یک رویکرد منطق فازی در مورد بهبود نقشه های بارش رادار آب و هوا
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یک رویکرد منطق فازی در مورد بهبود نقشه های بارش رادار آب و هوا

عنوان فارسی مقاله: بهبود نقشه های بارش رادار آب و هوا: یک رویکرد منطق فازی
عنوان انگلیسی مقاله: Improving weather radar precipitation maps: A fuzzy logic approach
مجله/کنفرانس: تحقیقات جوی - Atmospheric Research
رشته های تحصیلی مرتبط: جغرافیا، فیزیک
گرایش های تحصیلی مرتبط: آب و هواشناسی، مخاطرات آب و هوایی، تغییرات آب و هوایی اقلیمی، سنجش از دور و سیستم اطلاعات جغرافیایی
کلمات کلیدی فارسی: منطق فازی، بارش، مقیاس، رادار هوا، مبتنی بر موقعیت مکانی
کلمات کلیدی انگلیسی: Fuzzy logic، Precipitation، Gauges، Weather radar، Location-based
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus - Master Journals List - JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.atmosres.2019.104710
دانشگاه: Dept. of Geography and Environmental Development and Dept. of Psychology, Ben Gurion University, Israel
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2020
ایمپکت فاکتور: 4/413 در سال 2019
شاخص H_index: 85 در سال 2020
شاخص SJR: 1/464 در سال 2019
شناسه ISSN: 0169-8095
شاخص Quartile (چارک): Q1 در سال 2019
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 15
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: دارد
کد محصول: E14365
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

Abstract


1- Introduction


2- Methodology


3- Results


4- Discussion


5- Conclusion


References

نمونه متن انگلیسی مقاله

Abstract


Weather radar can provide spatially explicit precipitation grids. However interference, ground clutter and various causes of attenuation introduce uncertainty into the result. Typically, rain gauge observations, recognized as a precise measure of precipitation at point locations, are used to adjust weather radar grids to obtain more accurate precipitation maps. This adjustment involves one or more of various geostatistic techniques. Yet, since gauges are sparsely located, a geostatistic approach is sometimes limited or even not applicable.
This work adopts an alternative to radar adjustment by merging location-based variables with rain grids from weather radar. Recognizing that location-based variables: elevation, slope, aspect and distance from the coast all affect precipitation, these are applied to the original weather radar grid to produce an altered precipitation distribution.
The merging procedure presented here uses fuzzy logic, whereby all variables, as well as the original radar are assigned probabilities known as membership functions (MF), then a joint membership function (JMF) combines all MFs in the fuzzy set, each multiplied by its weight, to create a precipitation probability grid. This JMF probability grid is validated with gauge observation data. We show up to 30% higher correlation coefficients between gauges and the JMF grid than between gauges and the original radar. The improved correlation results from the flexibility of fuzzy logic in transforming location-based variables to probabilities.


Background


Estimating spatially distributed precipitation grids is a prerequisite to flood management and flood forecasting (Merz et al. (2014)). Hydrological models need basin-scale, spatially explicit precipitation data, among other inputs, to construct accurate flood forecasts (Todini et al. (2005)) for surface runoff management. Rain radar can produce such spatial precipitation distributions, however the challenges in calibrating and correcting for the various sources of error (detailed in Villarini et al. (2008)) create spatial and temporal uncertainty in the precipitation distribution (Cecinati et al. (2017), Krajewski and Smith, 2002). Nevertheless, the underlying motivation for research in improving precipitation maps rests in the needs of hydrological modeling and flood forecasting. Since weather radar became an accepted source of spatially distributed rainfall (Krajewski and Smith (2002), Morin et al. (2003)), extensive research has examined adjustment procedures to merge rain gauge observations with weather radar. Gauge data are accepted as reference observations (see for example Colli et al. (2013)), but represent point locations. Such point data can adjust weather radar grids through several geostatistic methods, reviewed and evaluated by Goovaerts (2000), Berndt et al. (2014) and McKee and Binns (2016). Kriging based methods have been examined by Kebaili Bargaoui and Chebbi (2009), Adhikary et al. (2017), and Ly et al. (2013). A comparison of various kriging methods where elevation was the secondary variable was done by Carrera-Hernández and Gaskin (2007). Another unique algorithm known as Conditional Merging, developed and evaluated by Sinclair and Pegram (2005), applies multiple kriging steps to achieve successful adjustment (Kim et al. (2007)) of weather radar grids.

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