Journal articles on the topic 'Numerical weather forecasting Australia, Southeastern'
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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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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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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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Sharples, Jason J., Graham A. Mills, Richard H. D. McRae, and Rodney O. Weber. "Foehn-Like Winds and Elevated Fire Danger Conditions in Southeastern Australia." Journal of Applied Meteorology and Climatology 49, no. 6 (June 1, 2010): 1067–95. http://dx.doi.org/10.1175/2010jamc2219.1.
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Abstract Bushfires in southeastern Australia are a serious environmental problem, and consistently cause loss of life and damage to property and other assets. Understanding synoptic processes that can lead to dangerous fire weather conditions throughout the region is therefore an important undertaking aimed at improving community safety, protection of assets, and fire suppression tactics and strategies. In southeastern Australia severe fire weather is often associated with dry cool changes or coastally modified cold fronts. Less well known, however, are synoptic events that can occur in connection with the topography of the region, such as cross-mountain flows and foehn-like winds, which can also lead to abrupt changes in fire weather variables that ultimately result in locally elevated fire danger. This paper focuses on foehn-like occurrences over the southeastern mainland, which are characterized by warm, dry winds on the lee side of the Australian Alps. The characteristics of a number of foehn-like occurrences are analyzed based on observational data and the predictions of a numerical weather model. The analyses confirm the existence of a foehn effect over parts of southeastern Australia and suggest that its occurrence is primarily due to the partial orographic blocking of relatively moist low-level air and the subsidence of drier upper-level air in the lee of the mountains. The regions prone to foehn occurrence, the influence of the foehn on fire weather variables, and the connection between the foehn and mountain waves are also discussed.
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McInnes, Kathleen L., John L. McBride, and Lance M. Leslie. "Cold Fronts over Southeastern Australia: Their Representation in an Operational Numerical Weather Prediction Model." Weather and Forecasting 9, no. 3 (September 1994): 384–409. http://dx.doi.org/10.1175/1520-0434(1994)009<0384:cfosat>2.0.co;2.
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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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Miller, Paul W., and Thomas L. Mote. "Characterizing severe weather potential in synoptically weakly forced thunderstorm environments." Natural Hazards and Earth System Sciences 18, no. 4 (April 27, 2018): 1261–77. http://dx.doi.org/10.5194/nhess-18-1261-2018.
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Abstract. Weakly forced thunderstorms (WFTs), short-lived convection forming in synoptically quiescent regimes, are a contemporary forecasting challenge. The convective environments that support severe WFTs are often similar to those that yield only non-severe WFTs and, additionally, only a small proportion of individual WFTs will ultimately produce severe weather. The purpose of this study is to better characterize the relative severe weather potential in these settings as a function of the convective environment. Thirty-one near-storm convective parameters for > 200 000 WFTs in the Southeastern United States are calculated from a high-resolution numerical forecasting model, the Rapid Refresh (RAP). For each parameter, the relative odds of WFT days with at least one severe weather event is assessed along a moving threshold. Parameters (and the values of them) that reliably separate severe-weather-supporting from non-severe WFT days are highlighted. Only two convective parameters, vertical totals (VTs) and total totals (TTs), appreciably differentiate severe-wind-supporting and severe-hail-supporting days from non-severe WFT days. When VTs exceeded values between 24.6 and 25.1 ∘C or TTs between 46.5 and 47.3 ∘C, odds of severe-wind days were roughly 5× greater. Meanwhile, odds of severe-hail days became roughly 10× greater when VTs exceeded 24.4–26.0 ∘C or TTs exceeded 46.3–49.2 ∘C. The stronger performance of VT and TT is partly attributed to the more accurate representation of these parameters in the numerical model. Under-reporting of severe weather and model error are posited to exacerbate the forecasting challenge by obscuring the subtle convective environmental differences enhancing storm severity.
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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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Moscatello, Agata, Mario Marcello Miglietta, and Richard Rotunno. "Numerical Analysis of a Mediterranean “Hurricane” over Southeastern Italy." Monthly Weather Review 136, no. 11 (November 1, 2008): 4373–97. http://dx.doi.org/10.1175/2008mwr2512.1.
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Abstract The presence of a subsynoptic-scale vortex over the Mediterranean Sea in southeastern Italy on 26 September 2006 has been recently documented by the authors. The transit of the cyclone over land allowed an accurate diagnosis of the structure of the vortex, based on radar and surface station data, showing that the cyclone had features similar to those observed in tropical cyclones. To investigate the cyclone in greater depth, numerical simulations have been performed using the Weather Research and Forecasting (WRF) model, set up with two domains, in a two-way-nested configuration. Model simulations are able to properly capture the timing and intensity of the small-scale cyclone. Moreover, the present simulated cyclone agrees with the observational analysis of this case, identifying in this small-scale depression the typical characteristics of a Mediterranean tropical-like cyclone. An analysis of the mechanisms responsible for the genesis, development, and maintenance of the cyclone has also been performed. Sensitivity experiments show that cyclogenesis on the lee side of the Atlas Mountains is responsible for the generation of the cyclone. Surface sensible and latent heat fluxes become important during the subsequent phase of development in which the lee-vortex shallow depression evolved as it moved toward the south of Sicily. During this phase, the latent heating, associated with convective motions triggered by a cold front entering the central Mediterranean area, was important for the intensification and contraction of the horizontal scale of the vortex. The small-scale cyclone subsequently deepened as it moved over the Ionian Sea and then maintained its intensity during its later transit over the Adriatic Sea; in this later stage, latent heat release continued to play a major role in amplifying and maintaining the vortex, while the importance of the surface fluxes diminished.
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Ma, Yimin, Xinmei Huang, Graham A. Mills, and Kevin Parkyn. "Verification of Mesoscale NWP Forecasts of Abrupt Cold Frontal Wind Changes." Weather and Forecasting 25, no. 1 (February 1, 2010): 93–112. http://dx.doi.org/10.1175/2009waf2222259.1.
