Improving an Extreme Rainfall Detection System with GPM IMERG data

Mazzoglio, Paola; Laio, Francesco; Balbo, Simone; Boccardo, Piero; Disabato, Franca

doi:10.3390/rs11060677

Cited by 59 publications

(54 citation statements)

References 15 publications

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“…floods). This has been extensively discussed in previous research (Hollmann et al, 2013a;Mazzoglio et al, 2019) Technical comments 16. Comment: Sentence in line 128 requires revision.…”

Section: Interactive Commentmentioning

confidence: 74%

Response to Reviewer's Comments

Hamouda¹

2020

Preprint

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The authors would like to thank Dr. Husain Najafi for his valuable comments. We really appreciate the time invested in reviewing the manuscript, which indeed help to improve the quality of our paper. We have followed the comments carefully and responded to each comment or suggestion.The reviewer has touched upon important points that need to be addressed in our revision of the manuscript. The authors' strived to make sure that the applied methods in C1

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“…floods). This has been extensively discussed in previous research (Hollmann et al, 2013a;Mazzoglio et al, 2019) Technical comments 16. Comment: Sentence in line 128 requires revision.…”

Section: Interactive Commentmentioning

confidence: 74%

Response to Reviewer's Comments

Hamouda¹

2020

Preprint

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“…Then the interpolated precipitation at each station was In this study, the evaluations were conducted in three steps. In the first step, the performance of the three NWP models and SPEs in terms of capturing the spatial distribution of precipitation for the three flood events (17)(18)(19)(20)(21)(22)(24)(25)(26) March, and 31 March to 2 April 2019, respectively) were compared. The numerical forecast and SPE data have a 50 × 50 km and~10 × 10 km spatial resolution, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…In another study, Sunikumar et al demonstrated the ability of IMERG to follow the intraseasonal variability with minor differences observed in the maximum values of precipitation during the rainy season over Japan, Philippines, and Nepal [25]. Mazzoglio et al improved an extreme precipitation detection system using IMERG data and stated that this product guarantees good results when the precipitation aggregation interval is equal to or greater than 12 h [26]. However, no comparison has been made so far between the ensemble forecasts with IMERG product for extreme flood events over Iran.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Precipitation Estimation from the NWP Models and Satellite Products for the Spring 2019 Severe Floods in Iran

2019

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Precipitation monitoring and early warning systems are required to reduce negative flood impacts. In this study, the performance of ensemble precipitation forecasts of three numerical weather prediction (NWP) models within the THORPEX interactive grand global ensemble (TIGGE) as well as the integrated multi-satellite retrievals for global precipitation measurement (GPM), namely IMERG, for precipitation estimates were evaluated in recent severe floods in Iran over the March-April 2019 period. The evaluations were conducted in three aspects: spatial distribution of precipitation, mean areal precipitation in three major basins hard hit by the floods, and the dichotomous evaluation in four precipitation thresholds (25, 50, 75, and 100 mm per day). The results showed that the United Kingdom Met Office (UKMO) model, in terms of spatial coverage and satellite estimates as well as the precipitation amount, were closer to the observations. Moreover, with regard to mean precipitation at the basin scale, UKMO and European Center for Medium-Range Weather Forecasts (ECMWF) models in the Gorganrud Basin, ECMWF in the Karkheh Basin and UKMO in the Karun Basin performed better than others in flood forecasting. The National Centers for Environmental Forecast (NCEP) model performed well at low precipitation thresholds, while at high thresholds, its performance decreased significantly. On the contrary, the accuracy of IMERG improved when the precipitation threshold increased. The UKMO had better forecasts than the other models at the 100 mm/day precipitation threshold, whereas the ECMWF had acceptable forecasts in all thresholds and was able to forecast precipitation events with a lower false alarm ratio and better detection when compared to other models. disturbing the stratospheric polar vortex, which can lead to a breakdown of this circulation with the potential to also significantly impact the troposphere in mid-to late-winter and early spring [6,7].Iran's climate is generally semi-arid and is subject to frequent flooding, causing major damage to people and society. In spring 2019, major floods occurred almost concurrently in different parts of the country. The first flood event occurred in late winter to early spring 2019 in the northeastern provinces due to heavy precipitation over the March 17-22 period. In at least one station, over 280 mm of precipitation was recorded over the six-day period. The subsequent second and third flood events occurred in the March 24-26 and March 31-April 2 periods, respectively, where most of the precipitation fell in the southwest, causing widespread damage to the people and infrastructure while filling/causing an overflow of most reservoirs. The total economic cost of these floods is estimated to be $3.5 billion U.S. dollars. Studies on the causes of the March-April 2019 severe floods are still ongoing, although exceptional precipitation and climate change attribution are on the minds of most experts.Given the heavy negative impacts imposed by the 2019 flood events, the monitoring and forecas...

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“…The definition of the accuracy in representing extreme rainfall events for varying time aggregation intervals is one more problem. A study of Mazzoglio et al [241] concluded that satellite precipitation data guarantee good results when the rainfall aggregation interval is ≥12 h; a 24-h aggregation interval ensures a probability of detection >80%. At the same time, the results depend on the adopted spatial domain and require a multiscale approach to regional trend analysis [242].…”

Section: Observing Extremesmentioning

confidence: 99%

Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate

2019

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The water cycle is the most essential supporting physical mechanism ensuring the existence of life on Earth. Its components encompass the atmosphere, land, and oceans. The cycle is composed of evaporation, evapotranspiration, sublimation, water vapor transport, condensation, precipitation, runoff, infiltration and percolation, groundwater flow, and plant uptake. For a correct closure of the global water cycle, observations are needed of all these processes with a global perspective. In particular, precipitation requires continuous monitoring, as it is the most important component of the cycle, especially under changing climatic conditions. Passive and active sensors on board meteorological and environmental satellites now make reasonably complete data available that allow better measurements of precipitation to be made from space, in order to improve our understanding of the cycle’s acceleration/deceleration under current and projected climate conditions. The article aims to draw an up-to-date picture of the current status of observations of precipitation from space, with an outlook to the near future of the satellite constellation, modeling applications, and water resource management.

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Improving an Extreme Rainfall Detection System with GPM IMERG data

Cited by 59 publications

References 15 publications

Response to Reviewer's Comments

Response to Reviewer's Comments

Assessment of Precipitation Estimation from the NWP Models and Satellite Products for the Spring 2019 Severe Floods in Iran

Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate

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