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One of the principal aims of the Sydney 2000 Forecast Demonstration Project was to assess the utility of advanced nowcasting systems to operational severe weather forecasters. This paper describes the application of the products of a variety of systems by forecasters during a severe weather event in Sydney, Australia, on 3 November 2000. During this day a severe storm developed to the south of the metropolitan area and tracked north producing large, damaging hail, heavy rainfall, and at least three tornadoes. A number of severe weather warnings were issued by the Australian Bureau of Meteorology to a variety of customers throughout the day. This paper investigates how the novel nowcast products were used by the forecasters and the impact they had on the forecast and warning dissemination procedure. The products used are contrasted with those that were available or could have been made available at various stages of the storm development and the efficiency of use of these products is discussed. The severe weather forecasters expressed their satisfaction with the systems and believed that the additional information enhanced the quality and timeliness of the warnings issued during the event. [ABSTRACT FROM AUTHOR]

One of the main goals of the Sydney 2000 Forecast Demonstration Project was to demonstrate the efficacy and utility of automated severe weather detection radar algorithms. As a contribution to this goal, this paper describes the radar-based severe weather algorithms used in the project, their performance, and related radar issues. Participants in this part of the project included the National Severe Storm Laboratory (NSSL) Warning Decision Support System (WDSS), the Meteorological Service of Canada Canadian Radar Decision Support (CARDS) system, the National Center for Atmospheric Research Thunderstorm Initiation, Tracking, Analysis, and Nowcasting (TITAN) system, and a precipitation-typing algorithm from the Bureau of Meteorology Research Centre polarized C-band polarimetric (C-Pol) radar. Three radars were available: the S-band reflectivity-only operational radar, the C-band Doppler Kurnell radar, and the C-band Doppler polarization C-Pol radar. The radar algorithms attempt to diagnose the presence of storm cells; provide storm tracks; identify mesocyclone circulations, downbursts and/or microbursts, and hail; and provide storm ranking. The tracking and identification of cells was undertaken using TITAN and WDSS. Three versions of TITAN were employed to track weak and strong cells. Results show TITAN cell detection thresholds influence the ability of the algorithm to clearly identify storm cells and also the ability to correctly track the storms. WDSS algorithms are set up with lower-volume thresholds and provided many more tracks. WDSS and CARDS circulation algorithms were adapted to the Southern Hemisphere. CARDS had lower detection thresholds and, hence, detected more circulations than WDSS. Radial-velocity-based and reflectivity-based downburst algorithms were available from CARDS. Since the reflectivity-based algorithm was based on features aloft, it provided an earlier indication of strong surface winds. Three different hail algorithms from WDSS, CARDS, and C-Pol provided output on the presence, the probability, and the size of hail. Although the algorithms differed considerably they provided similar results. Size distributions were similar to observations. The WDSS provided a ranking algorithm to identify the most severe storm. Many of the algorithms had been adapted and altered to account for differences in radar technology, configuration, and meteorological regime. The various combinations of different algorithms and different radars provided an unprecedented opportunity to study the impact of radar technology on the performance of the severe weather algorithms. The algorithms were able to operate on both single- and dual-pulse repetition frequency Doppler radars and on C- and S-band radars with minimal changes. The biggest influence on the algorithms was data quality. Beamwidth smoothing limited the effective range of the algorithms and ground clutter and ground clutter filtering affected the quality of the low-level radial velocities and the detection of low-level downbursts. Cycle time of the volume scans significantly affected the tracking results. [ABSTRACT FROM AUTHOR]

