Relevant bibliographies by topics / Numerical weather forecasting Australia

Academic literature on the topic 'Numerical weather forecasting Australia'

Author: Grafiati
Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Numerical weather forecasting Australia"

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Perera, Kushan C., Andrew W. Western, Bandara Nawarathna, and Biju George. "Forecasting daily reference evapotranspiration for Australia using numerical weather prediction outputs." Agricultural and Forest Meteorology 194 (August 2014): 50–63. http://dx.doi.org/10.1016/j.agrformet.2014.03.014.

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Engel, Chermelle, and Elizabeth E. Ebert. "Gridded Operational Consensus Forecasts of 2-m Temperature over Australia." Weather and Forecasting 27, no. 2 (April 1, 2012): 301–22. http://dx.doi.org/10.1175/waf-d-11-00069.1.

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Abstract This paper describes an extension of an operational consensus forecasting (OCF) scheme from site forecasts to gridded forecasts. OCF is a multimodel consensus scheme including bias correction and weighting. Bias correction and weighting are done on a scale common to almost all multimodel inputs (1.25°), which are then downscaled using a statistical approach to an approximately 5-km-resolution grid. Local and international numerical weather prediction model inputs are found to have coarse scale biases that respond to simple bias correction, with the weighted average consensus at 1.25° outperforming all models at that scale. Statistical downscaling is found to remove the systematic representativeness error when downscaling from 1.25° to 5 km, though it cannot resolve scale differences associated with transient small-scale weather.
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Shrestha, D. L., D. E. Robertson, Q. J. Wang, T. C. Pagano, and H. A. P. Hapuarachchi. "Evaluation of numerical weather prediction model precipitation forecasts for short-term streamflow forecasting purpose." Hydrology and Earth System Sciences 17, no. 5 (May 21, 2013): 1913–31. http://dx.doi.org/10.5194/hess-17-1913-2013.

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Abstract. The quality of precipitation forecasts from four Numerical Weather Prediction (NWP) models is evaluated over the Ovens catchment in Southeast Australia. Precipitation forecasts are compared with observed precipitation at point and catchment scales and at different temporal resolutions. The four models evaluated are the Australian Community Climate Earth-System Simulator (ACCESS) including ACCESS-G with a 80 km resolution, ACCESS-R 37.5 km, ACCESS-A 12 km, and ACCESS-VT 5 km. The skill of the NWP precipitation forecasts varies considerably between rain gauging stations. In general, high spatial resolution (ACCESS-A and ACCESS-VT) and regional (ACCESS-R) NWP models overestimate precipitation in dry, low elevation areas and underestimate in wet, high elevation areas. The global model (ACCESS-G) consistently underestimates the precipitation at all stations and the bias increases with station elevation. The skill varies with forecast lead time and, in general, it decreases with the increasing lead time. When evaluated at finer spatial and temporal resolution (e.g. 5 km, hourly), the precipitation forecasts appear to have very little skill. There is moderate skill at short lead times when the forecasts are averaged up to daily and/or catchment scale. The precipitation forecasts fail to produce a diurnal cycle shown in observed precipitation. Significant sampling uncertainty in the skill scores suggests that more data are required to get a reliable evaluation of the forecasts. The non-smooth decay of skill with forecast lead time can be attributed to diurnal cycle in the observation and sampling uncertainty. Future work is planned to assess the benefits of using the NWP rainfall forecasts for short-term streamflow forecasting. Our findings here suggest that it is necessary to remove the systematic biases in rainfall forecasts, particularly those from low resolution models, before the rainfall forecasts can be used for streamflow forecasting.
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Le Marshall, John, Robert Norman, David Howard, Susan Rennie, Michael Moore, Jan Kaplon, Yi Xiao, et al. "Corrigendum to: Using global navigation satellite system data for real-time moisture analysis and forecasting over the Australian region I. The system." Journal of Southern Hemisphere Earth Systems Science 70, no. 1 (2020): 394. http://dx.doi.org/10.1071/es19009_co.

