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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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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 Discussions 10, no. 5 (May 29, 2013): 6765–806. http://dx.doi.org/10.5194/hessd-10-6765-2013.
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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 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 periods. 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 multivariate normal distribution. Ensemble forecasts produced using the approach are shown to be more skilful than the raw NWP predictions both for individual forecast periods and for cumulative totals throughout the forecast periods. 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 forecast for flood and short term streamflow forecasting.
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Inoue, Mana, Alexander D. Fraser, Neil Adams, Scott Carpentier, and Helen E. Phillips. "An Assessment of Numerical Weather Prediction–Derived Low-Cloud-Base Height Forecasts." Weather and Forecasting 30, no. 2 (April 1, 2015): 486–97. http://dx.doi.org/10.1175/waf-d-14-00052.1.
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Abstract As demand for flight operations in Antarctica grows, accurate weather forecasting of cloud properties such as extent, cloud base, and cloud-top altitude becomes essential. The primary aims of this work are to ascertain relationships between numerical weather prediction (NWP) model output variables and surface-observed cloud properties and to develop low-cloud-base (<2000 m) height prediction algorithms for use across Antarctica to assist in low-cloud forecasting for aircraft operations. NWP output and radiosonde data are assessed against surface observations, and the relationship between the relative humidity RH profile and the height of the observed low-cloud base is investigated. The ability of NWP-derived RH and ice–water cloud optical depth profiles to represent the observed low-cloud conditions around each of the three Australian stations in East Antarctica is assessed. NWP-derived RH is drier than that reported by radiosonde from ground level up to ~2000 m. This trend reverses in the higher troposphere, and the largest positive difference is observed at ~10 000 m. A consequence is very low RH thresholds are needed for low-cloud-base height prediction using NWP RH profiles. RH and optical depth–based threshold techniques all show skill in reproducing the observed cloud-base height at all Australian Antarctic stations, but the radiosonde-derived RH technique is superior in all cases. This comparison of three low-cloud-base height retrieval techniques provides the first documented assessment of the relative efficacy of each technique in Antarctica.
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Prasad, Abhnil Amtesh, and Merlinde Kay. "Assessment of Simulated Solar Irradiance on Days of High Intermittency Using WRF-Solar." Energies 13, no. 2 (January 13, 2020): 385. http://dx.doi.org/10.3390/en13020385.
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Improvements in the short-term predictability of irradiance in numerical weather prediction models can assist grid operators in managing intermittent solar-generated electricity. In this study, the performance of the Weather Research and Forecasting (WRF) model when simulating different components of solar irradiance was tested under days of high intermittency at Mildura, a site located on the border of New South Wales and Victoria, Australia. Initially, four intermittent and clear case days were chosen, later extending to a full year study in 2005. A specific configuration and augmentation of the WRF model (version 3.6.1) designed for solar energy applications (WRF-Solar) with an optimum physics ensemble derived from literature over Australia was used to simulate solar irradiance with four nested domains nudged to ERA-Interim boundary conditions at grid resolutions (45, 15, 5, and 1.7 km) centred over Mildura. The Bureau of Meteorology (BOM) station dataset available at minute timescales and hourly derived satellite irradiance products were used to validate the simulated products. Results showed that on days of high intermittency, simulated solar irradiance at finer resolution was affected by errors in simulated humidity and winds (speed and direction) affecting clouds and circulation, but the latter improves at coarser resolutions; this is most likely from reduced displacement errors in clouds.
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Hapuarachchi, Hapu Arachchige Prasantha, Mohammed Abdul Bari, Aynul Kabir, Mohammad Mahadi Hasan, Fitsum Markos Woldemeskel, Nilantha Gamage, Patrick Daniel Sunter, et al. "Development of a national 7-day ensemble streamflow forecasting service for Australia." Hydrology and Earth System Sciences 26, no. 18 (September 29, 2022): 4801–21. http://dx.doi.org/10.5194/hess-26-4801-2022.
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Abstract. Reliable streamflow forecasts with associated uncertainty estimates are essential to manage and make better use of Australia's scarce surface water resources. Here we present the development of an operational 7 d ensemble streamflow forecasting service for Australia to meet the growing needs of users, primarily water and river managers, for probabilistic forecasts to support their decision making. We test the modelling methodology for 100 catchments to learn the characteristics of different rainfall forecasts from Numerical Weather Prediction (NWP) models, the effect of statistical processing on streamflow forecasts, the optimal ensemble size, and parameters of a bootstrapping technique for calculating forecast skill. A conceptual rainfall–runoff model, GR4H (hourly), and lag and route channel routing model that are in-built in the Short-term Water Information Forecasting Tools (SWIFT) hydrologic modelling package are used to simulate streamflow from input rainfall and potential evaporation. The statistical catchment hydrologic pre-processor (CHyPP) is used for calibrating rainfall forecasts, and the error reduction and representation in stages (ERRIS) model is used to reduce hydrological errors and quantify hydrological uncertainty. Calibrating raw forecast rainfall with CHyPP is an efficient method to significantly reduce bias and improve reliability for up to 7 lead days. We demonstrate that ERRIS significantly improves forecast skill up to 7 lead days. Forecast skills are highest in temperate perennially flowing rivers, while it is lowest in intermittently flowing rivers. A sensitivity analysis for optimising the number of streamflow ensemble members for the operational service shows that more than 200 members are needed to represent the forecast uncertainty. We show that the bootstrapping block size is sensitive to the forecast skill calculation. A bootstrapping block size of 1 month is recommended to capture maximum possible uncertainty. We present benchmark criteria for accepting forecast locations for the public service. Based on the criteria, 209 forecast locations out of a possible 283 are selected in different hydro-climatic regions across Australia for the public service. The service, which has been operational since 2019, provides daily updates of graphical and tabular products of ensemble streamflow forecasts along with performance information, for up to 7 lead days.
