Relevant bibliographies by topics / Hydrodynamical short range weather forecasting

Academic literature on the topic 'Hydrodynamical short range weather forecasting'

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Published: 26 July 2024
Last updated: 28 July 2024

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Journal articles on the topic "Hydrodynamical short range weather forecasting"

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Berkovich, L. V., and Yu V. Tkacheva. "Operative short-range hydrodynamic weather forecasting at the points and its efficiency." Russian Meteorology and Hydrology 35, no. 12 (December 2010): 813–16. http://dx.doi.org/10.3103/s1068373910120046.

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Mariani, S., M. Casaioli, and P. Malguzzi. "Towards a new BOLAM-MOLOCH suite for the SIMM forecasting system: implementation of an optimised configuration for the HyMeX Special Observation Periods." Natural Hazards and Earth System Sciences Discussions 2, no. 1 (January 22, 2014): 649–80. http://dx.doi.org/10.5194/nhessd-2-649-2014.

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Abstract. In this work, the performance of two versions of the Sistema Idro-Meteo-Mare (SIMM) forecasting system, aiming at predicting weather, waves and sea surge in the Mediterranean basin and, in particular, around the Italian coasts, are compared for two high-impact case studies corresponding to the Intense Operation Period (IOP) 16 and 18 of the first monitoring campaign of the HYdrological cycle in Mediterranean EXperiment (HyMeX). The first SIMM version tested – currently operational – is based on the meteorological hydrostatic BOlogna Limited Area Model (BOLAM) one-way nested over two domains, the Mediterranean-embedded Costal WAve Forecasting system (Mc-WAF), and the Shallow water HYdrodynamic Finite Element Model (SHYFEM). The second version tested is the one initially implemented for the HyMeX monitoring campaigns, which is composed by an optimised new configuration of BOLAM defined over a wider, higher-resolution domain, the nonhydrostatic convection permitting model MOLOCH, the Mc-WAF component, and SHYFEM. Both SIMM versions are initialised with data from the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS). The accumulated precipitation obtained by applying all the above meteorological model chains at the two case studies is compared with observations. In general, the precipitation forecast quality turns out to improve with increasing resolution, the best result being obtained with the MOLOCH model. Storm surge and tidal forecasts, obtained by forcing SHYFEM with the BOLAM and ECMWF IFS surface wind and atmospheric pressure fields, are compared with observations of tidal elevation measured at the ISPRA "Punta della Salute" tide-gauge, located in the Lagoon of Venice. Results indicate that, for the IOP18, short-term forecasts obtained with BOLAM outperform the ECMWF IFS one, while the opposite seems apparently true for longer-term predictions.
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Dawson, C. ,., J. F. V. Vincent, and A.-M. Rocca. "Short-range weather forecasting." Trends in Plant Science 3, no. 2 (February 1998): 45. http://dx.doi.org/10.1016/s1360-1385(97)01189-8.

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Murray, R. "SHORT-RANGE WEATHER FORECASTING." Weather 42, no. 11 (November 1987): 346–50. http://dx.doi.org/10.1002/j.1477-8696.1987.tb04869.x.

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Hewage, Pradeep, Ardhendu Behera, Marcello Trovati, Ella Pereira, Morteza Ghahremani, Francesco Palmieri, and Yonghuai Liu. "Temporal convolutional neural (TCN) network for an effective weather forecasting using time-series data from the local weather station." Soft Computing 24, no. 21 (April 23, 2020): 16453–82. http://dx.doi.org/10.1007/s00500-020-04954-0.

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Abstract Non-predictive or inaccurate weather forecasting can severely impact the community of users such as farmers. Numerical weather prediction models run in major weather forecasting centers with several supercomputers to solve simultaneous complex nonlinear mathematical equations. Such models provide the medium-range weather forecasts, i.e., every 6 h up to 18 h with grid length of 10–20 km. However, farmers often depend on more detailed short-to medium-range forecasts with higher-resolution regional forecasting models. Therefore, this research aims to address this by developing and evaluating a lightweight and novel weather forecasting system, which consists of one or more local weather stations and state-of-the-art machine learning techniques for weather forecasting using time-series data from these weather stations. To this end, the system explores the state-of-the-art temporal convolutional network (TCN) and long short-term memory (LSTM) networks. Our experimental results show that the proposed model using TCN produces better forecasting compared to the LSTM and other classic machine learning approaches. The proposed model can be used as an efficient localized weather forecasting tool for the community of users, and it could be run on a stand-alone personal computer.
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Altaf, M. U., T. Butler, X. Luo, C. Dawson, T. Mayo, and I. Hoteit. "Improving Short-Range Ensemble Kalman Storm Surge Forecasting Using Robust Adaptive Inflation." Monthly Weather Review 141, no. 8 (July 25, 2013): 2705–20. http://dx.doi.org/10.1175/mwr-d-12-00310.1.