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The use of high spatial and temporal resolution data assimilation and forecasting around Australia’s capital cities and rural land provided an opportunity to improve moisture analysis and forecasting. To support this endeavour, RMIT University and Geoscience Australia worked with the Bureau of Meteorology (BoM) to provide real-time GNSS (global navigation satellite system) zenith total delay (ZTD) data over the Australian region, from which a high-resolution total water vapour field for SE Australia could be determined. The ZTD data could play an important role in high-resolution data assimilation by providing mesoscale moisture data coverage from existing GNSS surface stations over significant areas of the Australian continent. The data were used by the BoM’s high-resolution ACCESS-C3 capital city numerical weather prediction (NWP) systems, the ACCESS-G3 Global system and had been used by the ACCESS-R2-Regional NWP model. A description of the data collection and analysis system is provided. An example of the application of these local GNSS data for a heavy rainfall event over SE Australia/Victoria is shown using the 1.5-km resolution ACCESS-C3 model, which was being prepared for operational use. The results from the test were assessed qualitatively, synoptically and also examined quantitatively using the Fractions Skills Score which showed the reasonableness of the forecasts and demonstrated the potential for improving rainfall forecasts over south-eastern Australia by the inclusion of ZTD data in constructing the moisture field. These data have been accepted for operational use in NWP.
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Marshall, John Le, Robert Norman, David Howard, Susan Rennie, Michael Moore, Jan Kaplon, Yi Xiao, et al. "Using global navigation satellite system data for real-time moisture analysis and forecasting over the Australian region I. The system." Journal of Southern Hemisphere Earth Systems Science 69, no. 1 (2019): 161. http://dx.doi.org/10.1071/es19009.

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The use of high spatial and temporal resolution data assimilation and forecasting around Australia’s capital cities and rural land provided an opportunity to improve moisture analysis and forecasting. To support this endeavour, RMIT University and Geoscience Australia worked with the Bureau of Meteorology (BoM) to provide real-time GNSS (global navigation satellite system) zenith total delay (ZTD) data over the Australian region, from which a high-resolution total water vapour field for SE Australia could be determined. The ZTD data could play an important role in high-resolution data assimilation by providing mesoscale moisture data coverage from existing GNSS surface stations over significant areas of the Australian continent. The data were used by the BoM’s high-resolution ACCESS-C3 capital city numerical weather prediction (NWP) systems, the ACCESS-G3 Global system and had been used by the ACCESS-R2-Regional NWP model. A description of the data collection and analysis system is provided. An example of the application of these local GNSS data for a heavy rainfall event over SE Australia/Victoria is shown using the 1.5-km resolution ACCESS-C3 model, which was being prepared for operational use. The results from the test were assessed qualitatively, synoptically and also examined quantitatively using the Fractions Skills Score which showed the reasonableness of the forecasts and demonstrated the potential for improving rainfall forecasts over south-eastern Australia by the inclusion of ZTD data in constructing the moisture field. These data have been accepted for operational use in NWP.
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Shrestha, D. L., D. E. Robertson, Q. J. Wang, T. C. Pagano, and P. Hapuarachchi. "Evaluation of numerical weather prediction model precipitation forecasts for use in short-term streamflow forecasting." Hydrology and Earth System Sciences Discussions 9, no. 11 (November 5, 2012): 12563–611. http://dx.doi.org/10.5194/hessd-9-12563-2012.

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Abstract. The quality of precipitation forecasts from four Numerical Weather Prediction (NWP) models is evaluated over the Ovens catchment in southeast Australia. Precipitation forecasts are compared with observed precipitation at point and catchment scales and at different temporal resolutions. The four models evaluated are the Australian Community Climate Earth-System Simulator (ACCESS) including ACCESS-G with a 80 km resolution, ACCESS-R 37.5 km, ACCESS-A 12 km, and ACCESS-VT 5 km. The high spatial resolution NWP models (ACCESS-A and ACCESS-VT) appear to be relatively free of bias (i.e. <30%) for 24 h total precipitation forecasts. The low resolution models (ACCESS-R and ACCESS-G) have widespread systematic biases as large as 70%. When evaluated at finer spatial and temporal resolution (e.g. 5 km, hourly) against station observations, the precipitation forecasts appear to have very little skill. There is moderate skill at short lead times when the forecasts are averaged up to daily and/or catchment scale. The skill decreases with increasing lead times and the global model ACCESS-G does not have significant skill beyond 7 days. The precipitation forecasts fail to produce a diurnal cycle shown in observed precipitation. Significant sampling uncertainty in the skill scores suggests that more data are required to get a reliable evaluation of the forecasts. Future work is planned to assess the benefits of using the NWP rainfall forecasts for short-term streamflow forecasting. Our findings here suggest that it is necessary to remove the systematic biases in rainfall forecasts, particularly those from low resolution models, before the rainfall forecasts can be used for streamflow forecasting.
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Anonymous. "Peer review report 1 On “Forecasting Daily Reference Evapotranspiration for Australia using Numerical Weather Prediction outputs”." Agricultural and Forest Meteorology 201 (January 2015): 634–35. http://dx.doi.org/10.1016/j.agrformet.2015.08.204.

