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Добірка наукової літератури з теми "Fronts (Meteorology) Forecasting Mathematical models"

Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 29 січня 2023

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Статті в журналах з теми "Fronts (Meteorology) Forecasting Mathematical models"

Pérez, Isidro A., Mª Ángeles García, Mª Luisa Sánchez, Nuria Pardo, and Beatriz Fernández-Duque. "Key Points in Air Pollution Meteorology." International Journal of Environmental Research and Public Health 17, no. 22 (November 11, 2020): 8349. http://dx.doi.org/10.3390/ijerph17228349.

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Although emissions have a direct impact on air pollution, meteorological processes may influence inmission concentration, with the only way to control air pollution being through the rates emitted. This paper presents the close relationship between air pollution and meteorology following the scales of atmospheric motion. In macroscale, this review focuses on the synoptic pattern, since certain weather types are related to pollution episodes, with the determination of these weather types being the key point of these studies. The contrasting contribution of cold fronts is also presented, whilst mathematical models are seen to increase the analysis possibilities of pollution transport. In mesoscale, land–sea and mountain–valley breezes may reinforce certain pollution episodes, and recirculation processes are sometimes favoured by orographic features. The urban heat island is also considered, since the formation of mesovortices determines the entry of pollutants into the city. At the microscale, the influence of the boundary layer height and its evolution are evaluated; in particular, the contribution of the low-level jet to pollutant transport and dispersion. Local meteorological variables have a major influence on calculations with the Gaussian plume model, whilst some eddies are features exclusive to urban environments. Finally, the impact of air pollution on meteorology is briefly commented on.

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Small, J., L. Shackleford, and G. Pavey. "Ocean feature models − their use and effectiveness in ocean acoustic forecasting." Annales Geophysicae 15, no. 1 (January 31, 1997): 101–12. http://dx.doi.org/10.1007/s00585-997-0101-7.

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Abstract. The aim of this paper is to test the effectiveness of feature models in ocean acoustic forecasting. Feature models are simple mathematical representations of the horizontal and vertical structures of ocean features (such as fronts and eddies), and have been used primarily for assimilating new observations into forecasts and for compressing data. In this paper we describe the results of experiments in which the models have been tested in acoustic terms in eddy and frontal environments in the Iceland Faeroes region. Propagation-loss values were obtained with a 2D parabolic-equation (PE) model, for the observed fields, and compared to PE results from the corresponding feature models and horizontally uniform (range-independent) fields. The feature models were found to represent the smoothed observed propagation-loss field to within an rms error of 5 dB for the eddy and 7 dB for the front, compared to 10–15-dB rms errors obtained with the range-independent field. Some of the errors in the feature-model propagation loss were found to be due to high-amplitude 'oceanographic noise' in the field. The main conclusion is that the feature models represent the main acoustic properties of the ocean but do not show the significant effects of small-scale internal waves and fine-structure. It is recommended that feature models be used in conjunction with stochastic models of the internal waves, to represent the complete environmental variability.

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ALAM, MAHBOOB, and MOHD AMJAD. "A precipitation forecasting model using machine learning on big data in clouds environment." MAUSAM 72, no. 4 (November 1, 2021): 781–90. http://dx.doi.org/10.54302/mausam.v72i4.3546.

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Numerical weather prediction (NWP) has long been a difficult task for meteorologists. Atmospheric dynamics is extremely complicated to model, and chaos theory teaches us that the mathematical equations used to predict the weather are sensitive to initial conditions; that is, slightly perturbed initial conditions could yield very different forecasts. Over the years, meteorologists have developed a number of different mathematical models for atmospheric dynamics, each making slightly different assumptions and simplifications, and hence each yielding different forecasts. It has been noted that each model has its strengths and weaknesses forecasting in different situations, and hence to improve performance, scientists now use an ensemble forecast consisting of different models and running those models with different initial conditions. This ensemble method uses statistical post-processing; usually linear regression. Recently, machine learning techniques have started to be applied to NWP. Studies of neural networks, logistic regression, and genetic algorithms have shown improvements over standard linear regression for precipitation prediction. Gagne et al proposed using multiple machine learning techniques to improve precipitation forecasting. They used Breiman’s random forest technique, which had previously been applied to other areas of meteorology. Performance was verified using Next Generation Weather Radar (NEXRAD) data. Instead of using an ensemble forecast, it discusses the usage of techniques pertaining to machine learning to improve the precipitation forecast. This paper is to present an approach for mapping of precipitation data. The project attempts to arrive at a machine learning method which is optimal and data driven for predicting precipitation levels that aids farmers thereby aiming to provide benefits to the agricultural domain.

