Improving the MSGMPE Accuracy for the Northern of Tunisia by the Multispectral Analysis of the Cloud Field from MSG SEVIRI

Dhib, Saoussen; Mannaerts, Chris M.; Bargaoui, Zoubeïda

doi:10.1007/978-3-319-09300-0_23

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“…Another important scope is to evaluate the MW data TMI over Tunisia by combining it with IR temperature brightness. We get encouraging primary results for the instance of the IR brightness temperature performance to detect heavy rainfall (Dhib et al 2014). At the same time, we expect to understand the accuracy of TRMM 3B42 product from TMI data which is used in the first step of processing of TRMM.…”

Section: Discussionmentioning

confidence: 62%

Comparison of two bias correction methods for TRMM 3B42 satellite daily rainfall estimates over Northern Tunisia

et al. 2021

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The overall objective of this study is to evaluate and correct the Tropical Rainfall Measuring Mission (TRMM) 3B42 algorithm for Northern Tunisia focusing on heavy rainfall events. Two types of correction methods are tested. The first is the combined scheme (CoSch) which was applied in two different ways. CoSch (1) combines satellite data with interpolated in situ data. However, CoSch (2) combines satellite data with a not interpolated in situ map where the pixel value is randomly selected from the rainfall stations belonging this pixel. The second type of correction is the best linear unbiased estimator. The study period is from January 2007 to August 2009. The in situ database is composed of an average of 318 rain gauges. Heavy events are defined as those daily events exceeding 50 mm/day for at least one station. A total of 77 heavy rainfall events result from this selection criterion; 35 events were recorded during the dry period (May to October) and 42 during the wet season (November to April). We first investigate the boxplots of the various evaluation indicators for raw TRMM. The best achievement is for moderate events. The worst performance is for very light and light events. Moreover, we noticed that raw TRMM estimates perform better during wet season. The error decomposition underlined that the highest underestimated values are localized in orographic areas in Le Kef, also in Cap Bon. However, the rainfall overestimation appeared in the central part of the study area (Bizerte and Zaghouan). About the bias correction method comparison, CoSch (1) performance showed a stronger correction than the unbiased estimator which outperforms CoSch (2). As for raw TRMM, CoSch (1) reports better correlation during wet season. The correction of probability of detection (POD) is more important for the wet season reaching 0.9 by the CoSch (1) and unbiased estimator methods. The threat score (TS) coefficients are found not sensitive to the season whatever the correction method.

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Section: Discussionmentioning

confidence: 62%