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Dai, Jingru, Michael J. Manton, Steven T. Siems, and Elizabeth E. Ebert. "Estimation of Daily Winter Precipitation in the Snowy Mountains of Southeastern Australia." Journal of Hydrometeorology 15, no. 3 (June 1, 2014): 909–20. http://dx.doi.org/10.1175/jhm-d-13-081.1.
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Abstract Wintertime precipitation in the Snowy Mountains provides water for agriculture, industry, and domestic use in inland southeastern Australia. Unlike most of Australia, much of this precipitation falls as snow, and it is recorded by a private network of heated tipping-bucket gauges. These observations are used in the present study to assess the accuracy of a poor man’s ensemble (PME) prediction of precipitation in the Snowy Mountains based on seven numerical weather prediction models. While the PME performs quite well, there is significant underestimation of precipitation intensity. It is shown that indicators of the synoptic environment can be used to improve the PME estimates of precipitation. Four synoptic regimes associated with different precipitation classes are identified from upper-air data. The reliability of the PME forecasts can be sharpened by considering the precipitation in each of the four synoptic classes. A linear regression, based on the synoptic classification and the PME estimate, is used to reduce the forecast errors. The potential to extend the method for forecasting purposes is discussed.
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Jha, Sanjeev K., Durga L. Shrestha, Tricia A. Stadnyk, and Paulin Coulibaly. "Evaluation of ensemble precipitation forecasts generated through post-processing in a Canadian catchment." Hydrology and Earth System Sciences 22, no. 3 (March 23, 2018): 1957–69. http://dx.doi.org/10.5194/hess-22-1957-2018.
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Abstract. Flooding in Canada is often caused by heavy rainfall during the snowmelt period. Hydrologic forecast centers rely on precipitation forecasts obtained from numerical weather prediction (NWP) models to enforce hydrological models for streamflow forecasting. The uncertainties in raw quantitative precipitation forecasts (QPFs) are enhanced by physiography and orography effects over a diverse landscape, particularly in the western catchments of Canada. A Bayesian post-processing approach called rainfall post-processing (RPP), developed in Australia (Robertson et al., 2013; Shrestha et al., 2015), has been applied to assess its forecast performance in a Canadian catchment. Raw QPFs obtained from two sources, Global Ensemble Forecasting System (GEFS) Reforecast 2 project, from the National Centers for Environmental Prediction, and Global Deterministic Forecast System (GDPS), from Environment and Climate Change Canada, are used in this study. The study period from January 2013 to December 2015 covered a major flood event in Calgary, Alberta, Canada. Post-processed results show that the RPP is able to remove the bias and reduce the errors of both GEFS and GDPS forecasts. Ensembles generated from the RPP reliably quantify the forecast uncertainty.
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Di Giuseppe, Francesca, Florian Pappenberger, Fredrik Wetterhall, Blazej Krzeminski, Andrea Camia, Giorgio Libertá, and Jesus San Miguel. "The Potential Predictability of Fire Danger Provided by Numerical Weather Prediction." Journal of Applied Meteorology and Climatology 55, no. 11 (November 2016): 2469–91. http://dx.doi.org/10.1175/jamc-d-15-0297.1.
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AbstractA global fire danger rating system driven by atmospheric model forcing has been developed with the aim of providing early warning information to civil protection authorities. The daily predictions of fire danger conditions are based on the U.S. Forest Service National Fire-Danger Rating System (NFDRS), the Canadian Forest Service Fire Weather Index Rating System (FWI), and the Australian McArthur (Mark 5) rating systems. Weather forcings are provided in real time by the European Centre for Medium-Range Weather Forecasts forecasting system at 25-km resolution. The global system’s potential predictability is assessed using reanalysis fields as weather forcings. The Global Fire Emissions Database (GFED4) provides 11 yr of observed burned areas from satellite measurements and is used as a validation dataset. The fire indices implemented are good predictors to highlight dangerous conditions. High values are correlated with observed fire, and low values correspond to nonobserved events. A more quantitative skill evaluation was performed using the extremal dependency index, which is a skill score specifically designed for rare events. It revealed that the three indices were more skillful than the random forecast to detect large fires on a global scale. The performance peaks in the boreal forests, the Mediterranean region, the Amazon rain forests, and Southeast Asia. The skill scores were then aggregated at the country level to reveal which nations could potentially benefit from the system information to aid decision-making and fire control support. Overall it was found that fire danger modeling based on weather forecasts can provide reasonable predictability over large parts of the global landmass.
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Tomašević, Ivana Čavlina, Kevin K. W. Cheung, Višnjica Vučetić, and Paul Fox-Hughes. "Comparison of Wildfire Meteorology and Climate at the Adriatic Coast and Southeast Australia." Atmosphere 13, no. 5 (May 7, 2022): 755. http://dx.doi.org/10.3390/atmos13050755.
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Wildfire is one of the most complex natural hazards. Its origin is a combination of anthropogenic factors, urban development and weather plus climate factors. In particular, weather and climate factors possess many spatiotemporal scales and various degrees of predictability. Due to the complex synergy of the human and natural factors behind the events, every wildfire is unique. However, there are indeed common meteorological and climate factors leading to the high fire risk before certain ignition mechanismfigures occur. From a scientific point of view, a better understanding of the meteorological and climate drivers of wildfire in every region would enable more effective seasonal to annual outlook of fire risk, and in the long term, better applications of climate projections to estimate future scenarios of wildfire. This review has performed a comparison study of two fire-prone regions: southeast Australia including Tasmania, and the Adriatic coast in Europe, especially events in Croatia. The former is well known as part of the ‘fire continent’, and major resources have been put into wildfire research and forecasting. The Adriatic coast is a region where some of the highest surface wind speeds, under strong topographic effect, have been recorded and, over the years, have coincided with wildfire ignitions. Similar synoptic background and dynamic origins of the meso-micro-scale meteorological conditions of these high wind events as well as the accompanied dryness have been identified between some of the events in the two regions. We have also reviewed how the researchers from these two regions have applied different weather indices and numerical models. The status of estimating fire potential under climate change for both regions has been evaluated. This review aims to promote a global network of information exchange to study the changing anthropogenic and natural factors we have to confront in order to mitigate and adapt the impacts and consequences from wildfire.
