B. S. Gohil

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.

Evaluation of OSCAR ocean surface current product in the tropical Indian Ocean using in situ data

The OSCAR (ocean surface current analysis real-time), which is a product derived from various satellite observations, has been evaluated in the tropical Indian Ocean (TIO) in two different ways. First, the OSCAR-derived monthly climatology has been compared with available drifter-derived climatology in the TIO. From the comparison of the two climatologies, one can infer that OSCAR product is able to capture the variabilities of the well-known surface current systems in the TIO reasonably well. Fourier analysis of the major current systems, as reproduced by OSCAR, shows that the dominant annual and semiannual periodicities, known to exist in these systems, have been faithfully picked up by OSCAR. Next, the evaluation has been carried out by comparing the OSCAR currents with currents measured by moored buoys. The zonal component of OSCAR-current is in good agreement with corresponding component of buoy-observed current with a correlation exceeding 0.7, while the match between the meridional components is poorer. The locations of the peaks of the mean and eddy kinetic energies are matching in both the climatologies, although the peak in the drifter climatology is stronger than the same in the OSCAR product. Finally, an important feature of Indian Ocean circulation, namely the reverse Wyrtki jet, occurring during anomalous dipole years, has been well-reproduced by OSCAR currents.

Correction to “Directional Stability and Conservation of Scattering (DiSCS)-Based Directional-Ambiguity Removal Algorithm for Improving Wind Fields From Scatterometer: A QuikSCAT Example” [Jul 10 592-595]

A new directional-ambiguity removal algorithm has been developed for improving the wind fields derived from a scatterometer. Exploiting the natural spatial variability of ocean surface wind directions and invoking the conservation of backscattering, a new directional stability-weighted low-pass filter has been designed to complement the circular median filtering. It improves the directional-ambiguity removal by reducing the directional noise while retaining the natural wind flow. On the basis of analysis, which will be described later, indicating the lower spatial stability (conversely, the variability) of the wind direction and the lower direction-retrieval skills of the scatterometer-derived winds at low-intensity winds (<; 3 m/s) as compared to those for winds above 3 m/s, the ambiguity removal has been performed separately for these two wind regimes. The proposed Directional Stability and Conservation of Scattering (DiSCS) algorithm is applicable to the full swath of the scatterometer. Using the DiSCS algorithm, the wind fields derived from QuikSCAT Level-2A backscatter data over global oceans for 57 orbits during July 1-4, 2005, have been compared with the winds from the National Centers for Environmental Predictions and European Centre for Medium-Range Weather Forecasts models. Similar comparisons of QuikSCAT Level-2B finished products “direction interval retrieval with thresholded nudging” winds (referred to hereinafter as FP) with the winds from these two models have been performed. The QuikSCAT-derived winds are found to be closer to the model winds as compared to those QuikSCAT FP winds.

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.

A two-frequency logarithmic differential technique for retrieving precipitable water from the satellite microwave radiometer (SAMIR-II) on board BHASKARA II

The Satellite Microwave Radiometer (SAMIR-II) on board the second Indian remote sensing satellite Bhaskara-II launched on November 20, 1981 measured microwave radiation at signals of 19.35, 22.235, and 31.4 GHz. The observations are primarily affected by water vapor (WV), cloud liquid water, and wind speed. In general regression approach, a linear combination of all the three channels or its subset could be used for parameter retrieval. An improved algorithm using a logarithmic function of the differential signal of 22.235 and 19.35 GHz as predictor for retrieving water vapor has been developed. The effects of cloud liquid water and wind speed cancel out in the present algorithm because of very small differences in cloud liquid transmittances and emissivities at 1935 and 22.235 GHz. The in situ comparisons of the satellite derived precipitable water with radiosonde measurements gave an rms difference of 0.31 gm/cm2 for water vapor range of 2.9 to 6.4 gm/cm2. The analysis showed an improvement of 44 percent over the general regression approach where brightness temperatures were used as predictors. However, the present differential approach gave an rms accuracy of 0.31 gm/cm2, identical to the accuracy obtained when functions of the brightness temperatures were used as predictors in the regression. The WV distribution over the Arabian sea has been studied for the periods 25 May to 25 June 1982 and 23 November 1981 to 31 January 1982. The study reveals the presence of high moisture value (6 gm/cm2) over the north and northeast part of the Arabian sea with a relatively low (2.5 to 3.5 gm/cm2) moisture content over the east African coast. The winter season indicates the low value (3 to 4 gm/cm2) of WV over the Arabia sea with the persistance of high equatorial moisture value (5 gm/cm2). Both summer and winter seasons WV distribution show a strong latitudinal variation.