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Abstract During a wildfire, a sharp wind change can lead to an abrupt increase in fire activity and change the rate of spread, endangering firefighters working on what had been the flank of the fire. In southeastern Australia, routine forecast of cold-frontal wind change arrival times is a critical component of the fire weather forecasting service, and mesoscale NWP model predictions are integral to this forecast process. An event-based verification method has been developed in order to verify these mesoscale NWP model forecasts of wind changes. The approach is based on fuzzy-rule techniques and objectively determines the timing of significant (fire weather) wind changes from time series of observations at a single surface station. In this paper these rules are applied to observational and NWP model forecast time series at observation locations over five fire seasons to determine objective “observed wind change times” and “forecast wind change times” for significant frontal wind changes in southeastern Australia. These forecast wind change times are compared with those observed, and also with those determined subjectively by forecasters at the Victorian Regional Forecast Centre. This provides an objective verification of NWP wind change forecasts and a measure of contemporary NWP model skill against which future model improvements may be measured. Case studies of two wind change events at selected stations are also presented to demonstrate some of the strengths, weaknesses, and characteristics of this verification technique.
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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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Speer, MS, LM Leslie, JR Colquhoun, and E. Mitchell. "The Sydney Australia Wildfires of January 1994 - Meteorological Conditions and High Resolution Numerical Modeling Experiments." International Journal of Wildland Fire 6, no. 3 (1996): 145. http://dx.doi.org/10.1071/wf9960145.
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Southeastern Australia is particularly vulnerable to wildfires during the spring and summer months, and the threat of devastation is present most years. In January 1994, the most populous city in Australia, Sydney, was ringed by wildfires, some of which penetrated well into suburban areas and there were many other serious fires in coastal areas of New South Wales (NSW). In recent years much research activity in Australia has focussed on the development of high resolution limited area models, for eventual operational prediction of meteorological conditions associated with high levels of wildfire risk. In this study, the period January 7-8, 1994 was chosen for detailed examination, as it was the most critical period during late December 1993/early January 1994 for the greater Sydney area. Routine forecast guidance from the Australian Bureau of Meteorology's operational numerical weather prediction (NWP) models was very useful in that both the medium and short range models predicted synoptic patterns suggesting extreme fire weather conditions up to several days in advance. However, vital information of a detailed nature was lacking. A new high resolution model was run at the operational resolution of 150 km and the much higher resolutions of 25 km and 5 km. The new model showed statistically significant greater skill in predicting details of wind, relative humidity and temperature patterns both near the surface and above the boundary layer. It also produced skilful predictions of the Forest Fire Danger Index.
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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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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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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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Zhang, Fuhan, Xiaodong Wang, Jiping Guan, Meihan Wu, and Lina Guo. "RN-Net: A Deep Learning Approach to 0–2 Hour Rainfall Nowcasting Based on Radar and Automatic Weather Station Data." Sensors 21, no. 6 (March 11, 2021): 1981. http://dx.doi.org/10.3390/s21061981.
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Precipitation has an important impact on people’s daily life and disaster prevention and mitigation. However, it is difficult to provide more accurate results for rainfall nowcasting due to spin-up problems in numerical weather prediction models. Furthermore, existing rainfall nowcasting methods based on machine learning and deep learning cannot provide large-area rainfall nowcasting with high spatiotemporal resolution. This paper proposes a dual-input dual-encoder recurrent neural network, namely Rainfall Nowcasting Network (RN-Net), to solve this problem. It takes the past grid rainfall data interpolated by automatic weather stations and doppler radar mosaic data as input data, and then forecasts the grid rainfall data for the next 2 h. We conduct experiments on the Southeastern China dataset. With a threshold of 0.25 mm, the RN-Net’s rainfall nowcasting threat scores have reached 0.523, 0.503, and 0.435 within 0.5 h, 1 h, and 2 h. Compared with the Weather Research and Forecasting model rainfall nowcasting, the threat scores have been increased by nearly four times, three times, and three times, respectively.
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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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Ruiz, Renata Sampaio da Rocha, Rosio Del Pilar Camayo Maita, Haroldo Fraga de Campos Velho, Saulo Freitas, and Valdir Innocentini. "ACOPLAMENTO BRAMS-WW3 PARA PREVISÃO DE ONDAS OCEÂNICAS." Ciência e Natura 38 (July 20, 2016): 401. http://dx.doi.org/10.5902/2179460x20283.
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A version of the coupled ocean waves WaveWatch III (WW3) model with mesoscale meteorological model BRAMS is presented. The WW3 is a model for waves forecasting. The BRAMS model is used for numerical weather prediction over South America, with horizontal resolution of 5 km. Both models are executed operationally by the CPTEC-INPE. The boundary conditions for the BRAMS model are provided by the global atmospheric circulation model of the CPTEC-INPE. The simulation with BRAMS for14/Oct/2014 generated a wind field with better agreement with the observations (satellite Metop-A, ASCAT sensor) to the coast of southeastern Brazil) than the field wind generated by the SFM model. The simulation with wind field generated by BRAMS produces higher waves than ones predicted with GFS wind field.
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Du, Yu, Yi-Leng Chen, and Qinghong Zhang. "Numerical Simulations of the Boundary Layer Jet off the Southeastern Coast of China." Monthly Weather Review 143, no. 4 (March 31, 2015): 1212–31. http://dx.doi.org/10.1175/mwr-d-14-00348.1.
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Abstract A strong coastal boundary layer jet (CBLJ) (~8 m s−1) off the southeastern coast of China (around 28°N, 122°E) is found from the July 2006–11 hourly model data simulated by the Advanced Research Weather Research and Forecasting Model (WRF-ARW) with a 9-km horizontal grid. The southerly CBLJ has a jet core at the 925-hPa level, located along the western periphery of the west Pacific subtropical high (WPSH). The CBLJ is mainly contributed by large-scale enhancement by diurnal forcing and orographic effects by the coastal terrain along the southeastern China coast and the terrain of Taiwan. Although the geostrophic winds offshore are faster in the afternoon due to the larger east–west pressure gradient caused by land surface heating over the China plain, the CBLJ has a nocturnal (~0200 LST) maximum. In the afternoon hours, easterly ageostrophic winds driven by differential land–sea thermal heating develop at low levels. After sunset, with the disappearance of land surface heating, the ageostrophic winds offshore veer southward by the Coriolis force and combine with the southerly geostrophic flow resulting in a nocturnal maximum in the CBLJ. Furthermore, from two model sensitivity experiments (NoTW and LowFJ), it is apparent that the terrain of Taiwan and Fujian exerts a secondary influence (1–2 m s−1) on the strength of the CBLJ. The orographic blocking by the terrain of Taiwan and Fujian is more significant with a larger (~1 m s−1) southerly wind component north of the Taiwan Strait at night than in the afternoon hours.