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Brown, Andrew, Andrew Dowdy, and Elizabeth E. Ebert. "The Relationship between High-Presentation Asthma Days in Melbourne, Australia, and Modeled Thunderstorm Environments." Weather and Forecasting 37, no. 3 (March 2022): 313–27. http://dx.doi.org/10.1175/waf-d-21-0109.1.
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Abstract Epidemic asthma events represent a significant risk to emergency services as well as the wider community. In southeastern Australia, these events occur in conjunction with relatively high amounts of grass pollen during the late spring and early summer, which may become concentrated in populated areas through atmospheric convergence caused by a number of physical mechanisms including thunderstorm outflow. Thunderstorm forecasts are therefore important for identifying epidemic asthma risk factors. However, the representation of thunderstorm environments using regional numerical weather prediction models, which are a key aspect of the construction of these forecasts, have not yet been systematically evaluated in the context of epidemic asthma events. Here, we evaluate diagnostics of thunderstorm environments from historical simulations of weather conditions in the vicinity of Melbourne, Australia, in relation to the identification of epidemic asthma cases based on hospital data from a set of controls. Skillful identification of epidemic asthma cases is achieved using a thunderstorm diagnostic that describes near-surface water vapor mixing ratio. This diagnostic is then used to gain insights on the variability of meteorological environments related to epidemic asthma in this region, including diurnal variations, long-term trends, and the relationship with large-scale climate drivers. Results suggest that there has been a long-term increase in days with high water vapor mixing ratio during the grass pollen season, with large-scale climate drivers having a limited influence on these conditions. Significance Statement We investigate the atmospheric conditions associated with epidemic thunderstorm asthma events in Melbourne, Australia, using historical model simulations of the weather. Conditions appear to be associated with high atmospheric moisture content, which relates to environments favorable for severe thunderstorms, but also potentially pollen rupturing as suggested by previous studies. These conditions are shown to be just as important as the concentration of grass pollen for a set of epidemic thunderstorm asthma events in this region. This means that weather model simulations of thunderstorm conditions can be incorporated into the forecasting process for epidemic asthma in Melbourne, Australia. We also investigate long-term variability in atmospheric conditions associated with severe thunderstorms, including relationships with the large-scale climate and long-term trends.
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Yang, Qichun, Quan J. Wang, Kirsti Hakala, and Yating Tang. "Bias-correcting input variables enhances forecasting of reference crop evapotranspiration." Hydrology and Earth System Sciences 25, no. 9 (September 2, 2021): 4773–88. http://dx.doi.org/10.5194/hess-25-4773-2021.
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Abstract. Reference crop evapotranspiration (ETo) is calculated using a standard formula with temperature, vapor pressure, solar radiation, and wind speed as input variables. ETo forecasts can be produced when forecasts of these input variables from numerical weather prediction (NWP) models are available. As raw ETo forecasts are often subject to systematic errors, statistical calibration is needed for improving forecast quality. The most straightforward and widely used approach is to directly calibrate raw ETo forecasts constructed with the raw forecasts of input variables. However, the predictable signal in ETo forecasts may not be fully implemented by this approach, which does not deal with error propagation from input variables to ETo forecasts. We hypothesize that correcting errors in input variables as a precursor to forecast calibration will lead to more skillful ETo forecasts. To test this hypothesis, we evaluate two calibration strategies that construct raw ETo forecasts with the raw (strategy i) or bias-corrected (strategy ii) input variables in ETo forecast calibration across Australia. Calibrated ETo forecasts based on bias-corrected input variables (strategy ii) demonstrate lower biases, higher correlation coefficients, and higher skills than forecasts produced by the calibration using raw input variables (strategy i). This investigation indicates that improving raw forecasts of input variables could effectively reduce error propagation and enhance ETo forecast calibration. We anticipate that future NWP-based ETo forecasting will benefit from adopting the calibration strategy developed in this study to produce more skillful ETo forecasts.
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Mukkavilli, S. K., A. A. Prasad, R. A. Taylor, A. Troccoli, and M. J. Kay. "Mesoscale Simulations of Australian Direct Normal Irradiance, Featuring an Extreme Dust Event." Journal of Applied Meteorology and Climatology 57, no. 3 (March 2018): 493–515. http://dx.doi.org/10.1175/jamc-d-17-0091.1.
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AbstractDirect normal irradiance (DNI) is the main input for concentrating solar power (CSP) technologies—an important component in future energy scenarios. DNI forecast accuracy is sensitive to radiative transfer schemes (RTSs) and microphysics in numerical weather prediction (NWP) models. Additionally, NWP models have large regional aerosol uncertainties. Dust aerosols can significantly attenuate DNI in extreme cases, with marked consequences for applications such as CSP. To date, studies have not compared the skill of different physical parameterization schemes for predicting hourly DNI under varying aerosol conditions over Australia. The authors address this gap by aiming to provide the first Weather and Forecasting (WRF) Model DNI benchmarks for Australia as baselines for assessing future aerosol-assimilated models. Annual and day-ahead simulations against ground measurements at selected sites focusing on an extreme dust event are run. Model biases are assessed for five shortwave RTSs at 30- and 10-km grid resolutions, along with the Thompson aerosol-aware scheme in three different microphysics configurations: no aerosols, fixed optical properties, and monthly climatologies. From the annual simulation, the best schemes were the Rapid Radiative Transfer Model for global climate models (RRTMG), followed by the new Goddard and Dudhia schemes, despite the relative simplicity of the latter. These top three RTSs all had 1.4–70.8 W m−2 lower mean absolute error than persistence. RRTMG with monthly aerosol climatologies was the best combination. The extreme dust event had large DNI mean bias overpredictions (up to 4.6 times), compared to background aerosol results. Dust storm–aware DNI forecasts could benefit from RRTMG with high-resolution aerosol inputs.
