Seasonal intercomparison of observational rainfall datasets over India during the southwest monsoon season

Prakash, Satya; Mitra, Ashis K.; Momin, Imranali M.; Rajagopal, E. N.; Basu, Swati; Collins, Michael R.; Turner, Andrew G.; Rao, K. Achuta; Ashok, Karumuri

doi:10.1002/joc.4129

Cited by 108 publications

(95 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figure 9 shows the decadal trends of extreme rainfall (i.e., 95th percentile precipitation for JJA) in the datasets over the period 1998-2007 calculated through a linear regression coefficient. Except for APHRODITE, ERA-Interim, and JRA55, the rest shows a remarkable increasing trend over central India, which is consistent with the finding of Rana et al (2015) and Prakash et al (2014). Singh et al (2013) found that APHRODITE has opposite trend signs for extreme precipitation intensity from the 2000s onwards when compared with the IMD dataset (2140 rain-gauge station).…”

Section: Interannual Variabilitysupporting

confidence: 82%

Evaluation of precipitation extremes over the Asian domain: observation and modelling studies

et al. 2018

View full text Add to dashboard Cite

In this study, a comparison in the precipitation extremes as exhibited by the seven reference datasets is made to ascertain whether the inferences based on these datasets agree or they differ. These seven datasets, roughly grouped in three categories i.e. rain-gauge based (APHRODITE, CPC-UNI), satellite-based (TRMM, GPCP1DD) and reanalysis based (ERA-Interim, MERRA, and JRA55), having a common data period 1998-2007 are considered. Focus is to examine precipitation extremes in the summer monsoon rainfall over South Asia, East Asia and Southeast Asia. Measures of extreme precipitation include the percentile thresholds, frequency of extreme precipitation events and other quantities. Results reveal that the differences in displaying extremes among the datasets are small over South Asia and East Asia but large differences among the datasets are displayed over the Southeast Asian region including the maritime continent. Furthermore, precipitation data appear to be more consistent over East Asia among the seven datasets. Decadal trends in extreme precipitation are consistent with known results over South and East Asia. No trends in extreme precipitation events are exhibited over Southeast Asia. Outputs of the Coupled Model Intercomparison Project Phase 5 (CMIP5) simulation data are categorized as high, medium and lowresolution models. The regions displaying maximum intensity of extreme precipitation appear to be dependent on model resolution. High-resolution models simulate maximum intensity of extreme precipitation over the Indian sub-continent, medium-resolution models over northeast India and South China and the low-resolution models over Bangladesh, Myanmar and Thailand. In summary, there are differences in displaying extreme precipitation statistics among the seven datasets considered here and among the 29 CMIP5 model data outputs.

show abstract

Section: Interannual Variabilitysupporting

confidence: 82%

Evaluation of precipitation extremes over the Asian domain: observation and modelling studies

et al. 2018

View full text Add to dashboard Cite

show abstract

“…These may suggest that all of these data sets are affected by some common factors in determining the characteristics of these data sets. For example, the station data sets included in each analysis data set may provide high consistency in the spatial distribution pattern, but different analysis schemes may lead to a larger spread in the magnitude of their variability because of different basis functions employed in different interpolation schemes (e.g., Xie and Arkin, 1995;Prakash et al, 2014). This is just a hypothesis and needs close examination in future studies.…”

Section: Summary and Discussionmentioning

confidence: 99%

“…These new data sets also introduce uncertainties in calculating regional climate characteristics because of the differences amongst them. Based on these concerns, two recent studies by Prakash et al (2014) and Kim et al (2015) examined uncertainty in calculating precipitation climatology over India and its surrounding regions using multiple precipitation analysis data sets. These two studies have revealed independently that there exist substantial amounts of differences amongst today's gridded precipitation data sets resulting in uncertainties in the calculated precipitation climatology and that the uncertainty and the spread amongst multiple data sets vary according to regions as well as seasons.…”