Statistical and case study–oriented comparisons of the quantitative precipitation nowcasting (QPN) schemes demonstrated during the first World Weather Research Programme (WWRP) Forecast Demonstration Project (FDP), held in Sydney, Australia, during 2000, served to confirm many of the earlier reported findings regarding QPN algorithm design and performance. With a few notable exceptions, nowcasting algorithms based upon the linear extrapolation of observed precipitation motion (Lagrangian persistence) were generally superior to more sophisticated, nonlinear nowcasting methods. Centroid trackers [Thunderstorm Identification, Tracking, Analysis and Nowcasting System (TITAN)] and pattern matching extrapolators using multiple vectors (Auto-nowcaster and Nimrod) were most reliable in convective scenarios. During widespread, stratiform rain events, the pattern matching extrapolators were superior to centroid trackers and wind advection techniques (Gandolf, Nimrod). There is some limited case study and statistical evidence from the FDP to support the use of more sophisticated, nonlinear QPN algorithms. In a companion paper in this issue, Wilson et al. demonstrate the advantages of combining linear extrapolation with algorithms designed to predict convective initiation, growth, and decay in the Auto-nowcaster. Ebert et al. show that the application of a nonlinear scheme [Spectral Prognosis (S-PROG)] designed to smooth precipitation features at a rate consistent with their observed temporal persistence tends to produce a nowcast that is superior to Lagrangian persistence in terms of rms error. However, the value of this approach in severe weather forecasting is called into question due to the rapid smoothing of high-intensity precipitation features. [ABSTRACT FROM AUTHOR]

The Sydney 2000 Olympic Games World Weather Research Programme Forecast Demonstration Project (WWRP FDP) aimed to demonstrate the utility and impact of modern nowcast systems. The project focused on the use of radar processing systems and products for nowcasting, including severe weather. The forecast problems facing the Australian Bureau of Meteorology (BoM) on these short timescales during the FDP are briefly described. The observing system is then discussed and enhancements to the network that supported the Olympic Games forecast requirements and the WWRP FDP project are outlined. In particular, issues related to radar calibration and quality control are discussed in some detail. The paper concludes with a brief discussion on the observing system requirements to meet such modern nowcast systems, areas of further development, and impacts that the FDP had on BoM nowcasting systems. The need for end-to-end design of systems from data gathering, to analysis and product generation is emphasized. [ABSTRACT FROM AUTHOR]

The Sydney 2000 (S2000) Forecast Demonstration Project (FDP) was initiated by the World Meteorological Organization (WMO) World Weather Research Programme (WWRP) to enable the world meteorological community to cooperatively demonstrate advanced technologies and methods for accurate and specific short-term weather forecasting (nowcasting). FDP output was developed in support of the Sydney 2000 Olympics and trialed throughout and beyond the Olympic period. As is the case with all WWRP projects, the WWRP S2000 FDP included an assessment of the social, societal, and economic impacts of the project's forecasts. The Impacts Study considered how Bureau of Meteorology (BoM) forecasters employed the enhanced FDP information to produce more “useful” nowcasts, and how selected end users accessed, utilized, and acted upon these enhanced forecasts. End users included the Sydney Organizing Committee for the Olympic Games (SOCOG) and a small selection of other BoM clients. With few storms or severe weather events during the 2.5-month trial period the opportunity to fully evaluate the impact of the FDP technologies was limited. Nevertheless, positive social, societal, and economic impacts were clearly indicated and additional potential benefits were identified by users. This paper details the WWRP S2000 FDP environment, discusses the processes and outcomes of the WWRP FDP Impacts Study, and outlines the benefits and implications of this type of research to both the producers and users of weather forecast products. [ABSTRACT FROM AUTHOR]

