Spatiotemporal Trends and Variations of the Rainfall Amount, Intensity, and Frequency in TRMM Multi-satellite Precipitation Analysis (TMPA) Data

The spatiotemporal mean rain rate (MR) can be characterized by the rain frequency (RF) and the conditional rain rate (CR). We computed these parameters for each season using the TMPA 3-hourly, 0.25° gridded data for the 1998–2017 period at a quasi-global scale, 50°N~50°S. For the global long-term av...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Qian Liu, Long S. Chiu, Xianjun Hao, Chaowei Yang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	precipitation rain frequency mean rain rate conditional rain rate EOF analysis Science Q
Acceso en línea:	https://doaj.org/article/47382ce0699f4eca9579cb105cd6342a
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:47382ce0699f4eca9579cb105cd6342a
record_format	dspace
spelling	oai:doaj.org-article:47382ce0699f4eca9579cb105cd6342a2021-11-25T18:54:53ZSpatiotemporal Trends and Variations of the Rainfall Amount, Intensity, and Frequency in TRMM Multi-satellite Precipitation Analysis (TMPA) Data10.3390/rs132246292072-4292https://doaj.org/article/47382ce0699f4eca9579cb105cd6342a2021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/22/4629https://doaj.org/toc/2072-4292The spatiotemporal mean rain rate (MR) can be characterized by the rain frequency (RF) and the conditional rain rate (CR). We computed these parameters for each season using the TMPA 3-hourly, 0.25° gridded data for the 1998–2017 period at a quasi-global scale, 50°N~50°S. For the global long-term average, MR, RF, and CR are 2.83 mm/d, 10.55%, and 25.05 mm/d, respectively. The seasonal time series of global mean RF and CR show significant decreasing and increasing trends, respectively, while MR depicts only a small but significant trend. The seasonal anomaly of RF decreased by 5.29% and CR increased 13.07 mm/d over the study period, while MR only slightly decreased by −0.029 mm/day. The spatiotemporal patterns in MR, RF, and CR suggest that although there is no prominent trend in the total precipitation amount, the frequency of rainfall events becomes smaller and the average intensity of a single event becomes stronger. Based on the co-variability of RF and CR, the paper optimally classifies the precipitation over land and ocean into four categories using K-means clustering. The terrestrial clusters are consistent with the dry and wet climatology, while categories over the ocean indicate high RF and medium CR in the Inter Tropical Convergence Zone (ITCZ) region; low RF with low CR in oceanic dry zones; and low RF and high CR in storm track areas. Empirical Orthogonal Function (EOF) analysis was then performed, and these results indicated that the major pattern of MR is characterized by an El Niño-Southern Oscillation (ENSO) signal while RF and CR variations are dominated by their trends.Qian LiuLong S. ChiuXianjun HaoChaowei YangMDPI AGarticleprecipitationrain frequencymean rain rateconditional rain rateEOF analysisScienceQENRemote Sensing, Vol 13, Iss 4629, p 4629 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	precipitation rain frequency mean rain rate conditional rain rate EOF analysis Science Q
spellingShingle	precipitation rain frequency mean rain rate conditional rain rate EOF analysis Science Q Qian Liu Long S. Chiu Xianjun Hao Chaowei Yang Spatiotemporal Trends and Variations of the Rainfall Amount, Intensity, and Frequency in TRMM Multi-satellite Precipitation Analysis (TMPA) Data
description	The spatiotemporal mean rain rate (MR) can be characterized by the rain frequency (RF) and the conditional rain rate (CR). We computed these parameters for each season using the TMPA 3-hourly, 0.25° gridded data for the 1998–2017 period at a quasi-global scale, 50°N~50°S. For the global long-term average, MR, RF, and CR are 2.83 mm/d, 10.55%, and 25.05 mm/d, respectively. The seasonal time series of global mean RF and CR show significant decreasing and increasing trends, respectively, while MR depicts only a small but significant trend. The seasonal anomaly of RF decreased by 5.29% and CR increased 13.07 mm/d over the study period, while MR only slightly decreased by −0.029 mm/day. The spatiotemporal patterns in MR, RF, and CR suggest that although there is no prominent trend in the total precipitation amount, the frequency of rainfall events becomes smaller and the average intensity of a single event becomes stronger. Based on the co-variability of RF and CR, the paper optimally classifies the precipitation over land and ocean into four categories using K-means clustering. The terrestrial clusters are consistent with the dry and wet climatology, while categories over the ocean indicate high RF and medium CR in the Inter Tropical Convergence Zone (ITCZ) region; low RF with low CR in oceanic dry zones; and low RF and high CR in storm track areas. Empirical Orthogonal Function (EOF) analysis was then performed, and these results indicated that the major pattern of MR is characterized by an El Niño-Southern Oscillation (ENSO) signal while RF and CR variations are dominated by their trends.
format	article
author	Qian Liu Long S. Chiu Xianjun Hao Chaowei Yang
author_facet	Qian Liu Long S. Chiu Xianjun Hao Chaowei Yang
author_sort	Qian Liu
title	Spatiotemporal Trends and Variations of the Rainfall Amount, Intensity, and Frequency in TRMM Multi-satellite Precipitation Analysis (TMPA) Data
title_short	Spatiotemporal Trends and Variations of the Rainfall Amount, Intensity, and Frequency in TRMM Multi-satellite Precipitation Analysis (TMPA) Data
title_full	Spatiotemporal Trends and Variations of the Rainfall Amount, Intensity, and Frequency in TRMM Multi-satellite Precipitation Analysis (TMPA) Data
title_fullStr	Spatiotemporal Trends and Variations of the Rainfall Amount, Intensity, and Frequency in TRMM Multi-satellite Precipitation Analysis (TMPA) Data
title_full_unstemmed	Spatiotemporal Trends and Variations of the Rainfall Amount, Intensity, and Frequency in TRMM Multi-satellite Precipitation Analysis (TMPA) Data
title_sort	spatiotemporal trends and variations of the rainfall amount, intensity, and frequency in trmm multi-satellite precipitation analysis (tmpa) data
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/47382ce0699f4eca9579cb105cd6342a
work_keys_str_mv	AT qianliu spatiotemporaltrendsandvariationsoftherainfallamountintensityandfrequencyintrmmmultisatelliteprecipitationanalysistmpadata AT longschiu spatiotemporaltrendsandvariationsoftherainfallamountintensityandfrequencyintrmmmultisatelliteprecipitationanalysistmpadata AT xianjunhao spatiotemporaltrendsandvariationsoftherainfallamountintensityandfrequencyintrmmmultisatelliteprecipitationanalysistmpadata AT chaoweiyang spatiotemporaltrendsandvariationsoftherainfallamountintensityandfrequencyintrmmmultisatelliteprecipitationanalysistmpadata
_version_	1718410520825954304

Spatiotemporal Trends and Variations of the Rainfall Amount, Intensity, and Frequency in TRMM Multi-satellite Precipitation Analysis (TMPA) Data

Ejemplares similares