Preprints
https://doi.org/10.5194/egusphere-2023-1258
https://doi.org/10.5194/egusphere-2023-1258
30 Aug 2023
 | 30 Aug 2023

Assessing sampling and retrieval errors of GPROF precipitation estimates over The Netherlands

Linda Bogerd, Hidde Leijnse, Aart Overeem, and Remko Uijlenhoet

Abstract. The Goddard Profiling algorithm (GPROF) converts radiometer observations aboard Global Precipitation Measurement (GPM) constellation satellites to precipitation estimates. Analyzing the accuracy of GPROF’s estimates is vital to further improve the algorithm. Such analyses often use high-quality ground-based estimates as reference with a different spatial resolution. Often, the reference is resampled to match the satellite’s resolution. However, the implemented sampling method to simulate the satellite’s resolution varies amongst studies, which limits the transferability of conclusions. additionally, GPROF combines observations from various sensors and frequency channels, each with its own footprint size. Hence, uncertainties related to sampling are added on top of the uncertainty introduced when converting brightness temperatures to precipitation intensities. The contribution of sampling to the total amount of uncertainty remains unknown.

Here, we quantify the uncertainty related to sampling while analyzing the current performance of GPROF over the Netherlands during a four year period (2017–2020). In this area, shallow and light precipitation frequently occur. Both precipitation types are often subject to research, as both types are difficult to detect with space-borne sensors. Only GPROF estimates based on observations from the conical-scanning radiometers of the GPM constellation are used. We investigate the uncertainty related to sampling by simulating the reference precipitation as satellite footprints that vary in size, geometry, and applied weighting technique. The reference estimates are gauge-adjusted radar precipitation estimates from two ground-based weather radars from the Royal Netherlands Meteorological Institute (KNMI). Echo top heights (ETH) retrieved from the same radars are used to classify the precipitation as shallow, medium, or deep.

The method used to spatially average the reference into a satellite footprint, i.e. using Gaussian weighting or the arithmetic mean, is found to exhibit a minimal influence on the retrieved estimate. The size of the sampled area is found to be the most influential. Still, the effect of using different footprint sizes cannot explain all the differences between the ground- and satellite based precipitation estimates. Additionally, the discrepancies between GPROF and the reference are largest for low ETH, while the relative bias between the different footprint sizes and implemented weighting methods increase with increasing ETH. Lastly, our results do not show a clear difference between coastal simulations and simulations over land. We conclude that the uncertainty introduced by merging different channels and sensors cannot fully explain the errors introduced by the retrieval algorithm. Hence, retrieval errors are found to be more prominent than sampling uncertainties, in particular for shallow and light precipitation.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.

Journal article(s) based on this preprint

15 Jan 2024
Assessing sampling and retrieval errors of GPROF precipitation estimates over the Netherlands
Linda Bogerd, Hidde Leijnse, Aart Overeem, and Remko Uijlenhoet
Atmos. Meas. Tech., 17, 247–259, https://doi.org/10.5194/amt-17-247-2024,https://doi.org/10.5194/amt-17-247-2024, 2024
Short summary
Linda Bogerd, Hidde Leijnse, Aart Overeem, and Remko Uijlenhoet

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-1258', Anonymous Referee #1, 20 Sep 2023
    • AC1: 'Reply on RC1', Linda Bogerd, 06 Nov 2023
  • RC2: 'Comment on egusphere-2023-1258', T.H.M. Rientjes, 23 Oct 2023
    • AC2: 'Reply on RC2', Linda Bogerd, 06 Nov 2023

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-1258', Anonymous Referee #1, 20 Sep 2023
    • AC1: 'Reply on RC1', Linda Bogerd, 06 Nov 2023
  • RC2: 'Comment on egusphere-2023-1258', T.H.M. Rientjes, 23 Oct 2023
    • AC2: 'Reply on RC2', Linda Bogerd, 06 Nov 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Linda Bogerd on behalf of the Authors (10 Nov 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Publish subject to technical corrections (20 Nov 2023) by Marloes Penning de Vries
AR by Linda Bogerd on behalf of the Authors (20 Nov 2023)  Author's response   Manuscript 

Post-review adjustments

AA: Author's adjustment | EA: Editor approval
AA by Linda Bogerd on behalf of the Authors (05 Jan 2024)   Author's adjustment   Manuscript
EA: Adjustments approved (15 Jan 2024) by Marloes Penning de Vries

Journal article(s) based on this preprint

15 Jan 2024
Assessing sampling and retrieval errors of GPROF precipitation estimates over the Netherlands
Linda Bogerd, Hidde Leijnse, Aart Overeem, and Remko Uijlenhoet
Atmos. Meas. Tech., 17, 247–259, https://doi.org/10.5194/amt-17-247-2024,https://doi.org/10.5194/amt-17-247-2024, 2024
Short summary
Linda Bogerd, Hidde Leijnse, Aart Overeem, and Remko Uijlenhoet
Linda Bogerd, Hidde Leijnse, Aart Overeem, and Remko Uijlenhoet

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Short summary
Accurate and uniformly distributed precipitation estimates are crucial for applications like weather forecasts and flood early water systems. Ground-based observations are reliable but have limited spatial coverage and representation. Sensors aboard satellites are able to overcome this limitation, but are not as accurate as those derived from ground-based sensors. We studied the performance of space-based precipitation estimates to highlight in which situations improvements are needed.