Variability of Indian summer monsoon rainfall in daily data from gauge and satellite

Abstract

[1] It has long been thought that tropical rainfall retrievals from satellites have large errors. Here we show, using a new daily 1 degree gridded rainfall data set based on about 1800 gauges from the India Meteorology Department (IMD), that modern satellite estimates are reasonably close to observed rainfall over the Indian monsoon region. Daily satellite rainfalls from the Global Precipitation Climatology Project (GPCP 1DD) and the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) are available since 1998. The high summer monsoon (June-September) rain over the Western Ghats and Himalayan foothills is captured in TMPA data. Away from hilly regions, the seasonal mean and intraseasonal variability of rainfall (averaged over regions of a few hundred kilometers linear dimension) from both satellite products are about 15% of observations. Satellite data generally underestimate both the mean and variability of rain, but the phase of intraseasonal variations is accurate. On synoptic timescales, TMPA gives reasonable depiction of the pattern and intensity of torrential rain from individual monsoon low-pressure systems and depressions. A pronounced biennial oscillation of seasonal total central India rain is seen in all three data sets, with GPCP 1DD being closest to IMD observations. The new satellite data are a promising resource for the study of tropical rainfall variability.

Precipitation climatology over India: validation with observations and reanalysis datasets and spatial trends

“…3a, c, e. The contribution of summer rainfall is the most significant for the annual rainfall. The highest rainfall is observed over the WG and the Himalayan foothills, which is attributed to the regional orography (Rahman et al 2009). About 70 % of the annual rainfall occurs during the summer monsoon months (June, July, August, and September) with large spatial and temporal variability (Fig.…”

Appearance of extreme monsoonal rainfall events and their impact on erosion in the Himalaya

“…The spatial asynchrony of climate change in the Himalaya is depicted by this regional analysis: climatic forcing throughout the Himalaya is not similar, as the Asian monsoon is a dynamic system tightly coupled to global teleconnections that change 38 B. Bookhagen their magnitudes at varying timescales (e.g. Webster 1987, Clemens et al 1991, Clemens et al 1996, Zhisheng et al 2001, Anderson et al 2002, Fleitmann et al 2003, Gupta et al 2003, Rahman et al 2009). Thus, part of the difficulty in prediction is related to the different climatic-forcing time scales and time lags in system response to the observed processes.…”

“…Because both local factors and global teleconnections influence monsoon strength, a successful prediction of the Indian monsoon season is commonly deemed to be almost impossible (e.g. Webster 1987, Webster et al 1998, Francis and Gadgil 2009, Rahman et al 2009). In particular, rainfall observation and prediction in remote, high mountain terrains need to be improved.…”

Assessment of small-scale variability of rainfall and multi-satellite precipitation estimates using measurements from a dense rain gauge network in Southeast India

“…It is, therefore, highly essential to perform validation independently at finer temporal scales over different climatic regions. As mentioned above, the evaluation of MPEs for monthly and seasonal monsoon precipitation was done to some extent over India (Rahman et al, 2009;Uma et al, 2013;Prakash et al, 2014;Sunilkumar et al, 2015). However, a detailed study on the validation of MPEs at shorter timescales (sub-daily and daily) does not exist due to the lack of suitable measurements.…”