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Dupuy, Florian, Gert-Jan Duine, Pierre Durand, Thierry Hedde, Eric Pardyjak, and Pierre Roubin. "Valley Winds at the Local Scale: Correcting Routine Weather Forecast Using Artificial Neural Networks." Atmosphere 12, no. 2 (January 20, 2021): 128. http://dx.doi.org/10.3390/atmos12020128.
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In regions of complex topography, local flows are difficult to forecast on a routine basis, especially in stable conditions, due to the coarse resolution of operational models. The Cadarache valley (southeastern France) features this sort of complex topography. The Weather Research and Forecasting (WRF) model is run daily to forecast the weather in this region with a horizontal resolution of 3 km. Such a resolution cannot resolve all topography details of the small Cadarache valley, and therefore its local wind patterns. Other variables, however, that are less dependent on the subgrid topography, are satisfactorily forecasted, and used as inputs to an artificial neural network (ANN) designed to reproduce wind observations inside the valley from WRF forecasts. A variable selection procedure identified 5 key input variables that best drive the ANN. With respect to the WRF output, the ANN significantly improves forecasted low-level winds, both for speed and direction. This study demonstrates the potential for the ANN technique to be used as a correcting tool to forecast weather conditions at the local scale when numerical modeling is performed at a resolution too coarse to take into account the effect of local topography.
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Wan, Bingcheng, Zhiqiu Gao, Fei Chen, and Chungu Lu. "Impact of Tibetan Plateau Surface Heating on Persistent Extreme Precipitation Events in Southeastern China." Monthly Weather Review 145, no. 9 (September 2017): 3485–505. http://dx.doi.org/10.1175/mwr-d-17-0061.1.
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This paper combines observations, climatic analysis, and numerical modeling to investigate the Tibetan Plateau’s (TP) surface heating conditions’ influence on extreme persistent precipitation events (PEPEs) in southeastern China. Observations indicated an increase of TP surface air temperature 3–4 days prior to extreme persistent precipitation events in southeastern China. NCEP reanalysis data revealed a significant low pressure anomaly in southern China and a high pressure anomaly in northern China during extreme persistent precipitation event periods. Using correlation analysis and random resampling nonparametric statistics, a typical PEPE event from 17 to 25 June 2010 was selected for numerical simulation. The Weather Research and Forecasting (WRF) Model was used to investigate the impact of the TP’s surface heating on the evolution of this event. Three contrasting WRF experiments were conducted with different surface heating strengths by changing initial soil moisture over the TP. Different soil conditions generate different intensities of surface sensible heat fluxes and boundary layer structures over the TP resulting in two main effects on downstream convective rainfall: modulating large-scale atmospheric circulations and modifying the water vapor transport at southern China. Increased surface heating in the TP strengthens a high pressure system over the Yangtze Plain, thereby blocking the northward movement of precipitation. It also enhances the water vapor transport from the South China Sea to southern China. The combined effects substantially increase precipitation over most of the southeastern China region.
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Gregory, Paul A., Lawrie J. Rikus, and Jeffrey D. Kepert. "Testing and Diagnosing the Ability of the Bureau of Meteorology’s Numerical Weather Prediction Systems to Support Prediction of Solar Energy Production." Journal of Applied Meteorology and Climatology 51, no. 9 (September 2012): 1577–601. http://dx.doi.org/10.1175/jamc-d-10-05027.1.
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AbstractThe ability of the Australian Bureau of Meteorology’s numerical weather prediction (NWP) systems to predict solar exposure (or insolation) was tested, with the aim of predicting large-scale solar energy several days in advance. The bureau’s Limited Area Prediction System (LAPS) and Mesoscale Assimilation model (MALAPS) were examined for the 2008 calendar year. Comparisons were made with estimates of solar exposure obtained from satellites for the whole Australian continent, as well as site-based exposure observations taken at eight locations across Australia. Monthly-averaged forecast solar exposure over Australia showed good agreement with satellite estimates; the day-to-day exposure values showed some consistent biases, however. Differences in forecast solar exposure were attributed to incorrect representation of convective cloud in the tropics during summer as well as clouds formed by orographic lifting over mountainous areas in southeastern Australia. Comparison with site-based exposure observations was conducted on a daily and hourly basis. The site-based exposure measurements were consistent with the findings from the analysis against satellite data. Hourly analysis at selected sites confirmed that models predicted the solar exposure accurately through low-level clouds (e.g., cumulus), provided that the forecast cloud coverage was accurate. The NWP models struggle to predict solar exposure through middle and high clouds formed by ice crystals (e.g., altocumulus). Sites located in central Australia showed that the monthly-averaged errors in daily solar exposure forecast by the NWP systems were within 5%–10%, up to two days in advance. These errors increased to 20%–30% in the tropics and coastal areas.
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Zhang, Sara Q., Milija Zupanski, Arthur Y. Hou, Xin Lin, and Samson H. Cheung. "Assimilation of Precipitation-Affected Radiances in a Cloud-Resolving WRF Ensemble Data Assimilation System." Monthly Weather Review 141, no. 2 (February 1, 2013): 754–72. http://dx.doi.org/10.1175/mwr-d-12-00055.1.
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Abstract Assimilation of remotely sensed precipitation observations into numerical weather prediction models can improve precipitation forecasts and extend prediction capabilities in hydrological applications. This paper presents a new regional ensemble data assimilation system that assimilates precipitation-affected microwave radiances into the Weather Research and Forecasting Model (WRF). To meet the challenges in satellite data assimilation involving cloud and precipitation processes, hydrometeors produced by the cloud-resolving model are included as control variables and ensemble forecasts are used to estimate flow-dependent background error covariance. Two assimilation experiments have been conducted using precipitation-affected radiances from passive microwave sensors: one for a tropical storm after landfall and the other for a heavy rain event in the southeastern United States. The experiments examined the propagation of information in observed radiances via flow-dependent background error auto- and cross covariance, as well as the error statistics of observational radiance. The results show that ensemble assimilation of precipitation-affected radiances improves the quality of precipitation analyses in terms of spatial distribution and intensity in accumulated surface rainfall, as verified by independent ground-based precipitation observations.
