scholarly journals Establishing the dominant source of uncertainty in drought indicators

2013 ◽  
Vol 10 (11) ◽  
pp. 13407-13440 ◽  
Author(s):  
G. Naumann ◽  
E. Dutra ◽  
P. Barbosa ◽  
F. Pappenberger ◽  
F. Wetterhall ◽  
...  
Keyword(s):  
Potential Evapotranspiration ◽  
Standardized Precipitation Index ◽  
Global Precipitation Climatology Project ◽  
Rain Gauge ◽  
Eastern Africa ◽  
Drought Monitoring ◽  
Monthly Precipitation ◽  
Precipitation Climatology ◽  
Drought Indicators ◽  
Global Precipitation

Abstract. Drought monitoring is a key component to mitigate impacts of droughts. Lack of reliable and up-to-date datasets is a common challenge across the Globe. This study investigates different datasets and drought indicators on their capability to improve drought monitoring in Africa. The study was performed for four river basins located in different climatic regions (the Oum er-Rbia in Morocco, the Blue Nile in Eastern Africa, the Upper Niger in Western Africa, and the Limpopo in South-Eastern Africa) as well as the Greater Horn of Africa. The five precipitation datasets compared are the ECMWF ERA – Interim reanalysis, the Tropical Rainfall Measuring Mission satellite monthly rainfall product 3B43, the Global Precipitation Climatology Centre gridded precipitation dataset, the Global Precipitation Climatology Project Global Monthly Merged Precipitation Analyses, and the Climate Prediction Center Merged Analysis of Precipitation. The set of drought indicators used includes the Standardized Precipitation Index, the Standardized Precipitation–Evaporation Index, Soil Moisture Anomalies and Potential Evapotranspiration. A comparison of the annual cycle and monthly precipitation time series shows a good agreement in the timing of the rainy seasons. The main differences between the datasets are in the ability to represent the magnitude of the wet seasons and extremes. Moreover, for the areas affected by drought, all the drought indicators agree on the time of drought onset and recovery although there is disagreement on the extent of the affected area. In regions with limited rain gauge data the estimation of the different drought indicators is characterised by a higher uncertainty. Further comparison suggests that the main source of error in the computation of the drought indicators is the uncertainty in the precipitation datasets rather than the estimation of the distribution parameters of the drought indicators.

scholarly journals Comparison of drought indicators derived from multiple data sets over Africa

2014 ◽  
Vol 18 (5) ◽  
pp. 1625-1640 ◽  
Author(s):  
G. Naumann ◽  
E. Dutra ◽  
P. Barbosa ◽  
F. Pappenberger ◽  
F. Wetterhall ◽  
...  
Keyword(s):  
Eastern Africa ◽  
Drought Monitoring ◽  
Data Sets ◽  
Monthly Precipitation ◽  
Precipitation Data ◽  
Data Set ◽  
Precipitation Climatology ◽  
Multiple Data Sets ◽  
Drought Indicators ◽  
Global Precipitation

Abstract. Drought monitoring is a key component to mitigate impacts of droughts. Lack of reliable and up-to-date precipitation data sets is a common challenge across the globe. This study investigates different data sets and drought indicators on their capability to improve drought monitoring in Africa. The study was performed for four river basins located in different climatic regions (the Oum er-Rbia in Morocco, the Blue Nile in eastern Africa, the Upper Niger in western Africa, and the Limpopo in southeastern Africa) as well as the Greater Horn of Africa. The five precipitation data sets compared are the ECMWF ERA-Interim reanalysis, the Tropical Rainfall Measuring Mission satellite monthly rainfall product 3B-43, the Global Precipitation Climatology Centre gridded precipitation data set, the Global Precipitation Climatology Project Global Monthly Merged Precipitation Analyses, and the Climate Prediction Center Merged Analysis of Precipitation. The set of drought indicators used includes the Standardized Precipitation Index, the Standardized Precipitation-Evaporation Index, and Soil Moisture Anomalies. A comparison of the annual cycle and monthly precipitation time series shows a good agreement in the timing of the rainy seasons. The main differences between the data sets are in the ability to represent the magnitude of the wet seasons and extremes. Moreover, for the areas affected by drought, all the drought indicators agree on the time of drought onset and recovery although there is disagreement on the extent of the affected area. In regions with limited rain gauge data the estimation of the different drought indicators is characterized by a higher uncertainty. Further comparison suggests that the main source of differences in the computation of the drought indicators is the uncertainty in the precipitation data sets rather than the estimation of the distribution parameters of the drought indicators.