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Balaji, V. "Climbing down Charney’s ladder: machine learning and the post-Dennard era of computational climate science." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379, no. 2194 (February 15, 2021): 20200085. http://dx.doi.org/10.1098/rsta.2020.0085.

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The advent of digital computing in the 1950s sparked a revolution in the science of weather and climate. Meteorology, long based on extrapolating patterns in space and time, gave way to computational methods in a decade of advances in numerical weather forecasting. Those same methods also gave rise to computational climate science, studying the behaviour of those same numerical equations over intervals much longer than weather events, and changes in external boundary conditions. Several subsequent decades of exponential growth in computational power have brought us to the present day, where models ever grow in resolution and complexity, capable of mastery of many small-scale phenomena with global repercussions, and ever more intricate feedbacks in the Earth system. The current juncture in computing, seven decades later, heralds an end to what is called Dennard scaling, the physics behind ever smaller computational units and ever faster arithmetic. This is prompting a fundamental change in our approach to the simulation of weather and climate, potentially as revolutionary as that wrought by John von Neumann in the 1950s. One approach could return us to an earlier era of pattern recognition and extrapolation, this time aided by computational power. Another approach could lead us to insights that continue to be expressed in mathematical equations. In either approach, or any synthesis of those, it is clearly no longer the steady march of the last few decades, continuing to add detail to ever more elaborate models. In this prospectus, we attempt to show the outlines of how this may unfold in the coming decades, a new harnessing of physical knowledge, computation and data. This article is part of the theme issue ‘Machine learning for weather and climate modelling’.

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Kumar, Saket, Rajkumar Viral, Vikas Deep, Purushottam Sharma, Manoj Kumar, Mufti Mahmud, and Thompson Stephan. "Forecasting major impacts of COVID-19 pandemic on country-driven sectors: challenges, lessons, and future roadmap." Personal and Ubiquitous Computing, March 26, 2021. http://dx.doi.org/10.1007/s00779-021-01530-7.

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AbstractThe pandemic caused by the coronavirus disease 2019 (COVID-19) has produced a global health calamity that has a profound impact on the way of perceiving the world and everyday lives. This has appeared as the greatest threat of the time for the entire world in terms of its impact on human mortality rate and many other societal fronts or driving forces whose estimations are yet to be known. Therefore, this study focuses on the most crucial sectors that are severely impacted due to the COVID-19 pandemic, in particular reference to India. Considered based on their direct link to a country’s overall economy, these sectors include economic and financial, educational, healthcare, industrial, power and energy, oil market, employment, and environment. Based on available data about the pandemic and the above-mentioned sectors, as well as forecasted data about COVID-19 spreading, four inclusive mathematical models, namely—exponential smoothing, linear regression, Holt, and Winters, are used to analyse the gravity of the impacts due to this COVID-19 outbreak which is also graphically visualized. All the models are tested using data such as COVID-19 infection rate, number of daily cases and deaths, GDP of India, and unemployment. Comparing the obtained results, the best prediction model is presented. This study aims to evaluate the impact of this pandemic on country-driven sectors and recommends some strategies to lessen these impacts on a country’s economy.

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Ruppert, James H., Steven E. Koch, Xingchao Chen, Yu Du, Anton Seimon, Y. Qiang Sun, Junhong Wei, and Lance F. Bosart. "Mesoscale Gravity Waves and Midlatitude Weather: A tribute to Fuqing Zhang." Bulletin of the American Meteorological Society, August 10, 2021, 1–57. http://dx.doi.org/10.1175/bams-d-20-0005.1.