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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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Lovat, Alexane, Béatrice Vincendon, and Véronique Ducrocq. "Hydrometeorological evaluation of two nowcasting systems for Mediterranean heavy precipitation events with operational considerations." Hydrology and Earth System Sciences 26, no. 10 (May 23, 2022): 2697–714. http://dx.doi.org/10.5194/hess-26-2697-2022.
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Abstract. Heavy precipitation events and subsequent flash floods regularly affect the Mediterranean coastal regions. In these situations, forecasting rainfall and river discharges is crucial especially up to 6 h, which is a relevant lead time for emergency services in times of crisis. The present study investigates the hydrometeorological skills of two new nowcasting systems: a numerical weather model AROME-NWC and a nowcasting system blending numerical weather prediction and extrapolation of radar estimation called PIAF. Their performance is assessed for 10 past heavy precipitation events that occurred in southeastern France. Precipitation forecasts are evaluated at a 15- min time resolution and the availability times of forecasts, based on the operational Météo-France suites, are taken into account when performing the evaluation. Rainfall observations and forecasts were first compared using a point-to-point approach. Then the evaluation was conducted from an hydrological point of view, by comparing observed and forecast precipitation over watersheds affected by floods. In general, the results led to the same conclusions for both evaluations. On the very first lead times, up to 1 h 15 min and 1 h 30 min of forecast, the performance of PIAF was higher than AROME-NWC. For longer lead times (up to 3 h) their performances were generally equivalent. An assessment of river discharges simulated with the ISBA-TOP coupled system, which is dedicated to Mediterranean flash flood simulations and driven by AROME-NWC and PIAF rainfall forecasts, was also performed on two exceptional past flash flood events. The results obtained for these two events show that using AROME-NWC or PIAF rainfall forecasts is promising for flash flood forecasting in terms of peak intensity, timing, and first wave of discharge, with an anticipation of these phenomena that can reach several hours.
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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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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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Tsai, Yu-Lin, Tso-Ren Wu, Chuan-Yao Lin, Simon C. Lin, Eric Yen, and Chun-Wei Lin. "Discrepancies on Storm Surge Predictions by Parametric Wind Model and Numerical Weather Prediction Model in a Semi-Enclosed Bay: Case Study of Typhoon Haiyan." Water 12, no. 12 (November 26, 2020): 3326. http://dx.doi.org/10.3390/w12123326.
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This study explores the discrepancies of storm surge predictions driven by the parametric wind model and the numerical weather prediction model. Serving as a leading-order storm wind predictive tool, the parametric Holland wind model provides the frictional-free, steady-state, and geostrophic-balancing solutions. On the other hand, WRF-ARW (Weather Research and Forecasting-Advanced Research WRF) provides the results solving the 3D time-integrated, compressible, and non-hydrostatic Euler equations, but time-consuming. To shed light on their discrepancies for storm surge predictions, the storm surges of 2013 Typhoon Haiyan in the Leyte Gulf and the San Pedro Bay are selected. The Holland wind model predicts strong southeastern winds in the San Pedro Bay after Haiyan makes landfall at the Leyte Island than WRF-ARW 3 km and WRF-ARW 1 km. The storm surge simulation driven by the Holland wind model finds that the water piles up in the San Pedro Bay and its maximum computed storm surges are almost twice than those driven by WRF-ARW. This study also finds that the storm surge prediction in the San Pedro Bay is sensitive to winds, which can be affected by the landfall location, the storm intensity, and the storm forward speed. The numerical experiment points out that the maximum storm surges can be amplified by more 5–6% inside the San Pedro Bay if Haiyan’s forward speed is increased by 10%.
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Zeng, Yong, and Lianmei Yang. "Triggering Mechanism of an Extreme Rainstorm Process near the Tianshan Mountains in Xinjiang, an Arid Region in China, Based on a Numerical Simulation." Advances in Meteorology 2020 (August 20, 2020): 1–12. http://dx.doi.org/10.1155/2020/8828060.
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The current study investigated the triggering mechanism of a record-breaking heavy rain process in the area near the Tianshan Mountains in Xinjiang, an arid region in China, from July 31 to August 1, 2016, based on the simulation using the Weather Research and Forecasting (WRF) model. The results illustrated that the rainstorm system was generated in the middle atmosphere of the western Aksu region near the Tianshan Mountains and gradually evolved into a multicell linear echo during system evolution. The cold air transported from the Tianshan Mountains partly reached the low altitudes during the downhill process, and the warm southwest air from Aksu was lifted, forming oblique updraft airflow. The other part of the cold air converged with the southeastern warm air in the middle atmosphere, and the transportation and convergence of the water vapor related to the southwestern, southeastern, and oblique updraft airflows provided good water vapor conditions for the storm system. Meanwhile, the inclined upward air transported cloud water and ice-phase particles to high altitudes, mixing the two and generating a large amount of supercooled cloud water, which was very beneficial for the development and maintenance of the storm system. These conditions were favorable for power, heat, water vapor, and water condensate particles, which enabled the development and maintenance of the rainstorm system on the convergence line, thus triggering this rare rainstorm process during the movement to the northeast.
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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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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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Figueroa, Silvio N., José P. Bonatti, Paulo Y. Kubota, Georg A. Grell, Hugh Morrison, Saulo R. M. Barros, Julio P. R. Fernandez, et al. "The Brazilian Global Atmospheric Model (BAM): Performance for Tropical Rainfall Forecasting and Sensitivity to Convective Scheme and Horizontal Resolution." Weather and Forecasting 31, no. 5 (September 21, 2016): 1547–72. http://dx.doi.org/10.1175/waf-d-16-0062.1.
