C. Mahesh

An evaluation of high-resolution multisatellite rainfall products over the Indian monsoon region

To date, more than half a dozen merged rainfall data sets are available to the research community. These data sets use different approaches for rainfall retrieval and combine different satellites or/and ground-based rainfall observations. However, these data sets appear to differ among themselves and deviate from in situ observations at regional and seasonal scales. Hence, it is becoming difficult to choose a suitable data set from these products for regional rainfall analyses. In the present study, four independently developed multisatellite high-resolution precipitation products (HRPPs), namely Climate Prediction Center Morphing (CMORPH) version 1.0, Naval Research Laboratory (NRL)–blended, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA)–3B42 version 7 are compared with quality-controlled gridded rain gauge data over India developed by the India Meteorological Department (IMD). A preliminary analysis is carried out for a 6 year period from 2004 to 2009 at daily scale for the summer monsoon season of June to September. Comparison of all-India seasonal (June to September) mean rainfall with rain gauge data shows a considerable underestimation by all HRPPs, although the underestimation is comparatively less for TMPA. Moreover, all the HRPPs are able to capture the important characteristic features of the summer monsoon rainfall such as intra-seasonal (active/break spells) and inter-annual (excess/deficient) variabilities reasonably well. Regional differences between observed rainfall and the HRPPs are also analysed. Results suggest that TMPA is comparatively closer to the ground-truth, possibly due to the incorporation of rain gauge observations. Furthermore, all the HRPPs show high probability of detection, low false alarm ratio, and high threat score in detection of rainfall events over most parts of India. It is also observed that all these HRPPs have certain issues in rainfall detection over the rain-shadow region of southeast peninsular India, semi-arid northwest parts of India, and hilly northern parts. Hence, results of the 6 year analysis over a region with a dense network of surface observations of rainfall suggest that the TMPA merged rainfall product is better than the other HRPPs due to (1) lower underestimation of rainfall, (2) higher correlation and lower root-mean-square error (RMSE), and (3) better performance over the west coast. Therefore, TMPA can be used with confidence as compared to other HRPPs for monsoon studies, particularly over the Indian land region with a considerable rain gauge network. This study also clarifies the fact that the merged satellite rainfall products with sufficient ground-truths can be the ideal product for monsoon and hydrological studies.

Comparison of TRMM Multi-satellite Precipitation Analysis (TMPA)-3B43 version 6 and 7 products with rain gauge data from ocean buoys

One of the most widely used high-resolution Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) research products, version 6 (V6) has gone through major changes, and consequently version 7 (V7) was recently released. In this study, these two recent versions of TMPA-3B43 product are compared with available rain gauge data from ocean buoys for a 13-year (1998–2010) period to evaluate the changes in error characteristics over the tropical oceans. The precipitation over high precipitation regimes is enhanced by 5–9% in the V7 product, primarily over the ocean. However, root mean square error is unexpectedly enhanced by 2–5% in V7 compared to V6, although the underestimation of high precipitation (more than 10 mm day−1) by V6 product is reduced by about 5–8% in the new version product.

Observed Relationship between Surface Freshwater Flux and Salinity in The North Indian Ocean

Abstract Using 10-year (2001-10) monthly evaporation, precipitation, and sea surface salinity (SSS) datasets, the relationship between local freshwater flux and SSS in the north Indian Ocean (NIO) is evaluated quantitatively. The results suggest a highly positive linear correlation between freshwater flux and SSS in the Arabian Sea (correlation coefficient, R=0.74) and the western equatorial Indian Ocean (R=0.73), whereas the linear relationships are relatively weaker in the Bay of Bengal (R=0.50) and the eastern equatorial Indian Ocean (R=0.40). Additionally, the interannual variations of freshwater flux and SSS and their mutual relationship are investigated in four sub-regions for pre-monsoon, monsoon, and post-monsoon seasons separately. The satellite retrievals of SSS from the Soil Moisture and Ocean Salinity (SMOS) and Aquarius missions can provide continuous and consistent SSS fields for a better understanding of its variability and the differences between the freshwater flux and SSS signals, which are commonly thought to be linearly related.

Meteorological Sub-divisional Scale Rainfall Monitoring Using Kalpana-1 VHRR Measurements

Geostationary satellites provide measurements over a wider geographical area with high temporal sampling, while microwave measurements are more accurate but sparse. For continuous monitoring of the Indian monsoon, geostationary platform would be ideal. In this study, INSAT (Indian National Satellite) Multi-spectral Rainfall Algorithm (IMSRA) has been used for the estimation of rainfall from Kalpana-1 very high resolution radiometer (VHRR) measurements. IMSRA benefits from the relative advantages of infrared and microwave sensors and is operational at the India Meteorological Department (IMD). In this paper, rainfall is estimated over India at meteorological sub-divisional scale during the south-west monsoon season of 2009 using Kalpana-1 satellite measurements. This is the first experimental attempt to generate meteorological sub-divisional scale rainfall maps using Kalpana-1 satellite measurements. The rainfall maps for the south-west monsoon season over the Indian land region are successfully utilised as a space input for the drought monitoring of the year 2009. The results have been compared with the IMD gauge-based accumulated rainfall maps at monthly and seasonal time scales. The qualitative comparison suggests that rainfall maps generated using the present methodology is in good agreement with the IMD rainfall maps. The quantitative comparison of the sub-divisional monthly accumulated rainfall shows a correlation of 0.77 and standard error of 71 mm over the non-orographic regions, whereas a correlation of 0.60 and standard error of 117 mm is observed over the orographic regions. The present study shows that Kalpana-1 satellite-based rainfall estimates (IMSRA technique) can act as a complementary tool for the monsoon monitoring over the Indian meteorological sub-divisions and can be used for various meteorological and hydrological applications.