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AbstractOver the course of his career, Fuqing Zhang drew vital new insights into the dynamics of meteorologically significant mesoscale gravity waves (MGWs), including their generation by unbalanced jet streaks, their interaction with fronts and organized precipitation, and their importance in midlatitude weather and predictability. Zhang was the first to deeply examine “spontaneous balance adjustment” – the process by which MGWs are continuously emitted as baroclinic growth drives the upper-level flow out of balance. Through his pioneering numerical model investigation of the large-amplitude MGW event of 4 January 1994, he additionally demonstrated the critical role of MGW–moist convection interaction in wave amplification.Zhang’s curiosity-turned-passion in atmospheric science covered a vast range of topics and led to the birth of new branches of research in mesoscale meteorology and numerical weather prediction. Yet, it was his earliest studies into midlatitude MGWs and their significant impacts on hazardous weather that first inspired him. Such MGWs serve as the focus of this review, wherein we seek to pay tribute to his groundbreaking contributions, review our current understanding, and highlight critical open science issues. Chief among such issues is the nature of MGW amplification through feedback with moist convection, which continues to elude our understanding. The pressing nature of this subject is underscored by the continued failure of operational numerical forecast models to adequately predict most large-amplitude MGW events. Further research into such issues therefore presents a valuable opportunity to improve the understanding and forecasting of this high-impact weather phenomenon, and in turn to preserve the spirit of Zhang’s dedication to this subject.

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Adavi, Z., R. Weber, and W. Rohm. "Pre-analysis of GNSS tomography solution using the concept of spread of model resolution matrix." Journal of Geodesy 96, no. 4 (April 2022). http://dx.doi.org/10.1007/s00190-022-01620-1.

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AbstractGNSS tropospheric tomography is one of the applications of the Global Navigation Satellite Systems (GNSS) signals which attracts more and more interest in the field of meteorology. This method can reconstruct the water vapour of the atmosphere, which has a considerable effect on weather forecasting and early warning systems of severe weather. In GNSS tomography, traditionally, a regular spaced 3D grid stretches from the GNSS network to the effective height of the troposphere in the area of interest. Therefore, this method can offer a permanent monitoring service for water vapour and wet refractivity fields at a low cost and a reasonable spatial resolution compared to conventional observations, like radiosonde and radio occultation profiles. Nevertheless, the quality of the reconstructed field is still one of the challenges in the GNSS tomography. In this research, we propose the concept of spread as a mathematical tool to provide a quality measure without using the reference field and calculating statistical measures like RMSE and Bias. Thereby, two synthetic and one real datasets (part of Germany and Czechia) covering overlapping periods between 29 May and 14 Jun of the year 2013 (DoY 149–165; DoY 160–165; DoY 160–165, 2013) have been tested to investigate the proposed method. According to the obtained results, the proposed tool shows a strong correlation (up to 0.81 for synthetic and 0.72 for real observations) with the standard deviation of the reconstructed wet refractivity with respect to the radiosonde profile reference. The correlation between spread and the Bias of the retrieved wet refractivity field is also significant. However, there is no clear picture depending on the applied spread computation models. Therefore, the spread of the resolution matrix can be used as a proxy for the accuracy of the tomography reconstruction field based on the quality of the observations, the initial field, and the design matrix.

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Дисертації з теми "Fronts (Meteorology) Forecasting Mathematical models"

May, Peter T. "VHF radar studies of the troposphere /." Title page, contents and summary only, 1986. http://web4.library.adelaide.edu.au/theses/09PH/09phm4666.pdf.

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Keefer, Timothy Orrin, and Timothy Orrin Keefer. "Likelihood development for a probabilistic flash flood forecasting model." Thesis, The University of Arizona, 1993. http://hdl.handle.net/10150/192077.

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An empirical method is developed for constructing likelihood functions required in a Bayesian probabilistic flash flood forecasting model using data on objective quantitative precipitation forecasts and their verification. Likelihoods based on categorical and probabilistic forecast information for several forecast periods, seasons, and locations are shown and compared. Data record length, forecast information type and magnitude, grid area, and discretized interval size are shown to affect probabilistic differentiation of amounts of potential rainfall. Use of these likelihoods in Bayes' Theorem to update prior probability distributions of potential rainfall, based on preliminary data, to posterior probability distributions, reflecting the latest forecast information, demonstrates that an abbreviated version of the flash flood forecasting methodology is currently practicable. For this application, likelihoods based on the categorical forecast are indicated. Apart from flash flood forecasting, it is shown that likelihoods can provide detailed insight into the value of information contained in particular forecast products.

