Ashis K. Mitra

Comparison of TMPA-3B42 Versions 6 and 7 Precipitation Products with Gauge-Based Data over India for the Southwest Monsoon Period

AbstractThe upgraded version 7 (V7) of the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) products is available to the user community. In this paper, two successive versions of the TMPA-3B42 research monitoring product, version 6 (V6) and V7, at the daily scale are evaluated over India during the southwest monsoon with gauge-based data for a 13-yr (1998–2010) period. Over typical monsoon rainfall zones, biases are improved by 5%–10% in V7 over the regions of higher rainfall like the west coast, northeastern, and central India. A similar reduced bias is seen in V7 over the rain-shadow region located in southeastern India. In terms of correlation, anomaly correlation, and RMSE, a marginal improvement is seen in V7. Additionally, in all-India summer monsoon rainfall amounts, mean, interannual values, and standard deviation show an overall improvement in V7. Different skill metrics over typical subregions within India show an improvement of the monsoon rainfall represe...

Gridded daily Indian monsoon rainfall for 14 seasons: Merged TRMM and IMD gauge analyzed values

Indian monsoon is an important component of earth’s climate system. Daily rainfall data for longer period is vital to study components and processes related to Indian monsoon. Daily observed gridded rainfall data covering both land and adjoining oceanic regions are required for numerical model validation and model development for monsoon. In this study, a new gridded daily Indian rainfall dataset at 1°×1° latitude/longitude resolution covering 14 monsoon seasons (1998–2011) are described. This merged satellite gauge rainfall dataset (NMSG) combines TRMM TMPA rainfall estimates with gauge information from IMD gridded data. Compared to TRMM and GPCP daily rainfall data, the current NMSG daily data has more information due to inclusion of local gauge analysed values. In terms of bias and skill scores this dataset is superior to other daily rainfall datasets. In a mean climatological sense and also for anomalous monsoon seasons, this merged satellite gauge data brings out more detailed features of monsoon rainfall. The difference of NMSG and GPCP looks significant. This dataset will be useful to researchers for monsoon intraseasonal studies and monsoon model development research.

Daily Rainfall for the Indian Monsoon Region from Merged Satellite and Rain Gauge Values: Large-Scale Analysis from Real-Time Data

Abstract A system for objectively producing daily large-scale analysis of rainfall for the Indian region has been developed and tested by using only available real-time rain gauge data and quantitative precipitation estimates from INSAT-1D IR data. The system uses a successive correction method to produce the analysis on a regular latitude–longitude grid. Quantitative precipitation estimates from the Indian National Satellite System (INSAT) operational geostationary satellite, INSAT-1D, IR data are used as the initial guess in the objective analysis method. Accumulated 24-h (daily) rainfall analyses are prepared each day by merging satellite and rain gauge data. The characteristics of the output from this analysis system have been examined by comparing the accumulated monthly observed rainfall with other available independent widely used datasets from the Global Precipitation Climatology Project (GPCP) and Climate Prediction Center Merged Analysis of Precipitation (CMAP) analyses. The monthly data prepare...

Daily rainfall analysis for Indian summer monsoon region

A simple method of daily rainfall analysis on a regular latitude by longitude grid over the Indian monsoon region is described. Daily rainfall estimates obtained from INSAT IR radiances and rain-gauge observations are combined to produce this analysis. A case of a typical westward moving monsoon depression during the 1994 monsoon season is chosen to present the characteristics of the rainfall analysed. The results obtained show that the analysis is able to represent adequately the large-scale distribution of the rainfall realized, which can further be used for modelling studies. By inserting rain-gauge observations it is seen that, in general, the final analysis removes the large negative bias imposed by the satellite estimates. Owing to the non-availability of the precipitable water estimates over mis region in real time, at present, there is a necessity to have alternative procedures for analysed rainfall distribution, especially for the purposes of physical initialization and other related applications of the tropical numerical weather prediction. When the Special Sensor Microwave Instrument (SSM/I) and other estimates from the Tropical Rainfall Measuring Mission, etc. become available in future the present scheme will be able to include these new products also for improving the quality and representation of rainfall over oceanic region.

