Towards an along‐track validation of HOAPS precipitation using OceanRAIN optical disdrometer data over the Atlantic Ocean

Burdanowitz, Jörg; Klepp, Christian; Bakan, Stephan; Buehler, Stefan A.

doi:10.1002/qj.3248

Cited by 10 publications

(6 citation statements)

References 35 publications

(51 reference statements)

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“…For OceanRAIN, the 99th percentile of 1 min precipitation rates shows a super CC scaling of almost 9 % K −1 in line with studies over land (e.g., Bürger et al, 2014;Schroeer and Kirchengast, 2018), while for ERA5 the 99th percentile of the hourly ERA5 precipitation rates increases by only 4.5 % K −1 (linear regression using Theil-Sen estimator). The tendency of a stronger scaling with higher temporal sampling confirms findings by Utsumi et al (2011) and Drobinski et al (2016).…”

Section: Summary and Concluding Remarkssupporting

confidence: 85%

The sensitivity of oceanic precipitation to sea surface temperature

et al. 2019

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Abstract. Our study forms the oceanic counterpart to numerous observational studies over land concerning the sensitivity of extreme precipitation to a change in air temperature. We explore the sensitivity of oceanic precipitation to changing sea surface temperature (SST) by exploiting two novel datasets at high resolution. First, we use the Ocean Rainfall And Ice-phase precipitation measurement Network (OceanRAIN) as an observational along-track shipboard dataset at 1 min resolution. Second, we exploit the most recent European Reanalysis version 5 (ERA5) at hourly resolution on a 31 km grid. Matched with each other, ERA5 vertical velocity allows the constraint of the OceanRAIN precipitation. Despite the inhomogeneous sampling along ship tracks, OceanRAIN agrees with ERA5 on the average latitudinal distribution of precipitation with fairly good seasonal sampling. However, the 99th percentile of OceanRAIN precipitation follows a super Clausius–Clapeyron scaling with a SST that exceeds 8.5 % K−1 while ERA5 precipitation scales with 4.5 % K−1. The sensitivity decreases towards lower precipitation percentiles, while OceanRAIN keeps an almost constant offset to ERA5 due to higher spatial resolution and temporal sampling. Unlike over land, we find no evidence for a decreasing precipitation event duration with increasing SST. ERA5 precipitation reaches a local minimum at about 26 ∘C that vanishes when constraining vertical velocity to strongly rising motion and excluding areas of weak correlation between precipitation and vertical velocity. This indicates that instead of moisture limitations as over land, circulation dynamics rather limit precipitation formation over the ocean. For the strongest rising motion, precipitation scaling converges to a constant value at all precipitation percentiles. Overall, high resolutions in observations and climate models are key to understanding and predicting the sensitivity of oceanic precipitation extremes to a change in SST.

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Section: Summary and Concluding Remarkssupporting

confidence: 85%

The sensitivity of oceanic precipitation to sea surface temperature

et al. 2019

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“…This may be attributed to the similar nature of satellite-based products and to the pixel-to-pixel comparison (with respect to the point-to-pixel comparison used in the OceanRain analysis). Although the along-track averaged precipitation of OceanRAIN provides better representation of the areal extent of satellite measurements, the track-to-area difference could be minimized by statistical adjustments as proposed by Burdanowitz et al [20]. Overall, this does not point to a conclusive evidence as to which reference (OceanRAIN or 3DPRD) should be used for validating the IMERG products.…”

Section: Discussionmentioning

confidence: 98%

“…IMERG V03 has been evaluated against OceanRAIN precipitation and showed an underestimation of shallow tropical rainfall [19]. Another recent study used OceanRAIN precipitation data for evaluating the HOAPS (Hamburg Ocean Atmosphere Parameters and fluxes from Satellite data) precipitation product across the Atlantic Ocean [20]. This study highlighted that the differences between HOAPS and OceanRAIN are governed by the point-to-area (along track-pixel) effect rather than the precipitation regime itself.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Level-3 Gridded Global Precipitation Mission (GPM) Products Over Oceans

Khan¹,

Maggioni²

2019

Remote Sensing

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The performance of Level-3 gridded Global Precipitation Mission (GPM)-based precipitation products (IMERG, Integrated Multi-satellite Retrievals for GPM) is assessed against two references over oceans: the OceanRAIN dataset, derived from oceanic shipboard disdrometers, and a satellite-based radar product (the Level-3 Dual-frequency Precipitation Radar, 3DPRD). Daily IMERG products (early, late, final) and microwave-only (MW) and Infrared-only (IR) precipitation components are evaluated at four different spatial resolutions (0.5 • , 1 • , 2 • , and 3 • ) during a 3-year study period (March 2014-February 2017). Their performance is assessed based on both categorical and continuous performance metrics, including correlation coefficient, probability of detection, success ratio, bias, and root mean square error (RMSE). A triple collocation analysis (TCA) is also presented to further investigate the performance of these satellite-based products. Overall, the IMERG products show an underestimation with respect to OceanRAIN. Rain events in OceanRAIN are correctly detected by all IMERG products~80% of the times. IR estimates show relatively large errors and low correlations with OceanRAIN compared to the other products. On the other hand, the MW component performs better than other products in terms of both categorical and continuous statistics. TCA reveals that 3DPRD performs consistently better than OceanRAIN in terms of RMSE and coefficient of determination at all spatial resolutions. This work is part of a larger effort to validate GPM products over nontraditional regions such as oceans.

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“…No attempt was made to match observations exactly in space and time due to the difficulty of point-to-area comparisons with ship-borne data and GPM (Burdanowitz et al, 2018;Loew et al, 2017). The right panel of Fig.…”

Section: Comparison To Gpmmentioning

confidence: 99%

On the distinctiveness of oceanic raindrop regimes

Duncan

Eriksson

Pfreundschuh

et al. 2019

Preprint

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<p><strong>Abstract.</strong> Representation of the drop size distribution (DSD) of rainfall is a key element of characterizing precipitation in models and retrievals, with a functional form necessary to calculate the precipitation flux and the drops' interaction with radiation. With newly available oceanic disdrometer measurements, this study investigates the validity of commonly used DSDs, potentially useful a priori constraints for retrievals, and the forward model errors caused by DSD variability. These data are also compared to leading satellite-based estimates of oceanic DSDs. Forward model errors due to DSD variability are shown to be significant for both active and passive sensors. The modified gamma distribution is found to be generally adequate to describe rain DSDs, but may cause systematic errors for high latitude or stratocumulus rain retrievals; depending on the application, an exponential or generalized gamma function may be preferable for representing oceanic DSDs. An unsupervised classification algorithm finds a variety of DSD shapes that differ from commonly used DSDs, but does not find a singular set that best describes the global variability. Finally, DSD shapes are found to be not particularly distinctive of regional or large-scale environments, but rather occur at varying frequencies over the global oceans.</p>

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Towards an along‐track validation of HOAPS precipitation using OceanRAIN optical disdrometer data over the Atlantic Ocean

Cited by 10 publications

References 35 publications

The sensitivity of oceanic precipitation to sea surface temperature

The sensitivity of oceanic precipitation to sea surface temperature

Assessment of Level-3 Gridded Global Precipitation Mission (GPM) Products Over Oceans

On the distinctiveness of oceanic raindrop regimes

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