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Abstract This paper presents a robust ensemble filtering methodology for storm surge forecasting based on the singular evolutive interpolated Kalman (SEIK) filter, which has been implemented in the framework of the H∞ filter. By design, an H∞ filter is more robust than the common Kalman filter in the sense that the estimation error in the H∞ filter has, in general, a finite growth rate with respect to the uncertainties in assimilation. The computational hydrodynamical model used in this study is the Advanced Circulation (ADCIRC) model. The authors assimilate data obtained from Hurricanes Katrina and Ike as test cases. The results clearly show that the H∞-based SEIK filter provides more accurate short-range forecasts of storm surge compared to recently reported data assimilation results resulting from the standard SEIK filter.
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McCandless, T. C., G. S. Young, S. E. Haupt, and L. M. Hinkelman. "Regime-Dependent Short-Range Solar Irradiance Forecasting." Journal of Applied Meteorology and Climatology 55, no. 7 (July 2016): 1599–613. http://dx.doi.org/10.1175/jamc-d-15-0354.1.

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AbstractThis paper describes the development and testing of a cloud-regime-dependent short-range solar irradiance forecasting system for predictions of 15-min-average clearness index (global horizontal irradiance). This regime-dependent artificial neural network (RD-ANN) system classifies cloud regimes with a k-means algorithm on the basis of a combination of surface weather observations, irradiance observations, and GOES-East satellite data. The ANNs are then trained on each cloud regime to predict the clearness index. This RD-ANN system improves over the mean absolute error of the baseline clearness-index persistence predictions by 1.0%, 21.0%, 26.4%, and 27.4% at the 15-, 60-, 120-, and 180-min forecast lead times, respectively. In addition, a version of this method configured to predict the irradiance variability predicts irradiance variability more accurately than does a smart persistence technique.
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Iversen, Trond. "Tellus A Special Issue on probabilistic short-range weather forecasting." Tellus A: Dynamic Meteorology and Oceanography 63, no. 3 (January 2011): 371–72. http://dx.doi.org/10.1111/j.1600-0870.2011.00520.x.

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Lewis, John M. "Roots of Ensemble Forecasting." Monthly Weather Review 133, no. 7 (July 1, 2005): 1865–85. http://dx.doi.org/10.1175/mwr2949.1.

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Abstract The generation of a probabilistic view of dynamical weather prediction is traced back to the early 1950s, to that point in time when deterministic short-range numerical weather prediction (NWP) achieved its earliest success. Eric Eady was the first meteorologist to voice concern over strict determinism—that is, a future determined by the initial state without account for uncertainties in that state. By the end of the decade, Philip Thompson and Edward Lorenz explored the predictability limits of deterministic forecasting and set the stage for an alternate view—a stochastic–dynamic view that was enunciated by Edward Epstein. The steps in both operational short-range NWP and extended-range forecasting that justified a coupling between probability and dynamical law are followed. A discussion of the bridge from theory to practice follows, and the study ends with a genealogy of ensemble forecasting as an outgrowth of traditions in the history of science.
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Gouweleeuw, B. T., J. Thielen, G. Franchello, A. P. J. De Roo, and R. Buizza. "Flood forecasting using medium-range probabilistic weather prediction." Hydrology and Earth System Sciences 9, no. 4 (October 7, 2005): 365–80. http://dx.doi.org/10.5194/hess-9-365-2005.

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Abstract. Following the developments in short- and medium-range weather forecasting over the last decade, operational flood forecasting also appears to show a shift from a so-called single solution or 'best guess' deterministic approach towards a probabilistic approach based on ensemble techniques. While this probabilistic approach is now more or less common practice and well established in the meteorological community, operational flood forecasters have only started to look for ways to interpret and mitigate for end-users the prediction products obtained by combining so-called Ensemble Prediction Systems (EPS) of Numerical Weather Prediction (NWP) models with rainfall-runoff models. This paper presents initial results obtained by combining deterministic and EPS hindcasts of the global NWP model of the European Centre for Medium-Range Weather Forecasts (ECMWF) with the large-scale hydrological model LISFLOOD for two historic flood events: the river Meuse flood in January 1995 and the river Odra flood in July 1997. In addition, a possible way to interpret the obtained ensemble based stream flow prediction is proposed.
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Dissertations / Theses on the topic "Hydrodynamical short range weather forecasting"

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Grimit, Eric P. "Probabilistic mesoscale forecast error prediction using short-range ensembles /." Thesis, Connect to this title online; UW restricted, 2004. http://hdl.handle.net/1773/10064.