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Fabbian, Dustin, Richard de Dear, and Stephen Lellyett. "Application of Artificial Neural Network Forecasts to Predict Fog at Canberra International Airport." Weather and Forecasting 22, no. 2 (April 1, 2007): 372–81. http://dx.doi.org/10.1175/waf980.1.
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Abstract The occurrence of fog can significantly impact air transport operations, and plays an important role in aviation safety. The economic value of aviation forecasts for Sydney Airport alone in 1993 was estimated at $6.8 million (Australian dollars) for Quantas Airways. The prediction of fog remains difficult despite improvements in numerical weather prediction guidance and models of the fog phenomenon. This paper assesses the ability of artificial neural networks (ANNs) to provide accurate forecasts of such events at Canberra International Airport (YSCB). Unlike conventional statistical techniques, ANNs are well suited to problems involving complex nonlinear interactions and therefore have potential in application to fog prediction. A 44-yr database of standard meteorological observations obtained from the Australian Bureau of Meteorology was used to develop, train, test, and validate ANNs designed to predict fog occurrence. Fog forecasting aids were developed for 3-, 6-, 12-, and 18-h lead times from 0600 local standard time. The forecasting skill of various ANN architectures was assessed through analysis of relative operating characteristic curves. Results indicate that ANNs are able to offer good discrimination ability at all four lead times. The results were robust to error perturbation for various input parameters. It is recommended that such models be included when preparing forecasts for YSCB, and that the technique should be extended in its application to cover other similarly fog-prone aviation locations.
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Son, Bongkyo, and Kideok Do. "Optimization of SWAN Wave Model to Improve the Accuracy of Winter Storm Wave Prediction in the East Sea." Journal of Ocean Engineering and Technology 35, no. 4 (August 31, 2021): 273–86. http://dx.doi.org/10.26748/ksoe.2021.019.
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In recent years, as human casualties and property damage caused by hazardous waves have increased in the East Sea, precise wave prediction skills have become necessary. In this study, the Simulating WAves Nearshore (SWAN) third-generation numerical wave model was calibrated and optimized to enhance the accuracy of winter storm wave prediction in the East Sea. We used Source Term 6 (ST6) and physical observations from a large-scale experiment conducted in Australia and compared its results to Komen’s formula, a default in SWAN. As input wind data, we used Korean Meteorological Agency's (KMA’s) operational meteorological model called Regional Data Assimilation and Prediction System (RDAPS), the European Centre for Medium Range Weather Forecasts’ newest 5th generation re-analysis data (ERA5), and Japanese Meteorological Agency's (JMA’s) meso-scale forecasting data. We analyzed the accuracy of each model’s results by comparing them to observation data. For quantitative analysis and assessment, the observed wave data for 6 locations from KMA and Korea Hydrographic and Oceanographic Agency (KHOA) were used, and statistical analysis was conducted to assess model accuracy. As a result, ST6 models had a smaller root mean square error and higher correlation coefficient than the default model in significant wave height prediction. However, for peak wave period simulation, the results were incoherent among each model and location. In simulations with different wind data, the simulation using ERA5 for input wind datashowed the most accurate results overall but underestimated the wave height in predicting high wave events compared to the simulation using RDAPS and JMA meso-scale model. In addition, it showed that the spatial resolution of wind plays a more significant role in predicting high wave events. Nevertheless, the numerical model optimized in this study highlighted some limitations in predicting high waves that rise rapidly in time caused by meteorological events. This suggests that further research is necessary to enhance the accuracy of wave prediction in various climate conditions, such as extreme weather.
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McInerney, David, Mark Thyer, Dmitri Kavetski, Richard Laugesen, Fitsum Woldemeskel, Narendra Tuteja, and George Kuczera. "Seamless streamflow forecasting at daily to monthly scales: MuTHRE lets you have your cake and eat it too." Hydrology and Earth System Sciences 26, no. 21 (November 10, 2022): 5669–83. http://dx.doi.org/10.5194/hess-26-5669-2022.
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Abstract. Subseasonal streamflow forecasts inform a multitude of water management decisions, from early flood warning to reservoir operation. Seamless forecasts, i.e. forecasts that are reliable and sharp over a range of lead times (1–30 d) and aggregation timescales (e.g. daily to monthly) are of clear practical interest. However, existing forecast products are often non-seamless, i.e. developed and applied for a single timescale and lead time (e.g. 1 month ahead). If seamless forecasts are to be a viable replacement for existing non-seamless forecasts, it is important that they offer (at least) similar predictive performance at the timescale of the non-seamless forecast. This study compares forecasts from two probabilistic streamflow post-processing (QPP) models, namely the recently developed seamless daily Multi-Temporal Hydrological Residual Error (MuTHRE) model and the more traditional (non-seamless) monthly QPP model used in the Australian Bureau of Meteorology's dynamic forecasting system. Streamflow forecasts from both post-processing models are generated for 11 Australian catchments, using the GR4J hydrological model and pre-processed rainfall forecasts from the Australian Community Climate and Earth System Simulator – Seasonal (ACCESS-S) numerical weather prediction model. Evaluating monthly forecasts with key performance metrics (reliability, sharpness, bias, and continuous ranked probability score skill score), we find that the seamless MuTHRE model achieves essentially the same performance as the non-seamless monthly QPP model for the vast majority of metrics and temporal stratifications (months and years). As such, MuTHRE provides the capability of seamless daily streamflow forecasts with no loss of performance at the monthly scale – the modeller can proverbially “have their cake and eat it too”. This finding demonstrates that seamless forecasting technologies, such as the MuTHRE post-processing model, are not only viable but also a preferred choice for future research development and practical adoption in streamflow forecasting.
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Shrestha, Durga Lal, David E. Robertson, James C. Bennett, and Q. J. Wang. "Improving Precipitation Forecasts by Generating Ensembles through Postprocessing." Monthly Weather Review 143, no. 9 (August 31, 2015): 3642–63. http://dx.doi.org/10.1175/mwr-d-14-00329.1.
