Version 1
: Received: 22 May 2023 / Approved: 23 May 2023 / Online: 23 May 2023 (05:36:57 CEST)
How to cite:
Altoom, M.B.; Adam, E.; Ali, K.A. Evaluating Current Satellite Sensors in Capturing the Spatio-temporal Rainfall Variability across North Darfur State, Sudan. Preprints2023, 2023051586. https://doi.org/10.20944/preprints202305.1586.v1
Altoom, M.B.; Adam, E.; Ali, K.A. Evaluating Current Satellite Sensors in Capturing the Spatio-temporal Rainfall Variability across North Darfur State, Sudan. Preprints 2023, 2023051586. https://doi.org/10.20944/preprints202305.1586.v1
Altoom, M.B.; Adam, E.; Ali, K.A. Evaluating Current Satellite Sensors in Capturing the Spatio-temporal Rainfall Variability across North Darfur State, Sudan. Preprints2023, 2023051586. https://doi.org/10.20944/preprints202305.1586.v1
APA Style
Altoom, M.B., Adam, E., & Ali, K.A. (2023). Evaluating Current Satellite Sensors in Capturing the Spatio-temporal Rainfall Variability across North Darfur State, Sudan. Preprints. https://doi.org/10.20944/preprints202305.1586.v1
Chicago/Turabian Style
Altoom, M.B., Elhadi Adam and Khalid Adem Ali. 2023 "Evaluating Current Satellite Sensors in Capturing the Spatio-temporal Rainfall Variability across North Darfur State, Sudan" Preprints. https://doi.org/10.20944/preprints202305.1586.v1
Abstract
Accurate rainfall measurement is vital when investigating spatial and temporal precipitation variability at different scales. However, there are many regions around the world, such as North Darfur State in Sudan, where ground-based observations are few. Satellite-based precipitation products can fill such regions' spatial and temporal rainfall data gaps. Six satellite rainfall prod-ucts, namely the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), African Rainfall Climatology Version 2 (ARC2.0), Climate Hazards Group In-frared Precipitation with Station Data (CHIRPS2.0), the Integrated Multi-satellitE Retrievals for Global Precipitation Measurements (GPM) Final Run v 6 (GPM IMERG6), Precipitation Estima-tion from Remote Sensing Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), and the Tropical Applications of Meteorology using SATellite and ground-based observations (TAMSAT) v3.1 were evaluated to assess their accuracy in estimating rainfall amounts variability trends in the study area. The global-based satellite rainfall products were assessed at monthly and annual time scales by applying a point-to-pixel comparison with ground-based rain gauge data for the period 2000–2019. Based on the overall statistical results at monthly and temporal yearly scales, five satellite precipitation products (TMPA, CHIRPS, GPM IMERG6, PERSIANN-CDR, and TAMSATv3.1) overestimated rainfall amounts by values ranging from 1.49% to 82.69%. In contrast, the ARC2 product underestimated rainfall amounts by values ranging from-16.9% to-20.25%. The TAMSATv3.1, CHIRPS, and TMPA performed relatively better, showing stronger correlations and higher values of Nash-Sutcliffe efficiency. This study showed that the TAMSATv3.1 and CHIRPS products could reasonably estimate rainfall amounts in the North Darfur State.
Keywords
Rain gauge; observations; satellite-based precipitation; spatio-temporal; TMPA; CHIRPS; ARC2; and North Darfur State; Sudan
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
