Articles | Volume 17, issue 1
https://doi.org/10.5194/amt-17-247-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-17-247-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 Jan 2024
Research article |
| 15 Jan 2024
| 15 Jan 2024
Assessing sampling and retrieval errors of GPROF precipitation estimates over the Netherlands
Hydrology and Environmental Hydraulics Group, Wageningen University and Research, Wageningen, the Netherlands
R&D Observations and Data Technology, Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands
Hidde Leijnse
R&D Observations and Data Technology, Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands
Aart Overeem
R&D Observations and Data Technology, Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands
Department of Water Management, Faculty of Civil Engineering & Geosciences, Delft University of Technology, Delft, the Netherlands
Remko Uijlenhoet
Department of Water Management, Faculty of Civil Engineering & Geosciences, Delft University of Technology, Delft, the Netherlands
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Xuan Chen, Job Augustijn van der Werf, Arjan Droste, Miriam Coenders-Gerrits, and Remko Uijlenhoet
Hydrol. Earth Syst. Sci., 29, 3447–3480, https://doi.org/10.5194/hess-29-3447-2025, https://doi.org/10.5194/hess-29-3447-2025, 2025
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The review highlights the need to integrate urban land surface and hydrological models to better predict and manage compound climate events in cities. We find that inadequate representation of water surfaces, hydraulic systems and detailed building representations are key areas for improvement in future models. Coupled models show promise but face challenges at regional and neighbourhood scales. Interdisciplinary communication is crucial to enhance urban hydrometeorological simulations.
Claudia C. Brauer, Ruben O. Imhoff, and Remko Uijlenhoet
EGUsphere, https://doi.org/10.5194/egusphere-2025-1712, https://doi.org/10.5194/egusphere-2025-1712, 2025
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In lowland catchments, flood severity is determined by both the amount of rain and how wet the soil is prior to the rain event. We investigated the trade-off between these two factors and how this affects peaks in the river discharge, for both the current and future climate. We found that with climate change floods will increase in winter and spring, but decease in fall. The total number and severity of floods will increase. This can help water managers to design climate robust water management.
Nathalie Rombeek, Markus Hrachowitz, and Remko Uijlenhoet
EGUsphere, https://doi.org/10.5194/egusphere-2025-1502, https://doi.org/10.5194/egusphere-2025-1502, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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On 29 October 2024 Valencia (Spain) was struck by torrential rainfall, triggering devastating floods in this area. In this study, we quantify and describe the spatial and temporal structure of this rainfall event using personal weather stations (PWSs). These PWSs provide near real-time observations at a temporal resolution of ~5 min. This study shows the potential of PWSs for real-time rainfall monitoring and potentially flood early warning systems by complementing dedicated rain gauge networks.
Luuk D. van der Valk, Oscar K. Hartogensis, Miriam Coenders-Gerrits, Rolf W. Hut, and Remko Uijlenhoet
EGUsphere, https://doi.org/10.5194/egusphere-2025-1128, https://doi.org/10.5194/egusphere-2025-1128, 2025
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Commercial microwave links (CMLs), part of mobile phone networks, transmit comparable signals as instruments specially designed to estimate evaporation. Therefore, we investigate if CMLs could be used to estimate evaporation, even though they have not been designed for this purpose. Our results illustrate the potential of using CMLs to estimate evaporation, especially given their global coverage, but also outline some major drawbacks, often a consequence of unfavourable design choices for CMLs.
Aart Overeem, Hidde Leijnse, Mats Veldhuizen, and Bastiaan Anker
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-160, https://doi.org/10.5194/essd-2025-160, 2025
Revised manuscript accepted for ESSD
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The Dutch real-time gauge-adjusted radar product provides 5 min precipitation accumulations every 5 min covering the Netherlands and the area around it. It plays a key role in hydrological decision-support systems and as input for short-term weather forecasts. Major changes were implemented on 31 January 2023 and the associated quality improvement is presented. Moreover, the employed radar and rain gauge datasets and the algorithms needed to produce this real-time radar product are described.