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Abstract This article describes the main features of the Brazilian Global Atmospheric Model (BAM), analyses of its performance for tropical rainfall forecasting, and its sensitivity to convective scheme and horizontal resolution. BAM is the new global atmospheric model of the Center for Weather Forecasting and Climate Research [Centro de Previsão de Tempo e Estudos Climáticos (CPTEC)], which includes a new dynamical core and state-of-the-art parameterization schemes. BAM’s dynamical core incorporates a monotonic two-time-level semi-Lagrangian scheme, which is carried out completely on the model grid for the tridimensional transport of moisture, microphysical prognostic variables, and tracers. The performance of the quantitative precipitation forecasts (QPFs) from two convective schemes, the Grell–Dévényi (GD) scheme and its modified version (GDM), and two different horizontal resolutions are evaluated against the daily TRMM Multisatellite Precipitation Analysis over different tropical regions. Three main results are 1) the QPF skill was improved substantially with GDM in comparison to GD; 2) the increase in the horizontal resolution without any ad hoc tuning improves the variance of precipitation over continents with complex orography, such as Africa and South America, whereas over oceans there are no significant differences; and 3) the systematic errors (dry or wet biases) remain virtually unchanged for 5-day forecasts. Despite improvements in the tropical precipitation forecasts, especially over southeastern Brazil, dry biases over the Amazon and La Plata remain in BAM. Improving the precipitation forecasts over these regions remains a challenge for the future development of the model to be used not only for numerical weather prediction over South America but also for global climate simulations.
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Georgescu, F., S. Tascu, M. Caian, and D. Banciu. "A severe blizzard event in Romania – a case study." Natural Hazards and Earth System Sciences 9, no. 2 (April 27, 2009): 623–34. http://dx.doi.org/10.5194/nhess-9-623-2009.
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Abstract. During winter cold strong winds associated with snowfalls are not unusual for South and Southeastern Romania. The episode of 2–4 January 2008 was less usual due to its intensity and persistence. It happened after a long period (autumn 2006–autumn 2007) of mainly southerly circulations inducing warm weather, when the absolute record of the maximum temperature was registered. The important snowfalls and snowdrifts, leading to a consistent snow layer (up to 100 cm), produced serious transport and electricity supply perturbations. Since this atypical local weather event was not correctly represented by the operational numerical forecasts, several cross-comparison numerical simulations were performed to analyze the relative role of the coupler/coupling models and to compare two ways of process-scale uncertainties mitigation: optimizing the forecast range and performing ensemble forecast through the perturbation of the lateral boundary conditions. The results underline, for this case, the importance of physical parametrization package on the first place and secondary, the importance of the model horizontal resolution. The resolution increase is beneficial only in the local process representation; on larger scale it may either improve or decrease the accuracy effect, depending on the specified nudging between large-scale and small-scale information. The event capture is likely to be favored by two elements: a more appropriate time-scale of the event's physics and the quality of the transmitted large-scale information. Concerning the time scale, the statistics on skill as a function of forecast range are shown to be a useful tool in order to increase the accuracy of the numerical simulations. Ensembles forecasting versus resolution increase experiments indicate, for such atypical events, an interesting supply in the forecast accuracy through the ensemble method when applied to correct the minimum skill of the deterministic forecast.
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Case, Jonathan L., Sujay V. Kumar, Jayanthi Srikishen, and Gary J. Jedlovec. "Improving Numerical Weather Predictions of Summertime Precipitation over the Southeastern United States through a High-Resolution Initialization of the Surface State." Weather and Forecasting 26, no. 6 (December 1, 2011): 785–807. http://dx.doi.org/10.1175/2011waf2222455.1.
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Abstract It is hypothesized that high-resolution, accurate representations of surface properties such as soil moisture and sea surface temperature are necessary to improve simulations of summertime pulse-type convective precipitation in high-resolution models. This paper presents model verification results of a case study period from June to August 2008 over the southeastern United States using the Weather Research and Forecasting numerical weather prediction model. Experimental simulations initialized with high-resolution land surface fields from the National Aeronautics and Space Administration’s (NASA) Land Information System (LIS) and sea surface temperatures (SSTs) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) are compared to a set of control simulations initialized with interpolated fields from the National Centers for Environmental Prediction’s (NCEP) 12-km North American Mesoscale model. The LIS land surface and MODIS SSTs provide a more detailed surface initialization at a resolution comparable to the 4-km model grid spacing. Soil moisture from the LIS spinup run is shown to respond better to the extreme rainfall of Tropical Storm Fay in August 2008 over the Florida peninsula. The LIS has slightly lower errors and higher anomaly correlations in the top soil layer but exhibits a stronger dry bias in the root zone. The model sensitivity to the alternative surface initial conditions is examined for a sample case, showing that the LIS–MODIS data substantially impact surface and boundary layer properties. The Developmental Testbed Center’s Meteorological Evaluation Tools package is employed to produce verification statistics, including traditional gridded precipitation verification and output statistics from the Method for Object-Based Diagnostic Evaluation (MODE) tool. The LIS–MODIS initialization is found to produce small improvements in the skill scores of 1-h accumulated precipitation during the forecast hours of the peak diurnal convective cycle. Because there is very little union in time and space between the forecast and observed precipitation systems, results from the MODE object verification are examined to relax the stringency of traditional gridpoint precipitation verification. The MODE results indicate that the LIS–MODIS-initialized model runs increase the 10 mm h−1 matched object areas (“hits”) while simultaneously decreasing the unmatched object areas (“misses” plus “false alarms”) during most of the peak convective forecast hours, with statistically significant improvements of up to 5%. Simulated 1-h precipitation objects in the LIS–MODIS runs more closely resemble the observed objects, particularly at higher accumulation thresholds. Despite the small improvements, however, the overall low verification scores indicate that much uncertainty still exists in simulating the processes responsible for airmass-type convective precipitation systems in convection-allowing models.
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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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Hitzl, David Eugene, Yi-Leng Chen, and Hiep Van Nguyen. "Numerical Simulations and Observations of Airflow through the ‘Alenuihāhā Channel, Hawaii." Monthly Weather Review 142, no. 12 (December 1, 2014): 4696–718. http://dx.doi.org/10.1175/mwr-d-13-00312.1.