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Zhang, Yongguang. "Peer review report 2 On “Forecasting Daily Reference Evapotranspiration for Australia using Numerical Weather Prediction outputs”." Agricultural and Forest Meteorology 201 (January 2015): 438. http://dx.doi.org/10.1016/j.agrformet.2015.08.205.

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Bochenek, Bogdan, and Zbigniew Ustrnul. "Machine Learning in Weather Prediction and Climate Analyses—Applications and Perspectives." Atmosphere 13, no. 2 (January 23, 2022): 180. http://dx.doi.org/10.3390/atmos13020180.

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In this paper, we performed an analysis of the 500 most relevant scientific articles published since 2018, concerning machine learning methods in the field of climate and numerical weather prediction using the Google Scholar search engine. The most common topics of interest in the abstracts were identified, and some of them examined in detail: in numerical weather prediction research—photovoltaic and wind energy, atmospheric physics and processes; in climate research—parametrizations, extreme events, and climate change. With the created database, it was also possible to extract the most commonly examined meteorological fields (wind, precipitation, temperature, pressure, and radiation), methods (Deep Learning, Random Forest, Artificial Neural Networks, Support Vector Machine, and XGBoost), and countries (China, USA, Australia, India, and Germany) in these topics. Performing critical reviews of the literature, authors are trying to predict the future research direction of these fields, with the main conclusion being that machine learning methods will be a key feature in future weather forecasting.
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Robertson, D. E., D. L. Shrestha, and Q. J. Wang. "Post-processing rainfall forecasts from numerical weather prediction models for short-term streamflow forecasting." Hydrology and Earth System Sciences 17, no. 9 (September 27, 2013): 3587–603. http://dx.doi.org/10.5194/hess-17-3587-2013.

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Abstract. Sub-daily ensemble rainfall forecasts that are bias free and reliably quantify forecast uncertainty are critical for flood and short-term ensemble streamflow forecasting. Post-processing of rainfall predictions from numerical weather prediction models is typically required to provide rainfall forecasts with these properties. In this paper, a new approach to generate ensemble rainfall forecasts by post-processing raw numerical weather prediction (NWP) rainfall predictions is introduced. The approach uses a simplified version of the Bayesian joint probability modelling approach to produce forecast probability distributions for individual locations and forecast lead times. Ensemble forecasts with appropriate spatial and temporal correlations are then generated by linking samples from the forecast probability distributions using the Schaake shuffle. The new approach is evaluated by applying it to post-process predictions from the ACCESS-R numerical weather prediction model at rain gauge locations in the Ovens catchment in southern Australia. The joint distribution of NWP predicted and observed rainfall is shown to be well described by the assumed log-sinh transformed bivariate normal distribution. Ensemble forecasts produced using the approach are shown to be more skilful than the raw NWP predictions both for individual forecast lead times and for cumulative totals throughout all forecast lead times. Skill increases result from the correction of not only the mean bias, but also biases conditional on the magnitude of the NWP rainfall prediction. The post-processed forecast ensembles are demonstrated to successfully discriminate between events and non-events for both small and large rainfall occurrences, and reliably quantify the forecast uncertainty. Future work will assess the efficacy of the post-processing method for a wider range of climatic conditions and also investigate the benefits of using post-processed rainfall forecasts for flood and short-term streamflow forecasting.
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Dissertations / Theses on the topic "Numerical weather forecasting Australia"

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Yan, Hanjun. "Numerical methods for data assimilation in weather forecasting." HKBU Institutional Repository, 2018. https://repository.hkbu.edu.hk/etd_oa/555.