Luuk D. van der Valk, Oscar K. Hartogensis, Miriam Coenders-Gerrits, Rolf W. Hut, Bas Walraven, and Remko Uijlenhoet
EGUsphere, https://doi.org/10.5194/egusphere-2024-2974, https://doi.org/10.5194/egusphere-2024-2974, 2025
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Commercial microwave links (CMLs), part of mobile phone networks, transmit comparable signals as instruments specially designed to estimate evaporation. Therefore, we investigate if CMLs could be used to estimate evaporation, even though they have not been designed for this purpose. Our results illustrate the potential of using CMLs to estimate evaporation, especially given their global coverage, but also outline some major drawbacks, often a consequence of unfavourable design choices for CMLs.
Nathalie Rombeek, Markus Hrachowitz, Arjan Droste, and Remko Uijlenhoet
EGUsphere, https://doi.org/10.5194/egusphere-2024-3207, https://doi.org/10.5194/egusphere-2024-3207, 2024
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Rain gauge networks from personal weather stations (PWSs) have a network density 100 times higher than dedicated rain gauge networks in the Netherlands. However, PWSs are prone to several sources of error, as they are generally not installed and maintained according to international guidelines. This study systematically quantifies and describes the uncertainties arising from PWS rainfall estimates. In particular, the focus is on the highest rainfall accumulations.
Abbas El Hachem, Jochen Seidel, Tess O'Hara, Roberto Villalobos Herrera, Aart Overeem, Remko Uijlenhoet, András Bárdossy, and Lotte de Vos
Hydrol. Earth Syst. Sci., 28, 4715–4731, https://doi.org/10.5194/hess-28-4715-2024, https://doi.org/10.5194/hess-28-4715-2024, 2024
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This study presents an overview of open-source quality control (QC) algorithms for rainfall data from personal weather stations (PWSs). The methodology and usability along technical and operational guidelines for using every QC algorithm are presented. All three QC algorithms are available for users to explore in the OpenSense sandbox. They were applied in a case study using PWS data from the Amsterdam region in the Netherlands. The results highlight the necessity for data quality control.
Athanasios Tsiokanos, Martine Rutten, Ruud J. van der Ent, and Remko Uijlenhoet
Hydrol. Earth Syst. Sci., 28, 3327–3345, https://doi.org/10.5194/hess-28-3327-2024, https://doi.org/10.5194/hess-28-3327-2024, 2024
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We focus on past high-flow events to find flood drivers in the Geul. We also explore flood drivers’ trends across various timescales and develop a new method to detect the main direction of a trend. Our results show that extreme 24 h precipitation alone is typically insufficient to cause floods. The combination of extreme rainfall and wet initial conditions determines the chance of flooding. Precipitation that leads to floods increases in winter, whereas no consistent trends are found in summer.
Luuk D. van der Valk, Miriam Coenders-Gerrits, Rolf W. Hut, Aart Overeem, Bas Walraven, and Remko Uijlenhoet
Atmos. Meas. Tech., 17, 2811–2832, https://doi.org/10.5194/amt-17-2811-2024, https://doi.org/10.5194/amt-17-2811-2024, 2024
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Microwave links, often part of mobile phone networks, can be used to measure rainfall along the link path by determining the signal loss caused by rainfall. We use high-frequency data of multiple microwave links to recreate commonly used sampling strategies. For time intervals up to 1 min, the influence of sampling strategies on estimated rainfall intensities is relatively little, while for intervals longer than 5–15 min, the sampling strategy can have significant influences on the estimates.
Aart Overeem, Hidde Leijnse, Gerard van der Schrier, Else van den Besselaar, Irene Garcia-Marti, and Lotte Wilhelmina de Vos
Hydrol. Earth Syst. Sci., 28, 649–668, https://doi.org/10.5194/hess-28-649-2024, https://doi.org/10.5194/hess-28-649-2024, 2024
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Ground-based radar precipitation products typically need adjustment with rain gauge accumulations to achieve a reasonable accuracy. Crowdsourced rain gauge networks have a much higher density than conventional ones. Here, a 1-year personal weather station (PWS) gauge dataset is obtained. After quality control, the 1 h PWS gauge accumulations are merged with pan-European radar accumulations. The potential of crowdsourcing to improve radar precipitation products in (near) real time is confirmed.
