Vijay K. Agarwal

A New Algorithm for Wind-Vector Retrieval From Scatterometers

A new efficient algorithm for retrieving wind-vector solutions from scatterometers is developed based on a criterion of minimum normalized standard deviation (NSD) of wind speed derived from backscatter measurements using a geophysical model function (GMF). Its performance has been evaluated through simulations using QSCAT-1 GMF and the QuikSCAT observational geometry. The present algorithm, named the NSD algorithm, is found to be computationally more efficient (two to three times) besides being at par with the maximum-likelihood estimator (MLE) algorithm in terms of retrieval skill, retrieval errors, and distribution of solutions, on the basis of simulations as well as comparison of limited QuikSCAT-data-derived winds with National Centers for Environmental Prediction and European Centre for Medium-Range Weather Forecasts model winds. Simulation results and analysis of sample QuikSCAT data are presented.

Impact of Satellite-Derived Forcings on Numerical Ocean Model Simulations and Study of Sea Surface Salinity Variations in the Indian Ocean

This study focuses on two major aspects: the impact of satellite forcings (winds and precipitation) on the simulations of a multilayer Indian Ocean (IO) model (IOM) and the analysis of the processes responsible for salinity variations in the Indian Ocean during dipole years (1994 and 1997). It is observed that the European Remote Sensing Satellite-2 (ERS-2) scatterometer wind-driven solutions describe the interannual variabilities of sea surface temperature (SST) more realistically than the National Centers for Environmental Prediction (NCEP) wind-driven solutions. The equatorial westward current jet [hereafter referred to as reverse Wyrtki jet (RWJ)] originating near the Sumatra coast in response to anomalous easterlies during fall of 1994 and 1997 is quite strong in the scatterometer-forced solutions. This RWJ is found to be weak in the NCEP solution. Two more experiments differing by their precipitation forcings [climatological and interannually varying Global Precipitation Climatology Project (GPCP) rainfall] are carried out. Model-simulated variables like SST, sea surface salinity (SSS), and mixed layer depth (MLD) have been compared with in situ observations to verify the performance of the model. The model suggests a dipolelike structure in surface salinity during late 1994 and 1997, with low salinity in the central equatorial Indian Ocean (EIO) and high salinity near the Sumatra coast. The low-salinity tongue is caused by the transport of fresh surface waters via RWJ, which is further strengthened by a southward branch (which is absent in normal years) coming from the Bay of Bengal. A major inference of the study is that the low-salinity tongue is caused mainly by advection, not by a direct effect of precipitation. On the contrary, the high salinity near the Sumatra coast is due to the strong upwelling caused by anomalous easterlies. Another inference made out of this study is that there is apparently a definite signature of the evolution of the dipole event in the MLD approximately 2 months prior to the peak occurring in SSS in the south EIO.

Study of the mixed layer depth variations within the north Indian Ocean using a 1-D model

[1] Mixed layer depth (MLD) over the north Indian Ocean (30°S to 30°N and 40°E to 110°E) is computed using the simple one-dimensional model of Price et al. [1986] forced by satellite-derived parameters (winds and chlorophyll). Seasonal chlorophyll observations obtained from the Coastal Zone Color Scanner allow us to examine how biology interacts with physics in the upper ocean by changing the absorption of light and thus the heating by penetrative solar radiation, an effect we refer to as biological heating. Our analysis focus mainly on two aspects: the importance of varying biology in the model simulations relative to runs with constant biology and secondly, the contribution of biology to the seasonal variability of the MLD. The model results are compared with observations from a surface mooring deployed for I year (October 1994 to October 1995) in the central Arabian Sea and also with available conductivity-temperature-depth (CTD) observations from the Arabian Sea during the period 1994-1995. The effect of biological heating on the upper ocean thermal structure in central Arabian Sea is found to be greatest in August. In other months it is either the wind, which is the controlling factor in mixed layer variations, or the density variations due to winter cooling and internal dynamics. A large number of CTD observations collected under the Joint Global Ocean Flux study and World Ocean Circulation Experiment have been used to validate model results. We find an overall improvement by approximately 2-3 m in root-mean-square error in MLD estimates when seasonally varying chlorophyll observations are used in the model.

Simulated Sea Surface Salinity Variability in the Tropical Indian Ocean

Abstract A long-period (15 yr) simulation of sea surface salinity (SSS) obtained from a hindcast run of an ocean general circulation model (OGCM) forced by the NCEP–NCAR daily reanalysis product is analyzed in the tropical Indian Ocean (TIO). The objective of the study is twofold: assess the capability of the model to provide realistic simulations of SSS and characterize the SSS variability in view of upcoming satellite salinity missions. Model fields are evaluated in terms of mean, standard deviation, and characteristic temporal scales of SSS variability. Results show that the standard deviations range from 0.2 to 1.5 psu, with larger values in regions with strong seasonal transitions of surface currents (south of India) and along the coast in the Bay of Bengal (strong Kelvin-wave-induced currents). Comparison of simulated SSS with collocated SSS measurements from the National Oceanographic Data Center and Argo floats resulted in a high correlation of 0.85 and a root-mean-square error (RMSE) of 0.4 psu. ...

