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similarity algorithm

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تکرار جستجوی کلیدواژه similarity algorithm در نشریات گروه علوم انسانی
تکرار جستجوی کلیدواژه similarity algorithm در مقالات مجلات علمی
  • نگار سیبانی، سید حسین ثنایی نژاد، بیژن قهرمان
    در مواجه با خطر سیل و یا خسارات ناشی از خشکسالی، برآورد میزان بارش و الگوی تغییرات مکانی آن در یک منطقه گسترده، یکی از چالش های مهم در علوم هواشناسی، کشاورزی و هیدرولوژی است. اندازه گیری محلی بارندگی در مناطق دور افتاده به دلیل هزینه زیاد و محدودیت های عملیاتی دشوار است. بدین علت در تحقیق حاضر به منظور تعیین الگوی مکانی-زمانی بارش و امکان تلفیق داده ها، سه نوع مختلف از تولیدات بارندگی شامل داده های ماهواره ای (TRMM3B42)، داده های حاصل از مدل پیش بینی عددی جوی (MM5) و اندازه گیری های زمینی (نقشه های حاصل از روش زمین آمار (KED))، مورد مطالعه قرار گرفتند. این مطالعه در بازه زمانی سال های 2000 تا 2010 میلادی و برای منطقه شمال شرق ایران به صورت ماهانه، فصلی و سالانه انجام شد. داده ها با استفاده از شاخص اعتبارسنجی RMSE و الگوریتم تشابه با یکدیگر مقایسه شدند. نتایج نشان دادند که یکی از ضعف های روش زمین آمار نبودن اطلاعات کافی در ارتفاعات بالای (1500) متر منطقه است. همچنین دقت تصاویر ماهواره ای در فصل های گرم بیشتر بود؛ بطوریکه در ماه آگوست مقدار 7/1 RMSE = به دست آمد. در فصل زمستان (ماه ژانویه) بیشترین مقدار 02/14 RMSE = حاصل شد که این امر عملکرد ضعیف تولیدات ماهواره ای TRMM در مناطق پوشیده از یخ را نشان می دهد. در اعتبارسنجی مدل MM5 بیشترین و کمترین مقدار RMSE به ترتیب 64/6 و 05/1 به دست آمد. علاوه بر این مدل MM5 تا حدود زیادی در شبیه سازی مقادیر بارندگی سالانه بیش برآورد داشت. نتایج تحلیل های مکانی- زمانی الگوریتم تشابه نیز نشان دادند که عملکرد مدل MM5 در مقیاس ماهانه و فصلی و تعیین مناطق بارندگی بهتر از تصاویر ماهواره ای TRMM بود. همچنین هر سه محصول الگوی مکانی بارندگی در مقیاس فصلی و سالانه را به خوبی نشان دادند.
    کلید واژگان: الگوریتم تشابه، بارندگی، TRMM
    Negar Siabia, Seyed Hossein Sanaei Nejadb, Bijan Ghahramanc
    1.
    Introduction
    Precise estimates of rainfall in areas with complex geographical features in the field of
    climatology, agricultural meteorology and hydrology is very important. TRMM satellite
    is the first international effort to measure rainfall from space reliably (Smith, 2007).
    Another set of data that has become available in recent years is the output of numerical
    prediction models. Akter and Islam (2007) used MM5 model for weather prediction
    especially for rainfall in Bangladesh. They compared MM5 outputs with 3B42RT
    production of TRMM, rain gage and radar data and concluded that MM5 is reliable for
    rainfall prediction. Ochoa et al. (2014) compared 3B42 product of TRMM with
    simulated rainfall data by WRF model. Their results showed that TRMM data is more
    applicable for presenting spatial distribution of annual rainfall. In addition to the
    methods of statistical comparison, the similarity algorithm (Herzfeld & Merriam, 1990)
    was also used in this study. This algorithm compares a large number of data
    simultaneously, which can be in the form of maps or models output. In Iran, very few
    studies have compared the output of numerical prediction models with TRMM products
    of rainfall. The aim of this study was to evaluate and compare the rainfall data using
    similarity algorithm for different locations and time periods in order to fill a gap in the
    space-time data.