Impact of a nonlocal closure scheme in a simulation of a monsoon system over India

Abstract The impact of two parameterization schemes for the atmospheric boundary layer in predicting monsoon circulation over the Indian region has been studied using a Global Spectral Model. The performance of the nonlocal closure scheme for the boundary layer has been tested in the operational global model of the National Centre for Medium Range Weather Forecasting (NCMRWF) for its possible implementation and operational use. Keeping the parameterization schemes for all other physical processes the same, the performance of the nonlocal closure scheme is studied and compared with the performance of the operational local closure scheme of the boundary layer processes. Incorporation of the nonlocal closure scheme shows marginal impact in the prediction of the flow pattern. However, systematic improvement in the precipitation distribution over the Indian region is seen with the incorporation of a nonlocal closure scheme during the month of August 1999. Location of the precipitation maximum along the west co...

Agreement between monthly land rainfall estimates from TRMM-PR and gauge-based observations over South Asia

There is a demand for reliable rainfall data-set over the South Asia region covering both land and ocean for model validation/development and various applications. For satellite rainfall estimates (SREs), the algorithm development groups also need validation information on SRE. The Tropical Rainfall Measuring Mission (TRMM) project has produced recently improved version 7 (V7) rainfall data-sets. Version 6 (V6) and V7 of 3A25, the surface rainfall products derived from TRMM precipitation radar (PR), are compared with gauge-based observations at 0.5° latitude/longitude resolution for the period of 1998–2007 over the South Asian land region. Both 3A25V7 and 3A25V6 represent the mean rainfall distribution patterns reasonably well. However, 3A25 products overestimate rainfall over the Indonesian region compared to gauge-based data. For some parts of South Asia, SREs show considerable difference in the magnitude of coefficient of variation compared to gauge-based information. At seasonal scale, a contrasting feature in bias over India during the pre-monsoon and monsoon seasons is noticed from both the versions of 3A25 data-set. In general, 3A25 rainfall data-sets are able to capture the interannual variability of rainfall over South Asia. The frequency distribution of monthly rainfall rate reveals that 3A25 products marginally underestimate rainfall below 10 mm day−1 and overestimate higher rainfall rate compared to gauge-based data. Overall, 3A25V7 product is marginally better than its previous version (3A25V6) over the South Asian land region.

Boreal summer sub-seasonal variability of the South Asian monsoon in the Met Office GloSea5 initialized coupled model

Boreal summer sub-seasonal variability in the Asian monsoon, otherwise known as the monsoon intra-seasonal oscillation (MISO), is one of the dominant modes of intraseasonal variability in the tropics, with large impacts on total monsoon rainfall and India’s agricultural production. However, our understanding of the mechanisms involved in MISO is incomplete and its simulation in various numerical models is often flawed. In this study, we focus on the objective evaluation of the fidelity of MISO simulation in the Met Office Global Seasonal forecast system version 5 (GloSea5), an initialized coupled model. We analyze a series of nine-member hindcasts from GloSea5 over 1996–2009 during the peak monsoon period (July–August) over the South-Asian monsoon domain focusing on aspects of the time-mean background state and air–sea interaction processes pertinent to MISO. Dominant modes during this period are evident in power spectrum analysis, but propagation and evolution characteristics of the MISO are not realistic. We find that simulated air–sea interactions in the central Indian Ocean are not supportive of MISO initiation in that region, likely a result of the low surface wind variance there. As a consequence, the expected near-quadrature phase relationship between SST and convection is not represented properly over the central equatorial Indian Ocean, and northward propagation from the equator is poorly simulated. This may reinforce the equatorial rainfall mean state bias in GloSea5.

Chapter 14 – Status of High-Resolution Multisatellite Precipitation Products Across India

