Evaluation of ERA-Interim, MERRA, NCEP-DOE R2 and CFSR Reanalysis precipitation Data using Gauge Observation over Ethiopia for a period of 33 years
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The vital demand of reliable climatic and hydrologic data of fine spatial and temporal resolution triggered the employment of reanalysis datasets as a surrogate in most of the hydrological modelling exercises. This study examines the performance of four widely used reanalysis datasets: ERA-Interim, NCEP-DOE R2, MERRA and CFSR, in reproducing the spatio-temporal characteristics of observed daily precipitation of different stations spread across Ethiopia, East Africa. The appropriateness of relying on reanalysis datasets for hydrologic modelling, climate change impact assessment and regional modelling studies is assessed using various statistical and non-parametric techniques. ERA-Interim is found to exhibit higher correlation and least root mean square error values with observed daily rainfall, which is followed by CFSR and MERRA in most of the stations. The variability of daily precipitation is better captured by ERA, CFSR and MERRA, while NCEP-DOE R2 overestimated the spread of the precipitation data. While ERA overestimates the probability of moderate rainfall, it is seemingly better in capturing the probability of low rainfall. CFSR captures the overall distribution reasonable well. NCEP-DOE R2 appears to be outperforming others in capturing the probabilities of higher magnitude rainfall. Climatological seasonal cycle and the characteristics of wet and dry spells are compared further, where ERA seemingly replicates the pattern more effectively. However, observed rainfall exhibits higher frequency of short wet spells when compared to that of any reanalysis datasets. MERRA relatively underperforms in simulating the wet spell characteristics of observed daily rainfall. CFSR overestimates the mean wet spell length and mean dry spell length. Spatial trend analysis indicates that the northern and central western Ethiopia show increasing trends, whereas the Central and Eastern Ethiopia as well as the Southern Ethiopia stations show either no trend or decreasing trend. Overall, ERA-Interim and CFSR are better in depicting various characteristics of daily rainfall in Ethiopian region.Keywords:
Climate Forecast System
Abstract The NCEP Climate Forecast System (CFS) is an important source of information for seasonal climate prediction in many Asian countries affected by monsoon climate. The authors provide a comprehensive analysis of the prediction of the Asian summer monsoon (ASM) by the new CFS version 2 (CFSv2) using the hindcast for 1983–2010, focusing on seasonal-to-interannual time scales. Many ASM features are well predicted by the CFSv2, including heavy monsoon rainfall centers, large-scale monsoon circulation patterns, and monsoon onset and retreat features. Several commonly used dynamical monsoon indices and their associated precipitation and circulation patterns can be predicted several months in advance. The CFSv2 has better skill in predicting the Southeast Asian monsoon than predicting the South Asian monsoon. Compared to CFS version 1 (CFSv1), the CFSv2 has increased skill in predicting large-scale monsoon circulation and precipitation features but decreased skill for the South Asian monsoon, although some biases in the CFSv1 still exist in the CFSv2, especially the weaker-than-observed western Pacific subtropical high and the exaggerated strong link of the ASM to ENSO. Comparison of CFSv2 hindcast with output from Atmospheric Model Intercomparison Project (AMIP) and Coupled Model Intercomparison Project (CMIP) simulations indicates that exclusion of ocean–atmosphere coupling leads to a weaker ASM. Compared to AMIP, both hindcast and CMIP show a more realistic annual cycle of precipitation, and the interannual variability of the ASM is better in hindcast. However, CMIP does not show any advantage in depicting the processes associated with the interannual variability of major dynamical monsoon indices compared to AMIP.
Hindcast
Climate Forecast System
East Asian Monsoon
Predictability
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ABSTRACT The capability to predict the leading modes of daily variability for South Asian monsoon in the climate forecast system version 2 (CFSv2) of the National Centers for Environmental Prediction (NCEP) is investigated. The CFSv2 model forecast at four pentad leads named as P1–P4 has been used in this study. The multi‐channel singular spectrum analysis (MSSA) on the daily anomalies of precipitation over the South Asian monsoon region for the period of 2001–2014 with a lag window of 61 days has been applied for June–July–August–September (JJAS) for all the four pentad lead forecasts of model and observation. It is encouraging that the space–time structure and propagation characteristics are exactly similar to observation up to P1 lead forecast. The model produces stationary modes and the northwest to southeast tilt significantly reduces from P2 lead onwards. The relationship of oscillatory and persisting modes with Indo–Pacific Ocean SST has been investigated. It is found that the SST over Pacific Ocean is independent of oscillatory mode in the case of P1 and P2 lead forecasts as in the case of observation. The model reproduces the observed correlation of Indian monsoon rainfall (IMR) index for the seasonally persisting mode with SST over Indo–Pacific Ocean up to P3 lead forecast which is a significant improvement. The contribution of El Niño‐Southern Oscillation (ENSO) mode to total anomaly over India is large in Pl lead and it decreases from P2 to P4 lead forecast.
