme frost events at a location can be understood in light of event dependence on organized waveguide modes.
Abstract
We define and examine extreme frost events at three station locations across southern Australia. A synoptic assessment of the events shows that they are generally characterized by passage of a front or trough followed by a developing blocking high. Frost typically occurs at the leading edge of the block. The very cold air pool leading to the frost event is the result of descent of cold, dry midtropospheric air parcels from regions poleward of the station. The air is exceptionally cold because it is advected across the strong meridional temperature gradients in the storm track. The air is dry because this equatorward meridional pathway requires descent and so must have origins well above the surface in the dryer midtroposphere. The position of the block and location of the dry descent are dynamically determined by large-scale waveguide modes in the polar jet waveguide. The role of the waveguide modes is deduced from composites of midtropospheric flow anomalies over the days preceding and after the frost events. These show organized wavenumber 3 or 4 wave trains, with the block associated with the frost formed as a node of the wave train. The wave trains resemble known waveguide modes such as the Pacific–South America mode, and the frost event projects clearly onto these modes during their life cycle. The strong interannual and decadal variability of extreme frost events at a location can be understood in light of event dependence on organized waveguide modes.
Calibrating Ground-Based Radars against TRMM and GPMAbstract
Calibration error represents a significant source of uncertainty in quantitative applications of ground-based radar (GR) reflectivity data. Correcting it requires knowledge of the true reflectivity at well-defined locations and times during a volume scan. Previous work has demonstrated that observations from certain spaceborne radar (SR) platforms may be suitable for this purpose. Specifically, the Ku-band precipitation radars on board the Tropical Rainfall Measuring Mission (TRMM) satellite and its successor, the Global Precipitation Measurement (GPM) mission Core Observatory satellite together provide nearly two decades of well-calibrated reflectivity measurements over low-latitude regions (±35°). However, when comparing SR and GR reflectivities, great care must be taken to account for differences in instrument sensitivity and frequency, and to ensure that the observations are spatially and temporally coincident. Here, a volume-matching method, developed as part of the ground validation network for GPM, is adapted and used to quantify historical calibration errors for three S-band radars in the vicinity of Sydney, Australia. Volume-matched GR–SR sample pairs are identified over a 7-yr period and carefully filtered to isolate reflectivity differences associated with GR calibration error. These are then used in combination with radar engineering work records to derive a piecewise-constant time series of calibration error for each site. The efficacy of this approach is verified through comparisons between GR reflectivities in regions of overlapping coverage, with improved agreement when the estimated errors are removed.
Abstract
Calibration error represents a significant source of uncertainty in quantitative applications of ground-based radar (GR) reflectivity data. Correcting it requires knowledge of the true reflectivity at well-defined locations and times during a volume scan. Previous work has demonstrated that observations from certain spaceborne radar (SR) platforms may be suitable for this purpose. Specifically, the Ku-band precipitation radars on board the Tropical Rainfall Measuring Mission (TRMM) satellite and its successor, the Global Precipitation Measurement (GPM) mission Core Observatory satellite together provide nearly two decades of well-calibrated reflectivity measurements over low-latitude regions (±35°). However, when comparing SR and GR reflectivities, great care must be taken to account for differences in instrument sensitivity and frequency, and to ensure that the observations are spatially and temporally coincident. Here, a volume-matching method, developed as part of the ground validation network for GPM, is adapted and used to quantify historical calibration errors for three S-band radars in the vicinity of Sydney, Australia. Volume-matched GR–SR sample pairs are identified over a 7-yr period and carefully filtered to isolate reflectivity differences associated with GR calibration error. These are then used in combination with radar engineering work records to derive a piecewise-constant time series of calibration error for each site. The efficacy of this approach is verified through comparisons between GR reflectivities in regions of overlapping coverage, with improved agreement when the estimated errors are removed.
Abstract
We develop and compare variants of coupled data assimilation (DA) systems based on ensemble optimal interpolation (EnOI) and ensemble transform Kalman filter (ETKF) methods. The assimilation system is first tested on a small paradigm model of the coupled tropical–extratropical climate system, then implemented for a coupled general circulation model (GCM). Strongly coupled DA was employed specifically to assess the impact of assimilating ocean observations [sea surface temperature (SST), sea surface height (SSH), and sea surface salinity (SSS), Argo, XBT, CTD, moorings] on the atmospheric state analysis update via the cross-domain error covariances from the coupled-model background ensemble. We examine the relationship between ensemble spread, analysis increments, and forecast skill in multiyear ENSO prediction experiments with a particular focus on the atmospheric response to tropical ocean perturbations. Initial forecast perturbations generated from bred vectors (BVs) project onto disturbances at and below the thermocline with similar structures to ETKF perturbations. BV error growth leads ENSO SST phasing by 6 months whereupon the dominant mechanism communicating tropical ocean variability to the extratropical atmosphere is via tropical convection modulating the Hadley circulation. We find that bred vectors specific to tropical Pacific thermocline variability were the most effective choices for ensemble initialization and ENSO forecasting.
