Intense precipitation events during the monsoon season in Bangladesh as captured by satellite-based products

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  • 1 International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
  • | 2 International Research Institute for Climate and Society (IRI), The Earth Institute, Columbia University, Palisades NY, U.S.A.
  • | 3 Bangladesh Meteorological Department, Dhaka, Bangladesh
  • | 4 International Maize and Wheat Improvement Center (CIMMYT), Dhaka, Bangladesh
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Abstract

Extreme precipitation events are a serious threat to societal well-being over rainy areas such as Bangladesh. The reliability of studies of extreme events depends on data quality and their spatial and temporal distribution, although these subjects remain knowledge gaps in many countries. This work focuses on the analysis of four satellite-based precipitation products for monitoring intense rainfall events: the Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS), the PERSIANN-Climate Data Record (PERSIANN-CDR), the Integrated Multisatellite Retrievals (IMERG), and the CPC Morphing Technique (CMORPH). Five indices of intense rainfall were considered for the period 2000-2019 and a set of 31 rain gauges for evaluation. The number and amount of precipitation associated with intense rainfall events are systematically underestimated or overestimated throughout the country. While random errors are higher over the wetter and higher-elevation north- and southeastern parts of Bangladesh, biases are more homogeneous. CHIRPS, PERSIANN-CDR and IMERG perform similar capturing total seasonal rainfall, but variability is better represented by CHIRPS and IMERG. Better results were obtained by IMERG, followed by PERSIANN-CDR and CHIRPS, in terms of climatological intensity indices based on percentiles, although the three products exhibited systematic errors. IMERG and CMORPH systematically overestimate the occurrence of intense precipitation events. IMERG showed the best performance representing events over a value of 20 mm/day; CMORPH exhibited random and systematic errors strongly associated with a poor representation of interannual variability in seasonal total rainfall. The results suggest that the datasets have different potential use and such differences should be considered in future applications regarding extreme rainfall events and risk assessment in Bangladesh.

Corresponding author email: c.montes@cgiar.org

Abstract

Extreme precipitation events are a serious threat to societal well-being over rainy areas such as Bangladesh. The reliability of studies of extreme events depends on data quality and their spatial and temporal distribution, although these subjects remain knowledge gaps in many countries. This work focuses on the analysis of four satellite-based precipitation products for monitoring intense rainfall events: the Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS), the PERSIANN-Climate Data Record (PERSIANN-CDR), the Integrated Multisatellite Retrievals (IMERG), and the CPC Morphing Technique (CMORPH). Five indices of intense rainfall were considered for the period 2000-2019 and a set of 31 rain gauges for evaluation. The number and amount of precipitation associated with intense rainfall events are systematically underestimated or overestimated throughout the country. While random errors are higher over the wetter and higher-elevation north- and southeastern parts of Bangladesh, biases are more homogeneous. CHIRPS, PERSIANN-CDR and IMERG perform similar capturing total seasonal rainfall, but variability is better represented by CHIRPS and IMERG. Better results were obtained by IMERG, followed by PERSIANN-CDR and CHIRPS, in terms of climatological intensity indices based on percentiles, although the three products exhibited systematic errors. IMERG and CMORPH systematically overestimate the occurrence of intense precipitation events. IMERG showed the best performance representing events over a value of 20 mm/day; CMORPH exhibited random and systematic errors strongly associated with a poor representation of interannual variability in seasonal total rainfall. The results suggest that the datasets have different potential use and such differences should be considered in future applications regarding extreme rainfall events and risk assessment in Bangladesh.

Corresponding author email: c.montes@cgiar.org
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