Comparison of Gridded Multisatellite Rainfall Estimates with Gridded Gauge Rainfall over West Africa

2008 ◽  
Vol 47 (1) ◽  
pp. 185-205 ◽  
Author(s):  
Benjamin L. Lamptey
Keyword(s):  
Global Precipitation Climatology Project ◽  
Rain Gauge ◽  
Guinea Coast ◽  
Rain Gauge Data ◽  
Precipitation Climatology ◽  
Global Precipitation Climatology Centre ◽  
Surface Climate ◽  
Gauge Data ◽  
Global Precipitation ◽  
Rainfall Climatology

Abstract Two monthly gridded precipitation datasets of the Global Precipitation Climatology Project (GPCP; the multisatellite product) and the Global Precipitation Climatology Centre (GPCC) Variability Analysis of Surface Climate Observations (VASClimO; rain gauge data) are compared for a 22-yr period, from January 1979 to December 2000, over land areas (i.e., latitudes 4°–20°N and longitudes 18°W–15°E). The two datasets are consistent with respect to the spatial distribution of the annual and seasonal rainfall climatology over the domain and along latitudinal bands. However, the satellite generally overestimates rainfall. The inability of the GPCC data to capture the bimodal rainfall pattern along the Guinea coast (i.e., south of latitude 8°N) is an artifact of the interpolation of the rain gauge data. For interannual variability, the gridded multisatellite and gridded gauge datasets agree on the sign of the anomaly 15 out of the 22 yr (68% of the time) for region 1 (between longitude 5° and 18°W and north of latitude 8°N) and 18 out of the 22 yr (82% of the time) for region 2 (between longitude 5°W and 15°E and north of latitude 8°N). The datasets agreed on the sign of the anomaly 14 out of the 22 yr (64% of the time) over the Guinea Coast. The magnitudes of the anomaly are very different in all years. Most of the years during which the two datasets did not agree on the sign of the anomaly were years with El Niño events. The ratio of the seasonal root-mean-square differences to the seasonal mean rainfall range between 0.24 and 2.60. The Kendall’s tau statistic indicated statistically significant trends in both datasets, separately.

The Global Precipitation Climatology Project

2007 ◽  
pp. 25-35 ◽  
Author(s):  
Arnold Gruber ◽  
Bruno Rudolf ◽  
Mark M. Morrissey ◽  
Toshiyuki Kurino ◽  
John E. Janowiak ◽  
...  
Keyword(s):  
Global Precipitation Climatology Project ◽  
Precipitation Climatology ◽  
Global Precipitation

scholarly journals The Global Precipitation Climatology Project (GPCP) Combined Precipitation Dataset

1997 ◽  
Vol 78 (1) ◽  
pp. 5-20 ◽  
Author(s):  
George J. Huffman ◽  
Robert F. Adler ◽  
Philip Arkin ◽  
Alfred Chang ◽  
Ralph Ferraro ◽  
...  
Keyword(s):  
Global Precipitation Climatology Project ◽  
Precipitation Climatology ◽  
Global Precipitation

scholarly journals Spatial and Temporal Variation of Precipitation in Greece and Surrounding Regions Based on Global Precipitation Climatology Project Data

2008 ◽  
Vol 21 (6) ◽  
pp. 1349-1370 ◽  
Author(s):  
N. Hatzianastassiou ◽  
B. Katsoulis ◽  
J. Pnevmatikos ◽  
V. Antakis
Keyword(s):  
Large Scale ◽  
Precipitation Amount ◽  
Global Precipitation Climatology Project ◽  
Spatial And Temporal Variation ◽  
Mean Annual Precipitation ◽  
Study Region ◽  
Precipitation Climatology ◽  
Spatial Coverage ◽  
Precipitation Decrease ◽  
Global Precipitation