Louise J. Schreyers, Tim H. M. van Emmerik, Thanh-Khiet L. Bui, Khoa L. van Thi, Bart Vermeulen, Hong-Q. Nguyen, Nicholas Wallerstein, Remko Uijlenhoet, and Martine van der Ploeg
Hydrol. Earth Syst. Sci., 28, 589–610, https://doi.org/10.5194/hess-28-589-2024, https://doi.org/10.5194/hess-28-589-2024, 2024
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River plastic emissions into the ocean are of global concern, but the transfer dynamics between fresh water and the marine environment remain poorly understood. We developed a simple Eulerian approach to estimate the net and total plastic transport in tidal rivers. Applied to the Saigon River, Vietnam, we found that net plastic transport amounted to less than one-third of total transport, highlighting the need to better integrate tidal dynamics in plastic transport and emission models.
Bich Ngoc Tran, Johannes van der Kwast, Solomon Seyoum, Remko Uijlenhoet, Graham Jewitt, and Marloes Mul
Hydrol. Earth Syst. Sci., 27, 4505–4528, https://doi.org/10.5194/hess-27-4505-2023, https://doi.org/10.5194/hess-27-4505-2023, 2023
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Satellite data are increasingly used to estimate evapotranspiration (ET) or the amount of water moving from plants, soils, and water bodies into the atmosphere over large areas. Uncertainties from various sources affect the accuracy of these calculations. This study reviews the methods to assess the uncertainties of such ET estimations. It provides specific recommendations for a comprehensive assessment that assists in the potential uses of these data for research, monitoring, and management.
Aart Overeem, Else van den Besselaar, Gerard van der Schrier, Jan Fokke Meirink, Emiel van der Plas, and Hidde Leijnse
Earth Syst. Sci. Data, 15, 1441–1464, https://doi.org/10.5194/essd-15-1441-2023, https://doi.org/10.5194/essd-15-1441-2023, 2023
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EURADCLIM is a new precipitation dataset covering a large part of Europe. It is based on weather radar data to provide local precipitation information every hour and combined with rain gauge data to obtain good precipitation estimates. EURADCLIM provides a much better reference for validation of weather model output and satellite precipitation datasets. It also allows for climate monitoring and better evaluation of extreme precipitation events and their impact (landslides, flooding).
Femke A. Jansen, Remko Uijlenhoet, Cor M. J. Jacobs, and Adriaan J. Teuling
Hydrol. Earth Syst. Sci., 26, 2875–2898, https://doi.org/10.5194/hess-26-2875-2022, https://doi.org/10.5194/hess-26-2875-2022, 2022
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We studied the controls on open water evaporation with a focus on Lake IJssel, the Netherlands, by analysing eddy covariance observations over two summer periods at two locations at the borders of the lake. Wind speed and the vertical vapour pressure gradient can explain most of the variation in observed evaporation, which is in agreement with Dalton's model. We argue that the distinct characteristics of inland waterbodies need to be taken into account when parameterizing their evaporation.
Wagner Wolff, Aart Overeem, Hidde Leijnse, and Remko Uijlenhoet
Atmos. Meas. Tech., 15, 485–502, https://doi.org/10.5194/amt-15-485-2022, https://doi.org/10.5194/amt-15-485-2022, 2022
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The existing infrastructure for cellular communication is promising for ground-based rainfall remote sensing. Rain-induced signal attenuation is used in dedicated algorithms for retrieving rainfall depth along commercial microwave links (CMLs) between cell phone towers. This processing is a source of many uncertainties about input data, algorithm structures, parameters, CML network, and local climate. Application of a stochastic optimization method leads to improved CML rainfall estimates.