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Abstract This paper evaluates a postprocessing method for deterministic quantitative precipitation forecasts (raw QPFs) from a numerical weather prediction model. The postprocessing aims to produce calibrated QPF ensembles that are bias free, more accurate than raw QPFs, and reliable for use in streamflow forecasting applications. The method combines a simplified version of the Bayesian joint probability (BJP) modeling approach and the Schaake shuffle. The BJP modeling approach relates raw QPFs and observed precipitation by modeling their joint distribution. It corrects biases in the raw QPFs and generates ensemble forecasts that reflect the uncertainty in the raw QPFs. The BJP modeling approach is applied to each lead time and each forecast location separately. The Schaake shuffle is then employed to produce calibrated QPFs with appropriate space–time correlations by linking ensemble members generated by the BJP modeling approach. Calibrated QPFs are produced for 10 Australian catchments that cover a wide range of climatic conditions and hydrological characteristics. The calibrated QPFs are bias free, contain smaller forecast errors than that of the raw QPFs, reliably quantify the forecast uncertainty at a range of lead times, and successfully discriminate common and rare events of precipitation occurrences at shorter lead times. The postprocessing method is able to instill realistic within-catchment spatial variability in the QPFs, which is crucial for accurate and reliable streamflow forecasting.
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Kulessa, A. S., A. Barrios, J. Claverie, S. Garrett, T. Haack, J. M. Hacker, H. J. Hansen, et al. "The Tropical Air–Sea Propagation Study (TAPS)." Bulletin of the American Meteorological Society 98, no. 3 (March 1, 2017): 517–37. http://dx.doi.org/10.1175/bams-d-14-00284.1.
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Abstract The purpose of the Tropical Air–Sea Propagation Study (TAPS), which was conducted during November–December 2013, was to gather coordinated atmospheric and radio frequency (RF) data, offshore of northeastern Australia, in order to address the question of how well radio wave propagation can be predicted in a clear-air, tropical, littoral maritime environment. Spatiotemporal variations in vertical gradients of the conserved thermodynamic variables found in surface layers, mixing layers, and entrainment layers have the potential to bend or refract RF energy in directions that can either enhance or limit the intended function of an RF system. TAPS facilitated the collaboration of scientists and technologists from the United Kingdom, the United States, France, New Zealand, and Australia, bringing together expertise in boundary layer meteorology, mesoscale numerical weather prediction (NWP), and RF propagation. The focus of the study was on investigating for the first time in a tropical, littoral environment the i) refractivity structure in the marine and coastal inland boundary layers; ii) the spatial and temporal behavior of momentum, heat, and moisture fluxes; and iii) the ability of propagation models seeded with refractive index functions derived from blended NWP and surface-layer models to predict the propagation of radio wave signals of ultrahigh frequency (UHF; 300 MHz–3 GHz), super-high frequency (SHF; 3–30 GHz), and extremely high frequency (EHF; 30–300 GHz). Coordinated atmospheric and RF measurements were made using a small research aircraft, slow-ascent radiosondes, lidar, flux towers, a kitesonde, and land-based transmitters. The use of a ship as an RF-receiving platform facilitated variable-range RF links extending to distances of 80 km from the mainland. Four high-resolution NWP forecasting systems were employed to characterize environmental variability. This paper provides an overview of the TAPS experimental design and field campaign, including a description of the unique data that were collected, preliminary findings, and the envisaged interpretation of the results.
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Griffiths, Deryn, Nicholas Loveday, Benjamin Price, Michael Foley, and Alistair McKelvie. "Circular Flip-Flop Index: quantifying revision stability of forecasts of direction." Journal of Southern Hemisphere Earth Systems Science 71, no. 3 (2021): 266. http://dx.doi.org/10.1071/es21010.
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The Flip-Flop Index, designed to quantify the extent to which a forecast changes from one issue time to the next, is extended to a Circular Flip-Flop Index for use with forecasts of wind direction, swell direction or similar. The index was devised so we could understand the degree of stability in wind direction forecasts. The Circular Flip Flop Index is independent of observations, has a relatively simple definition and does not penalise a sequence of forecasts that show a trend as long as the forecasts stay within a 180° sector. The Circular Flip-Flop Index is interpreted in terms of the impact of changing forecasts on decisions made by users of the forecast. The Circular Flip-Flop Index has been used to compare the stability of sequences of automated forecast guidance to the official Australian Bureau of Meteorology forecasts, which are prepared manually. It is the first objective assessment of the stability of forecasts of direction. The results show that the forecasts of wind direction from the automated forecast guidance, itself a consensus of many numerical weather models, are more stable than the official, manual forecasts. The Circular Flip-Flop Index does not measure skill but can play a complementary role in characterising and evaluating a forecasting system.
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Wolters, Lex, Gerard Cats, and Nils Gustafsson. "Data-Parallel Numerical Weather Forecasting." Scientific Programming 4, no. 3 (1995): 141–53. http://dx.doi.org/10.1155/1995/692717.
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In this article we describe the implementation of a numerical weather forecast model on a massively parallel computer system. This model is a production code used for routine weather forecasting at the meteorological institutes of several European countries. The modifications needed to achieve a data-parallel version of this model without explicit message passing are outlined. The achieved performance of different numerical solution methods within this model is presented and compared.
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Chemel, Charles, Maria R. Russo, John A. Pyle, Ranjeet S. Sokhi, and Cornelius Schiller. "Quantifying the Imprint of a Severe Hector Thunderstorm during ACTIVE/SCOUT-O3 onto the Water Content in the Upper Troposphere/Lower Stratosphere." Monthly Weather Review 137, no. 8 (August 1, 2009): 2493–514. http://dx.doi.org/10.1175/2008mwr2666.1.