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Wahl, Douglas Timothy. "Increasing range and lethality of Extended -Range Munitions (ERMS) using Numerical Weather Prediction (NWP) and the AUV workbench to compute a Ballistic Correction (BALCOR)." Thesis, Monterey, Calif. : Naval Postgraduate School, 2006. http://bosun.nps.edu/uhtbin/hyperion.exe/06Dec%5FWahl.pdf.

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Thesis (M.S. in Meteorology and Physical Oceanography)--Naval Postgraduate School, December 2006.
Thesis Advisor(s): Wendell Nuss, Don Brutzmann. "December 2006." Includes bibliographical references (p. 107-116). Also available in print.

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Sanabia, Elizabeth R. "Objective identification of environmental patterns related to tropical cyclone track forecast errors." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2006. http://library.nps.navy.mil/uhtbin/hyperion/06Sep%5FSanabia.pdf.

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Thesis (M.S. in Meteorology and Physical Oceanography)--Naval Postgraduate School, September 2006.
Thesis Advisor(s): Patrick A. Harr, Russell L. Elsberry. "September 2006." Includes bibliographical references (p. 43). Also available in print.

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Dars, Ghulam Hussain. "Climate Change Impacts on Precipitation Extremes over the Columbia River Basin Based on Downscaled CMIP5 Climate Scenarios." PDXScholar, 2013. https://pdxscholar.library.pdx.edu/open_access_etds/979.

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Hydro-climate extreme analysis helps understanding the process of spatio-temporal variation of extreme events due to climate change, and it is an important aspect in designing hydrological structures, forecasting floods and an effective decision making in the field of water resources design and management. The study evaluates extreme precipitation events over the Columbia River Basin (CRB), the fourth largest basin in the U.S., by simulating four CMIP5 global climate models (GCMs) for the historical period (1970-1999) and future period (2041-2070) under RCP85 GHG scenario. We estimated the intensity of extreme and average precipitation for both winter (DJF) and summer (JJA) seasons by using the GEV distribution and multi-model ensemble average over the domain of the Columbia River Basin. The four CMIP5 models performed very well at simulating precipitation extremes in the winter season. The CMIP5 climate models showed heterogeneous spatial pattern of summer extreme precipitation over the CRB for the future period. It was noticed that multi-model ensemble mean outperformed compared to the individual performance of climate models for both seasons. We have found that the multi-model ensemble shows a consistent and significant increase in the extreme precipitation events in the west of the Cascades Range, Coastal Ranges of Oregon and Washington State, the Canadian portion of the basin and over the Rocky Mountains. However, the mean precipitation is projected to decrease in both winter and summer seasons in the future period. The Columbia River is dominated by the glacial snowmelt, so the increase in the intensity of extreme precipitation and decrease in mean precipitation in the future period, as simulated by four CMIP5 models, is expected to aggravate the earlier snowmelt and contribute to the flooding in the low lying areas especially in the west of the Cascades Range. In addition, the climate change shift could have serious implications on transboundary water issues in between the United States and Canada. Therefore, adaptation strategies should be devised to cope the possible adverse effects of the changing the future climate so that it could have minimal influence on hydrology, agriculture, aquatic species, hydro-power generation, human health and other water related infrastructure.

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Sanz, Rodrigo Javier. "On antarctic wind engineering." Doctoral thesis, Universite Libre de Bruxelles, 2011. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209953.

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Antarctic Wind Engineering deals with the effects of wind on the built environment. The assessment of wind induced forces, wind resource and wind driven snowdrifts are the main tasks for a wind engineer when participating on the design of an Antarctic building. While conventional Wind Engineering techniques are generally applicable to the Antarctic environment, there are some aspects that require further analysis due to the special characteristics of the Antarctic wind climate and its boundary layer meteorology.