Ruben Imhoff, Claudia Brauer, Klaas-Jan van Heeringen, Hidde Leijnse, Aart Overeem, Albrecht Weerts, and Remko Uijlenhoet
Hydrol. Earth Syst. Sci., 25, 4061–4080, https://doi.org/10.5194/hess-25-4061-2021, https://doi.org/10.5194/hess-25-4061-2021, 2021
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Significant biases in real-time radar rainfall products limit the use for hydrometeorological forecasting. We introduce CARROTS (Climatology-based Adjustments for Radar Rainfall in an OperaTional Setting), a set of fixed bias reduction factors to correct radar rainfall products and to benchmark other correction algorithms. When tested for 12 Dutch basins, estimated rainfall and simulated discharges with CARROTS generally outperform those using the operational mean field bias adjustments.
Simone Gelsinari, Valentijn R. N. Pauwels, Edoardo Daly, Jos van Dam, Remko Uijlenhoet, Nicholas Fewster-Young, and Rebecca Doble
Hydrol. Earth Syst. Sci., 25, 2261–2277, https://doi.org/10.5194/hess-25-2261-2021, https://doi.org/10.5194/hess-25-2261-2021, 2021
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Estimates of recharge to groundwater are often driven by biophysical processes occurring in the soil column and, particularly in remote areas, are also always affected by uncertainty. Using data assimilation techniques to merge remotely sensed observations with outputs of numerical models is one way to reduce this uncertainty. Here, we show the benefits of using such a technique with satellite evapotranspiration rates and coupled hydrogeological models applied to a semi-arid site in Australia.
Jolijn van Engelenburg, Erik van Slobbe, Adriaan J. Teuling, Remko Uijlenhoet, and Petra Hellegers
Drink. Water Eng. Sci., 14, 1–43, https://doi.org/10.5194/dwes-14-1-2021, https://doi.org/10.5194/dwes-14-1-2021, 2021
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This study analysed the impact of extreme weather events, water quality deterioration, and a growing drinking water demand on the sustainability of drinking water supply in the Netherlands. The results of the case studies were compared to sustainability issues for drinking water supply that are experienced worldwide. This resulted in a set of sustainability characteristics describing drinking water supply on a local scale in terms of hydrological, technical, and socio-economic characteristics.
Cited articles
Aberson, K.: The spatial and temporal variability of the vertical dimension of rainstorms and their relation with precipitation intensity, internal report, https://cdn.knmi.nl/knmi/pdf/bibliotheek/knmipubIR/IR2011-03.pdf (last access: 19 December 2023), 2011. a
Beekhuis, H. and Holleman, I.: Highlights of the digital-IF upgrade of the Dutch national radar network, online report, https://cdn.knmi.nl/system/data_center_publications/files/000/068/061/original/erad2008drup_0120.pdf?1495621011 (last access: 19 December 2023), 2008. a
Bogerd, L., Overeem, A., Leijnse, H., and Uijlenhoet, R.: A comprehensive five-year evaluation of IMERG late run precipitation estimates over the Netherlands, J. Hydrometeorol., 22, 1855–1868, https://doi.org/10.1175/JHM-D-21-0002.1, 2021. a, b
Casella, D., Panegrossi, G., Sanò, P., Milani, L., Petracca, M., and Dietrich, S.: A novel algorithm for detection of precipitation in tropical regions using PMW radiometers, Atmos. Meas. Tech., 8, 1217–1232, https://doi.org/10.5194/amt-8-1217-2015, 2015. a