Air-sea interactions play an important role in atmospheric circulation and boundary layer conditions through changing convection processes and surface heat fluxes, particularly in coastal areas. These changes can affect the concentrations, distributions, and lifetimes of atmospheric pollutants. In this Part II paper, the performance of the Weather Research and Forecasting model with chemistry (WRF/Chem) and the coupled WRF/Chem with the Regional Ocean Model System (ROMS) (WRF/Chem-ROMS) are intercompared for their applications over quadruple-nested domains in Australia during the three following field campaigns: The Sydney Particle Study Stages 1 and 2 (SPS1 and SPS2) and the Measurements of Urban, Marine, and Biogenic Air (MUMBA). The results are used to evaluate the impact of air-sea interaction representation in WRF/Chem-ROMS on model predictions. At 3, 9, and 27 km resolutions, compared to WRF/Chem, the explicit air-sea interactions in WRF/Chem-ROMS lead to substantial improvements in simulated sea-surface temperature (SST), latent heat fluxes (LHF), and sensible heat fluxes (SHF) over the ocean, in terms of statistics and spatial distributions, during all three field campaigns. The use of finer grid resolutions (3 or 9 km) effectively reduces the biases in these variables during SPS1 and SPS2 by WRF/Chem-ROMS, whereas it further increases these biases for WRF/Chem during all field campaigns. The large differences in SST, LHF, and SHF between the two models lead to different radiative, cloud, meteorological, and chemical predictions. WRF/Chem-ROMS generally performs better in terms of statistics and temporal variations for temperature and relative humidity at 2 m, wind speed and direction at 10 m, and precipitation. The percentage differences in simulated surface concentrations between the two models are mostly in the range of ±10% for CO, OH, and O3, ±25% for HCHO, ±30% for NO2, ±35% for H2O2, ±50% for SO2, ±60% for isoprene and terpenes, ±15% for PM2.5, and ±12% for PM10. WRF/Chem-ROMS at 3 km resolution slightly improves the statistical performance of many surface and column concentrations. WRF/Chem simulations with satellite-constrained boundary conditions (BCONs) improve the spatial distributions and magnitudes of column CO for all field campaigns and slightly improve those of the column NO2 for SPS1 and SPS2, column HCHO for SPS1 and MUMBA, and column O3 for SPS2 at 3 km over the Greater Sydney area. The satellite-constrained chemical BCONs reduce the model biases of surface CO, NO, and O3 predictions at 3 km for all field campaigns, surface PM2.5 predictions at 3 km for SPS1 and MUMBA, and surface PM10 predictions at all grid resolutions for all field campaigns. A more important role of chemical BCONs in the Southern Hemisphere, compared to that in the Northern Hemisphere reported in this work, indicates a crucial need in developing more realistic chemical BCONs for O3 in the relatively clean SH. [ABSTRACT FROM AUTHOR]

Air pollution and associated human exposure are important research areas in Greater Sydney, Australia. Several field campaigns were conducted to characterize the pollution sources and their impacts on ambient air quality including the Sydney Particle Study Stages 1 and 2 (SPS1 and SPS2), and the Measurements of Urban, Marine, and Biogenic Air (MUMBA). In this work, the Weather Research and Forecasting model with chemistry (WRF/Chem) and the coupled WRF/Chem with the Regional Ocean Model System (ROMS) (WRF/Chem-ROMS) are applied during these field campaigns to assess the models' capability in reproducing atmospheric observations. The model simulations are performed over quadruple-nested domains at grid resolutions of 81-, 27-, 9-, and 3-km over Australia, an area in southeastern Australia, an area in New South Wales, and the Greater Sydney area, respectively. A comprehensive model evaluation is conducted using surface observations from these field campaigns, satellite retrievals, and other data. This paper evaluates the performance of WRF/Chem-ROMS and its sensitivity to spatial grid resolutions. The model generally performs well at 3-, 9-, and 27-km resolutions for sea-surface temperature and boundary layer meteorology in terms of performance statistics, seasonality, and daily variation. Moderate biases occur for temperature at 2-m and wind speed at 10-m in the mornings and evenings due to the inaccurate representation of the nocturnal boundary layer and surface heat fluxes. Larger underpredictions occur for total precipitation due to the limitations of the cloud microphysics scheme or cumulus parameterization. The model performs well at 3-, 9-, and 27-km resolutions for surface O3 in terms of statistics, spatial distributions, and diurnal and daily variations. The model underpredicts PM2.5 and PM10 during SPS1 and MUMBA but overpredicts PM2.5 and underpredicts PM10 during SPS2. These biases are attributed to inaccurate meteorology, precursor emissions, insufficient SO2 conversion to sulfate, inadequate dispersion at finer grid resolutions, and underprediction in secondary organic aerosol. The model gives moderate biases for net shortwave radiation and cloud condensation nuclei but large biases for other radiative and cloud variables. The performance of aerosol optical depth and latent/sensible heat flux varies for different simulation periods. Among all variables evaluated, wind speed at 10-m, precipitation, surface concentrations of CO, NO, NO2, SO2, O3, PM2.5, and PM10, aerosol optical depth, cloud optical thickness, cloud condensation nuclei, and column NO2 show moderate-to-strong sensitivity to spatial grid resolutions. The use of finer grid resolutions (3- or 9-km) can generally improve the performance for those variables. While the performance for most of these variables is consistent with that over the U.S. and East Asia, several differences along with future work are identified to pinpoint reasons for such differences. [ABSTRACT FROM AUTHOR]

