Global precipitation measurements for validating climate models

Tapiador, Francisco J.; Navarro, Andrés; Levizzani, Vincenzo; García‐Ortega, Eduardo; Huffman, George J.; Kidd, Chris; Kucera, Paul A.; Kummerow, Christian D.; Masunaga, Hirohiko; Petersen, Walt; Roca, Rémy; Sánchez, José Luis; Tao, Wei‐Kuo; Turk, F. Joseph

doi:10.1016/j.atmosres.2017.06.021

Cited by 135 publications

(117 citation statements)

References 134 publications

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“…Rainfall estimation by remote sensing, radar and satellite also rely on PSD information (Olsen et al, 1978;Atlas et al, 1999;Uijlenhoet and Sempere-Torres, 2006;Tapiador et al, 2017b). Disdrometer observations of PSD are also used to derive relationships between radar reflectivity and rainfall rates (known usually as Z-R relationships), despite the difficulties due to differences in altitude of the measurement (surface vs. cloud base) and the sensing area (a few cm 2 vs. km 2 ; Krajewski et al, 1998;Löffler-Mang and Blahak, 2001;Miriousky et al, 2004;Thurai and Bringi, 2008;Marzano et al, 2010;Jaffrain and Berne, 2012;Jameson et al, 2015;Raupach and Berne, 2016;Gires et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Comparison of precipitation measurements by OTT Parsivel<sup>2</sup> and Thies LPM optical disdrometers

Angulo‐Martínez

Beguería

Latorre

et al. 2018

Hydrol. Earth Syst. Sci.

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Abstract. Optical disdrometers are present weather sensors with the ability of providing detailed information on precipitation such as rain intensity, radar reflectivity or kinetic energy, together with discrete information on the particle size and fall velocity distribution (PSVD) of the hydrometeors. Disdrometers constitute a step forward towards a more complete characterization of precipitation, being useful in several research fields and applications. In this article the performance of two extensively used optical disdrometers, the most recent version of OTT Parsivel 2 disdrometer and Thies Clima Laser Precipitation Monitor (LPM), is evaluated. During 2 years, four collocated optical disdrometers, two Thies Clima LPM and two OTT Parsivel 2 , collected up to 100 000 min of data and up to 30 000 min with rain in more than 200 rainfall events, with intensities peaking at 277 mm h −1 in 1 minute. The analysis of these records shows significant differences between both disdrometer types for all integrated precipitation parameters, which can be explained by differences in the raw PSVD estimated by the two sensors. Thies LPM recorded a larger number of particles than Parsivel 2 and a higher proportion of small particles than OTT Parsivel 2 , resulting in higher rain rates and totals and differences in radar reflectivity and kinetic energy. These differences increased greatly with rainfall intensity. Possible causes of these differences, and their practical consequences, are discussed in order to help researchers and users in the choice of sensor, and at the same time pointing out limitations to be addressed in future studies.

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Section: Introductionmentioning

confidence: 99%

Comparison of precipitation measurements by OTT Parsivel<sup>2</sup> and Thies LPM optical disdrometers

Angulo‐Martínez

Beguería

Latorre

et al. 2018

Hydrol. Earth Syst. Sci.

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“…It is therefore safe to say that CEMS is a fully trusted model, at least according to Tapiador et al . 's classification (). Actually, some of the models in CMIP5 (such as the two NorESM1 models) are CESM “dialects” with varying degrees of improvement, changes in the tuning parameters, varied grid spacing and time steps, and added features to enhance detailed modelling of some key processes such as radiation.…”

Section: Datamentioning

confidence: 99%

“…The details of those datasets and other alternatives can be found in the references above and in Tapiador et al . (; ; ). The four datasets were selected for this study after considering the temporal span, coverage and wide use within the community of the available data.…”

Section: Datamentioning

confidence: 99%

“…Validating models by comparing model output and field observations involves many nuances (Tapiador et al, ). Observational databases are built on a set of assumptions and algorithmic options (such as selecting a particular drop size distribution and fixing the values of parameters for orbital radars) that are not always obvious to modellers.…”

Section: Introductionmentioning

confidence: 99%

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Discrepancies with satellite observations in the spatial structure of global precipitation as derived from global climate models