Section: Introductionmentioning

confidence: 99%

Uncertainties in calculating precipitation climatology in East Asia

Kim

Park

2016

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. This study examines the uncertainty in calculating the fundamental climatological characteristics of precipitation in the East Asia region from multiple fine-resolution gridded analysis data sets based on in situ rain gauge observations and data assimilations. Five observation-based gridded precipitation data sets are used to derive the long-term means, standard deviations in lieu of interannual variability and linear trends over the 28-year period from 1980 to 2007. Both the annual and summer (June-July-August) mean precipitation is examined. The agreement amongst these precipitation data sets is examined using two metrics including the signal-to-noise ratio (SNR) defined as the ratio between long-term means and the corresponding standard deviations, and Taylor diagrams, which allow examinations of the pattern correlation, the standard deviation, and the centered root mean square error. It is found that the five gauge-based precipitation analysis data sets agree well in the long-term mean and interannual variability in most of the East Asia region including eastern China, Manchuria, South Korea, and Japan, which are densely populated and have fairly highdensity observation networks. The regions of large interdata-set variations include Tibetan Plateau, Mongolia, northern Indo-China, and North Korea. The regions of large uncertainties are typically lightly populated and are characterized by severe terrain and/or extremely high elevations. Unlike the long-term mean and interannual variability, agreement between data sets in the linear trend is weak, both for the annual and summer mean values. In most of the East Asia region, the SNR for the linear trend is below 0.5: the interdata-set variability exceeds the multi-data ensemble mean.The uncertainty in the spatial distribution of long-term means among these data sets occurs both in the spatial pattern and variability, but the uncertainty for the interannual variability and time trend is much larger in the variability than in the pattern correlation. Thus, care must be taken in using long-term trends calculated from gridded precipitation analysis data for climate studies over the East Asia region.

show abstract

“…Spatial analyses created from interpolating station data [48,49] provide coarse scale climate trends [2,42], but in areas such as Mongolia, there are few meteorological stations [4]. Historically, areas with sparse populations have greater distances between stations [1,43] due to the lack of people and funding needed to perform the manual observations.…”

Section: Data Considerationsmentioning

confidence: 99%

“…[43], have a monthly time scale [48] that cannot be used to estimate details such as changes in the precipitation phase [55], providing an impetus for making spatial estimates of uncertainty in climate trends using herder observations ( Figure 5 and Table 2). Thus, in a data sparse region like Mongolia, herder observations can help in the assessment of climate change at a finer resolution ( Figure 5 and Table 2).…”

Section: Data Considerationsmentioning

confidence: 99%

Merging Indigenous Knowledge Systems and Station Observations to Estimate the Uncertainty of Precipitation Change in Central Mongolia

et al. 2018

View full text Add to dashboard Cite

Across the globe, station-based meteorological data are analyzed to estimate the rate of change in precipitation. However, in sparsely populated regions, like Mongolia, stations are few and far between, leaving significant gaps in station-derived precipitation patterns across space and over time. We combined station data with the observations of herders, who live on the land and observe nature and its changes across the landscape. Station-based trends were computed with the Mann-Kendall significance and Theil-Sen rate of change tests. We surveyed herders about their observations of changes in rain and snowfall amounts, rain intensity, and days with snow, using a closed-ended questionnaire and also recorded their qualitative observations. Herder responses were summarized using the Potential for Conflict Index (PCI 2 ), which computes the mean herder responses and their consensus. For one set of stations in the same forest steppe ecosystem, precipitation trends were similar and decreasing, and the herder-based PCI 2 consensus score matched differences between stations. For the other station set, trends were less consistent and the PCI 2 consensus did not match well, since the stations had different climates and ecologies. Herder and station-based uncertainties were more consistent for the snow variables than the rain variables. The combination of both data sources produced a robust estimate of climate change uncertainty.

show abstract

Seasonal intercomparison of observational rainfall datasets over India during the southwest monsoon season

Cited by 108 publications

References 24 publications

Evaluation of precipitation extremes over the Asian domain: observation and modelling studies

Evaluation of precipitation extremes over the Asian domain: observation and modelling studies

Uncertainties in calculating precipitation climatology in East Asia

Merging Indigenous Knowledge Systems and Station Observations to Estimate the Uncertainty of Precipitation Change in Central Mongolia

Contact Info

Product

Resources

About