2018
DOI: 10.5194/amt-11-4389-2018
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Towards variational retrieval of warm rain from passive microwave observations

Abstract: Abstract. An experimental retrieval of oceanic warm rain is presented, extending a previous variational algorithm to provide a suite of retrieved variables spanning non-raining through predominantly warm raining conditions. The warm rain retrieval is underpinned by hydrometeor covariances and drizzle onset data derived from CloudSat. Radiative transfer modelling and analysis of drop size variability from disdrometer observations permit state-dependent observation error covariances that scale with columnar rain… Show more

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Cited by 9 publications
(9 citation statements)
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“…These errors can be strongly correlated across nearby frequencies and can thus cause systematic biases in variational systems (e.g., 1DVAR, 3DVAR) if not taken into account. An example of including this type of forward model error into a variational rainfall retrieval for GPM was presented by Duncan et al (2018). Instead, the focus in this section is investigating the extent of forward model response inherent to variations in natural drop populations, without fitting a functional form to the observed drop counts.…”
Section: Radiative Transfer Impactsmentioning
confidence: 99%
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“…These errors can be strongly correlated across nearby frequencies and can thus cause systematic biases in variational systems (e.g., 1DVAR, 3DVAR) if not taken into account. An example of including this type of forward model error into a variational rainfall retrieval for GPM was presented by Duncan et al (2018). Instead, the focus in this section is investigating the extent of forward model response inherent to variations in natural drop populations, without fitting a functional form to the observed drop counts.…”
Section: Radiative Transfer Impactsmentioning
confidence: 99%
“…Forward model simulations of the radiative transfer were performed using the Atmospheric Radiative Transfer Simulator (ARTS) version 2.3 (Eriksson et al, 2011;Buehler et al, 2018). The ARTS model can handle custom particle size distributions (such as observational size bin data) as well as prescribed DSDs such as the MGD.…”
Section: Radiative Transfer Impactsmentioning
confidence: 99%
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“…For example, since an inversion of 14 channels for an observation space of 20 scans with 25 pixels each constitutes an observation vector (y) of 7000 elements, this is not trivial. Whereas some studies include covariances between co-registered observations due to correlated forward model errors (Weston et al, 2014;Bormann et al, 2016;Duncan et al, 2018), it is unclear whether sensor noise or other errors have any spatial correlations. The nature of sensor noise and its assumed characteristics pixel to pixel, i.e.…”
Section: Retrieval Setupmentioning
confidence: 99%
“…These errors can be strongly correlated across nearby frequencies and can thus cause systematic biases in variational systems if not taken into account. An example of including this type of forward model error into a variational rainfall retrieval for GPM was presented by Duncan et al (2018). Instead, the focus in this section is investigating the extent of forward model response inherent to variations in natural drop populations, without fitting a functional form to the observed drop counts.…”
Section: Radiative Transfer Impactsmentioning
confidence: 99%