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    Short-Term Single-Station Forecasting of Precipitation

    Source: Monthly Weather Review:;1984:;volume( 112 ):;issue: 006::page 1198
    Author:
    Miller, A. J.
    ,
    Leslie, L. M.
    DOI: 10.1175/1520-0493(1984)112<1198:STSSFO>2.0.CO;2
    Publisher: American Meteorological Society
    Abstract: Forecast probabilities of rain were calculated up to 12 hours in advance using a Markov chain model applied to three-hourly observations from five major Australian cities. The four weather states chosen in this first study were three cloudiness states (0?2 oktas, 3?5 oktas and 6?8 oktas) and a rain state. Second-order Markov models with time-of-day dependent transition probabilities were fitted after appropriate statistical testing. Forecasts were made using transition probabilities for summer and winter seasons. The skill of the Markov chain forecast probabilities of rain was evaluated in terms of Brier scores using to years of independent data, and compared with forecasts based upon persistence and climatology. The skill of the Markov model forecasts appreciably exceeded that of persistence and climatology and a real time trial of the procedure is being planned.
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      Short-Term Single-Station Forecasting of Precipitation

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    contributor authorMiller, A. J.
    contributor authorLeslie, L. M.
    date accessioned2017-06-09T16:04:53Z
    date available2017-06-09T16:04:53Z
    date copyright1984/06/01
    date issued1984
    identifier issn0027-0644
    identifier otherams-60459.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4201131
    description abstractForecast probabilities of rain were calculated up to 12 hours in advance using a Markov chain model applied to three-hourly observations from five major Australian cities. The four weather states chosen in this first study were three cloudiness states (0?2 oktas, 3?5 oktas and 6?8 oktas) and a rain state. Second-order Markov models with time-of-day dependent transition probabilities were fitted after appropriate statistical testing. Forecasts were made using transition probabilities for summer and winter seasons. The skill of the Markov chain forecast probabilities of rain was evaluated in terms of Brier scores using to years of independent data, and compared with forecasts based upon persistence and climatology. The skill of the Markov model forecasts appreciably exceeded that of persistence and climatology and a real time trial of the procedure is being planned.
    publisherAmerican Meteorological Society
    titleShort-Term Single-Station Forecasting of Precipitation
    typeJournal Paper
    journal volume112
    journal issue6
    journal titleMonthly Weather Review
    identifier doi10.1175/1520-0493(1984)112<1198:STSSFO>2.0.CO;2
    journal fristpage1198
    journal lastpage1205
    treeMonthly Weather Review:;1984:;volume( 112 ):;issue: 006
    contenttypeFulltext
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