The first issue in remote places like Antarctica is the lack of site wind measurements and meteorological information in general. In order to complement this shortage of information various meteorological databases have been surveyed. Global Reanalyses, produced by the European Met Office ECMWF, and RACMO/ANT mesoscale model simulations, produced by the Institute for Marine and Atmospheric Research of Utrecht University (IMAU), have been validated versus independent observations from a network of 115 automatic weather stations. The resolution of these models, of some tens of kilometers, is sufficient to characterize the wind climate in areas of smooth topography like the interior plateaus or the coastal ice shelves. In contrast, in escarpment and coastal areas, where the terrain gets rugged and katabatic winds are further intensified in confluence zones, the models lack resolution and underestimate the wind velocity.

The Antarctic atmospheric boundary layer (ABL) is characterized by the presence of strong katabatic winds that are generated by the presence of surface temperature inversions in sloping terrain. This inversion is persistent in Antarctica due to an almost continuous cooling by longwave radiation, especially during the winter night. As a result, the ABL is stably stratified most of the time and, only when the wind speed is high it becomes near neutrally stratified. This thesis also aims at making a critical review of the hypothesis underlying wind engineering models when extreme boundary layer situations are faced. It will be shown that the classical approach of assuming a neutral log-law in the surface layer can hold for studies of wind loading under strong winds but can be of limited use when detailed assessments are pursued.

The Antarctic landscape, mostly composed of very long fetches of ice covered terrain, makes it an optimum natural laboratory for the development of homogeneous boundary layers, which are a basic need for the formulation of ABL theories. Flux-profile measurements, made at Halley Research Station in the Brunt Ice Shelf by the British Antarctic Survery (BAS), have been used to analyze boundary layer similarity in view of formulating a one-dimensional ABL model. A 1D model of the neutral and stable boundary layer with a transport model for blowing snow has been implemented and verified versus test cases of the literature. A validation of quasi-stationary homogeneous profiles at different levels of stability confirms that such 1D models can be used to classify wind profiles to be used as boundary conditions for detailed 3D computational wind engineering studies.

A summary of the wind engineering activities carried out during the design of the Antarctic Research Station is provided as contextual reference and point of departure of this thesis. An elevated building on top of sloping terrain and connected to an under-snow garage constitutes a challenging environment for building design. Building aerodynamics and snowdrift management were tested in the von Karman Institute L1B wind tunnel for different building geometries and ridge integrations. Not only for safety and cost reduction but also for the integration of renewable energies, important benefits in the design of a building can be achieved if wind engineering is considered since the conceptual phase of the integrated building design process.

Doctorat en Sciences de l'ingénieur
info:eu-repo/semantics/nonPublished

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May, Peter T. "VHF radar studies of the troposphere / by Peter T. May." Thesis, 1986. http://hdl.handle.net/2440/20636.

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Compton, Andrea Jean. "The correlation of sea surface temperatures, sea level pressure and vertical wind shear with ten tropical cyclones between 1981-2010." Thesis, 2013. http://hdl.handle.net/1805/3669.

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Книги з теми "Fronts (Meteorology) Forecasting Mathematical models"

Gericke, Manfred. Frontenanalyse als Aufgabe der Variationsrechnung und der optimalen Steuerung. Berlin: Akademie-Verlag, 1990.

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Hayes, Pamela Speers. Prediction of precipitation in Western Washington State. Olympia, Wash: Washington State Dept. of Transportation, Planning, Research and Public Transportation Division, 1991.

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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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Saitō, Kazuo. Documentation of the Meteorological Research Institute Numerical Prediction Division unified nonhydrostatic model. Japan: Meteorological Research Institute, 2001.

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

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service), SpringerLink (Online, ed. Fronts, Waves and Vortices in Geophysical Flows. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2010.

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(Korea), Han'gang Hongsu T'ongjeso. Han'gang hongsu yebo: Imjin'gang, Ansŏngch'ŏn p'oham. Sŏul-si: Kukt'o Haeyangbu Han'gang Hongsu T'ongjeso, 2011.

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Parrett, Charles. Characteristics of extreme storms in Montana and methods for constructing synthetic storm hyetographs. Helena, Mont: U.S. Dept. of the Interior, U.S. Geological Survey, 1998.

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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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A, Matthews David. Nested model simulations of regional orographic precipition. Denver, Colo: U.S. Dept. of the Interior, Bureau of Reclamation, Technical Service Center, 1997.

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