Abstract
We develop and compare variants of coupled data assimilation (DA) systems based on ensemble optimal interpolation (EnOI) and ensemble transform Kalman filter (ETKF) methods. The assimilation system is first tested on a small paradigm model of the coupled tropical–extratropical climate system, then implemented for a coupled general circulation model (GCM). Strongly coupled DA was employed specifically to assess the impact of assimilating ocean observations [sea surface temperature (SST), sea surface height (SSH), and sea surface salinity (SSS), Argo, XBT, CTD, moorings] on the atmospheric state analysis update via the cross-domain error covariances from the coupled-model background ensemble. We examine the relationship between ensemble spread, analysis increments, and forecast skill in multiyear ENSO prediction experiments with a particular focus on the atmospheric response to tropical ocean perturbations. Initial forecast perturbations generated from bred vectors (BVs) project onto disturbances at and below the thermocline with similar structures to ETKF perturbations. BV error growth leads ENSO SST phasing by 6 months whereupon the dominant mechanism communicating tropical ocean variability to the extratropical atmosphere is via tropical convection modulating the Hadley circulation. We find that bred vectors specific to tropical Pacific thermocline variability were the most effective choices for ensemble initialization and ENSO forecasting.
Abstract
Recent studies have shown that regardless of model configuration, skill in predicting El Niño–Southern Oscillation (ENSO), in terms of target month and forecast lead time, remains largely dependent on the temporal characteristics of the boreal spring predictability barrier. Continuing the 2019 study by O’Kane et al., we compare multiyear ensemble ENSO forecasts from the Climate Analysis Forecast Ensemble (CAFE) to ensemble forecasts from state-of-the-art dynamical coupled models in the North American Multimodel Ensemble (NMME) project. The CAFE initial perturbations are targeted such that they are specific to tropical Pacific thermocline variability. With respect to individual NMME forecasts and multimodel ensemble averages, the CAFE forecasts reveal improvements in skill when predicting ENSO at lead times greater than 6 months, in particular when predictability is most strongly limited by the boreal spring barrier. Initial forecast perturbations generated exclusively as disturbances in the equatorial Pacific thermocline are shown to improve the forecast skill at longer lead times in terms of anomaly correlation and the random walk sign test. Our results indicate that augmenting current initialization methods with initial perturbations targeting instabilities specific to the tropical Pacific thermocline may improve long-range ENSO prediction.
Abstract
Recent studies have shown that regardless of model configuration, skill in predicting El Niño–Southern Oscillation (ENSO), in terms of target month and forecast lead time, remains largely dependent on the temporal characteristics of the boreal spring predictability barrier. Continuing the 2019 study by O’Kane et al., we compare multiyear ensemble ENSO forecasts from the Climate Analysis Forecast Ensemble (CAFE) to ensemble forecasts from state-of-the-art dynamical coupled models in the North American Multimodel Ensemble (NMME) project. The CAFE initial perturbations are targeted such that they are specific to tropical Pacific thermocline variability. With respect to individual NMME forecasts and multimodel ensemble averages, the CAFE forecasts reveal improvements in skill when predicting ENSO at lead times greater than 6 months, in particular when predictability is most strongly limited by the boreal spring barrier. Initial forecast perturbations generated exclusively as disturbances in the equatorial Pacific thermocline are shown to improve the forecast skill at longer lead times in terms of anomaly correlation and the random walk sign test. Our results indicate that augmenting current initialization methods with initial perturbations targeting instabilities specific to the tropical Pacific thermocline may improve long-range ENSO prediction.
Abstract
The CSIRO Climate retrospective Analysis and Forecast Ensemble system, version 1 (CAFE60v1) provides a large (96 member) ensemble retrospective analysis of the global climate system from 1960 to present with sufficiently many realizations and at spatiotemporal resolutions suitable to enable probabilistic climate studies. Using a variant of the ensemble Kalman filter, 96 climate state estimates are generated over the most recent six decades. These state estimates are constrained by monthly mean ocean, atmosphere, and sea ice observations such that their trajectories track the observed state while enabling estimation of the uncertainties in the approximations to the retrospective mean climate over recent decades. For the atmosphere, we evaluate CAFE60v1 in comparison to empirical indices of the major climate teleconnections and blocking with various reanalysis products. Estimates of the large-scale ocean structure, transports, and biogeochemistry are compared to those derived from gridded observational products and climate model projections (CMIP). Sea ice (extent, concentration, and variability) and land surface (precipitation and surface air temperatures) are also compared to a variety of model and observational products. Our results show that CAFE60v1 is a useful, comprehensive, and unique data resource for studying internal climate variability and predictability, including the recent climate response to anthropogenic forcing on multiyear to decadal time scales.
