@article{fdi:010060426,
  title = {{A}n error model for instantaneous satellite rainfall estimates : evaluation of {BRAIN}-{TMI} over {W}est {A}frica},
  author = {{K}irstetter, {P}. {E}. and {V}iltard, {N}. and {G}osset, {M}arielle},
  editor = {},
  language = {{ENG}},
  abstract = {{C}haracterising the error associated with satellite rainfall estimates based on space-borne passive and active microwave measurements is a major issue for many applications, such as water budget studies or assessment of natural hazards caused by extreme rainfall events. {W}e focus here on the error structure of the {B}ayesian {R}ain retrieval {A}lgorithm {I}ncluding {N}eural {N}etwork ({BRAIN}), the algorithm that provides instantaneous quantitative precipitation estimates at the surface based on the {MADRAS} radiometer on board the {M}egha-{T}ropiques satellite. {A} version of {BRAIN} using data from the {T}ropical {R}ainfall {M}easuring {M}ission ({TRMM}) {M}icrowave {I}mager ({TMI}) has been compared to reference values derived either from {TRMM} {P}recipitation {R}adar ({PR}) or from a ground validation ({GV}) dataset. {T}he ground-based measurements were provided by two densified rain-gauge networks in {W}est {A}frica, using a geostatistical framework. {T}he comparisons were carried out at the {BRAIN} retrieval scale for {TMI} (instantaneous and 12.5 km) and over a ten-year-long period. {T}he primary contribution of this study is to provide some insight into the most significant error sources of satellite rainfall retrieval. {T}his involves comparisons of rainfall detectability, distributions and spatial representativeness, as well as separation of systematic biases and random errors using {G}eneralized {A}dditive {M}odels for {L}ocation, {S}cale and {S}hape. {I}n spite of their different sampling properties, the three rain estimates were found to detect rainfall consistently. {T}he most important {BRAIN}-{TMI} error is due to the rain/no-rain delimitation which causes about 20% of volume rainfall loss relative to {PR} and {GV}. {BRAIN}-{TMI} presents a narrow {PDF} relative to {GV} and catches the spatial structure of the most active part of rain fields. {T}he conditional bias is significant (e.g. +2 mm h-1 for light-moderate rain rates, -2 mm h-1 for rain rates greater than 8 mm h-1) and the overall bias is within 10%. {T}he {PR} shows a significant underestimation for high rain rates with respect to {GV}. {T}he proposed framework could be applied to the evaluation of other passive microwave sensors ({SSMI}, {AMSR}-{E} or {MADRAS}) or rainfall satellite products.},
  keywords = {satellite-based rain estimation ; {QPE} ; conditional bias ; geostatistics ; rain-gauge ; radiometry ; {W}est {A}frican {M}onsoon ; {AFRIQUE} {DE} {L}'{OUEST}},
  booktitle = {},
  journal = {{Q}uarterly {J}ournal of the {R}oyal {M}eteorological {S}ociety},
  volume = {139},
  numero = {673},
  pages = {894--911},
  ISSN = {0035-9009},
  year = {2013},
  DOI = {10.1002/qj.1964},
  URL = {https://www.documentation.ird.fr/hor/fdi:010060426},
}