A feasibility of six-hourly rainfall forecast over central India using model output and remote sensing data

Rainfall forecast has prime importance in an agrarian country like India, wherein the agricultural production is solely dependent on monsoon rainfall. In this paper, an artificial neural network (ANN) technique is used to construct a non-linear mapping between output data from global forecast system (GFS) and rainfall from tropical rainfall measuring mission (TRMM) satellite measurements. The objective of the present study is to generate region-specific six-hourly quantitative rainfall forecast over central India using ANN and resilient propagation learning algorithm. Meteorological variables from the GFS model and precipitation product from TRMM multisatellite precipitation analysis (TMPA) are used as input data for training the network, which generate rainfall forecast for the next time step. The test was performed for central India during the summer monsoon period of 2010. In order to evaluate the potential of rainfall forecast skill over the studied region, the forecast precipitation has been intercompared with TMPA-3B42, and Kalpana-1 derived precipitation products and a statistical analysis was performed. The linear correlation between ANN forecast and TMPA-3B42 rainfall was 0.58, whereas it was 0.52 with Kalpana-1 derived precipitation estimates. The results show that the predicted precipitation by the present technique performs better than GFS model precipitation forecast, and the system indicates a potential for more accurate rainfall forecasting.

Shape Classification of AltiKa 40-Hz Waveforms using Linear Discriminant Analysis and Bayes Decision Rule in the Gujarat Coastal Region

Shape classification of the 40-Hz waveforms obtained by the recently launched AltiKa satellite has been attempted in the paper. Since retracking algorithms suitable for altimeter return echoes based on Brown model are not applicable for the echoes from coastal ocean, specific algorithms are to be devised for such echoes. In the coastal ocean, waveforms display a wide variety of shapes due to varying coastline geometry, and topography. Hence, a proper classification strategy is required for classifying the waveforms into various categories so that suitable retracker could be applied to each category for retrieving the oceanic parameters. The algorithm consists of three steps: feature selection, linear discriminant analysis, and Bayesian classifier. The classification algorithm has been applied to the waveforms in the close proximity of Gujarat coast. Independent validation has been done near the eastern coast of India. Confusion matrices obtained for both the coasts are quite encouraging. Individual examples of classification have been provided for the purpose of illustration.

Model Function for Wind Speed Retrieval from SARAL-AltiKa Radar Altimeter Backscatter: Case Studies with TOPEX and JASON Data

In this article, a forward model is developed for estimation of the microwave nadir-viewing radar backscatter of a sea surface at C-, Ku- and Ka-bands during clear sky conditions. The forward simulations are carried out based on electromagnetic field theory of stratified media under various conditions like, sea surface temperature, salinity and wind. This theoretical basis is tested as a model function for retrieval of oceanic wind speed from the radar backscatter measurements from earlier or existing altimeters such as TOPEX/Poseidon and Jason-2 during non-raining conditions. Results show that the present model provides a better agreement with real altimeter data for wind speed retrieval at Ku-band. Moreover, a multiple regression approach is utilized to find the underlying relationship between radar backscatter and wind speed at Ka-band that could be possibly utilized for the retrieval of oceanic wind speed from the AltiKa radar measurements onboard the recently launched Satellite with Argos and AltiKa (SARAL).

Observational Study of the Oceanic Surface Parameters in the Eastern Indian Ocean During Two Contrasting Dipole Years 2005 and 2006

The Indian Ocean dipole (IOD) is one of the major interannual climate variation signals in this region through the coupled air-sea interactions. In this letter, the oceanic surface parameters in the eastern Indian Ocean during two consecutive contrasting dipole years 2005 (negative) and 2006 (positive) are examined exclusively from satellite and in situ observations. Results showed that there was sea surface temperature anomaly up to ±2°C in the south eastern equatorial Indian Ocean (EEIO) during the mature phase (September-November) of the dipole. Except for the south EEIO, the south of the equator showed very high positive sea level anomaly (up to 45 cm) during the boreal fall (September-November) in 2006 under the influence of the strong positive IOD event. Similar coherent differences were observed in freshwater flux and sea surface salinity anomalies which show the large impact of these dipole events in the south EEIO box. Also, the seasonal rainfall during the northeast Indian monsoon for these two years shows substantial difference over southern India which reveals the paramount impact of IOD on the northeast monsoon rainfall.

Sea level anomalies in the tropical Indian Ocean during two contrasting southwest monsoon years

The sea level anomalies (SLA) in the tropical Indian Ocean (TIO) during two consecutive contrasting southwest monsoon years of 2002 (deficit) and 2003 (normal) are examined using multi-satellite measurements. The rainfall anomalies over the TIO in the month of July show distinct patterns during these two years. The more consistent patterns analogous to rainfall anomalies are reflected in the freshwater flux anomalies which is one of the major contributors of the local sea level change. As a result, the SLA shows distinct features in the north Indian Ocean during these two years. The surface atmospheric convergence and divergence patterns in the TIO are also investigated using multi-satellite wind vectors which are supposed to be correlated with the southwest monsoon rainfall and a key component of sea level change. The results suggest that the eustatic effect and near surface convergence/divergence of winds have significant impact on SLA locally.