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Data assimilation plays an important role in weather forecasting. The purpose of data assimilation is try to provide a more accurate atmospheric state for future forecast. Several existed methods currently used in this field fall into two categories: statistical data assimilation and variational data assimilation. This thesis focuses mainly on variational data assimilation. The original objective function of three dimensional data assimilation (3D-VAR) consists of two terms: the difference between the pervious forecast and analysis and the difference between the observations and analysis in observation space. Considering the inaccuracy of previous forecasting results, we replace the first term by the difference between the previous forecast gradients and analysis gradients. The associated data fitting term can be interpreted using the second-order finite difference matrix as the inverse of the background error covariance matrix in the 3D-VAR setting. In our approach, it is not necessary to estimate the background error covariance matrix and to deal with its inverse in the 3D-VAR algorithm. Indeed, the existence and uniqueness of the analysis solution of the proposed objective function are already established. Instead, the solution can be calculated using the conjugate gradient method iteratively. We present the experimental results based on WRF simulations. We show that the performance of this forecast gradient based DA model is better than that of 3D-VAR. Next, we propose another optimization method of variational data assimilation. Using the tensor completion in the cost function for the analysis, we replace the second term in the 3D-VAR cost function. This model is motivated by a small number of observations compared with the large portion of the grids. Applying the alternating direction method of multipliers to solve this optimization problem, we conduct numerical experiments on real data. The results show that this tensor completion based DA model is competitive in terms of prediction accuracy with 3D-VAR and the forecast gradient based DA model. Then, 3D-VAR and the two model proposed above lack temporal information, we construct a third model in four-dimensional space. To include temporal information, this model is based on the second proposed model, in which introduce the total variation to describe the change of atmospheric state. To this end, we use the alternating direction method of multipliers. One set of experimental results generates a positive performance. In fact, the prediction accuracy of our third model is better than that of 3D-VAR, the forecast gradient based DA model, and the tensor completion based DA model. Nevertheless, although the other sets of experimental results show that this model has a better performance than 3D-VAR and the forecast gradient based DA model, its prediction accuracy is slightly lower than the tensor completion based model.
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Torrisi, Lucio. "The numerical weather prediction system at the Italian Air Force Weather Service : impact of non-conventional observations and increased resolution /." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2004. http://library.nps.navy.mil/uhtbin/hyperion/04Jun%5FTorrisi.pdf.

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Ancell, Brian C. "The nature of adjoint sensitivities with respect to model parameters and their use in adaptive data assimilation /." Thesis, Connect to this title online; UW restricted, 2006. http://hdl.handle.net/1773/10042.

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Cordy, Paul David. "Applied automated numerical avalanche forecasting using electronic weather sensor data." Thesis, University of British Columbia, 2007. http://hdl.handle.net/2429/32241.

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Numerical avalanche prediction was used for Canadian highways avalanche forecasting for ten years before changes in information technology infrastructure rendered the original numerical avalanche forecasting model incompatible and therefore obsolete. Now these efforts are being renewed with greater automation by the use of electronic weather sensor data. Use of this data presents several challenges and opportunities. Automated hourly observations generate large datasets that require systems for filtering historic and current data; as well as fitness testing routines that dynamically extract independent validation samples from serially correlated datasets. These weather sensor data manipulation systems offer several advantages over traditional avalanche prediction models that are based on manually observed weather and surface snow information. Rapid dataset generation enables spatial scaling of predictions, easy generation and testing of memory variables, model comparison, and visual verification of predicted avalanche probability time series. These features will facilitate operational implementation of avalanche forecasting models for applied computer assisted avalanche forecasting-in highways avalanche control programs across British Columbia, Canada. In the winter of 2006/7, the Avalanche Forecast System (AFS) was applied in two avalanche prone transportation corridors. The AFS uses only electronic weather sensor data and incorporates all of the aforementioned capabilities. A nearest neighbour analysis is used to generate avalanche probabilities, however the AFS data management systems could also be made to operate with classical linear and modern non-linear statistical prediction methods. Automated filters eliminate erroneous data dynamically, permit investigation of various prediction targets (such as natural avalanche occurrences, or avalanches of different size classes), and a jackknife cross-validation routine generates fitness statistics by selecting test cases that are not temporally autocorrelated. The AFS was applied operationally in Kootenay Pass, near Salmo, BC, and also at Bear Pass, near Stewart, BC, where accuracy of 76% +/-2% and 71% +/-2% were achieved respectively.
Arts, Faculty of
Geography, Department of
Graduate
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Lawless, Amos S. "Development of linear models for data assimilation in numerical weather prediction." Thesis, University of Reading, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.365423.

