2022
DOI: 10.1029/2022jd036796
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A Characterization of Clouds and Precipitation Over the Southern Ocean From Synoptic to Micro Scales During the CAPRICORN Field Campaigns

Abstract: The Southern Ocean (SO) is a region of significant interest for its capacity to store excess heat and carbon. Yet large shortwave radiative biases over the SO continue to exist in both climate models and reanalysis products, which are primarily attributed to the poor representation of clouds, precipitation, aerosols, and their interactions in this region (

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Cited by 6 publications
(14 citation statements)
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“…Montoya Duque et al. (2023), further use variables: “IRprecipitation” and “HQprecipitation” (PMW precipitation is defined as High‐Quality, HQ, precipitation, Huffman et al. (2019)) to deduce whether the rain gauge calibrated precipitation rate estimates (precipCal) use the morphing‐only or the PMW‐only information.…”
Section: Observationsmentioning
confidence: 99%
See 3 more Smart Citations
“…Montoya Duque et al. (2023), further use variables: “IRprecipitation” and “HQprecipitation” (PMW precipitation is defined as High‐Quality, HQ, precipitation, Huffman et al. (2019)) to deduce whether the rain gauge calibrated precipitation rate estimates (precipCal) use the morphing‐only or the PMW‐only information.…”
Section: Observationsmentioning
confidence: 99%
“…Montoya Duque et al. (2023), constructed the Fractional Skill Score (FSS) based on the OceanPOL (e.g., C‐band radar) against the ERA5 and GPM (IMERG) data and found that ERA5 has better FSS scores than IMERG at large spatial scales for precipitation thresholds between 0.07 and 0.6 mm hr −1 . They also noted that ERA5 commonly overestimates the frequency of precipitation and underestimates the intensity of precipitation over the SO.…”
Section: Observationsmentioning
confidence: 99%
See 2 more Smart Citations
“…Precipitation estimates from reanalysis products strongly depend on model parameterizations and are at scales that do not resolve key processes and, therefore, potentially inherit the climate model biases over the region (Lang et al., 2018; Naud et al., 2014). Further, the reanalysis and satellite estimates do not agree with each other and have large observed errors (Montoya Duque et al., 2023).…”
Section: Introductionmentioning
confidence: 99%