Multi-frequency Scanning Microwave Radiometer Research Articles

A comparative assessment of IRS-P4 (MSMR)-derived sea surface temperature and sea surface wind speed over the north Indian Ocean

Indian Remote Sensing Satellite (IRS-P4) multi-frequency scanning microwave radiometer (MSMR) provides geophysical parameters like sea surface temperature (SST), sea surface wind speed (SSWS), integrated water vapour (IWV) and cloud liquid water (CLW). The retrieval procedure of these parameters given by Gohil et al. (2000, Geophysical parameter retrieval over global oceans from IRS-P4 (MSMR). In Preprints, Fifth Pacific Ocean Remote Sensing Conference, 5–8 December 2000, Goa, India (Goa: National Institute of Oceanography), pp. 207–211) was summarized by Sharma et al. (2002, Identification of large scale atmospheric and oceanic features from IRS-P4 multifrequency scanning microwave radiometer: preliminary results. Journal of Atmospheric and Oceanic Technology, 19, pp. 1127–1134) and Jena (2007, Studies on the retrieval, validation and applications of geophysical parameters from IRS-P4 (MSMR) data. PhD thesis, Berhampur University, Orissa). Demonstration of self-consistency of these parameters has primary scientific importance. This article deals with the validation of MSMR geophysical parameters such as SST and SSWS with in situ observations (buoy data) over the north Indian Ocean during 2000. Result shows that the MSMR-derived SST and SSWS can be utilized for several applications because of their reasonable accuracy and coverage even under cloudy condition.

Application of Artificial Neural Networks for Sea-Surface Wind-Speed Retrieval From IRS-P4 (MSMR) Brightness Temperature

Indian Remote Sensing Satellite Multifrequency Scanning Microwave Radiometer (MSMR)-measured brightness temperatures (TB) in 6.6-, 10.65-, 18-, and 21-GHz channels with dual polarizations were utilized to retrieve sea-surface wind speed (SSWS). A concurrent and collocated database was constructed on MSMR TB- and deep-sea (DS)-buoy-recorded wind speeds for the period of June 1999-July 2001 over the north Indian Ocean. A radial-basis-function-based artificial-neural-network (ANN) algorithm was developed to estimate SSWS from MSMR TB values. Multiple ANNs were generated by the systematic variation of the architecture of input- and hidden-layer nodes. The performance of the most successful algorithm was evaluated based on statistical summary and network performance. The accuracy of the ANN-based wind-speed algorithm was compared with DS-buoy observations, and the result was then compared with the output of the regression analysis between buoy- and MSMR operational-global-retrieval-algorithm (OGRA)-derived SSWS values. On the average, 84% (92%) of ANN-estimated MSMR SSWS observations are within ±2 m/s ( ±3 m/s) when compared with DS-buoy observations. The correlation and root mean square error of 0.80 and 1.79 m/s, respectively, for ANN-predicted SSWS values are much better than that obtained from OGRA. The performance of the ANN algorithm was also evaluated during a super cyclone (October 1999) over the Bay of Bengal. The ANN algorithm could capture the high cyclonic winds, and the values match reasonably well with Special Sensor Microwave/Imager and SeaWinds Scatterometer (QuikSCAT) operational wind products.

Application of Oceansat-1 MSMR analysed winds to marine navigation

The launch of the Indian Remote Sensing Satellite IRS-P4 (Oceansat-1) has opened up many important applications. Meteorological navigation is one of the new avenues, which was suffering due to the lack of a synoptic wave database. The research in this area was hindered mainly by the unavailability of real-time wind data. Now, it has become feasible to carry out routine wave forecasting. The Multi-frequency Scanning Microwave Radiometer (MSMR) channel of the satellite gives scalar wind information. This is analysed at the National Center for Medium-range Weather Forecast (NCMWF), India and converted to vector wind information. The 6-hourly u- and v-components of wind in a 1.5° grid covering both the north and south Indian Ocean are available to the research community for further application. In the present study the information has been used to force a third generation spectral wave model (WAM). The simulations are performed for the rough weather month of July 1999 for the regional grid system of the Indian Ocean. This simulated wave climate formed the basis for computing effective ship velocity in the irregular seaway. This study gives a quantitative estimation of change in ship velocity in the open Indian Ocean for a tanker. The optimal path is charted using Dijkstra's algorithm for the optimum route voyage from Calcutta to Singapore and vice versa.

A Comparative Assessment of Surface Wind Speed and Sea Surface Temperature over the Indian Ocean by TMI, MSMR, and ERA-40

A 2-yr (June 1999–June 2001) observation of ocean surface wind speed (SWS) and sea surface temperature (SST) derived from microwave radiometer measurements made by a multifrequency scanning microwave radiometer (MSMR) and the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) is compared with direct measurements by Indian Ocean buoys. Also, for the first time SWS and SST values of the same period obtained from 40-yr ECMWF Re-Analysis (ERA-40) have been evaluated with these buoy observations. The SWS and SST are shown to have standard deviations of 1.77 m s−1 and 0.60 K for TMI, 2.30 m s−1 and 2.0 K for MSMR, and 2.59 m s−1 and 0.68 K for ERA-40, respectively. Despite the fact that MSMR has a lower-frequency channel, larger values of bias and standard deviation (STD) are found compared to those of TMI. The performance of SST retrieval during the daytime is found to be better than that at nighttime. The analysis carried out for different seasons has raised an important question as to why one spaceborne instrument (TMI) yields retrievals with similar biases during both pre- and postmonsoon periods and the other (MSMR) yields drastically different results. The large bias at low wind speeds is believed to be due to the poorer sensitivity of microwave emissivity variations at low wind speeds. The extreme SWS case study (cyclonic condition) showed that satellite-retrieved SWS captured the trend and absolute magnitudes as reflected by in situ observations, while the model (ERA-40) failed to do so. This result has direct implications on the real-time application of satellite winds in monitoring extreme weather events.