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Bilodeau, Bernard. "Accuracy of a truncated barotropic spectral model : numerical versus analytical solutions." Thesis, McGill University, 1985. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=66037.

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Xue, Ming. "A nonhydrostatic numerical model in sigma-coordinates and simulations of mesoscale phenomena." Thesis, University of Reading, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.328942.

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Ramamurthy, Mohan K. "Four dimensional data assimilation in a limited area model for the monsoon region /." Full-text version available from OU Domain via ProQuest Digital Dissertations, 1986.

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Wahl, Sabrina [Verfasser]. "Uncertainty in mesoscale numerical weather prediction: probabilistic forecasting of precipitation / Sabrina Wahl." Bonn : Universitäts- und Landesbibliothek Bonn, 2015. http://d-nb.info/1080561099/34.

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Vetra-Carvalho, Sanita. "Properties of the ensemble Kalman filter for convective-scale numerical weather forecasting." Thesis, University of Reading, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.590111.

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Atmospheric data assimilation has now started to deal with high model resolution scales of O(lkm) where dynamical properties of the atmosphere exploited in larger scale models may no longer be valid. This leads to a problem in high-resolution data assimilation systems since balances such as the hydrostatic balance are still used to model forecast errors. From scale analysis arguments we recognise that such balances do not necessarily need to be valid at small scales and in this work we use the convective scale Met Office Global and Regional Ensemble Prediction System (MOGREPS) to show that indeed the hydrostatic balance at a horizontal resolution of 1.5 km ceases to be valid in the ensemble perturbations in regions where convection is present while it is valid in regions with no convection. We show that the horizontal threshold at which the hydrostatic balance becomes valid as a vertical average in the ensemble perturbations regardless of the presence of convection is 22 km. We also make use of ensemble methods to establish their applicability (0 convective scale models. In particular we apply (he ensemble square root filter (EnSRF) to a one-dimensional idealised column model wilh a parameterized cloud scheme and a discontinuous rain scheme. We show that the ensemble filter can caprure the true solution within a linear ('No cloud') model regime and non-linear ('Cloud') regime; however, if many good quality observations are used the ensemble fails to capture the true solution within the discontinuous CRain') regime. Interestingly, this can be alleviated if only a portion of the state space is observed. Moreover, having fewer spatial observations also improves the ensemble estimate for the ~mperature in the 'Rain' regime, while the estimate of state variables is slightly degraded in the 'No cloud' and 'Cloud' regimes.
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Books on the topic "Numerical weather forecasting Australia"

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CAS/JSC Working Group on Numerical Experimentation. Session. Report of the sixth session of the CAS/JSC Working Group on Numerical Experimentation: Melbourne, Australia, 24-28 September 1990. [Geneva, Switzerland]: World Meteorological Organization, 1991.

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Numerical weather and climate prediction. Cambridge: Cambridge University Press, 2011.

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Fundamentals of numerical weather prediction. Cambridge: Cambridge University Press, 2011.

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Spectral numerical weather prediction models. Philadelphia: Society for Industrial and Applied Mathematics, 2012.

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Atmospheric modeling, data assimilation, and predictability. Cambridge, U.K: Cambridge University Press, 2003.

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Jensen, Anna B. O. Numerical weather predictions for Network RTK. [Denmark]: National Survey and Cadastre, 2002.

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An introduction to numerical weather prediction techniques. Boca Raton, Fla: CRC Press, 1996.

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World Meteorological Organization. Executive Council. Session. Mesoscale forecasting and its applications. Geneva, Switzerland: Secretariat of the World Meteorological Organization, 1989.