Backpropagation neural-network-based retrieval of atmospheric water vapor and cloud liquid water from IRS-P4 MSMR

A new multiparameter retrieval algorithm based on a backpropagation neural network (BPNN) has been developed for deriving integrated water vapor (WV) and cloud liquid water (CLW) contents over oceans from brightness temperatures (BTs) measured by the Multi-frequency Scanning Microwave Radiometer (MSMR) launched onboard Indian Remote Sensing satellite IRS-P4. The MSMR measures brightness temperatures in vertical and horizontal polarizations at 6.0-, 10.65-, 18.0-, and 21.0-GHz frequencies. The data are available at three spatial grid resolutions of 150, 75, and 50 km. In this paper, a BPNN has been trained using brightness temperatures simulated through radiative transfer model and simulated surface and atmospheric parameters. The present algorithm has been compared with the operational MSMR retrieval algorithm based on statistical regression using the same dataset. The validation of WV with in situ data (Vaisala radiosonde) is presented. Moreover, comparison of WV and CLW derived from MSMR using BPNN with the finished products from the Special Sensor Microwave/Imager and the Tropical Rainfall Measuring Mission Microwave Imager has also been carried out. The complexity of the BPNN in retrieval of geophysical products, individually and simultaneously, has also been discussed. Simultaneous retrieval of WV and CLW improves the results.

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.

Derivation of Salinity Profiles in the Indian Ocean from Satellite Surface Observations

Subsurface salinity profiles in the Indian Ocean have been constructed using a combined empirical orthogonal function analysis technique and a nonlinear data-fitting algorithm, which is known as a genetic algorithm. The purpose is to establish a methodology to generate a 3-D structure of salinity using satellite-derived surface observations, which will be useful for assimilating in numerical models. This acquires greater significance in view of the upcoming satellite salinity missions. In this letter, we have been able to generate vertical profiles of salinity using a combination of sea surface temperature derived by the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager onboard TRMM and sea surface salinity from Argo floats. The root mean square error of the retrieved salinity varies from less than 0.05 psu-78 at the surface to about 0.15 psu-78 at greater depths. A sensitivity study of the retrieval algorithm with the anticipated uncertainties in surface salinity from the European Soil Moisture and Ocean Salinity satellite has also been carried out

Impact of Satellite-Derived Precipitation on Simulated Sea-Surface Salinity in the Tropical Indian Ocean

The impact of satellite-derived precipitation on variability of sea-surface salinity (SSS) in the tropical Indian Ocean has been studied using an ocean general-circulation model. Two different experiments have been conducted. In one of the experiments, the model has been forced by precipitation derived from National Center for Environmental Prediction (NCEP) reanalysis, while in the other one, the model has been forced by satellite-derived precipitation. The time span of the experiments is 2003-2005. The simulations have been compared with data from buoy located at 90°E and 1.5°. The comparison suggests that the simulation forced by satellite precipitation captures the high-frequency variability much better than that forced by NCEP precipitation. The reason for this lies in the fact that the regions of high-frequency variability in SSS coincide with the regions of high-frequency variability in the satellite precipitation. As far as the low-frequency part of the SSS variability is concerned, it was found that this was governed by advective process. Hence, satellite precipitation does not have significant impact on this scale of variability.

A study of air-sea interaction following the tsunami of 26 December 2004 in the eastern Indian Ocean

A time series analysis of various atmospheric as well as oceanic parameters derived from different satellites covering eastern Indian Ocean and Bay of Bengal has been carried out for the period December 2004 to February 2005. The purpose is to assess the likely perturbations in the air–sea exchanges associated with the tsunami event of 26 December 2004. Satellite derived sea level anomaly in the northern Bay of Bengal show a rise in the sea level of roughly about 5–6 cm after 26 December 2004. A significant cooling of the order of 0.5°C in a span of 5 days (21–27 December 2004) in the sea surface temperature (SST) is observed near Andaman and Nicobar Island. The formation of such anomalies is certainly associated with the rising of the sub‐surface water towards surface due to the enhanced turbulent exchange. An enhanced turbidity near Sumatra seen in the MODIS colour data in the week following 26 December 2004 is also suggestive of turbulent mixing. After an initial dip in the integrated water vapour (IWV) on 27 December 2004, an apparent growth in the water vapour loading is observed in the Tropical Rainfall Measuring Mission/Microwave Imager (TMI) data from 31 December 2004 to 11 January 2005 near Andaman and Nicobar Island. An analysis of the boundary layer parameters suggests that the increased water vapour loading in the atmosphere following the tsunami is not due to the winds and neither due to the increase in SST. One of the possible reasons could be direct injection of water into the atmosphere. The study could be used as an indicative to understand changes in a global context under sea level rise scenario.

Assimilation of altimeter significant wave height into a third-generation global spectral wave model

Data from three different altimeters (Topex/Poseidon, Jason-1, and European Remote Sensing Satellite 2) have been assimilated in a third-generation global spectral wave model forced by winds observed by scatterometer onboard QuikSCAT. Two different approaches of assimilation have been discussed. In the first approach, a simple scaling has been used to generate wave spectrum from altimeter-derived significant wave heights for assimilation in the model. In the second approach, the influence of altimeter observation has been spread to nearby grid points. Assimilation has been carried out every 6 h for five days. After the expiry of the assimilation phase, the model has been run in pure hindcast mode. Assimilation experiments have been carried out for the months of September and December 2002. Impact of assimilation has been found to be quite high in the Indian Ocean. It has been also found that the model is able to retain the memory of assimilation for a period of two and a half days as far as global ocean is concerned. This memory is more for the Indian Ocean. The wave spectrum generated by the model in the hindcast mode has been validated against the buoy-observed wave spectrum in the high sea conditions. The more significant impact has been seen in the case of altimeter track in the vicinity of the buoy.