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Abstract During the summer, sustained winds in the ‘Alenuihāhā Channel, Hawaii, may exceed 20 m s−1 with higher gusts. The Advanced Research Weather Research and Forecasting model is used to diagnose airflow in the Hawaiian coastal waters. High-resolution (2 km) runs are performed for July 2005 covering the ‘Alenuihāhā Channel and nested in a 6-km state domain. Under normal trade wind conditions (7–8 m s−1), winds at the channel entrance are 1–2 m s−1 faster than upstream due to the convergence of the deflected airflows by the islands of Maui and Hawaii, and accelerate through the channel due to along-gap pressure gradients and lower pressure in the wakes of both islands. The acceleration is accompanied by descending airflow (>9 cm s−1) in the exit region with lowering of the trade wind inversion. Deceleration occurs downstream of the channel exit with a rapid change from sinking motion to rising motion (>3 cm s−1). Under normal or strong trade wind conditions, the flow is subcritical [Froude number (Fr) < 1] upstream of the channel, supercritical (Fr > 1) in the exit region, and subcritical again (Fr < 1) downstream with a weak hydraulic jump. The localized sinking motion on the lee side of bordering ridgelines (>1 m s−1) is most significant in the afternoon hours and results in warming and lowering of surface pressure on the lee side, into the channel, and farther downstream. As a result, the channel winds and the wind speed maximum along the southeastern coast of Maui exhibit an afternoon maximum.
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Čavlina Tomašević, Ivana, Kevin K. W. Cheung, Višnjica Vučetić, Paul Fox-Hughes, Kristian Horvath, Maja Telišman Prtenjak, Paul J. Beggs, Barbara Malečić, and Velimir Milić. "The 2017 Split wildfire in Croatia: evolution and the role of meteorological conditions." Natural Hazards and Earth System Sciences 22, no. 10 (October 6, 2022): 3143–65. http://dx.doi.org/10.5194/nhess-22-3143-2022.
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Abstract. The Split wildfire in July 2017, which was one of the most severe wildfires in the history of this Croatian World Heritage Site, is the focus in this study. The Split fire is a good example of a wildfire–urban interface, with unexpected fire behavior including rapid downslope spread to the coastal populated area. This study clarifies the meteorological conditions behind the fire event, those that have limited the effectiveness of firefighting operations, and the rapid escalation and expansion of the fire zones within 30 h. The Split fire propagation was first reconstructed using radio logs, interviews with firefighters and pilots involved in the intervention, eyewitness statements, digital photographs from fire detection cameras, media, and the monthly firefighting journal. Four phases of fire development have been identified. Then, weather observations and numerical simulations using an enhanced-resolution operational model are utilized to analyze the dynamics in each phase of the fire runs. The synoptic background of the event includes large surface pressure gradient between the Azores anticyclone accompanied by a cold front and a cyclone over the southeastern Balkan Peninsula. At the upper level, there was a deep shortwave trough extending from the Baltic Sea to the Adriatic Sea, which developed into a cut-off low. Such synoptic conditions have resulted in the maximum fire weather index in 2017. Combined with topography, they also locally provoke the formation of the strong northeasterly bura wind along the Adriatic coast, which has been accompanied by a low-level jet (LLJ). The bura (downslope wind), with mid- to low-level gravity-wave breaking and turbulence mixing (as in the hydraulic jump theory), also facilitated the subsidence of dry air from the upper troposphere and rapid drying at the surface. This study demonstrates that numerical guidance that indicates the spatial and temporal occurrence of a LLJ is highly capable of explaining the Split fire evolution from the ignition potential to its extinguishment stage. Thus, in addition to the conventional fire weather indices, such products are able to improve fire weather behavior forecasting and in general more effective decision-making in fire management.
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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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White, Andrew T., Arastoo Pour-Biazar, Kevin Doty, Bright Dornblaser, and Richard T. McNider. "Improving Cloud Simulation for Air Quality Studies through Assimilation of Geostationary Satellite Observations in Retrospective Meteorological Modeling." Monthly Weather Review 146, no. 1 (December 28, 2017): 29–48. http://dx.doi.org/10.1175/mwr-d-17-0139.1.
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Abstract Development of clouds in space and time within numerical meteorological models as observed in nature is essential for producing an accurate representation of the physical atmosphere for input into air quality models. In this study, a new technique was developed to assimilate Geostationary Operational Environmental Satellite (GOES)-derived cloud fields into the Weather Research and Forecasting (WRF) meteorological model to improve the placement of clouds in space and time within the model. The simulations were performed on 36-, 12-, and 4-km grid-size domains covering the contiguous United States, the south-southeastern United States, and eastern Texas, respectively. The technique was tested over the month of August 2006. The results indicate that the assimilation technique significantly improves the agreement between the model-predicted and GOES-derived cloud fields. The daily average percentage increase in the cloud agreement was determined to be 14.02%, 11.29%, and 4.96% for the 36-, 12-, and 4-km domains, respectively. This was accomplished without degrading the model performance with respect to surface wind speed, temperature, and mixing ratio, which are important parameters for air quality applications; in some cases these variables were even slightly improved. The assimilation technique also produced improvements in the model-predicted precipitation and predicted downwelling shortwave radiation reaching the surface.
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Li, Zhe, Daniel B. Wright, Sara Q. Zhang, Dalia B. Kirschbaum, and Samantha H. Hartke. "Object-Based Comparison of Data-Driven and Physics-Driven Satellite Estimates of Extreme Rainfall." Journal of Hydrometeorology 21, no. 12 (December 2020): 2759–76. http://dx.doi.org/10.1175/jhm-d-20-0041.1.
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AbstractThe Global Precipitation Measurement (GPM) constellation of spaceborne sensors provides a variety of direct and indirect measurements of precipitation processes. Such observations can be employed to derive spatially and temporally consistent gridded precipitation estimates either via data-driven retrieval algorithms or by assimilation into physically based numerical weather models. We compare the data-driven Integrated Multisatellite Retrievals for GPM (IMERG) and the assimilation-enabled NASA-Unified Weather Research and Forecasting (NU-WRF) model against Stage IV reference precipitation for four major extreme rainfall events in the southeastern United States using an object-based analysis framework that decomposes gridded precipitation fields into storm objects. As an alternative to conventional “grid-by-grid analysis,” the object-based approach provides a promising way to diagnose spatial properties of storms, trace them through space and time, and connect their accuracy to storm types and input data sources. The evolution of two tropical cyclones are generally captured by IMERG and NU-WRF, while the less organized spatial patterns of two mesoscale convective systems pose challenges for both. NU-WRF rain rates are generally more accurate, while IMERG better captures storm location and shape. Both show higher skill in detecting large, intense storms compared to smaller, weaker storms. IMERG’s accuracy depends on the input microwave and infrared data sources; NU-WRF does not appear to exhibit this dependence. Findings highlight that an object-oriented view can provide deeper insights into satellite precipitation performance and that the satellite precipitation community should further explore the potential for “hybrid” data-driven and physics-driven estimates in order to make optimal usage of satellite observations.