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Bell, R. S. The Meteorological Office operational numerical weather prediction system. London: HMSO, 1987.

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Rochas, Michel. La météorologie: La prévision numérique du temps et du climat. Paris: Syros/Alternatives, 1993.

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Book chapters on the topic "Numerical weather forecasting Australia"

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Ross, Bruce B. "An Overview of Numerical Weather Prediction." In Mesoscale Meteorology and Forecasting, 720–51. Boston, MA: American Meteorological Society, 1986. http://dx.doi.org/10.1007/978-1-935704-20-1_30.

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Colman, Brad, Kirby Cook, and Bradley J. Snyder. "Numerical Weather Prediction and Weather Forecasting in Complex Terrain." In Springer Atmospheric Sciences, 655–92. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-4098-3_11.

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Pu, Zhaoxia, and Eugenia Kalnay. "Numerical Weather Prediction Basics: Models, Numerical Methods, and Data Assimilation." In Handbook of Hydrometeorological Ensemble Forecasting, 1–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-642-40457-3_11-1.

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Pu, Zhaoxia, and Eugenia Kalnay. "Numerical Weather Prediction Basics: Models, Numerical Methods, and Data Assimilation." In Handbook of Hydrometeorological Ensemble Forecasting, 67–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2019. http://dx.doi.org/10.1007/978-3-642-39925-1_11.

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Golding, Brian, Jenny Sun, Michael Riemer, Nusrat Yussouf, Helen Titley, Joanne Robbins, Beth Ebert, et al. "Connecting Weather and Hazard: A Partnership of Physical Scientists in Connected Disciplines." In Towards the “Perfect” Weather Warning, 149–200. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98989-7_6.

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AbstractAchieving consistency in the prediction of the atmosphere and related environmental hazards requires careful design of forecasting systems. In this chapter, we identify the benefits of seamless approaches to hazard prediction and the challenges of achieving them in a multi-institution situation. We see that different modelling structures are adopted in different disciplines and that these often relate to the user requirements for those hazards. We then explore the abilities of weather prediction to meet the requirements of these different disciplines. We find that differences in requirement and language can be major challenges to seamless data processing and look at some ways in which these can be resolved. We conclude with examples of partnerships in flood forecasting in the UK and wildfire forecasting in Australia.
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Mahura, Alexander, Alexander Baklanov, Claus Petersen, Niels W. Nielsen, and Bjarne Amstrup. "Verification and Case Studies for Urban Effects in HIRLAM Numerical Weather Forecasting." In Meteorological and Air Quality Models for Urban Areas, 143–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00298-4_14.

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Brabec, Marek, Pavel Krc, Krystof Eben, and Emil Pelikan. "Wind Speed Forecasting for a Large-Scale Measurement Network and Numerical Weather Modeling." In Contributions to Statistics, 361–73. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55789-2_25.

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Ahmad Mohtar, Intan Shafeenar, Wardah Tahir, Sahol Hamid Abu Bakar, and Ahmad Zikry Mohd Zuhari. "Use of Numerical Weather Prediction Model and Visible Weather Satellite Images for Flood Forecasting at Kelantan River Basin." In ISFRAM 2014, 283–94. Singapore: Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-287-365-1_23.

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Renko, Tanja, Sarah Ivušić, Maja Telišman Prtenjak, Vinko Šoljan, and Igor Horvat. "Waterspout Forecasting Method Over the Eastern Adriatic Using a High-Resolution Numerical Weather Model." In Pageoph Topical Volumes, 39–58. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11958-4_4.

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Hide, Raymond. "Forecasting short-term changes in the Earth's rotation using global numerical weather prediction models." In Variations in Earth Rotation, 145–46. Washington, D. C.: American Geophysical Union, 1990. http://dx.doi.org/10.1029/gm059p0145.

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Conference papers on the topic "Numerical weather forecasting Australia"

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Huang, Jing, Lawrence Rikus, and Yi Qin. "Probabilistic solar irradiance forecasting using numerical weather prediction ensembles over Australia." In 2020 IEEE 47th Photovoltaic Specialists Conference (PVSC). IEEE, 2020. http://dx.doi.org/10.1109/pvsc45281.2020.9300836.