Abstract Following the launch of the Tropical Rainfall Measuring Mission (TRMM) satellite, a precursor to the Global Precipitation Measurement (GPM) mission, several high-resolution multisatellite precipitation products were developed to study the characteristics of tropical and subtropical precipitation. However, multisatellite precipitation products are subject to region and season specific biases and inherent errors, which need to be comprehensively characterized before their integration in any specific application. In this chapter, recent evaluations of several popular TRMM-era high-resolution global or quasiglobal multisatellite precipitation products against gauge-based observations over the Indian subcontinent are highlighted, especially for the southwest monsoon season. The unique geography and complex precipitation processes associated with the southwest monsoon makes India a good test-bed to evaluate any satellite-derived precipitation estimates. All the studies showed that although most of the multisatellite precipitation estimates are able to capture large-scale monsoon rainfall patterns, they have biases and errors. The TRMM Multisatellite Precipitation Analysis (TMPA)-3B42 product is proven to be superior to other TRMM-era multisatellite precipitation estimates. With the launch of the GPM Core Observatory in 2014, two finer resolution multisatellite precipitation products—Integrated Multi-satellitE Retrievals for GPM (IMERG) and Global Satellite Mapping of Precipitation (GSMaP) version 6, were released. Both the GPM-based multisatellite precipitation estimates were also compared with the TMPA-3B42 and gauge-based observations. A preliminary analysis showed a noticeable improvement by GPM-based estimates over TMPA-3B42 in Indian monsoon precipitation estimation. However, a more comprehensive evaluation of GPM-based multisatellite precipitation estimates for longer periods is further required for their widest usage and applications in various sectors. Furthermore, the use of additional local rain gauges with multisatellite precipitation estimates would essentially enhance the quality of precipitation estimates for near real-time applications. There is an operational merged satellite-gauge precipitation estimate that exists specifically for the Indian monsoon region, which was recently upgraded with the IMERG estimate. The procedure of the development of this merged satellite-gauge precipitation estimate and its potential for near real-time applications are also highlighted.

Variability of sea surface salinity in the tropical Indian Ocean as inferred from Aquarius and in situ data sets

Sea surface salinity (SSS) is one of the key components of the Earth’s global water cycle. Reliable information on SSS is very important for ocean modelling, data assimilation, and ocean and climate research applications. In this study, SSS variability in the tropical Indian Ocean (TIO) was analysed using the Aquarius instrument on board the SAC-D satellite and in situ observations from the Research Moored Array for African–Asian–Australian Monsoon Analysis and Prediction (RAMA) buoys and Array for Real-Time Geostrophic Oceanography (ARGO) data sets for the period 2012–2013. Comparison of two recent versions (V2 and V3) of Aquarius-based SSS estimates to nine RAMA buoys on a daily timescale showed excellent mutual agreement. The systematic underestimation of SSS by satellite-based V2 products over the TIO shows a clear advantage for the new version product (V3). A larger root-mean-square error of the order of 0.50 psu in the satellite-based SSS was observed over the highly variable (larger standard deviation) Bay of Bengal region as compared with ARGO data sets. In the eastern equatorial Indian Ocean region, satellite-based SSS overestimated SSS below 34 psu and underestimated SSS of 34–35 psu as compared with ARGO data. However, the V3 SSS from Aquarius showed marginal improvement over V2 SSS. Monthly variation and fast Fourier analysis of the satellite-based SSS estimates are in reasonably good agreement with in situ observations which suggest that Aquarius is able to capture SSS variability in the TIO. The Aquarius-based V3 SSS showed a temporal autocorrelation of 0.6 over most parts of the TIO up to day 10, and decreased gradually with time. Overall analysis suggests that Aquarius-derived V3 SSS can detect variability in SSS satisfactorily in the TIO and is in reasonably good agreement with in situ observations.

Skill of Indian summer monsoon rainfall prediction in multiple seasonal prediction systems

We use seasonal forecasts from the Climate Historical Forecast Project (CHFP) to study the skill of multiple climate models in predicting Indian summer monsoon precipitation. The multi-model average of seasonal forecasts from eight prediction systems shows statistically significant skill for predicting Indian monsoon precipitation at seasonal lead times. Rapid convergence of tropical rainfall skill with ensemble size suggests that the skill of seasonal monsoon rainfall forecasts improves only marginally when using multi-model ensemble (MME) means as compared to the single most skillful system. There is also a large range in the skill of individual models. Some individual models show correlation skill as high as 0.6, which is similar to the MME mean, while others show low skill. We also investigate the effect of spatial averaging on the skill of predicting monsoon rainfall and show that the predictions averaged over a larger area than the verifying observations can yield higher skill due to the extended spatial coherence of monsoon rainfall variability. We also document current errors in seasonal prediction systems and show that these are more strongly related to the errors in El-Nino Southern Oscillation (ENSO) teleconnections than they are to mean rainfall biases. Finally, we examine the ENSO-monsoon relationship and confirm that this relationship is likely to be stationary, despite fluctuations in the observed relationship, which can simply be explained as sampling variability on an underlying stationary teleconnection between ENSO and the Indian monsoon.