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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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Demko, J. Cory, and Bart Geerts. "A Numerical Study of the Evolving Convective Boundary Layer and Orographic Circulation around the Santa Catalina Mountains in Arizona. Part II: Interaction with Deep Convection." Monthly Weather Review 138, no. 9 (September 1, 2010): 3603–22. http://dx.doi.org/10.1175/2010mwr3318.1.
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Abstract This is the second part of a study that examines the daytime evolution of the thermally forced boundary layer (BL) circulation over a relatively isolated mountain, about 30 km in diameter and 2 km high, and its interaction with locally initiated deep convection by means of numerical simulations validated with data collected in the 2006 Cumulus Photogrammetric, In Situ, and Doppler Observations (CuPIDO) field campaign in southeastern Arizona. Part I examined the BL circulation in cases with, at most, rather shallow orographic cumulus (Cu) convection; the present part addresses deep convection. The results are based on output from version 3 of the Weather Research and Forecasting model run at a horizontal resolution of 1 km. The model output verifies well against CuPIDO observations. In the absence of Cu convection, the thermally forced (solenoidal) circulation is largely contained within the BL over the mountain. Thunderstorm development deepens this BL circulation with inflow over the depth of the BL and outflow in the free troposphere aloft. Primary deep convection results from destabilization over elevated terrain and tends to be triggered along a convergence line, which arises from the solenoidal circulation but may drift downwind of the terrain crest. While the solenoidal anabatic flow converges moisture over the mountain, it also cools the air. Thus, a period of suppressed anabatic flow following a convective episode, at a time when surface heating is still intense, can trigger new and possibly deeper convection. The growth of deep convection may require enhanced convergent flow in the BL, but this is less apparent in the mountain-scale surface flow signal than the decay of orographic convection. A budget study over the mountain suggests that the precipitation efficiency of the afternoon convection is quite low, ~10% in this case.
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Yang, Geum-Hee, Yu-Jin Jo, Hyo-Jung Lee, Chang-Keun Song, and Cheol-Hee Kim. "Numerical Sensitivity Tests of Volatile Organic Compounds Emission to PM2.5 Formation during Heat Wave Period in 2018 in Two Southeast Korean Cities." Atmosphere 11, no. 4 (March 29, 2020): 331. http://dx.doi.org/10.3390/atmos11040331.
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A record-breaking severe heat wave was recorded in southeast Korea from 11 July to 15 August 2018, and the numerical sensitivity simulations of volatile organic compound (VOC) to secondarily generated particulate matter with diameter of less than 2.5 µm (PM2.5) concentrations were studied in the Busan and Ulsan metropolitan areas in southeast Korea. A weather research and forecasting (WRF) model coupled with chemistry (WRF-Chem) was employed, and we carried out VOC emission sensitivity simulations to investigate variations in PM2.5 concentrations during the heat wave period that occurred from 11 July to 15 August 2018. In our study, when anthropogenic VOC emissions from the Comprehensive Regional Emissions Inventory for Atmospheric Transport Experiment-2015 (CREATE-2015) inventory were increased by approximately a factor of five in southeast Korea, a better agreement with observations of PM2.5 mass concentrations was simulated, implying an underestimation of anthropogenic VOC emissions over southeast Korea. The simulated secondary organic aerosol (SOA) fraction, in particular, showed greater dominance during high temperature periods such as 19–21 July, 2018, with the SOA fractions of 42.3% (in Busan) and 34.3% (in Ulsan) among a sub-total of seven inorganic and organic components. This is considerably higher than observed annual mean organic carbon (OC) fraction (28.4 ± 4%) among seven components, indicating the enhancement of secondary organic aerosols induced by photochemical reactions during the heat wave period in both metropolitan areas. The PM2.5 to PM10 ratios were 0.69 and 0.74, on average, during the study period in the two cities. These were also significantly higher than the typical range in those cities, which was 0.5–0.6 in 2018. Our simulations implied that extremely high temperatures with no precipitation are significantly important to the secondary generation of PM2.5 with higher secondary organic aerosol fraction via photochemical reactions in southeastern Korean cities. Other possible relationships between anthropogenic VOC emissions and temperature during the heat wave episode are also discussed in this study.
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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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Zhang, Yang, Chinmay Jena, Kai Wang, Clare Paton-Walsh, Élise-Andrée Guérette, Steven Utembe, Jeremy David Silver, and and Melita Keywood. "Multiscale Applications of Two Online-Coupled Meteorology-Chemistry Models during Recent Field Campaigns in Australia, Part I: Model Description and WRF/Chem-ROMS Evaluation Using Surface and Satellite Data and Sensitivity to Spatial Grid Resolutions." Atmosphere 10, no. 4 (April 8, 2019): 189. http://dx.doi.org/10.3390/atmos10040189.