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"Forecasting daily reference evapotranspiration for Shepparton, Victoria, Australia using numerical weather prediction outputs." In 20th International Congress on Modelling and Simulation (MODSIM2013). Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc., 2013. http://dx.doi.org/10.36334/modsim.2013.l16.perera.

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Sandeepan, BS, Sashikant Nayak, and Vijay Panchang. "A Numerical Weather Forecasting System for Qatar." In Qatar Foundation Annual Research Conference Proceedings. Hamad bin Khalifa University Press (HBKU Press), 2016. http://dx.doi.org/10.5339/qfarc.2016.eepp2879.

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Faeldon, James, Karen Espana, and Delfin Jay Sabido. "Data-centric HPC for Numerical Weather Forecasting." In 2014 43nd International Conference on Parallel Processing Workshops (ICCPW). IEEE, 2014. http://dx.doi.org/10.1109/icppw.2014.23.

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Doroshenko, Anatoliy, Vitalii Shpyg, and Roman Kushnirenko. "Machine Learning to Improve Numerical Weather Forecasting." In 2020 IEEE 2nd International Conference on Advanced Trends in Information Theory (ATIT). IEEE, 2020. http://dx.doi.org/10.1109/atit50783.2020.9349325.

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Konstantinou, Theodoros, Nikolaos Savvopoulos, and Nikos Hatziargyriou. "Post-processing Numerical Weather Prediction for Probabilistic Wind Forecasting." In 2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS). IEEE, 2020. http://dx.doi.org/10.1109/pmaps47429.2020.9183641.

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Fernandez, Elvira, Igor Albizu, Garikoitz Buigues, Victor Valverde, Agurtzane Etxegarai, and Jon G. Olazarri. "Dynamic line rating forecasting based on numerical weather prediction." In 2015 IEEE Eindhoven PowerTech. IEEE, 2015. http://dx.doi.org/10.1109/ptc.2015.7232611.

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Sperandio, M., A. A. B. Ferreira, M. R. Moraes, and A. G. O. Goulart. "Short-term wind farm power forecasting with numerical weather prediction." In 2013 IV International Conference on Power Engineering, Energy and Electrical Drives (POWERENG). IEEE, 2013. http://dx.doi.org/10.1109/powereng.2013.6635648.

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Wolters, Lex, Gerard Cats, and Nils Gustafsson. "Limited area numerical weather forecasting on a massively parallel computer." In the 8th international conference. New York, New York, USA: ACM Press, 1994. http://dx.doi.org/10.1145/181181.181544.

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Tan, Jiqing. "A New Founded Error Contamination Mechanism in Numerical Weather Forecasting Models." In 2010 International Conference on Multimedia Technology (ICMT). IEEE, 2010. http://dx.doi.org/10.1109/icmult.2010.5631296.

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Reports on the topic "Numerical weather forecasting Australia"

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Buckley, R. L. Numerical Weather Forecasting at the Savannah River Site. Office of Scientific and Technical Information (OSTI), January 1999. http://dx.doi.org/10.2172/4787.

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Michaels, Michelle, Theodore Letcher, Sandra LeGrand, Nicholas Webb, and Justin Putnam. Implementation of an albedo-based drag partition into the WRF-Chem v4.1 AFWA dust emission module. Engineer Research and Development Center (U.S.), January 2021. http://dx.doi.org/10.21079/11681/42782.

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Employing numerical prediction models can be a powerful tool for forecasting air quality and visibility hazards related to dust events. However, these numerical models are sensitive to surface conditions. Roughness features (e.g., rocks, vegetation, furrows, etc.) that shelter or attenuate wind flow over the soil surface affect the magnitude and spatial distribution of dust emission. To aide in simulating the emission phase of dust transport, we used a previously published albedo-based drag partition parameterization to better represent the component of wind friction speed affecting the immediate soil sur-face. This report serves as a guide for integrating this parameterization into the Weather Research and Forecasting with Chemistry (WRF-Chem) model. We include the procedure for preprocessing the required input data, as well as the code modifications for the Air Force Weather Agency (AFWA) dust emission module. In addition, we provide an example demonstration of output data from a simulation of a dust event that occurred in the Southwestern United States, which incorporates use of the drag partition.
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Letcher, Theodore, Sandra LeGrand, and Christopher Polashenski. The Blowing Snow Hazard Assessment and Risk Prediction model : a Python based downscaling and risk prediction for snow surface erodibility and probability of blowing snow. Engineer Research and Development Center (U.S.), March 2022. http://dx.doi.org/10.21079/11681/43582.