    2. Material and
    Methods
    The study area consisted of North Khorasan, Khorasan Razavi and South Khorasan
    provinces in North East of Iran, which is geographically located between the longitudes
    of 55 to 61 degrees and latitudes of 30 to 38 degrees. The climate of the area is arid and
    semi arid. Total area is approximately 313000 square kilometers. In this study, three
    types of data were used. Ground-based observations used from synoptic and rain-gauge
    stations of Meteorology Organization. The seventh series products of TRMM 3B42 sensor containing three hours TRMM rainfall data with a spatial resolution of 0.25
    degree were downloaded for free from the site of NASA. MM5 model outputs which
    were in the form of images with a spatial resolution of 0.5× 0.5 degrees for the period of
    2000-2010 were also obtained from NASA and NOAA .In this study, KED as a
    geostatistical method was used to interpolate rainfall. For running geostatistics
    algorithms, GS and ArcGIS software were used. Similarity algorithm was executed
    for each grid point map and the similarity values were derived. After standardization by
    calculating the similarity value for the entire study area, F network model for similar
    map was created. In similarity algorithm, closest values to zero indicate a good
    similarity between the input maps in a specific location and higher values indicate
    weaker similarity. Standardization algorithms, similarity and analytical software
    programming in MATLAB were performed for each grid point of the map.
    3.
    Results And Discussion
    RMSE values for MM5 model were higher in the warm months. The highest RMSE
    values were obtained in late spring and early summer. This result proved that in the
    summer, rainfall was predicted less accurately than in the cold months in winter. RMSE
    values for TRMM showed a reverse pattern with MM5 model output. Maximum
    amount of RMSE for TRMM was obtained in January with 14 mm per month. The
    reason for this may be because microwave energy scattering from frozen ice on the
    ground. The scattering from rain or frozen rain in the atmosphere is similar. Similarity
    values in the area were scattered with uniform distribution that represents the least
    significant inter-annual variation is cold seasons. For the warm seasons, in the south and
    north of the area, similarity values vary from 1 to 2. Results showed that inter-annual
    variations of rainfall in warm seasons and in central areas is high. One of the reasons for
    these results can be errors in the observed data.
    By examining the time series of TRMM images using similarity algorithm, we found
    that in the cold season, the south zone of the study area had similarity values 0.05 to 0.1
    with a uniform distribution of values. However, higher similarity values were obtained
    for the northern and central areas where the distribution of similarity values was not
    uniform.
    Due to these facts, it can be concluded that rainfall production of TRMM data was
    relatively good in the cold season in south and relatively week in north and central parts
    of the region. In the warm season the least amount of similarity could be seen in the
    northeast part of the study area. But generally, TRMM estimated rainfall fairly in the
    warm season.
    4.
    Conclusion
    The validation results of MM5 model rainfall and TRMM monthly rainfall images
    showed that the model predicted rainfall amounts in the cold months better than in the
    warm months. However unlike the MM5 model, remote sensing images had the highest
    error in cold months. The reason was the presence of snow and ice on the ground in the
    cold months of winter. Considering inter-annual and seasonal changes, it became clear
    that there is much difference between inter-annual remote sensing image changes and the actual amounts of rainfall (KED). Nevertheless the model inter-annual changes were
    consistent with real data. Inter-annual changes of the model and the station data (KED)
    were higher in cold season.
    KED methods also retained spatial variability of rainfall as well as remote sensing data
    and model output. The estimates, especially above 1500 meters in the central regions,
    had low precision in the products. The results showed that in the absence of adequate
    rain gages in the region, MM5 output model and TRMM data could be used to fill the
    gaps.
    Keywords: MM5, Precipitation, Similarity algorithm, TRMM
نکته
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