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Abstract The development of a severe Hector thunderstorm that formed over the Tiwi Islands, north of Australia, during the Aerosol and Chemical Transport in Tropical Convection/Stratospheric-Climate Links with Emphasis on the Upper Troposphere and Lower Stratosphere (ACTIVE/SCOUT-O3) field campaign in late 2005, is simulated by the Advanced Research Weather Research and Forecasting (ARW) model and the Met Office Unified Model (UM). The general aim of this paper is to investigate the role of isolated deep convection over the tropics in regulating the water content in the upper troposphere/lower stratosphere (UT/LS). Using a horizontal resolution as fine as 1 km, the numerical simulations reproduce the timing, structure, and strength of Hector fairly well when compared with field campaign observations. The sensitivity of results from ARW to horizontal resolution is investigated by running the model in a large-eddy simulation mode with a horizontal resolution of 250 m. While refining the horizontal resolution to 250 m leads to a better representation of convection with respect to rainfall, the characteristics of the Hector thunderstorm are basically similar in space and time to those obtained in the 1-km-horizontal-resolution simulations. Several overshooting updrafts penetrating the tropopause are produced in the simulations during the mature stage of Hector. The penetration of rising towering cumulus clouds into the LS maintains the entrainment of air at the interface between the UT and the LS. Vertical exchanges resulting from this entrainment process have a significant impact on the redistribution of atmospheric constituents within the UT/LS region at the scale of the islands. In particular, a large amount of water is injected in the LS. The fate of the ice particles as Hector develops drives the water vapor mixing ratio to saturation by sublimation of the injected ice particles, moistening the air in the LS. The moistening was found to be fairly significant above 380 K and averaged about 0.06 ppmv in the range 380–420 K for ARW. As for UM, the moistening was found to be much larger (about 2.24 ppmv in the range of 380–420 K) than for ARW. This result confirms that convective transport can play an important role in regulating the water vapor mixing ratio in the LS.
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Shakina N.P., N. P. "Aviation weather forecasting based on numerical weather prediction products." Hydrometeorological research and forecasting 4 (2019): 241–56. http://dx.doi.org/10.37162/2618-9631-2019-4-241-256.
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Staniforth, Andrew, and Robert Benoit. "Numerical weather forecasting research in the Canadian weather service." Telematics and Informatics 2, no. 4 (January 1985): 279–87. http://dx.doi.org/10.1016/0736-5853(85)90035-8.
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HONDA, Yuki. "Numerical Weather Prediction Using a Super-Computer for Weather Forecasting." Journal of The Institute of Electrical Engineers of Japan 139, no. 7 (July 1, 2019): 434–37. http://dx.doi.org/10.1541/ieejjournal.139.434.
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Jianguo, Xia. "How much numerical products affect weather forecasting." Advances in Atmospheric Sciences 8, no. 1 (March 1991): 107–10. http://dx.doi.org/10.1007/bf02657369.
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Minh, Pham Thi, Bui Thi Tuyet, Tran Thi Thu Thao, and Le Thi Thu Hang. "Application of ensemble Kalman filter in WRF model to forecast rainfall on monsoon onset period in South Vietnam." VIETNAM JOURNAL OF EARTH SCIENCES 40, no. 4 (September 18, 2018): 367–94. http://dx.doi.org/10.15625/0866-7187/40/4/13134.
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This paper presents some results of rainfall forecast in the monsoon onset period in South Vietnam, with the use of ensemble Kalman filter to assimilate observation data into the initial field of the model. The study of rainfall forecasts are experimented at the time of Southern monsoon outbreaks for 3 years (2005, 2008 and 2009), corresponding to 18 cases. In each case, there are five trials, including satellite wind data assimilation, upper-air sounding data assimilation, mixed data (satellite wind+upper-air sounding data) assimilation and two controlled trials (one single predictive test and one multi-physical ensemble prediction), which is equivalent to 85 forecasts for one trial. Based on the statistical evaluation of 36 samples (18 meteorological stations and 18 trials), the results show that Kalman filter assimilates satellite wind data to forecast well rainfall at 48 hours and 72 hours ranges. With 24 hour forecasting period, upper-air sounding data assimilation and mixed data assimilation experiments predicted better rainfall than non-assimilation tests. The results of the assessment based on the phase prediction indicators also show that the ensemble Kalman filter assimilating satellite wind data and mixed data sets improve the rain forecasting capability of the model at 48 hours and 72 hour ranges, while the upper-air sounding data assimilation test produces satisfactory results at the 72 hour forecast range, and the multi-physical ensemble test predicted good rainfall at 24 hour and 48 hour forecasts. The results of this research initially lead to a new research approach, Kalman Filter Application that assimilates the existing observation data into input data of the model that can improve the quality of rainfall forecast in Southern Vietnam and overall country in general.References Bui Minh Tuan, Nguyen Minh Truong, 2013. 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Hudson, Debra, Oscar Alves, Harry H. Hendon, and Andrew G. Marshall. "Bridging the gap between weather and seasonal forecasting: intraseasonal forecasting for Australia." Quarterly Journal of the Royal Meteorological Society 137, no. 656 (March 21, 2011): 673–89. http://dx.doi.org/10.1002/qj.769.
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Zhang, Gang, Dazhi Yang, George Galanis, and Emmanouil Androulakis. "Solar forecasting with hourly updated numerical weather prediction." Renewable and Sustainable Energy Reviews 154 (February 2022): 111768. http://dx.doi.org/10.1016/j.rser.2021.111768.
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YODEN, Shigeo. "Numerical Weather Prediction and Chaos. Forecasting Forecast Skill." Journal of the Visualization Society of Japan 13, no. 50 (1993): 178–82. http://dx.doi.org/10.3154/jvs.13.178.
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Duan, Q., Z. Di, J. Quan, C. Wang, W. Gong, Y. Gan, A. Ye, et al. "Automatic Model Calibration: A New Way to Improve Numerical Weather Forecasting." Bulletin of the American Meteorological Society 98, no. 5 (May 1, 2017): 959–70. http://dx.doi.org/10.1175/bams-d-15-00104.1.