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Blowing snow is an extreme terrain hazard causing intermittent severe reductions in ground visibility and snow drifting. These hazards pose significant risk to operations in snow-covered regions. While many ingredients-based forecasting methods can be employed to predict where blowing snow is likely to occur, there are currently no physically based tools to predict blowing snow from a weather forecast. However, there are several different process models that simulate the transport of snow over short distances that can be adapted into a terrain forecasting tool. This report documents a downscaling and blowing-snow prediction tool that leverages existing frameworks for snow erodibility, lateral snow transport, and visibility, and applies these frameworks for terrain prediction. This tool is designed to work with standard numerical weather model output and user-specified geographic models to generate spatially variable forecasts of snow erodibility, blowing snow probability, and deterministic blowing-snow visibility near the ground. Critically, this tool aims to account for the history of the snow surface as it relates to erodibility, which further refines the blowing-snow risk output. Qualitative evaluations of this tool suggest that it can provide more precise forecasts of blowing snow. Critically, this tool can aid in mission planning by downscaling high-resolution gridded weather forecast data using even higher resolution terrain dataset, to make physically based predictions of blowing snow.
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Wilson, D., Vladimir Ostashev, Michael Shaw, Michael Muhlestein, John Weatherly, Michelle Swearingen, and Sarah McComas. Infrasound propagation in the Arctic. Engineer Research and Development Center (U.S.), December 2021. http://dx.doi.org/10.21079/11681/42683.

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This report summarizes results of the basic research project “Infrasound Propagation in the Arctic.” The scientific objective of this project was to provide a baseline understanding of the characteristic horizontal propagation distances, frequency dependencies, and conditions leading to enhanced propagation of infrasound in the Arctic region. The approach emphasized theory and numerical modeling as an initial step toward improving understanding of the basic phenomenology, and thus lay the foundation for productive experiments in the future. The modeling approach combined mesoscale numerical weather forecasts from the Polar Weather Research and Forecasting model with advanced acoustic propagation calculations. The project produced significant advances with regard to parabolic equation modeling of sound propagation in a windy atmosphere. For the polar low, interesting interactions with the stratosphere were found, which could possibly be used to provide early warning of strong stratospheric warming events (i.e., the polar vortex). The katabatic wind resulted in a very strong low-level duct, which, when combined with a highly reflective icy ground surface, leads to efficient long-distance propagation. This information is useful in devising strategies for positioning sensors to monitor environmental phenomena and human activities.
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LeGrand, Sandra, Christopher Polashenski, Theodore Letcher, Glenn Creighton, Steven Peckham, and Jeffrey Cetola. The AFWA dust emission scheme for the GOCART aerosol model in WRF-Chem v3.8.1. Engineer Research and Development Center (U.S.), August 2021. http://dx.doi.org/10.21079/11681/41560.

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Airborne particles of mineral dust play a key role in Earth’s climate system and affect human activities around the globe. The numerical weather modeling community has undertaken considerable efforts to accurately forecast these dust emissions. Here, for the first time in the literature, we thoroughly describe and document the Air Force Weather Agency (AFWA) dust emission scheme for the Georgia Institute of Technology–Goddard Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) aerosol model within the Weather Research and Forecasting model with chemistry (WRF-Chem) and compare it to the other dust emission schemes available in WRF-Chem. The AFWA dust emission scheme addresses some shortcomings experienced by the earlier GOCART-WRF scheme. Improved model physics are designed to better handle emission of fine dust particles by representing saltation bombardment. WRF-Chem model performance with the AFWA scheme is evaluated against observations of dust emission in southwest Asia and compared to emissions predicted by the other schemes built into the WRF-Chem GOCART model. Results highlight the relative strengths of the available schemes, indicate the reasons for disagreement, and demonstrate the need for improved soil source data.
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