After decades of study and significant data collection of time-varying swash on sandy beaches, there is no single deterministic prediction scheme for wave runup that eliminates prediction error – even bespoke, locally tuned predictors present scatter when compared to observations. Scatter in runup prediction is meaningful and can be used to create probabilistic predictions of runup for a given wave climate and beach slope. This contribution demonstrates this using a data-driven Gaussian process predictor; a probabilistic machine-learning technique. The runup predictor is developed using 1 year of hourly wave runup data (8328 observations) collected by a fixed lidar at Narrabeen Beach, Sydney, Australia. The Gaussian process predictor accurately predicts hourly wave runup elevation when tested on unseen data with a root-mean-squared error of 0.18 m and bias of 0.02 m. The uncertainty estimates output from the probabilistic GP predictor are then used practically in a deterministic numerical model of coastal dune erosion, which relies on a parameterization of wave runup, to generate ensemble predictions. When applied to a dataset of dune erosion caused by a storm event that impacted Narrabeen Beach in 2011, the ensemble approach reproduced ∼85 % of the observed variability in dune erosion along the 3.5 km beach and provided clear uncertainty estimates around these predictions. This work demonstrates how data-driven methods can be used with traditional deterministic models to develop ensemble predictions that provide more information and greater forecasting skill when compared to a single model using a deterministic parameterization – an idea that could be applied more generally to other numerical models of geomorphic systems. [ABSTRACT FROM AUTHOR]

The periodical Weather and Forecasting is starting its 19th year as an American Meteorological Society (AMS) journal, as of February 1, 2004. Over the past years the journal has continued to fulfill its purpose to provide a medium for interaction among AMS members worldwide who are involved and interested in operational forecasting. Authors plan to continue with the concept of special issues that have a single focus or theme. They are delighted that this first issue under their tenure is a special issue on the 2000 Sydney Olympics World Weather Research Programme Forecast Demonstration Project.

The Australian Bureau of Meteorology was the official provider of weather services for the 2000 Olympic and Paralympic Games in Sydney, Australia. The sailing events took place on Sydney Harbor and an offshore area adjacent to the harbor entrance. The forecasting of surface winds for the Sydney region presented a complex problem. The regional topography is intricate, and accurate wind forecasting at the 1-km scale for the harbor was beyond the resolution of Australian operational numerical weather prediction (NWP) models. A range of statistical products was developed to supplement the NWP output in an attempt to account for small-scale wind variations due to terrain and local sea–harbor breeze circulations. Significant effort was spent on facilitating athletes' and coaches' access to weather information, particularly through the Bureau of Meteorology Internet site. A team of meteorologists from around Australia was assigned the task of interpreting the output of an array of NWP and statistical models. The forecasters provided weather advice to regatta organizers, and other users, over a range of time scales. [ABSTRACT FROM AUTHOR]

During the Sydney 2000 Forecast Demonstration Project (FDP) a four-dimensional variational assimilation (4DVAR) scheme was run to analyze the low-level wind field with high spatial and temporal resolution. The 4DVAR scheme finds an optimal fit to the data and a background field under the constraints of a dry boundary layer model. During the FDP, the system assimilated data from two Doppler radars, a surface mesonet, and a boundary layer profiler, and provided low-level analyses every 10 min. After the FDP, a number of experiments have been performed to test the ability of the system to provide short-term forecasts (0–60 min) of the low-level wind and convergence. Herein, the performance of the system during the FDP and the forecast experiment's performed after the FDP are described. Two strong gust front cases and one sea-breeze case that occurred during the FDP are also examined. It is found that for the strong gust front cases, the numerical forecasts improve over persistence in the 1-h time frame, whereas for the slower-moving sea-breeze case, it is difficult to improve over a persistence forecast. [ABSTRACT FROM AUTHOR]