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Abstract Weather forecasting skill has been improved over recent years owing to advances in the representation of physical processes by numerical weather prediction (NWP) models, observational systems, data assimilation and postprocessing, new computational capability, and effective communications and training. There is an area that has received less attention so far but can bring significant improvement to weather forecasting—the calibration of NWP models, a process in which model parameters are tuned using certain mathematical methods to minimize the difference between predictions and observations. Model calibration of the NWP models is difficult because 1) there are a formidable number of model parameters and meteorological variables to tune, and 2) a typical NWP model is very expensive to run, and conventional model calibration methods require many model runs (up to tens of thousands) or cannot handle the high dimensionality of NWP models. This study demonstrates that a newly developed automatic model calibration platform can overcome these difficulties and improve weather forecasting through parameter optimization. We illustrate how this is done with a case study involving 5-day weather forecasting during the summer monsoon in the greater Beijing region using the Weather Research and Forecasting Model. The keys to automatic model calibration are to use global sensitivity analysis to screen out the most important parameters influencing model performance and to employ surrogate models to reduce the need for a large number of model runs. Through several optimization and validation studies, we have shown that automatic model calibration can improve precipitation and temperature forecasting significantly according to a number of performance measures.
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Zhang, Hailing, and Zhaoxia Pu. "Beating the Uncertainties: Ensemble Forecasting and Ensemble-Based Data Assimilation in Modern Numerical Weather Prediction." Advances in Meteorology 2010 (2010): 1–10. http://dx.doi.org/10.1155/2010/432160.
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Accurate numerical weather forecasting is of great importance. Due to inadequate observations, our limited understanding of the physical processes of the atmosphere, and the chaotic nature of atmospheric flow, uncertainties always exist in modern numerical weather prediction (NWP). Recent developments in ensemble forecasting and ensemble-based data assimilation have proved that there are promising ways to beat the forecast uncertainties in NWP. This paper gives a brief overview of fundamental problems and recent progress associated with ensemble forecasting and ensemble-based data assimilation. The usefulness of these methods in improving high-impact weather forecasting is also discussed.
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Orrell, D., L. Smith, J. Barkmeijer, and T. N. Palmer. "Model error in weather forecasting." Nonlinear Processes in Geophysics 8, no. 6 (December 31, 2001): 357–71. http://dx.doi.org/10.5194/npg-8-357-2001.
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Abstract. Operational forecasting is hampered both by the rapid divergence of nearby initial conditions and by error in the underlying model. Interest in chaos has fuelled much work on the first of these two issues; this paper focuses on the second. A new approach to quantifying state-dependent model error, the local model drift, is derived and deployed both in examples and in operational numerical weather prediction models. A simple law is derived to relate model error to likely shadowing performance (how long the model can stay close to the observations). Imperfect model experiments are used to contrast the performance of truncated models relative to a high resolution run, and the operational model relative to the analysis. In both cases the component of forecast error due to state-dependent model error tends to grow as the square-root of forecast time, and provides a major source of error out to three days. These initial results suggest that model error plays a major role and calls for further research in quantifying both the local model drift and expected shadowing times.
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Wang, Bo, Chun Ming Zhang, Fu Dong Liu, Yang Yang, Liang Wang, Rui Ping Guo, Ya Hua Qiao, Yong Ye Liu, Shao Qing Yu, and Qiong Zhang. "Review on the Application of Numerical Weather Prediction in the Simulation Research of Atmospheric Dispersion." Advanced Materials Research 953-954 (June 2014): 428–31. http://dx.doi.org/10.4028/www.scientific.net/amr.953-954.428.
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The spatial and temporal variations of wind field and the atmospheric turbulence characteristics are important factors that affect the migration and dispersion of local atmospheric pollutants. Since the numerical meteorological forecasting system with high resolution is the foundation of atmospheric dispersion simulation, more and more attention has been drawn to the application of numerical weather prediction systems in wind field prediction and atmospheric simulation. This paper reviewed the widespread application of numerical weather prediction systems in wind field prediction and atmospheric simulation with the focus on the mesoscale weather forecasting system with high resolution, for instance WRF and MM5, as well as the requirement for numerical weather prediction in the simulation research of wind field prediction and atmospheric dispersion.
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Shan e Zahra and Sabir Abbas. "Weather Forecasting Pridiction using Mamdani Fuzzifier." Lahore Garrison University Research Journal of Computer Science and Information Technology 3, no. 2 (June 28, 2019): 9–13. http://dx.doi.org/10.54692/lgurjcsit.2019.030272.
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A climate expectation display is under study in view of the neural system and fuzzy surmising framework, and after that apply it to anticipate every day fuzzy precipitation given meteorological premises for testing. A "fuzzy ranked based neural system", which reenacts successive relations among fuzzy sets utilizing the manufactured neural system. It is outstanding that the requirement for exact climate expectation is clear while thinking about the advantages. Nonetheless, the over the top quest for exactness in climate expectation makes a portion of the "precise" forecast comes about pointless and the numerical forecast show is regularly intricate and tedious.
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Chantry, Matthew, Tobias Thornes, Tim Palmer, and Peter Düben. "Scale-Selective Precision for Weather and Climate Forecasting." Monthly Weather Review 147, no. 2 (January 25, 2019): 645–55. http://dx.doi.org/10.1175/mwr-d-18-0308.1.
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Abstract Attempts to include the vast range of length scales and physical processes at play in Earth’s atmosphere push weather and climate forecasters to build and more efficiently utilize some of the most powerful computers in the world. One possible avenue for increased efficiency is in using less precise numerical representations of numbers. If computing resources saved can be reinvested in other ways (e.g., increased resolution or ensemble size) a reduction in precision can lead to an increase in forecast accuracy. Here we examine reduced numerical precision in the context of ECMWF’s Open Integrated Forecast System (OpenIFS) model. We posit that less numerical precision is required when solving the dynamical equations for shorter length scales while retaining accuracy of the simulation. Transformations into spectral space, as found in spectral models such as OpenIFS, enact a length scale decomposition of the prognostic fields. Utilizing this, we introduce a reduced-precision emulator into the spectral space calculations and optimize the precision necessary to achieve forecasts comparable with double and single precision. On weather forecasting time scales, larger length scales require higher numerical precision than smaller length scales. On decadal time scales, half precision is still sufficient precision for everything except the global mean quantities.