Tapiador

Navarro

Jiménez

et al. 2018

Quart J Royal Meteoro Soc

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One of the applications of satellite‐derived precipitation datasets, such as Global Precipitation Climatology Project (GPCP) or Climate Prediction Centre Merged Analysis of Precipitation (CMAP), is to validate output from numerical models, either numerical weather prediction (NWP) models, Regional Climate Models (RCMs), Global Climate Models (GCMs) or Earth System Models (ESMs). A qualitative comparison of total annual precipitation and climatology is the first step in detecting model deficiencies and thus improving our understanding of the world's climate. However, spatial (or association) analysis of the precipitation fields offers new insights into model performance and their ability to provide realistic predictions of rain and snowfall in both current and future climates. Here we analyse the spatial structure of precipitation according to 40 GCMs for 20 years (January 1980 to December 1999), quantitatively comparing the modelled precipitation against five observational datasets: three land‐only (CRU, PRECL and GPCC) and two global (GPCP and CMAP). We found discrepancies between the GCMs' predictions and the observational datasets and noted that satellite‐derived datasets are essential for pinpointing areas that require attention. The analyses also revealed a consistent trend towards less spatially correlated fields. This trend is not apparent in aggregated, traditional validation exercises but arises when spatial association indices are applied. So long as the trend is not an artefact in the observational datasets, then we suggest the tendency could be attributed to changes in stratiform/convective partitioning or the result of increasingly intense convection.

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“…The products after assimilating results in, for instance, significantly improved cyclone prediction, reflecting mostly in the cyclone's track, the associated frontal structure and the associated precipitation along the front [3]. Rainfall monitoring is also important to assess possible anthropogenic impacts on global climate [4][5][6], to monitor hydrometeorological natural disasters, such as flood and flash flood events [7][8][9][10][11], and to improve precipitation estimates in Earth System Models (ESMs) [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Decorrelation of Satellite Precipitation Estimates in Space and Time

et al. 2018

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Precise estimates of precipitation are required for many environmental tasks, including water resources management, improvement of numerical model outputs, nowcasting and evaluation of anthropogenic impacts on global climate. Nonetheless, the availability of such estimates is hindered by technical limitations. Rain gauge and ground radar measurements are limited to land, and the retrieval of quantitative precipitation estimates from satellite has several problems including the indirectness of infrared-based geostationary estimates, and the low orbit of those microwave instruments capable of providing a more precise measurement but suffering from poor temporal sampling. To overcome such problems, data fusion methods have been devised to take advantage of synergisms between available data, but these methods also present issues and limitations. Future improvements in satellite technology are likely to follow two strategies. One is to develop geostationary millimeter-submillimeter wave soundings, and the other is to deploy a constellation of improved polar microwave sensors. Here, we compare both strategies using a simulated precipitation field. Our results show that spatial correlation and RMSE would be little affected at the monthly scale in the constellation, but that the precise location of the maximum of precipitation could be compromised; depending on the application, this may be an issue.

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Global precipitation measurements for validating climate models

Cited by 135 publications

References 134 publications

Comparison of precipitation measurements by OTT Parsivel<sup>2</sup> and Thies LPM optical disdrometers

Comparison of precipitation measurements by OTT Parsivel<sup>2</sup> and Thies LPM optical disdrometers

Discrepancies with satellite observations in the spatial structure of global precipitation as derived from global climate models

Decorrelation of Satellite Precipitation Estimates in Space and Time

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Global precipitation measurements for validating climate models

Cited by 135 publications

References 134 publications

Comparison of precipitation measurements by OTT Parsivel&lt;sup&gt;2&lt;/sup&gt; and Thies LPM optical disdrometers

Comparison of precipitation measurements by OTT Parsivel&lt;sup&gt;2&lt;/sup&gt; and Thies LPM optical disdrometers

Discrepancies with satellite observations in the spatial structure of global precipitation as derived from global climate models

Decorrelation of Satellite Precipitation Estimates in Space and Time

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Product

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Comparison of precipitation measurements by OTT Parsivel<sup>2</sup> and Thies LPM optical disdrometers

Comparison of precipitation measurements by OTT Parsivel<sup>2</sup> and Thies LPM optical disdrometers