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Ryall, Gill. "An automated system for generating very-short-range forecasts of precipitation." Thesis, University of Sussex, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.284079.

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Landman, Stephanie. "A multi-model ensemble system for short-range weather prediction in South Africa." Diss., 2012. http://hdl.handle.net/2263/27018.

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Predicting the location and timing of rainfall events has important social and economic impacts. It is also important to have the ability to predict the amount of rainfall accurately. In operational centres forecasters use deterministic model output data as guidance for a subjective probabilistic rainfall forecast. The aim of this research is to determine the skill in an objective multi-model, multi-institute objective probabilistic forecast system. This was done by obtaining the rainfall forecast of two high-resolution regional models operational in South Africa. The first model is the Unified Model (UM) which is operational at the South African Weather Service. The UM contributed three members which differ in physics, data assimilation techniques and horisontal resolution. The second model is the Conformal-Cubic Atmospheric Model (CCAM) which is operational at the Council for Scientific and Industrial Research which in turn contributed two members to the ensemble system differing in horisontal resolution. A single-model ensemble was constructed for the UM and CCAM models respectively with each of the individual members having equal weights. The UM and CCAM single-model ensemble prediction models have been used in turn to construct a multi-model ensemble prediction system, using simple un-weighted averaging. The multi-model system was used to predict the 24-hour rainfall totals for three austral summer half-year seasons of 2006/07 to 2008/09. The forecast of this system was rigorously tested using observed rainfall data for the same period. From the multi-model system it has been found that the probabilistic forecast has good significant skill in predicting rainfall. The multi-model system proved to have skill and shows discrimination between events and non-events. This study has shown that it is possible to make an objective probabilistic rainfall forecast by constructing a multi-model, multi-institute system with high resolution regional models currently operational in South Africa. Thus, probabilistic rainfall forecasts with usable skill can be made with the use of a multi-model short-range ensemble prediction system over the South African domain. Such a system is not currently operational in South Africa. Copyright
Dissertation (MSc)--University of Pretoria, 2012.
Geography, Geoinformatics and Meteorology
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Singhai, Priyanshi. "Short to Medium Range Forecasting Skills of the GFS Model." Thesis, 2018. https://etd.iisc.ac.in/handle/2005/4486.

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The reliable prediction of the South Asian monsoon rainfall and its variability is crucial for various hydrological applications and early warning systems. This study analyzes Global Fore- cast System (GFS) model generated high-resolution precipitation forecast over the South-Asian region during June-September 2012. This work delineates the error characteristics of the model over land and ocean; how forecast errors vary at different hours of the day; the skill of the model in active and break cycle and clustering of the precipitation events. This study shows that forecast errors are much larger over the land than over ocean. More- over, the rate of increase of errors with lead time is rapid over the oceans where observed precipitation shows large day-to-variability. This is possibly due to the one-way air-sea interac- tion in the atmosphere-only model used for forecasting. Furthermore, over ocean, for a smaller range of RMS error there was not much variation in RMS error growth with lead time but for a higher range of RMS error, there was a rapid growth. Over land for a lower and higher value of RMS error, there was no variation in error growth with lead time. It has been also shown that the model had poor forecasting skills in predicting very heavy (>30 mmday􀀀1) precipitation over both land and ocean. The error decomposition analysis shows that error by pattern variation was contributing more than 90% of the total mean square error as compared to an error by mean di erence. This can be probably due to an error in daily and diurnal scale variation. On the daily scale, the transition of the occurrence of active and break phases was well captured by the model. However, the model had considerable difficulties in forecasting long intense break and heavy rainfall events. Diurnal cycle of precipitation in the model shows the phase error of about 6 hours over land. On the other hand, over oceans, there was no phase error in precipitation forecast. Moreover, there was a systematic bias over the ocean. This shift and bias in model forecasted phase result in large error over both land and ocean. Thus, efforts should be given to improve the phase and amplitude forecast of the diurnal cycle of precipitation from the model over the South Asian region.
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Potgieter, Christina Johanna. "Accuracy and skill of the Conformal-Cubic Atmospheric model in short-range weather forecasting over Southern Africa." Diss., 2007. http://hdl.handle.net/2263/28044.