Chang, N.-B. and Hong, Y.: Multiscale hydrologic remote sensing: perspectives and applications, CRC Press, ISBN 978-1-00-068727-9, 2012. a
Chen, F. and Li, X.: Evaluation of IMERG and TRMM 3B43 monthly precipitation products over mainland China, Remote Sens., 8, 472, https://doi.org/10.3390/rs8060472, 2016. a
Cristiano, E., ten Veldhuis, M.-C., and van de Giesen, N.: Spatial and temporal variability of rainfall and their effects on hydrological response in urban areas – a review, Hydrol. Earth Syst. Sci., 21, 3859–3878, https://doi.org/10.5194/hess-21-3859-2017, 2017. a
Ferraro, R. R., Peters-Lidard, C. D., Hernandez, C., Turk, F. J., Aires, F., Prigent, C., Lin, X., Boukabara, S.-A., Furuzawa, F. A., Gopalan, K., Harrison, K. W., Karbou, F., Li, L., Ringerud, S., Skofronick-Jackson, G. M., Tian, Y., and Wang, N.-Y.: An evaluation of microwave land surface emissivities over the continental United States to benefit GPM-era precipitation algorithms, IEEE Trans. Geosci. Remote, 51, 378–398, https://doi.org/10.1109/TGRS.2012.2199121, 2013. a
Foufoula-Georgiou, E., Ebtehaj, A. M., Zhang, S. Q., and Hou, A. Y.: Downscaling satellite precipitation with emphasis on extremes: a variational 1-norm regularization in the derivative domain, Surv. Geophys., 35, 765–783, https://doi.org/10.1007/s10712-013-9264-9, 2014. a
Guilloteau, C., Foufoula-Georgiou, E., and Kummerow, C. D.: Global multiscale evaluation of satellite passive microwave retrieval of precipitation during the TRMM and GPM eras: effective resolution and regional diagnostics for future algorithm development, J. Hydrometeorol., 18, 3051–3070, https://doi.org/10.1175/JHM-D-17-0087.1, 2017. a
Hayden, L. and Liu, C.: Differences in the diurnal variation of precipitation estimated by spaceborne radar, passive microwave radiometer, and IMERG, J. Geophys. Res.-Atmos., 126, e2020JD033020, https://doi.org/10.1029/2020JD033020, 2021. a
Hou, A. Y., Kakar, R. K., Neeck, S., Azarbarzin, A. A., Kummerow, C. D., Kojima, M., Oki, R., Nakamura, K., and Iguchi, T.: The Global Precipitation Measurement Mission, B. Am. Meteorol. Soc., 95, 701–722, https://doi.org/10.1175/BAMS-D-13-00164.1, 2014. a, b, c
Kidd, C. and Huffman, G.: Global precipitation measurement, Meteorol. Appl., 18, 334–353, https://doi.org/10.1002/met.284, 2011. a, b
Kidd, C. and Levizzani, V.: Chapter One – Quantitative precipitation estimation from satellite observations, in: Extreme Hydroclimatic Events and Multivariate Hazards in a Changing Environment, edited by: Maggioni, V. and Massari, C., 3–39, Elsevier, ISBN 978-0-12-814899-0, https://doi.org/10.1016/B978-0-12-814899-0.00001-8, 2019. a
Kidd, C., Tan, J., Kirstetter, P.-E., and Petersen, W. A.: Validation of the Version 05 Level 2 precipitation products from the GPM core observatory and constellation satellite sensors, Q. J. Roy. Meteorol. Soc., 144, 313–328, https://doi.org/10.1002/qj.3175, 2018. a
Kidd, C., Graham, E., Smyth, T., and Gill, M.: Assessing the impact of light/shallow precipitation retrievals from satellite-Based observations using surface radar and micro rain radar observations, Remote Sens., 13, 1708, https://doi.org/10.3390/rs13091708, 2021a. a
Kidd, C., Huffman, G., Maggioni, V., Chambon, P., and Oki, R.: The global satellite precipitation constellation: current status and future requirements, B. Am. Meteorol. Soc., 102, E1844–E1861, https://doi.org/10.1175/BAMS-D-20-0299.1, 2021b. a