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Air pollution and associated human exposure are important research areas in Greater Sydney, Australia. Several field campaigns were conducted to characterize the pollution sources and their impacts on ambient air quality including the Sydney Particle Study Stages 1 and 2 (SPS1 and SPS2), and the Measurements of Urban, Marine, and Biogenic Air (MUMBA). In this work, the Weather Research and Forecasting model with chemistry (WRF/Chem) and the coupled WRF/Chem with the Regional Ocean Model System (ROMS) (WRF/Chem-ROMS) are applied during these field campaigns to assess the models’ capability in reproducing atmospheric observations. The model simulations are performed over quadruple-nested domains at grid resolutions of 81-, 27-, 9-, and 3-km over Australia, an area in southeastern Australia, an area in New South Wales, and the Greater Sydney area, respectively. A comprehensive model evaluation is conducted using surface observations from these field campaigns, satellite retrievals, and other data. This paper evaluates the performance of WRF/Chem-ROMS and its sensitivity to spatial grid resolutions. The model generally performs well at 3-, 9-, and 27-km resolutions for sea-surface temperature and boundary layer meteorology in terms of performance statistics, seasonality, and daily variation. Moderate biases occur for temperature at 2-m and wind speed at 10-m in the mornings and evenings due to the inaccurate representation of the nocturnal boundary layer and surface heat fluxes. Larger underpredictions occur for total precipitation due to the limitations of the cloud microphysics scheme or cumulus parameterization. The model performs well at 3-, 9-, and 27-km resolutions for surface O3 in terms of statistics, spatial distributions, and diurnal and daily variations. The model underpredicts PM2.5 and PM10 during SPS1 and MUMBA but overpredicts PM2.5 and underpredicts PM10 during SPS2. These biases are attributed to inaccurate meteorology, precursor emissions, insufficient SO2 conversion to sulfate, inadequate dispersion at finer grid resolutions, and underprediction in secondary organic aerosol. The model gives moderate biases for net shortwave radiation and cloud condensation nuclei but large biases for other radiative and cloud variables. The performance of aerosol optical depth and latent/sensible heat flux varies for different simulation periods. Among all variables evaluated, wind speed at 10-m, precipitation, surface concentrations of CO, NO, NO2, SO2, O3, PM2.5, and PM10, aerosol optical depth, cloud optical thickness, cloud condensation nuclei, and column NO2 show moderate-to-strong sensitivity to spatial grid resolutions. The use of finer grid resolutions (3- or 9-km) can generally improve the performance for those variables. While the performance for most of these variables is consistent with that over the U.S. and East Asia, several differences along with future work are identified to pinpoint reasons for such differences.
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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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Laiti, L., D. Zardi, M. de Franceschi, and G. Rampanelli. "Analysis of the diurnal development of the <i>Ora del Garda</i> wind in the Alps from airborne and surface measurements." Atmospheric Chemistry and Physics Discussions 13, no. 7 (July 18, 2013): 19121–71. http://dx.doi.org/10.5194/acpd-13-19121-2013.
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Abstract. A lake-breeze and valley-wind coupled circulation system, known as Ora del Garda, typically arises in the late morning from the northern shorelines of Lake Garda (southeastern Italian Alps), and then channels into the Sarca and Lakes valleys to the north. After flowing over an elevated saddle, in the early afternoon this wind breaks out from the west into the nearby Adige Valley, hindering the regular development of the local up-valley wind by producing a strong and gusty anomalous flow in the area. Two targeted flights of an equipped motorglider were performed in the morning and afternoon of 23 August 2001 in the above valleys, exploring selected vertical slices of the atmosphere, from the lake's shore to the area where the two local airflows interact. At the same time, surface observations were collected during an intensive field measurement campaign held in the interaction area, as well as from routinely-operated weather stations disseminated along the whole study area, allowing the analysis of the different stages of the Ora del Garda development. From airborne measurements, an atmospheric boundary-layer (ABL) vertical structure, typical of deep Alpine valleys, was detected in connection with the wind flow, with rather shallow (∼500 m) convective mixed layers surmounted by deeper, weakly stable layers. On the other hand, close to the lake's shoreline the ABL was found to be stabilized down to very low heights, as an effect of the onshore advection of cold air by the lake breeze. Airborne potential temperature observations were mapped over high-resolution 3-D grids for each valley section explored by the flights, using a geostatistical technique called residual kriging (RK). RK-regridded fields revealed fine-scale features and inhomogeneities of ABL thermal structures associated with the complex thermally-driven wind field developing in the valleys. The combined analysis of surface observations and RK-interpolated fields revealed an irregular propagation of the lake-breeze front in the lower part of the valley, and cross-valley thermal asymmetries amenable both to the differential solar heating of the valley slopes and to the valley curvature in its upper part. The overflowing of the potentially cooler Ora del Garda air from the Lakes Valley in the afternoon produces a strong katabatic wind at the bottom of the underlying Adige Valley, which blows in cross-valley (i.e. westerly) direction and impinges on the opposite eastern valley sidewall. RK-regridded potential temperature field highlighted that this phenomenon gives origin to a "hydraulic jump" flow structure in the urban area north of the city of Trento, leading to the down-stream formation of a ∼1300 m deep well-mixed layer. The improved knowledge of the typical Ora del Garda flow patterns and associated ABL structures, deriving from the combined analysis of surface and airborne observations, has practical application in air quality forecasting for the study area, for it helps in the understanding of pollution transport and dispersion processes by thermally-driven winds in the region. Moreover, 3-D meteorological fields produced by RK are likely to be an excellent basis for comparison with results from high-resolution numerical simulations, as they provide a degree of spatial detail that is fully comparable to the spatial scales resolved by large-eddy simulations.
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Cho, Woojin, Jinyoung Park, Jihong Moon, Dong-Hyun Cha, Yu-min Moon, Hyeon-Sung Kim, Kyoung-jo Noh, and Sang-Hwan Park. "Effects of topography and sea surface temperature anomalies on heavy rainfall induced by Typhoon Chaba in 2016." Geoscience Letters 9, no. 1 (August 17, 2022). http://dx.doi.org/10.1186/s40562-022-00230-1.
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AbstractTyphoon Chaba made landfall on the Korean Peninsula in the fall of 2016, resulting in record-breaking rainfall in southeastern Korea. In particular, the Ulsan metropolitan region experienced the most severe floods due to heavy rainfall of 319 mm for just 3 h. The heavy rainfall was possibly associated with the mountainous southeastern Korea topography and the warm sea surface temperature (SST) anomaly in the East China Sea. In this study, the Weather Research and Forecasting (WRF) model was used to investigate the effects of topography and SST anomalies through high-resolution numerical experiments. Simulation using original topography showed more rainfall on the windward and less on the leeward slope compared to the experiment with reduced topography around Ulsan. The moist flow in the typhoon was raised by orographic uplift, enhancing precipitation on the windward side and summits of the mountains. The orographically induced updraft extended to the mid-troposphere and contributed to the upward vertical moisture flux associated with rainfall. Therefore, the mountainous topography around Ulsan affected the local change in rainfall induced by the simulated typhoon. In addition, SST on the track of the typhoon controlled storm intensity and caused extreme precipitation changes. The experiment using the original SST in the East China Sea simulated less decayed typhoons and produced more precipitation compared to the experiment wherein the positive SST anomaly in the East China Sea was removed. The warm SST anomaly hindered the weakening of the typhoon moving northward to the mid-latitudes. At landfall, the stronger typhoon contained more water vapor, transported more moisture inland, and generated increased precipitation.