Abstract In this study, the spatial and temporal distribution of precipitation in the broader Greek area is investigated for the 26-yr period 1979–2004 by using monthly mean satellite-based data, with complete spatial coverage, taken from the Global Precipitation Climatology Project (GPCP). The results show that there exists a clear contrast between the more rainy western Greek area (rainside) and the drier eastern one (rainshadow), whereas there is little precipitation over the islands, particularly in the southern parts. The computed long-term areal mean annual precipitation amount averaged for the study area is equal to P = 639.8 ± 44.8 mm yr−1, showing a decreasing trend of −2.32 mm yr−1 or −60.3 mm over the 26-yr study period, which corresponds to −9.4%. This decrease of precipitation, arising primarily in winter and secondarily in spring, is the result of a decreasing trend from 1979 through the 1980s, against an increase during the 1990s through the early 2000s, followed again by a decrease up to the year 2004. The performed analysis reveals an increasing trend of precipitation in the central and northern parts of the study region, contrary to an identified decreasing trend in the southern parts, which is indicative of threatening desertification processes in those areas in the context of climatic changes in the climatically sensitive Mediterranean basin. In addition, the analysis shows that the precipitation decrease is due to a corresponding decrease of maximum precipitation against rather unchanged minimum precipitation amounts. The analysis indicates that the changing precipitation patterns in the region during winter are significantly anticorrelated with the North Atlantic Oscillation (NAO) index values, against a positive correlation during summer, highlighting thus the role of large-scale circulation patterns for regional climates. The GPCP precipitation data are satisfactorily correlated with instrumental measurements from 36 stations uniformly distributed over the study area (correlation coefficient R = 0.74 for all stations; R = 0.63–0.91 for individual stations).

scholarly journals Estimating Climatological Bias Errors for the Global Precipitation Climatology Project (GPCP)

2012 ◽  
Vol 51 (1) ◽  
pp. 84-99 ◽  
Author(s):  
Robert F. Adler ◽  
Guojun Gu ◽  
George J. Huffman
Keyword(s):  
Error Estimate ◽  
Error Estimates ◽  
Relative Error ◽  
Absolute Magnitude ◽  
Global Precipitation Climatology Project ◽  
Bias Error ◽  
Precipitation Climatology ◽  
The Mean ◽  
The Tropics ◽  
Global Precipitation

AbstractA procedure is described to estimate bias errors for mean precipitation by using multiple estimates from different algorithms, satellite sources, and merged products. The Global Precipitation Climatology Project (GPCP) monthly product is used as a base precipitation estimate, with other input products included when they are within ±50% of the GPCP estimates on a zonal-mean basis (ocean and land separately). The standard deviation σ of the included products is then taken to be the estimated systematic, or bias, error. The results allow one to examine monthly climatologies and the annual climatology, producing maps of estimated bias errors, zonal-mean errors, and estimated errors over large areas such as ocean and land for both the tropics and the globe. For ocean areas, where there is the largest question as to absolute magnitude of precipitation, the analysis shows spatial variations in the estimated bias errors, indicating areas where one should have more or less confidence in the mean precipitation estimates. In the tropics, relative bias error estimates (σ/μ, where μ is the mean precipitation) over the eastern Pacific Ocean are as large as 20%, as compared with 10%–15% in the western Pacific part of the ITCZ. An examination of latitudinal differences over ocean clearly shows an increase in estimated bias error at higher latitudes, reaching up to 50%. Over land, the error estimates also locate regions of potential problems in the tropics and larger cold-season errors at high latitudes that are due to snow. An empirical technique to area average the gridded errors (σ) is described that allows one to make error estimates for arbitrary areas and for the tropics and the globe (land and ocean separately, and combined). Over the tropics this calculation leads to a relative error estimate for tropical land and ocean combined of 7%, which is considered to be an upper bound because of the lack of sign-of-the-error canceling when integrating over different areas with a different number of input products. For the globe the calculated relative error estimate from this study is about 9%, which is also probably a slight overestimate. These tropical and global estimated bias errors provide one estimate of the current state of knowledge of the planet’s mean precipitation.

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