Kim, K., Park, J., Baik, J., and Choi, M.: Evaluation of topographical and seasonal feature using GPM IMERG and TRMM 3B42 over Far-East Asia, Atmos. Res., 187, 95–105, https://doi.org/10.1016/j.atmosres.2016.12.007, 2017. a
Kubota, T., Ushio, T., Shige, S., Kida, S., Kachi, M., and Okamoto, K.: Verification of high-resolution satellite-based rainfall estimates around Japan using a gauge-calibrated ground-radar dataset, J. Meteorol. Soc. JPN II, 87A, 203–222, https://doi.org/10.2151/jmsj.87A.203, 2009. a
Kummerow, C., Hong, Y., Olson, W. S., Yang, S., Adler, R. F., McCollum, J., Ferraro, R., Petty, G., Shin, D.-B., and Wilheit, T. T.: The evolution of the Goddard profiling algorithm (GPROF) for rainfall estimation from passive microwave sensors, J. Appl. Meteorol. Climatol., 40, 1801–1820, https://doi.org/10.1175/1520-0450(2001)040<1801:TEOTGP>2.0.CO;2, 2001. a
Kummerow, C. D.: Introduction to passive microwave retrieval methods, in: Satellite Precipitation Measurement: Volume 1, edited by Levizzani, V., Kidd, C., Kirschbaum, D. B., Kummerow, C. D., Nakamura, K., and Turk, F. J., Advances in Global Change Research, 123–140 pp., Springer International Publishing, Cham, ISBN 978-3-030-24568-9, https://doi.org/10.1007/978-3-030-24568-9_7, 2020. a, b
Lee, Y.-R., Shin, D.-B., Kim, J.-H., and Park, H.-S.: Precipitation estimation over radar gap areas based on satellite and adjacent radar observations, Atmos. Meas. Tech., 8, 719–728, https://doi.org/10.5194/amt-8-719-2015, 2015. a
Leijnse, H.: Precipitation – radar 5 minute echo top height composites over the Netherlands, KNMI dataplatform [data set], https://dataplatform.knmi.nl/dataset/radar-tar-echotopheight-5min-1-0, last access: 19 December 2023. a
Leth, T. C. v., Leijnse, H., Overeem, A., and Uijlenhoet, R.: Rainfall spatiotemporal correlation and intermittency structure from micro-γ to meso-β scale in the Netherlands, J. Hydrometeorol., 22, 2227–2240, https://doi.org/10.1175/JHM-D-20-0311.1, 2021. a
Liu, C. and Zipser, E.: Differences between the surface precipitation estimates from the TRMM precipitation radar and passive microwave radiometer version 7 products, J. Hydrometeorol., 15, 2157–2175, https://doi.org/10.1175/JHM-D-14-0051.1, 2014. a
Lorenz, C. and Kunstmann, H.: The hydrological cycle in three state-of-the-art reanalyses: Intercomparison and performance analysis, J. Hydrometeorol., 13, 1397–1420, https://doi.org/10.1175/JHM-D-11-088.1, 2012. a
Maggioni, V., Meyers, P. C., and Robinson, M. D.: A review of merged high-resolution satellite precipitation product accuracy during the Tropical Rainfall Measuring Mission (TRMM) era, J. Hydrometeorol., 17, 1101–1117, https://doi.org/10.1175/JHM-D-15-0190.1, 2016. a, b, c
Maggioni, V., Massari, C., and Kidd, C.: Chapter 13 – Errors and uncertainties associated with quasiglobal satellite precipitation products, in: Precipitation Science, edited by: Michaelides, S., 377–390 pp., Elsevier, ISBN 978-0-12-822973-6, https://doi.org/10.1016/B978-0-12-822973-6.00023-8, 2022. a
McCollum, J. R. and Ferraro, R. R.: Microwave rainfall estimation over coasts, J. Atmos. Ocean. Technol., 22, 497–512, https://doi.org/10.1175/JTECH1732.1, 2005. a
Mega, T. and Shige, S.: Improvements of rain/no-rain classification methods for microwave radiometer over coasts by dynamic surface-type classification, J. Atmos. Ocean. Technol., 33, 1257–1270, https://doi.org/10.1175/JTECH-D-15-0127.1, 2016. a, b