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Fraley, Chris, Adrian Raftery, Tilmann Gneiting, McLean Sloughter, and Veronica Berrocal. "Probabilistic Weather Forecasting in R." R Journal 3, no. 1 (2011): 55. http://dx.doi.org/10.32614/rj-2011-009.
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Roulston, Mark S., Jerome Ellepola, Jost von Hardenberg, and Leonard A. Smith. "Forecasting wave height probabilities with numerical weather prediction models." Ocean Engineering 32, no. 14-15 (October 2005): 1841–63. http://dx.doi.org/10.1016/j.oceaneng.2004.11.012.
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Rosen, R. D., D. A. Salstein, T. Nehrkorn, J. O. Dickey, T. M. Eubanks, J. A. Steppe, M. R. P. McCalla, and A. J. Miller. "Forecasting length-of-day using numerical weather prediction models." Symposium - International Astronomical Union 128 (1988): 285–86. http://dx.doi.org/10.1017/s0074180900119618.
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A new approach to forecasting changes in length-of-day (δl.o.d) with lead times from one to ten days is examined. The approach is based on the high correlation that has been shown to exist between high frequency changes in l.o.d. and those in the atmosphere's angular momentum (M). Because forecasts of tropospheric values of M can be calculated from the zonal wind fields produced by operational numerical weather prediction models, it seems worth investigating whether these forecasts are sufficiently skillful to use to infer the evolution of δl.o.d. Here, we examine the quality of M forecasts made by the Medium Range Forecast (MRF) model of the U.S. National Meteorological Center (NMC). By comparing these forecasts against those based on a simple model of persistence, we find that skillful forecasts of M are being achieved on average by the MRF, although there has been much month-to-month variability in forecast quality. Overall, our results indicate that for prediction lead times of 1–10 days, dynamically-based forecasts of δl.o.d. represent a viable alternative to the empirical approaches currently in use.
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Tennekes, H. "KARL POPPER AND THE ACCOUNTABILITY OF NUMERICAL WEATHER FORECASTING." Weather 47, no. 9 (September 1992): 343–46. http://dx.doi.org/10.1002/j.1477-8696.1992.tb07201.x.
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Kukkonen, J., T. Balk, D. M. Schultz, A. Baklanov, T. Klein, A. I. Miranda, A. Monteiro, et al. "Operational, regional-scale, chemical weather forecasting models in Europe." Atmospheric Chemistry and Physics Discussions 11, no. 2 (February 21, 2011): 5985–6162. http://dx.doi.org/10.5194/acpd-11-5985-2011.
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Abstract. Numerical models that combine weather forecasting and atmospheric chemistry are here referred to as chemical weather forecasting models. Eighteen operational chemical weather forecasting models on regional and continental scales in Europe are described and compared in this article. Topics discussed in this article include how weather forecasting and atmospheric chemistry models are integrated into chemical weather forecasting systems, how physical processes are incorporated into the models through parameterization schemes, how the model architecture affect the predicted variables, and how air chemistry and aerosol processes are formulated. In addition, we discuss sensitivity analysis and evaluation of the models, user operational requirements, such as model availability and documentation, and output availability and dissemination. In this manner, this article allows for the evaluation of the relative strengths and weaknesses of the various modelling systems and modelling approaches. Finally, this article highlights the most prominent gaps of knowledge for chemical weather forecasting models and suggests potential priorities for future research directions, for the following selected focus areas: emission inventories, the integration of numerical weather prediction and atmospheric chemical transport models, boundary conditions and nesting of models, data assimilation of the various chemical species, improved understanding and parameterization of physical processes, better evaluation of models against data and the construction of model ensembles.
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Kukkonen, J., T. Olsson, D. M. Schultz, A. Baklanov, T. Klein, A. I. Miranda, A. Monteiro, et al. "A review of operational, regional-scale, chemical weather forecasting models in Europe." Atmospheric Chemistry and Physics 12, no. 1 (January 2, 2012): 1–87. http://dx.doi.org/10.5194/acp-12-1-2012.
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Abstract. Numerical models that combine weather forecasting and atmospheric chemistry are here referred to as chemical weather forecasting models. Eighteen operational chemical weather forecasting models on regional and continental scales in Europe are described and compared in this article. Topics discussed in this article include how weather forecasting and atmospheric chemistry models are integrated into chemical weather forecasting systems, how physical processes are incorporated into the models through parameterization schemes, how the model architecture affects the predicted variables, and how air chemistry and aerosol processes are formulated. In addition, we discuss sensitivity analysis and evaluation of the models, user operational requirements, such as model availability and documentation, and output availability and dissemination. In this manner, this article allows for the evaluation of the relative strengths and weaknesses of the various modelling systems and modelling approaches. Finally, this article highlights the most prominent gaps of knowledge for chemical weather forecasting models and suggests potential priorities for future research directions, for the following selected focus areas: emission inventories, the integration of numerical weather prediction and atmospheric chemical transport models, boundary conditions and nesting of models, data assimilation of the various chemical species, improved understanding and parameterization of physical processes, better evaluation of models against data and the construction of model ensembles.
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Schalkwijk, Jerôme, Harmen J. J. Jonker, A. Pier Siebesma, and Erik Van Meijgaard. "Weather Forecasting Using GPU-Based Large-Eddy Simulations." Bulletin of the American Meteorological Society 96, no. 5 (May 1, 2015): 715–23. http://dx.doi.org/10.1175/bams-d-14-00114.1.
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Abstract Since the advent of computers midway through the twentieth century, computational resources have increased exponentially. It is likely they will continue to do so, especially when accounting for recent trends in multicore processors. History has shown that such an increase tends to directly lead to weather and climate models that readily exploit the extra resources, improving model quality and resolution. We show that Large-Eddy Simulation (LES) models that utilize modern, accelerated (e.g., by GPU or coprocessor), parallel hardware systems can now provide turbulence-resolving numerical weather forecasts over a region the size of the Netherlands at 100-m resolution. This approach has the potential to speed the development of turbulence-resolving numerical weather prediction models.