Evaluation of precipitation prediction skill of IMD operational NWP system over Indian monsoon region

Bhowmik, S. K. Roy; Joardar, D.; Hatwar, H. R.

doi:10.1007/s00703-006-0198-3

Cited by 11 publications

(5 citation statements)

References 11 publications

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“…Habets et al (2004) used precipitation forecasts from two French NWP models as inputs to a hydrologic model. Roy Bhowmik et al (2007) evaluated precipitation predictive skill of the Indian Meteorological Department operational NWP system over the Indian monsoon region. Roberts (2008) assessed the spatial and temporal variation in the skill of precipitation forecasts from a NWP model.…”

Section: L Shrestha Et Al: Evaluation Of Numerical Weather Predimentioning

confidence: 99%

Evaluation of numerical weather prediction model precipitation forecasts for short-term streamflow forecasting purpose

Shrestha

Robertson

Wang

et al. 2013

Hydrol. Earth Syst. Sci.

119

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Abstract. The quality of precipitation forecasts from four Numerical Weather Prediction (NWP) models is evaluated over the Ovens catchment in Southeast Australia. Precipitation forecasts are compared with observed precipitation at point and catchment scales and at different temporal resolutions. The four models evaluated are the Australian Community Climate Earth-System Simulator (ACCESS) including ACCESS-G with a 80 km resolution, ACCESS-R 37.5 km, ACCESS-A 12 km, and ACCESS-VT 5 km.The skill of the NWP precipitation forecasts varies considerably between rain gauging stations. In general, high spatial resolution (ACCESS-A and ACCESS-VT) and regional (ACCESS-R) NWP models overestimate precipitation in dry, low elevation areas and underestimate in wet, high elevation areas. The global model (ACCESS-G) consistently underestimates the precipitation at all stations and the bias increases with station elevation. The skill varies with forecast lead time and, in general, it decreases with the increasing lead time. When evaluated at finer spatial and temporal resolution (e.g. 5 km, hourly), the precipitation forecasts appear to have very little skill. There is moderate skill at short lead times when the forecasts are averaged up to daily and/or catchment scale. The precipitation forecasts fail to produce a diurnal cycle shown in observed precipitation. Significant sampling uncertainty in the skill scores suggests that more data are required to get a reliable evaluation of the forecasts. The non-smooth decay of skill with forecast lead time can be attributed to diurnal cycle in the observation and sampling uncertainty.Future work is planned to assess the benefits of using the NWP rainfall forecasts for short-term streamflow forecasting. Our findings here suggest that it is necessary to remove the systematic biases in rainfall forecasts, particularly those from low resolution models, before the rainfall forecasts can be used for streamflow forecasting.

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Section: L Shrestha Et Al: Evaluation Of Numerical Weather Predimentioning

confidence: 99%

Evaluation of numerical weather prediction model precipitation forecasts for short-term streamflow forecasting purpose

Shrestha

Robertson

Wang

et al. 2013

Hydrol. Earth Syst. Sci.

119

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“…Here we calculate the 75th, 95th and 99th percentile of daily precipitation probability distribution function (PDF) at each grid point for June-August (JJA) of each year. The frequency of heavy rainfall is defined to be more than 30 mm/day (Bhowmik et al 2007;Kusumoki et al 2012;Agnihotri et al 2015). In addition, we analyze the distribution of the right tail of the precipitation distribution, defined as the range between the 99th and 90th percentile (Scoccimarro et al 2013(Scoccimarro et al , 2014, similar to Interquartile range (IQR), which is calculated as the 75-25th percentile.…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2018

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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.

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“…Habets et al (2004) used precipitation forecasts from two French NWP models as inputs to a hydrologic model. Roy Bhowmik et al (2007) evaluated precipitation predictive skill of the Indian Meteorological Department operational NWP system over the Indian monsoon region. Roberts (2008) 10 assessed the spatial and temporal variation in the skill of precipitation forecasts from a NWP model.…”

Section: Interactive Discussionmentioning

confidence: 99%

Evaluation of numerical weather prediction model precipitation forecasts for use in short-term streamflow forecasting

Shrestha¹,

Robertson²,

Wang³

et al. 2012

Preprint

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The quality of precipitation forecasts from four Numerical Weather Prediction (NWP) models is evaluated over the Ovens catchment in southeast Australia. Precipitation forecasts are compared with observed precipitation at point and catchment scales and at different temporal resolutions. The four models evaluated are the Australian Community Climate Earth-System Simulator (ACCESS) including ACCESS-G with a 80 km resolution, ACCESS-R 37.5 km, ACCESS-A 12 km, and ACCESS-VT 5 km. <br><br> The high spatial resolution NWP models (ACCESS-A and ACCESS-VT) appear to be relatively free of bias (i.e. <30%) for 24 h total precipitation forecasts. The low resolution models (ACCESS-R and ACCESS-G) have widespread systematic biases as large as 70%. When evaluated at finer spatial and temporal resolution (e.g. 5 km, hourly) against station observations, the precipitation forecasts appear to have very little skill. There is moderate skill at short lead times when the forecasts are averaged up to daily and/or catchment scale. The skill decreases with increasing lead times and the global model ACCESS-G does not have significant skill beyond 7 days. The precipitation forecasts fail to produce a diurnal cycle shown in observed precipitation. Significant sampling uncertainty in the skill scores suggests that more data are required to get a reliable evaluation of the forecasts. <br><br> Future work is planned to assess the benefits of using the NWP rainfall forecasts for short-term streamflow forecasting. Our findings here suggest that it is necessary to remove the systematic biases in rainfall forecasts, particularly those from low resolution models, before the rainfall forecasts can be used for streamflow forecasting

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Evaluation of precipitation prediction skill of IMD operational NWP system over Indian monsoon region

Cited by 11 publications

References 11 publications

Evaluation of numerical weather prediction model precipitation forecasts for short-term streamflow forecasting purpose

Evaluation of numerical weather prediction model precipitation forecasts for short-term streamflow forecasting purpose

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

Evaluation of numerical weather prediction model precipitation forecasts for use in short-term streamflow forecasting

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