A description of the global land-surface precipitation data products of the Global Precipitation Climatology Centre with sample applications including centennial (trend) analysis from 1901–present

Becker, Andreas; Finger, Peter; Meyer-Christoffer, Anja; Rudolf, B.; Schamm, Kirstin; Schneider, Ulrike; Ziese, Markus

doi:10.5194/essd-5-71-2013

Cited by 773 publications

(588 citation statements)

References 56 publications

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“…We used monthly values of the daytime observations of ''first-guess SST'' (Reynolds Optimally Interpolated SST, Version 2 product) from the Pathfinder algorithm, for the period 1982-2009. Furthermore, we used monthly 1.0 gridded Global Precipitation Climatology Centre ''full data reanalysis'' version 6.0 precipitation [Becker et al, 2013 ;Schneider et al, 2011] for the same period. We calculated the Pearson correlation coefficients between monthly mean SST anomalies averaged over the various source regions and the Niño 3.4 (ENSO) region [see Trenberth, 1997], and the gridded monthly values of the precipitation data set with zero lag.…”

Section: Sst and Continental Precipitationmentioning

confidence: 99%

Oceanic sources of continental precipitation and the correlation with sea surface temperature

Ent

Savenije

2013

Water Resources Research

103

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[1] Identifying the sources of continental precipitation has received increasing attention in recent years. With the use of various numerical methods, sources of precipitation have been identified from local to global scales. In this paper we identify the oceanic sources based on an atmospheric backtracking analysis of continental precipitation. We find that the strongest source areas are located close to the continents. In general, we define an oceanic area as a significant source when on average more than 20% of the total evaporation, and at least 250 mm/yr of evaporation ends up as continental precipitation. We grouped these identified source areas into 15 regions and performed a forward tracking analysis of oceanic evaporation. We identified the areas on the adjacent continents that receive this oceanic moisture and whether this is nearby or remote. Moreover, we showed how the oceanic sources vary over the year in time and space. Furthermore, we correlated sea surface temperatures (SSTs) in the 15 source regions and the Niño 3.4 region with precipitation on all continents. For South America, we found that the El Niño Southern Oscillation (altering wind patterns) has a larger effect on precipitation than local SSTs. For West Africa, however, we show that SST in the source regions is strongly correlated with precipitation in the rainy season. In Australia, both local SST and the Niño 3.4 region appear to have a big influence on precipitation. As such this research provides new insight in the oceanatmosphere-land coupling, which can be useful for studying seasonal weather predictions as well as climate change impact.Citation: van der Ent, R. J., and H. H. G. Savenije (2013), Oceanic sources of continental precipitation and the correlation with sea surface temperature, Water Resour. Res., 49,[3993][3994][3995][3996][3997][3998][3999][4000][4001][4002][4003][4004]

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Section: Sst and Continental Precipitationmentioning

confidence: 99%

Oceanic sources of continental precipitation and the correlation with sea surface temperature

Ent

Savenije

2013

Water Resources Research

103

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“…Due to the higher timeliness of the CAMS data set (Fan and van den Dool, 2008), CAMS is more important for the drought index calculation. The station data are interpolated by means of a Cressman-based objective analysis scheme and a background climatology.…”

Section: Data Usedmentioning

confidence: 99%

“…It is based on monthly totals calculated from SYNOP reports, interpolated to a regular grid with 1 • spatial resolution using a modified SPHEREMAP scheme (Willmott et al, 1985) and a background climatology. A detailed description of the data set, data base and interpolation is given in Becker et al (2013).…”

Section: Data Usedmentioning

confidence: 99%

“…The "SPEI Global Drought Monitor" provides global maps and gridded data of the SPEI with 0.5 • spatial resolution (http://sac.csic.es/spei/; last access: 5 March 2014). There is a monitoring tool based on precipitation data from the GPCC and temperature data from the NOAA NCEP CPC GHCN_CAMS gridded data set (Fan and van den Dool, 2008). The estimation of the PET is based on the parameterization from Thornthwaite (1948).…”

Section: Some Existing Drought Index Data Sets and Monitoring Toolsmentioning

confidence: 99%

“…Monthly mean temperatures were applied from the NOAA NCEP CPC GHCN_CAMS gridded data set (Fan and van den Dool, 2008), where the temperature data are taken from GHCN version 2 (Peterson and Vose, 1997) and CAMS (Ropelewski et al, 1984). Due to the higher timeliness of the CAMS data set (Fan and van den Dool, 2008), CAMS is more important for the drought index calculation.…”

Section: Data Usedmentioning

confidence: 99%

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The GPCC Drought Index – a new, combined and gridded global drought index

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et al. 2014

Earth Syst. Sci. Data

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Abstract. The Global Precipitation Climatology Centre Drought Index (GPCC-DI) provides estimations of water supply anomalies with respect to long-term statistics. It is a combination of the Standardized Precipitation Index with adaptations from Deutscher Wetterdienst (SPI-DWD) and the Standardized Precipitation Evapotranspiration Index (SPEI). Precipitation data were taken from the Global Precipitation Climatology Centre (GPCC) and temperature data from NOAA's Climate Prediction Center (CPC). The GPCC-DI is available with several accumulation periods of 1, 3, 6, 9, 12, 24 and 48 months for different applications. It is issued monthly starting in January 2013. Typically, it is released on the 10th day of the following month, depending on the availability of the input data. It is calculated on a regular grid with 1 • spatial resolution. All accumulation periods are integrated into one netCDF file for each month. This data set is referenced by the

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Regularized principal covariates regression and its application to finding coupled patterns in climate fields

Fischer

2014

JGR Atmospheres

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There are many different methods for investigating the coupling between two climate fields, which are all based on the multivariate regression model. Each different method of solving the multivariate model has its own attractive characteristics, but often the suitability of a particular method for a particular problem is not clear. Continuum regression methods search the solution space between the conventional methods and thus can find regression model subspaces that mix the attractive characteristics of the end-member subspaces. Principal covariates regression is a continuum regression method that is easily applied to climate fields and makes use of two end-members: principal components regression and redundancy analysis. In this study, principal covariates regression is extended to additionally span a third end-member (partial least squares or maximum covariance analysis). The new method, regularized principal covariates regression, has several attractive features including the following: it easily applies to problems in which the response field has missing values or is temporally sparse, it explores a wide range of model spaces, and it seeks a model subspace that will, for a set number of components, have a predictive skill that is the same or better than conventional regression methods. The new method is illustrated by applying it to the problem of predicting the southern Australian winter rainfall anomaly field using the regional atmospheric pressure anomaly field. Regularized principal covariates regression identifies four major coupled patterns in these two fields. The two leading patterns, which explain over half the variance in the rainfall field, are related to the subtropical ridge and features of the zonally asymmetric circulation.

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A description of the global land-surface precipitation data products of the Global Precipitation Climatology Centre with sample applications including centennial (trend) analysis from 1901–present

Cited by 773 publications

References 56 publications

Oceanic sources of continental precipitation and the correlation with sea surface temperature

Oceanic sources of continental precipitation and the correlation with sea surface temperature

The GPCC Drought Index – a new, combined and gridded global drought index

Regularized principal covariates regression and its application to finding coupled patterns in climate fields

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