R. M. Gairola

The SARAL/AltiKa Altimetry Satellite Mission

The India-France SARAL/AltiKa mission is the first Ka-band altimetric mission dedi-cated to oceanography. The mission objectives are primarily the observation of the oceanic mesoscales but also include coastal oceanography, global and regional sea level monitoring, data assimilation, and operational oceanography. Secondary objectives include ice sheet and inland waters monitoring. One year after launch, the results widely confirm the nominal expectations in terms of accuracy, data quality and data availability in general. Todays performances are compliant with specifications with an overall observed performance for the Sea Surface Height RMS of 3.4 cm to be compared to a 4 cm requirement. Some scientific examples are provided that illustrate some salient features of todays SARAL/AltiKa data with regard to standard altimetry: data availability, data accuracy at the mesoscales, data usefulness in costal area, over ice sheet, and for inland waters.

Validation of high-resolution TRMM-3B43 precipitation product using rain gauge measurements over Kyrgyzstan

This paper presents the validation of monthly precipitation using Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis (TMPA)—3B43 product with conventional rain gauge observations for the period 1998–2007 over Kyrgyzstan. This study is carried out to quantify the accuracy of TMPA-3B43 product over the high latitude and complex orographic region. The present work is quite important because it is highly desirable to compare the TMPA precipitation product with the ground truth data at a regional scale, so that the satellite product can be fine-tuned at that scale. For the validation, four different types of spatial collocation have been used: station wise, climatic zone wise, topographically and seasonal. The analysis has been done at the same spatial and temporal scales in order to eliminate the sampling biases in the comparisons. The results show that TMPA-3B43 product has statistically significant correlation (r = 0.36–0.88) with rain gauge data over the most parts of the country. The minimum linear correlation is observed around the large continental water bodies (e.g., Issyk-Kul lake; r = 0.17–0.19). The overall result suggests that the precipitation estimated using TMPA-3B43 product performs reasonably well over the plain regions and even over the orographic regions except near the big lake regions. Also, the negative bias suggests the systematic underestimation of high precipitation by TMPA-3B43 product. The analyses suggest the need of a better algorithm for precipitation estimation over this region separately to capture the different types of rain events more reliably.

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.

Empirical orthogonal function analysis of humidity profiles over the Indian Ocean and an assessment of their retrievability using satellite microwave radiometry

The paper examines the variability of vertical humidity profiles over the Indian oceanic region using a set of 1200 radiosonde observations spanning 10 years (1982–1991). The examination is based upon the method of empirical orthogonal function (EOF) analysis. The first EOF explains 61% of the total variance and the first three EOFs together account for 85% of the total variability. The first principal component is almost perfectly correlated with the total precipitable water (TPW) and the second one is well correlated with the ratio of boundary layer moisture and TPW. This fact and an inequality derived from the analysis of the variances of individual terms of the EOF expansion of specific humidity are utilised to establish an algorithm for retrieving humidity profile from satellite microwave measurement of TPW over the region of study. Power of the retrieval technique is demonstrated using 127 independent radiosonde measurements and by plotting the profiles of rms error and bias. The method is found to be distinctly superior compared to a power-law retrieval. A few examples of profile retrieval from satellite measurements of TPW have been checked against colocated radiosonde measurements. Some examples of retrieval show that the method is uniquely able to capture the humidity variability in the boundary layer, particularly the high moisture loading in the monsoon season.

Rain rate measurements over global oceans from IRS-P4 MSMR

In this paper rain estimation capability of MSMR is explored. MSMR brightness temperature data of six channels corresponding to three frequencies of 10, 18 and 21 GHz are colocated with the TRMM Microwave Imager (TMI) derived rain rates to find a new empirical algorithm for rain rate by multiple regression. Multiple correlation analysis involving various combinations of channels in linear and non-linear forms and rain rate from TMI is carried out, and thus the best possible algorithm for rain rate measurement was identified which involved V and H polarized brightness temperature measurements at 10 and 18 GHz channels. This algorithm explained about 82 per cent correlation (r) with rain rate, and 1.61 mm h-1 of error of estimation.Further, this algorithm is used for generating global average rain rate map for two contrasting months of August (2000) and January (2001) of northern and southern hemispheric summers, respectively. MSMR derived monthly averaged rain rates are compared with similar estimates from TRMM Precipitation Radar (PR), and it was found that MSMR derived rain rates match well, quantitatively and qualitatively, with that from PR.