Abstract
The CSIRO Climate retrospective Analysis and Forecast Ensemble system, version 1 (CAFE60v1) provides a large (96 member) ensemble retrospective analysis of the global climate system from 1960 to present with sufficiently many realizations and at spatiotemporal resolutions suitable to enable probabilistic climate studies. Using a variant of the ensemble Kalman filter, 96 climate state estimates are generated over the most recent six decades. These state estimates are constrained by monthly mean ocean, atmosphere, and sea ice observations such that their trajectories track the observed state while enabling estimation of the uncertainties in the approximations to the retrospective mean climate over recent decades. For the atmosphere, we evaluate CAFE60v1 in comparison to empirical indices of the major climate teleconnections and blocking with various reanalysis products. Estimates of the large-scale ocean structure, transports, and biogeochemistry are compared to those derived from gridded observational products and climate model projections (CMIP). Sea ice (extent, concentration, and variability) and land surface (precipitation and surface air temperatures) are also compared to a variety of model and observational products. Our results show that CAFE60v1 is a useful, comprehensive, and unique data resource for studying internal climate variability and predictability, including the recent climate response to anthropogenic forcing on multiyear to decadal time scales.
Abstract
The 12 weather and climate models participating in the Global Land–Atmosphere Coupling Experiment (GLACE) show both a wide variation in the strength of land–atmosphere coupling and some intriguing commonalities. In this paper, the causes of variations in coupling strength—both the geographic variations within a given model and the model-to-model differences—are addressed. The ability of soil moisture to affect precipitation is examined in two stages, namely, the ability of the soil moisture to affect evaporation, and the ability of evaporation to affect precipitation. Most of the differences between the models and within a given model are found to be associated with the first stage—an evaporation rate that varies strongly and consistently with soil moisture tends to lead to a higher coupling strength. The first-stage differences reflect identifiable differences in model parameterization and model climate. Intermodel differences in the evaporation–precipitation connection, however, also play a key role.
Abstract
The 12 weather and climate models participating in the Global Land–Atmosphere Coupling Experiment (GLACE) show both a wide variation in the strength of land–atmosphere coupling and some intriguing commonalities. In this paper, the causes of variations in coupling strength—both the geographic variations within a given model and the model-to-model differences—are addressed. The ability of soil moisture to affect precipitation is examined in two stages, namely, the ability of the soil moisture to affect evaporation, and the ability of evaporation to affect precipitation. Most of the differences between the models and within a given model are found to be associated with the first stage—an evaporation rate that varies strongly and consistently with soil moisture tends to lead to a higher coupling strength. The first-stage differences reflect identifiable differences in model parameterization and model climate. Intermodel differences in the evaporation–precipitation connection, however, also play a key role.
Abstract
Africa is poised for a revolution in the quality and relevance of weather predictions, with potential for great benefits in terms of human and economic security. This revolution will be driven by recent international progress in nowcasting, numerical weather prediction, theoretical tropical dynamics, and forecast communication, but will depend on suitable scientific investment being made. The commercial sector has recognized this opportunity and new forecast products are being made available to African stakeholders. At this time, it is vital that robust scientific methods are used to develop and evaluate the new generation of forecasts. The Global Challenges Research Fund (GCRF) African Science for Weather Information and Forecasting Techniques (SWIFT) project represents an international effort to advance scientific solutions across the fields of nowcasting, synoptic and short-range severe weather prediction, subseasonal-to-seasonal (S2S) prediction, user engagement, and forecast evaluation. This paper describes the opportunities facing African meteorology and the ways in which SWIFT is meeting those opportunities and identifying priority next steps. Delivery and maintenance of weather forecasting systems exploiting these new solutions requires a trained body of scientists with skills in research and training, modeling and operational prediction, and communications and leadership. By supporting partnerships between academia and operational agencies in four African partner countries, the SWIFT project is helping to build capacity and capability in African forecasting science. A highlight of SWIFT is the coordination of three weather forecasting “Testbeds”—the first of their kind in Africa—which have been used to bring new evaluation tools, research insights, user perspectives, and communications pathways into a semioperational forecasting environment.
Abstract
Africa is poised for a revolution in the quality and relevance of weather predictions, with potential for great benefits in terms of human and economic security. This revolution will be driven by recent international progress in nowcasting, numerical weather prediction, theoretical tropical dynamics, and forecast communication, but will depend on suitable scientific investment being made. The commercial sector has recognized this opportunity and new forecast products are being made available to African stakeholders. At this time, it is vital that robust scientific methods are used to develop and evaluate the new generation of forecasts. The Global Challenges Research Fund (GCRF) African Science for Weather Information and Forecasting Techniques (SWIFT) project represents an international effort to advance scientific solutions across the fields of nowcasting, synoptic and short-range severe weather prediction, subseasonal-to-seasonal (S2S) prediction, user engagement, and forecast evaluation. This paper describes the opportunities facing African meteorology and the ways in which SWIFT is meeting those opportunities and identifying priority next steps. Delivery and maintenance of weather forecasting systems exploiting these new solutions requires a trained body of scientists with skills in research and training, modeling and operational prediction, and communications and leadership. By supporting partnerships between academia and operational agencies in four African partner countries, the SWIFT project is helping to build capacity and capability in African forecasting science. A highlight of SWIFT is the coordination of three weather forecasting “Testbeds”—the first of their kind in Africa—which have been used to bring new evaluation tools, research insights, user perspectives, and communications pathways into a semioperational forecasting environment.