Climate Forecast System
Anomaly (physics)
Mode (computer interface)
Lead (geology)
Singular Spectrum Analysis
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ABSTRACT It is known that the El Niño – Southern Oscillation ( ENSO ) episodes have a great influence on South American precipitation and its extreme events during austral autumn (from March until May, MAM ) and winter (from June until August, JJA ) that occur after the ENSO peak (normally this happens on austral summer). Recent papers have studied the two types of ENSO and their influence on atmosphere–ocean system. This study analysed the influence of Central and East equatorial Pacific ENSO on South American seasonal/monthly mean precipitation and its extreme events during MAM and JJA . The composites of precipitation anomalies, during these two types of ENSO , show that there are different, even opposite patterns over South America. In MAM , there is an increased precipitation in southeastern South America and a decrease in the northeast South America during East El Niño ( EEN ) and an increased precipitation in central Brazil during Central El Niño ( CEN ). In JJA , the signs of anomaly precipitation are opposite between CEN (less precipitation) and EEN (more precipitation) over southeastern South America. The extreme precipitation events show patterns consistent with the precipitation anomaly patterns, but, normally, the changes in the frequency of extremes precipitation events affect more extensive areas than the total precipitation. If monthly or seasonal atmospheric anomalies in a certain region during one of the types of ENSO are similar (opposite) to the atmospheric anomalies associated with extreme precipitation events in this region, then there is enhancement (suppression) of the frequency of extreme events in this region during this type of ENSO .
Anomaly (physics)
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Wet season
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A radar quantitative precipitation estimation group system is described in this paper which is established based on rain gauge adjustment techniques and is aimed to apply to meteorological operations.It provides hourly precipitation field in 10 minute interval and 1 km×1 km spacial resolution.Evaluation results by use of the data in the past three years show that the more the adjustment rain gauges,the higher the estimated precipitation accuracy. The relative error of hourly estimation during the year 2003 is about 40%,and even lower than 20%for the total precipitation in a precipitation event.The longer and larger the rain event lasts and covers,the higher the accuracy as well if the density of rain gauge station keeps fixed.
Quantitative precipitation estimation
Quantitative precipitation forecast
Weather radar
Precipitation types
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The Madden–Julian oscillation (MJO) is arguably the most important intraseasonal mode of climate variability, given its significant modulation of global climate variations and attendant societal impacts. Advancing the current understanding and simulation of the MJO using state-of-the-art climate data and modeling systems is thus a necessary goal for improving MJO prediction capability. MJO variability is assessed in NOAA/NCEP reanalyses and two versions of the Climate Forecast System (CFS), CFS version 1 (CFSv1) and its update version 2 (CFSv2). The analysis leans on a variety of diagnostic procedures and includes MJO sensitivity to varying El Niño–Southern Oscillation (ENSO) phases. It is found that significant improvements have been realized in the representation of MJO variations in the new NCEP Climate Forecast System reanalysis (CFSR) as evidenced by outgoing longwave radiation (OLR) power spectral analysis and more coherent propagation characteristics of precipitation and 850-hPa zonal winds over the Eastern Hemisphere in CFSR-only depictions. Conversely, while modest improvements are realized in the CFSv2 as compared to CFSv1, in general the simulation of the MJO continues to be a challenge. Both versions produce strong eastward propagating variance of convection and wind fields in the intraseasonal frequency band. However, the simulated MJO propagates slower than the observed with difficulties traversing the Maritime Continent into the western Pacific, as noted in many previous modeling studies. The CFS shows robust intraseasonal simulations over the west Pacific during El Niño years with diminished simulation capability over the Indian Ocean during La Niña years. This is likely a manifestation of the preference for La Niña MJO activity to occur over the Indian Ocean and the simulation challenges over that domain.
Madden–Julian oscillation
Climate Forecast System
Outgoing longwave radiation
Predictability
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Climate Forecast System
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The seasonal prediction skill of the Asian summer monsoon is assessed using retrospective predictions (1982–2009) from the ECMWF System 4 (SYS4) and NCEP CFS version 2 (CFSv2) seasonal prediction systems. In both SYS4 and CFSv2, a cold bias of sea-surface temperature (SST) is found over the equatorial Pacific, North Atlantic, Indian Oceans and over a broad region in the Southern Hemisphere relative to observations. In contrast, a warm bias is found over the northern part of North Pacific and North Atlantic. Excessive precipitation is found along the ITCZ, equatorial Atlantic, equatorial Indian Ocean and the maritime continent. The southwest monsoon flow and the Somali Jet are stronger in SYS4, while the south-easterly trade winds over the tropical Indian Ocean, the Somali Jet and the subtropical northwestern Pacific high are weaker in CFSv2 relative to the reanalysis. In both systems, the prediction of SST, precipitation and low-level zonal wind has greatest skill in the tropical belt, especially over the central and eastern Pacific where the influence of El Nino-Southern Oscillation (ENSO) is dominant. Both modeling systems capture the global monsoon and the large-scale monsoon wind variability well, while at the same time performing poorly in simulating monsoon precipitation. The Asian monsoon prediction skill increases with the ENSO amplitude, although the models simulate an overly strong impact of ENSO on the monsoon. Overall, the monsoon predictive skill is lower than the ENSO skill in both modeling systems but both systems show greater predictive skill compared to persistence.
Climate Forecast System
Madden–Julian oscillation
Intertropical Convergence Zone
East Asian Monsoon
Forcing (mathematics)
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Anticyclone
Climate Forecast System
Forcing (mathematics)
Kelvin wave
Walker circulation
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Madden–Julian oscillation
Outgoing longwave radiation
Climate Forecast System
Anomaly (physics)
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