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.

Development of Geophysical Model Functions for Oceansat-2 Scatterometer

This letter deals with the development of geophysical model functions (GMFs) specific to Oceansat-2 Scatterometer (OSCAT) for the operational retrieval of global ocean surface vector winds from OSCAT. It makes use of OSCAT-measured radar backscatter and the surface analysis winds at 10-m neutral-stability height from the European Centre for Medium-Range Weather Forecasts (ECMWF) model for the period from November 2009 to April 2010. The OSCAT-specific GMF has been used for retrieving the vector solutions from backscatter data as well as for the directional ambiguity removal. The procedures and the conditions incorporated for the same are presented in this letter.

Intercomparison of OSCAT Winds With Numerical-Model-Generated Winds

Subsequent to the launch of an ocean scatterometer onboard the Oceansat-2 satellite, hereby referred to as OSCAT, on September 23, 2009, the nine-month period from November 2009 to July 2010 was the validation phase for the retrieved ocean surface winds. This letter focuses on one section of the validation campaign, where the validation study for OSCAT winds with European Centre for Medium-Range Weather Forecasts (ECMWF) and National Centers for Environmental Prediction (NCEP) analyses is carried out. It is found that, in the 4-24-m/s range of wind speed, the rms error in OSCAT wind speed with ECMWF analysis is 1.4 m/s while that with NCEP analysis is 1.7 m/s. In the case of retrieved wind direction, the rms error is 17.2° with ECMWF analyses, while that for NCEP is 18.8°. These statistics are well within the mission goal of 2-m/s accuracy in wind speed and 20 ° in wind directions. This letter discusses the comparison results for different geographical regions and wind speed ranges.

Intercomparison of IRS-P4-MSMR derived geophysical products with DMSP-SSM/I and TRMM-TMI finished products

In this paper, MSMR geophysical products like Integrated Water Vapour (IWV), Ocean Surface Wind Speed (OWS) and Cloud Liquid Water (CLW) in different grids of 50, 75 and 150 kms are compared with similar products available from other satellites like DMSP-SSM/I and TRMMTMI. MSMR derived IWV, OWS and CLW compare well with SSM/I and TMI finished products. Comparison of MSMR derived CLW with that derived from TMI and SSM/I is relatively in less agreement. This is possibly due to the use of 37 GHz in SSM/I and TMI that is highly sensitive to CLW, while 37 GHz channels are not available on MSMR. Monthly comparison of MSMR geophysical products with those from TMI is all carried out for climatological purpose. The monthly comparisons were much better compared to instantaneous comparisons. In this paper, details of the data analysis and comparison results are presented. The usefulness of the MSMR vis-à-vis other sensors is also discussed.

Wind vector retrieval algorithm for Oceansat-2 scatterometer

The forthcoming Indian satellite Oceansat-2 to be launched in 2007 will carry a microwave scatterometer and an ocean colour monitor onboard. The scatterometer, a Ku-band pencil beam sensor similar to that onboard Quikscat satellite, will provide surface vector winds over global oceans with a two days repetivity. An algorithm for retrieving wind vector from scatterometer has been developed with a solution ranking criteria of minimum normalized standard deviation (NSD) of wind speeds derived using backscatter measurements through a geophysical model function (GMF). Using Quikscat observational geometry and QSCAT-1 GMF, simulation based evaluation of algorithm performance under different noise conditions and its comparison with standard algorithm known as Maximum Likelihood Estimator (MLE) algorithm have been performed. Besides having retrieval performance closely comparable with MLE, the present algorithm has quality and rain flagging provisions. Moreover, it is computationally efficient with least subjectivity on various retrieval related parameters. These features are equally desirable for the operational implementation. Results of simulation studies related to retrieval, quality control and rain flagging along with its implementation to limited Quikscat data are presented.