Comparison of GPCP Monthly and Daily Precipitation Estimates with High-Latitude Gauge Observations

Bolvin, David T.; Adler, Robert F.; Huffman, George J.; Nelkin, Eric; Poutiainen, Jani

doi:10.1175/2009jamc2147.1

Cited by 73 publications

(37 citation statements)

References 15 publications

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“…Global precipitation analyses, not surprisingly, are typically assessed via comparisons to ground-based gauge networks (Bolvin et al 2009;Huffman et al 1997;Xie and Arkin 1997). Some disadvantages of such a validation approach exist.…”

Section: Discussionmentioning

confidence: 99%

“…When the same correction was applied to the two gauge networks, the GPCP bias was substantially reduced. More recently, Bolvin et al (2009) compared a new, corrected GPCP version 2 product to a high-quality, high-density gauge network of the Finnish Meteorological Institute (FMI) spanning the period 1995-2007. They found a result consistent with Adler et al (2003), such that when the FMI gauge data were adjusted for undercatch, very good agreement between the two datasets was obtained.…”

Section: Introductionmentioning

confidence: 99%

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Assessing High-Latitude Winter Precipitation from Global Precipitation Analyses Using GRACE

Swenson

2010

Journal of Hydrometeorology

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This study compares cold-season, high-latitude precipitation estimates from two global, merged satellitegauge precipitation analyses-Global Precipitation Climatology Project (GPCP) and Climate Prediction Center Merged Analysis of Precipitation (CMAP)-to total water storage anomalies produced from the Gravity Recovery and Climate Experiment (GRACE). In general, spatial patterns and interannual variability are highly correlated between the datasets, although significant differences are also observed. Differences vary by region but typically increase at higher latitudes. Furthermore, results indicate that the gauge undercatch correction used by GPCP may be overestimated. These comparisons may be useful for assessing precipitation estimates over large regions, where in situ gauge networks may be sparse.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessing High-Latitude Winter Precipitation from Global Precipitation Analyses Using GRACE

Swenson

2010

Journal of Hydrometeorology

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“…Monthly and daily precipitation estimates from GPCP have been widely evaluated against gauge data (e.g. Bolvin et al 2009;McPhee and Margulis 2005;Nicholson et al 2003). The precipitation data have been interpolated to the same 2.5° grid as the front data in order to facilitate the linking of the two datasets as in Catto et al (2012a).…”

Section: Reanalysis Datamentioning

confidence: 99%

A global evaluation of fronts and precipitation in the ACCESS model

Catto¹,

Jakob²,

Nicholls³

2013

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How global precipitation will change in the future is of great socio-economic importance. It is therefore vital that climate models are able to adequately simulate the characteristics of precipitation and the individual precipitation events. Fronts play an important role in providing precipitation and they can be associated with heavy rain and flooding.In this paper the ACCESS1.3 atmosphere model is evaluated in terms of frontal and non-frontal precipitation. An objective front identification method is applied to data from reanalysis and the model. The fronts are then linked to daily precipitation from observational estimates and the model. The proportion of precipitation associated with fronts and the average intensity of precipitation associated with fronts are then calculated and compared.The frequency of fronts and the proportion of precipitation associated with fronts are well captured by the model. The intensity of precipitation when a front is present is underestimated by the model in most regions, consistent with many previous studies. Decomposing the total precipitation error into components associated with the frequency and intensity of both frontal and non-frontal precipitation shows that the non-frontal precipitation errors contribute the most to the total error, and that there are compensating errors in the model. This regime-and process-based method of model evaluation provides a useful tool to gain deeper understanding into the sources of precipitation errors in climate models.

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“…Furthermore, some precipitation datasets use precipitation observations that are not corrected for gauge undercatch. Others use various adjustment techniques to eliminate these biases (Adler et al, 2003;Bolvin, Adler, Huffman, Nelkin, & Poutiainen, 2009;Schneider et al, 2014). The GPCP dataset (Adler et al, 2003) combines gauge measurements with satellite estimates to provide more complete spatial coverage in areas with low station density.…”

Section: Introductionmentioning

confidence: 99%

An Evaluation of Temperature and Precipitation Surface-Based and Reanalysis Datasets for the Canadian Arctic, 1950–2010

Rapaić¹,

Brown

Markovic³

et al. 2015

Atmosphere-Ocean

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The spatial and temporal consistency of seasonal air temperature and precipitation in eight widely used gridded observation-based climate datasets (CANGRD, CRU-TS3.1, CRUTEM4.1, GISTEMP, GPCC, GPCP, HadCRUT3, and UDEL) and eight reanalyses (20CR, CFSR, ERA-40, ERA-Interim, JRA25, MERRA, NARR, and NCEP2) was evaluated over the Canadian Arctic for the 1950-2010 period. The evaluation used the CANGRD dataset, which is based on homogenized temperature and adjusted precipitation from climate stations, as a reference. Dataset agreement and bias were observed to exhibit important spatial, seasonal, and temporal variability over the Canadian Arctic with the largest spread occurring between datasets over mountain and coastal regions and over the Canadian Arctic Archipelago. Reanalysis datasets were typically warmer and wetter than surface observation-based datasets, with CFSR and 20CR exhibiting biases in total annual precipitation on the order of 300 mm. Warm bias in 20CR exceeded 12°C in winter over the western Arctic. Analysis of the temporal consistency of datasets over the 1950-2010 period showed evidence of discontinuities in several datasets as well as a noticeable increase in dataset spread in the period after approximately 2000. Declining station networks, increased automation, and the inclusion of new satellite data streams in reanalyses are potential contributing factors to this phenomenon. Evaluation of trends over the 1950-2010 period showed a relatively consistent picture of warming and increased precipitation over the Canadian Arctic from all datasets, with CANGRD giving moistening trends two times larger than the multi-dataset average related to the adjustment of the station precipitation data. The study results indicate that considerable care is needed when using gridded climate datasets in local or regional scale applications in the Canadian Arctic.

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Comparison of GPCP Monthly and Daily Precipitation Estimates with High-Latitude Gauge Observations

Cited by 73 publications

References 15 publications

Assessing High-Latitude Winter Precipitation from Global Precipitation Analyses Using GRACE

Assessing High-Latitude Winter Precipitation from Global Precipitation Analyses Using GRACE

A global evaluation of fronts and precipitation in the ACCESS model

An Evaluation of Temperature and Precipitation Surface-Based and Reanalysis Datasets for the Canadian Arctic, 1950–2010

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