Munchak, S. J. and Skofronick-Jackson, G.: Evaluation of precipitation detection over various surfaces from passive microwave imagers and sounders, Atmos. Res., 131, 81–94, https://doi.org/10.1016/j.atmosres.2012.10.011, 2013. a
O, S., Foelsche, U., Kirchengast, G., Fuchsberger, J., Tan, J., and Petersen, W. A.: Evaluation of GPM IMERG Early, Late, and Final rainfall estimates using WegenerNet gauge data in southeastern Austria, Hydrol. Earth Syst. Sci., 21, 6559–6572, https://doi.org/10.5194/hess-21-6559-2017, 2017. a
Overeem, A.: Precipitation – 5 minute precipitation accumulations from climatological gauge-adjusted radar dataset for The Netherlands, KNMI dataplatform [data set], https://dataplatform.knmi.nl/dataset/rad-nl25-rac-mfbs-5min-netcdf4-2-0, last access: 19 December 2023. a
Overeem, A., Buishand, T. A., and Holleman, I.: Extreme rainfall analysis and estimation of depth-duration-frequency curves using weather radar, Water Resour. Res., 45, https://doi.org/10.1029/2009WR007869, 2009a. a
Overeem, A., Holleman, I., and Buishand, A.: Derivation of a 10-year radar-based climatology of rainfall, J. Appl. Meteorol. Climatol., 48, 1448–1463, https://doi.org/10.1175/2009JAMC1954.1, 2009b. a
Overeem, A., Leijnse, H., and Uijlenhoet, R.: Measuring urban rainfall using microwave links from commercial cellular communication networks, Water Resour. Res., 47, W12505, https://doi.org/10.1029/2010WR010350, 2011. a
Petty, G. W. and Bennartz, R.: Field-of-view characteristics and resolution matching for the Global Precipitation Measurement (GPM) Microwave Imager (GMI), Atmos. Meas. Tech., 10, 745–758, https://doi.org/10.5194/amt-10-745-2017, 2017. a
Pfreundschuh, S., Brown, P. J., Kummerow, C. D., Eriksson, P., and Norrestad, T.: GPROF-NN: a neural-network-based implementation of the Goddard Profiling Algorithm, Atmos. Meas. Tech., 15, 5033–5060, https://doi.org/10.5194/amt-15-5033-2022, 2022. a, b, c
Prigent, C.: Precipitation retrieval from space: an overview, Compt. Rendus Geosci., 342, 380–389, https://doi.org/10.1016/j.crte.2010.01.004, 2010. a
Randel, D. L., Kummerow, C. D., and Ringerud, S.: The Goddard Profiling (GPROF) precipitation retrieval algorithm, in: Satellite Precipitation Measurement: Volume 1, edited by: Levizzani, V., Kidd, C., Kirschbaum, D. B., Kummerow, C. D., Nakamura, K., and Turk, F. J., Advances in Global Change Research, 141–152 pp., Springer International Publishing, ISBN 978-3-030-24568-9, https://doi.org/10.1007/978-3-030-24568-9_8, 2020. a, b, c
Reed, J.: Precipitation Data Directory, NASA's Precipitation Processing Center [data set], https://gpm.nasa.gov/data/directory (last access: 19 December 2023), 2023. a
Saltikoff, E., Friedrich, K., Soderholm, J., Lengfeld, K., Nelson, B., Becker, A., Hollmann, R., Urban, B., Heistermann, M., and Tassone, C.: An overview of using weather radar for climatological studies: successes, challenges, and potential, B. Am. Meteorol. Soc., 100, 1739–1752, https://doi.org/10.1175/BAMS-D-18-0166.1, 2019. a
Shin, D.-B. and Kummerow, C.: Parametric rainfall retrieval algorithms for passive microwave radiometers, J. Appl. Meteorol. Climatol., 42, 1480–1496, https://doi.org/10.1175/1520-0450(2003)042<1480:PRRAFP>2.0.CO;2, 2003. a