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Books on the topic "Hydrodynamical short range weather forecasting"

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A, Bortnikov S., and Gosudarstvennyĭ komitet SSSR po gidrometeorologii i kontroli͡u︡ prirodnoĭ sredy., eds. Voprosy gidrodinamicheskogo kratkosrochnogo prognoza pogody i mezometeorologii. Leningrad: Gidrometeoizdat, 1988.

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Velʹtishchev, N. F. Mesometeorology and short-range forecasting. Geneva, Switzerland: World Meteorological Organization, 1990.

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Empirical methods in short-term climate prediction. Oxford: Oxford University Press, 2007.

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Malaysia, Malaysia Jabatan Meteorologi, ed. Evaluation of a mesoscale short range ensemble forecast system for Peninsular Malaysia during the 2010/2011 northeast monsoon season. Petaling Jaya, Selangor Darul Ehsan: Malaysian Meteorological Department (MMD), Ministry of Science, Technologi and Innovation (MOSTI), 2014.

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International Symposium on Short and Medium Range Numerical Weather Prediction (1986 Tokyo, Japan). Short- and medium- range numerical weather prediction: Collection of papers presented at the WMO/IUGG NWP Symposium, Tokyo, 4-8 August 1986. Tokyo: Meteorological Society of Japan, 1987.

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United States. National Aeronautics and Space Administration., ed. Utilization of satellite data and regional scale numerical models in short range weather forecasting: Final report, NASA Grant no. NSG-5162. [Washington, DC: National Aeronautics and Space Administration, 1985.

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Workshop on Boundary Layer Models in Short Range Weather Forecasting (1986 De Bilt, Utrecht, Netherlands). Report of the Workshop on Boundary Layer Models in Short Range Weather Forecasting, including the abstract of all the presentations during the workshop (De Bilt, The Netherlands, 10-12 March, 1986). De Bilt: Koninklijk Nederlands Meteorologisch Instituut, 1986.

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United States. National Aeronautics and Space Administration, ed. Utilization of satellite data and regional scale numerical models in short range weather forecasting: Final report, NASA Grant no. NSG-5162. [Washington, DC: National Aeronautics and Space Administration, 1985.

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John F. Kennedy Space Center. and United States. National Aeronautics and Space Administration. Scientific and Technical Information Program., eds. Statistical short-range guidance for peak wind speed forecasts on Kennedy Space Center/Cape Canaveral Air Force Station: Phase 1 results. [Washington, D.C.]: National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Program, 2002.

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John F. Kennedy Space Center. and United States. National Aeronautics and Space Administration. Scientific and Technical Information Program., eds. Statistical short-range guidance for peak wind speed forecasts on Kennedy Space Center/Cape Canaveral Air Force Station: Phase 1 results. [Washington, D.C.]: National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Program, 2002.

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Book chapters on the topic "Hydrodynamical short range weather forecasting"

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Haupt, Sue Ellen. "Short-Range Forecasting for Energy." In Weather & Climate Services for the Energy Industry, 97–107. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-68418-5_7.

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Nilsson, S., and J. O. Brunsberg. "Promis 600; An Operational System for Very Short Range Weather Forecasting in Sweden." In Weather Radar Networking, 391–400. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0551-1_43.

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Lyell, Christopher Sean, Usha Nattala, Rakesh Chandra Joshi, Zaher Joukhadar, Jonathan Garber, Simon Mutch, Assaf Inbar, et al. "A forest fuel dryness forecasting system that integrates an automated fuel sensor network, gridded weather, landscape attributes and machine learning models." In Advances in Forest Fire Research 2022, 21–27. Imprensa da Universidade de Coimbra, 2022. http://dx.doi.org/10.14195/978-989-26-2298-9_1.