Comparison of large-scale global land precipitation from multisatellite and reanalysis products with gauge-based GPCC data sets

Reliable information of land precipitation along with other atmospheric variables is crucial for monsoon studies, ecosystem modelling, crop modelling and numerous other applications. In this paper, three multisatellite and three reanalysis precipitation products, namely Global Precipitation Climatology Project (GPCP), Climate Prediction Center Mapping of Precipitation (CMAP1 and CMAP2), European Center for Medium Range Weather Forecasts Reanalysis-Interim (ERA-I) and National Center for Environmental Prediction (NCEP1 and NCEP2), are compared with the recent version of gauge-based gridded Global Precipitation Climatology Centre (GPCC) data sets over the global land region. The analysis is done at monthly scale and at 2.5° latitude × 2.5° longitude resolution for a 25-year (1986–2010) period. Large-scale prominent features of precipitation and its variability are qualitatively represented by all the precipitation products. However, the magnitudes considerably differ among themselves. Among the six precipitation products, GPCP performs better than the others when compared to the gridded GPCC data sets. Among the three reanalysis precipitation products, ERA-I is better than NCEP1 and NCEP2 in general. Even though NCEP2 is improved over NCEP1 over the mid-latitudes, NCEP2 has more serious problem over the orographic regions than that of NCEP1. Moreover, all the precipitation estimates exhibit similar kind of interannual variability over the global and tropical land regions. Additionally, the comparison is done for the six global monsoon regions for the regional analysis which shows that all the precipitation estimates exhibit similar kind of interannual variability in the seasonal monsoon precipitation. However, there are some regional differences among these precipitation products in the representation of monsoon variability.

A combined deficit index for regional agricultural drought assessment over semi-arid tract of India using geostationary meteorological satellite data

Abstract The untimely onset and uneven distribution of south-west monsoon rainfall lead to agricultural drought causing reduction in food-grain production with high vulnerability over semi-arid tract (SAT) of India. A combined deficit index (CDI) has been developed from tri-monthly sum of deficit in antecedent rainfall and deficit in monthly vegetation vigor with a lag period of one month between the two. The formulation of CDI used a core biophysical (e.g., NDVI) and a hydro-meteorological (e.g., rainfall) variables derived using observation from Indian geostationary satellites. The CDI was tested and evaluated in two drought years (2009 and 2012) within a span of five years (2009–2013) over SAT. The index was found to have good correlation (0.49–0.68) with standardized precipitation index (SPI) computed from rain-gauge measurements but showed lower correlation with anomaly in monthly land surface temperature (LST). Significant correlations were found between CDI and reduction in agricultural carbon productivity (0.67–0.83), evapotranspiration (0.64–0.73), agricultural grain yield (0.70–0.85). Inconsistent correlation between CDI and ET reduction was noticed in 2012 in contrast to consistent correlation between CDI and reduction in carbon productivity both in 2009 and 2012. The comparison of CDI-based drought-affected area with those from existing operational approach showed 75% overlapping regions though class-to-class matching was only 40–45%. The results demonstrated that CDI is a potential indicator for assessment of late-season regional agricultural drought based on lag-response between water supply and crop vigor.

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.

An empirical algorithm for cloud liquid water from MSMR and its utilization in rain identification

In this paper, an empirical method to estimate cloud liquid water from Indian Remote Sensing P4 (IRS-P4) Multi-frequency Scanning Microwave Radiometer (MSMR) measurements is presented. MSMR brightness temperatures are collocated with concurrent observations of the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI)-derived cloud liquid water. The multiple-correlation coefficient between TMI-derived cloud liquid water and logarithmic of MSMR-derived brightness temperatures, and their differences at 18- and 21-GHz channels, is found to be about 82.4%. The relationship thus obtained has an rms error of 8.75 mgcm/sup -2/ in the measurements of cloud liquid water from MSMR with respect to TMI measurements. Verification of the algorithm is carried out with another set of concurrent measurements from MSMR and TMI. Further, the MSMR-derived cloud liquid water over the global oceans and for extreme weather conditions (cyclone) are compared with that from TMI and the Special Sensor Microwave/Imager (SSM/I) for independent verification. The cloud liquid water from MSMR is further used to successfully delineate rain events for quantitative estimation of rain rate from MSMR.