Skofronick-Jackson, G., Petersen, W. A., Berg, W., Kidd, C., Stocker, E. F., Kirschbaum, D. B., Kakar, R., Braun, S. A., Huffman, G. J., Iguchi, T., Kirstetter, P. E., Kummerow, C., Meneghini, R., Oki, R., Olson, W. S., Takayabu, Y. N., Furukawa, K., and Wilheit, T.: The Global Precipitation Measurement (GPM) Mission for science and society, B. Am. Meteorol. Soc., 98, 1679–1695, https://doi.org/10.1175/BAMS-D-15-00306.1, 2017. a
Skofronick-Jackson, G., Berg, W., Kidd, C., Kirschbaum, D. B., Petersen, W. A., Huffman, G. J., and Takayabu, Y. N.: Global Precipitation Measurement (GPM): Unified precipitation estimation from space, in: Remote Sensing of Clouds and Precipitation, edited by: Andronache, C., Springer Remote Sensing/Photogrammetry, 175–193 pp., Springer International Publishing, Cham, ISBN 978-3-319-72583-3, https://doi.org/10.1007/978-3-319-72583-3_7, 2018a. a, b
Skofronick‐Jackson, G., Kirschbaum, D., Petersen, W., Huffman, G., Kidd, C., Stocker, E., and Kakar, R.: The Global Precipitation Measurement (GPM) mission's scientific achievements and societal contributions: reviewing four years of advanced rain and snow observations, Q. J. Roy. Meteorol. Soc., 144, 27–48, https://doi.org/10.1002/qj.3313, 2018b. a
Stephens, G. L. and Kummerow, C. D.: The remote sensing of clouds and precipitation from space: A review, J. Atmos. Sci., 64, 3742–3765, https://doi.org/10.1175/2006JAS2375.1, 2007. a
Sun, Q., Miao, C., Duan, Q., Ashouri, H., Sorooshian, S., and Hsu, K.-L.: A Review of Global Precipitation Data Sets: Data Sources, Estimation, and Intercomparisons, Rev. Geophys., 56, 79–107, https://doi.org/10.1002/2017RG000574, 2018. a
Tan, J., Cho, N., Oreopoulos, L., and Kirstetter, P.: Evaluation of GPROF V05 precipitation retrievals under different cloud regimes, J. Hydrometeorol., 23, 389–402, https://doi.org/10.1175/JHM-D-21-0154.1, 2022. a, b
Tang, G., Clark, M. P., Papalexiou, S. M., Ma, Z., and Hong, Y.: Have satellite precipitation products improved over last two decades? A comprehensive comparison of GPM IMERG with nine satellite and reanalysis datasets, Remote Sens. Environ., 240, 111697, https://doi.org/10.1016/j.rse.2020.111697, 2020. a
Wang, Y., You, Y., and Kulie, M.: Global virga precipitation distribution derived from three spaceborne radars and its contribution to the false radiometer precipitation detection, Geophys. Res. Lett., 45, 4446–4455, https://doi.org/10.1029/2018GL077891, 2018. a
You, Y., Peters-Lidard, C., Turk, J., Ringerud, S., and Yang, S.: Improving overland precipitation retrieval with brightness temperature temporal variation, J. Hydrometeorol., 18, 2355–2383, https://doi.org/10.1175/JHM-D-17-0050.1, 2017. a
You, Y., Petkovic, V., Tan, J., Kroodsma, R., Berg, W., Kidd, C., and Peters-Lidard, C.: Evaluation of V05 precipitation estimates from GPM constellation radiometers using KuPR as the reference, J. Hydrometeorol., 21, 705–728, https://doi.org/10.1175/JHM-D-19-0144.1, 2020. a, b
Short summary
Algorithms merge satellite radiometer data from various frequency channels, each tied to a different footprint size. We studied the uncertainty associated with sampling (over the Netherlands using 4 years of data) as precipitation is highly variable in space and time by simulating ground-based data as satellite footprints. Though sampling affects precipitation estimates, it doesn’t explain all discrepancies. Overall, uncertainties in the algorithm seem more influential than how data is sampled.
Algorithms merge satellite radiometer data from various frequency channels, each tied to a...