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Accurate and timely forecasting of forest fuel moisture is critical for decision making in the context of bushfire risk and prescribed burning. The moisture content in forest fuels is a driver of ignition probability and contributes to the success of fuel hazard reduction burns. Forecasting capacity is extremely limited because traditional modelling approaches have not kept pace with rapid technological developments of field sensors, weather forecasting and data-driven modelling approaches. This research aims to develop and test a 7-day-ahead forecasting system for forest fuel dryness that integrates an automated fuel sensor network, gridded weather, landscape attributes and machine learning models. The integrated system was established across a diverse range of 30 sites in south-eastern Australia. Fuel moisture was measured hourly using 10-hour automated fuel sticks. A subset of long-term sites (5 years of data) was used to evaluate the relative performance of a selection of machine learning (Light Gradient Boosting Machine (LightGBM) and Recurrent Neural Network (RNN) based Long-Short Term Memory (LSTM)), statistical (VARMAX) and process-based models. The best performing models were evaluated at all 30 sites where data availability was more limited, demonstrating the models' performance in a real-world scenario on operational sites prone to data limitations. The models were driven by daily 7-day continent-scale gridded weather forecasts, in-situ fuel moisture observation and site variables. The model performance was evaluated based on the capacity to successfully predict minimum daily fuel dryness within the burnable range for fuel reduction (11 – 16%) and bushfire risk (
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Xiong, Anming, Haibo Xu, and Zhenglong Chen. "Frequency-Domain Analysis of Atmospheric Electric Field and Thunderstorm Forecasting." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia231228.

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The atmospheric electric field (AEF) has significant importance in thunderstorm warning. This paper first analyzed AEF variations during thunderstorms based on 386 samples and then divided the period into four stages, in which a certain time stage provided valuable information for lightning prediction. It was defined as the “Stage T.” Fast Fourier transform was employed to study the frequency-domain patterns of the AEF in this stage. The result showed that there were notable differences in the frequency spectrum distribution between thunderstorm and non-thunderstorm weather conditions. During thunderstorms, the AEF frequency spectrum amplitude was higher and the waveform exhibited a wide range of variations compared to non-thunderstorm conditions. Then we used the Euclidean distance classifier to discriminate between thunderstorm events and non-thunderstorm events in the modeling samples for lightning forecasting. The remaining 256 samples were used to validate and evaluate the effectiveness of the algorithm. For prediction time of 5min, 15min and 25min, probability of detection(POD) is greater than 77.78%, false alarm rate (FAR) is less than 16.67%, critical success index (CSI) is greater than 0.57. These results indicated that this method was effective for short-term thunderstorm forecasting.
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Conference papers on the topic "Hydrodynamical short range weather forecasting"

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Chance, Barbara A., Robert E. Introne, Jr., and David Izraelevitz. "Automated Meteorological Satellite Image Interpretation: An Aid To Short-Range Weather Forecasting." In 1987 Cambridge Symposium, edited by Paul Janota. SPIE, 1988. http://dx.doi.org/10.1117/12.942638.

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Carlstrom, Anders, Jacob Christensen, Johan Embretsen, Anders Emrich, and Peter de Maagt. "A Geostationary Atmospheric Sounder for now-casting and short-range weather forecasting." In 2009 IEEE Antennas and Propagation Society International Symposium (APSURSI). IEEE, 2009. http://dx.doi.org/10.1109/aps.2009.5172067.

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Chiou, Paul T., Chia-Rong Chen, Pao-Liang Chang, and Guo-Ji Jian. "Status and outlook of very short range forecasting system in Central Weather Bureau, Taiwan." In Fourth International Asia-Pacific Environmental Remote Sensing Symposium 2004: Remote Sensing of the Atmosphere, Ocean, Environment, and Space, edited by W. Paul Menzel and Toshiki Iwasaki. SPIE, 2005. http://dx.doi.org/10.1117/12.601195.

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DeLeon, Rey, Kyle Felzien, and Inanc Senocak. "Toward a GPU-Accelerated Immersed Boundary Method for Wind Forecasting Over Complex Terrain." In ASME 2012 Fluids Engineering Division Summer Meeting collocated with the ASME 2012 Heat Transfer Summer Conference and the ASME 2012 10th International Conference on Nanochannels, Microchannels, and Minichannels. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/fedsm2012-72145.

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A short-term wind power forecasting capability can be a valuable tool in the renewable energy industry to address load-balancing issues that arise from intermittent wind fields. Although numerical weather prediction models have been used to forecast winds, their applicability to micro-scale atmospheric boundary layer flows and ability to predict wind speeds at turbine hub height with a desired accuracy is not clear. To address this issue, we develop a multi-GPU parallel flow solver to forecast winds over complex terrain at the micro-scale, where computational domain size can range from meters to several kilometers. In the solver, we adopt the immersed boundary method and the Lagrangian dynamic large-eddy simulation model and extend them to atmospheric flows. The computations are accelerated on GPU clusters with a dual-level parallel implementation that interleaves MPI with CUDA. We evaluate the flow solver components against test problems and obtain preliminary results of flow over Bolund Hill, a coastal hill in Denmark.
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