Soil moisture initialization for climate prediction: Assimilation of scanning multifrequency microwave radiometer soil moisture data into a land surface model

Climate model prediction skill is currently limited in response to poor land surface soil moisture state initialization. However, initial soil moisture state prediction skill can potentially be enhanced by the assimilation of remotely sensed near‐surface soil moisture data in off‐line simulation. This study is one of the first to evaluate such potential using actual remote sensing data together with field observations. Here the ensemble Kalman filter (Kalman, 1960) is used to assimilate scanning multifrequency microwave radiometer derived near‐surface soil moisture data from 1979 to 1987 into the catchment‐based land surface model (CLSM). CLSM is used by the NASA Goddard Modeling and Assimilation Office global climate model. Enhancement to land surface soil moisture initialization skill is evaluated for Eurasia using the ground soil moisture measurements collected in Russia, Mongolia, and China. As initial model and observation error predictions were poor, the assimilation improved both the surface and root zone soil moisture estimates only when the observation error was less than the model error. This emphasizes the need for good quality remotely sensed soil moisture data sets, together with reliable observation and model error assessments, in order to ensure improved soil moisture estimates through data assimilation. When the relative magnitude of predicted observation and model error was matched to the error determined from field observation comparison, improvements in root zone and surface soil moisture estimates were guaranteed given unbiased model and satellite observations.

Evaluation of the Oceansat‐1 Multi‐frequency Scanning Microwave Radiometer and its potential for soil moisture retrieval

The Multi‐frequency Scanning Microwave Radiometer (MSMR) aboard the Indian Space Research Organization—Oceansat‐1 platform measured land surface brightness temperature at a C‐band frequency and provided an opportunity for exploring large‐scale soil moisture retrieval during its two‐year period of operation. These data may provide a valuable extension to the Scanning Multichannel Microwave Radiometer (SMMR) and the Advanced Microwave Scanning Radiometer (AMSR) since they covered a portion of the time period between the two missions. This investigation was one of the first to utilize the MSMR data for a land application and, as a result, several data quality issues had to be addressed. These included geolocation accuracy, calibration (particularly over land), erroneous data, and the significance of anthropogenic radio‐frequency interference (RFI). Calibration of the low frequency channels was evaluated using inter‐comparisons between the Tropical Rainfall Measuring Mission/Microwave Imager (TRMM/TMI) and the MSMR brightness temperatures. Biases (TMI T B>MSMR T B) of 3.4 and 3.6 K were observed over land for the MSMR 10.65 GHz horizontal and vertical polarization channels, respectively. These results suggested that additional calibration of the MSMR data was required. Comparisons between the MSMR measured brightness temperature and ground measured volumetric soil moisture collected during the South Great Plain experiment (SGP99) indicated that the lower frequency and horizontal polarization observations had higher sensitivity to soil moisture. Using a previously developed soil emission model, multi‐temporal regional soil moisture distributions were retrieved for the continental United States. Comparisons between the MSMR based soil moisture and ground measured volumetric soil moisture indicated a standard error of estimate of 0.052 m3/m3.

Inter‐comparison of NOAA‐AVHRR and IRS‐P4 (MSMR) derived sea surface temperatures

Due to the limitations of infrared remote sensing, sea surface temperature (SST) can be derived only under clear sky conditions from the infrared channels like those in National Oceanic and Atmospheric Administration (NOAA) advanced very high resolution radiometer (AVHRR), where as microwave radiometers can provide SST even under cloudy conditions. However, the accuracy of SST derived from the microwave sensor is less with a poor spatial resolution. In this study, SSTs over the Arabian Sea derived from NOAA‐AVHRR and Indian Remote Sensing Satellite (IRS‐P4) multi‐frequency scanning microwave radiometer (MSMR) observations have been compared on weekly basis with a view to blend these two observations, so that SST can be provided continuously even under cloudy conditions. The NOAA‐AVHRR derived pathfinder SSTs with spatial resolution of 18 km were averaged to 1.5°×1.5° grid resolutions to match with MSMR observations. The analysis was carried out during 2000. Statistical analysis of the NOAA‐AVHRR SST shows that the spatial variation of SST within 1.5° grid is negligible compared with the MSMR accuracy of 1.52°C. Thus the comparison of SST from these two sources with different spatial resolution is reasonable. The RMS difference is 1.55°C with a correlation coefficient of 0.73. After removing the seasonal bias, the RMS difference reduced to 0.66°C and the coefficient of correlation improved to 0.89. The correlation coefficient between the two observations has further improved to 0.90 and the RMS difference reduced to 0.53°C when the averaging was done using 5°×5° grid resolution. The accuracy of satellite derived SSTs are also evaluated with the moored buoy observations over the Arabian Sea. The accuracy of MSMR SST observations have improved if 95% confidence level data is considered. The results indicate the possibility of replacing the data gaps in AVHRR SSTs with MSMR estimations after adjusting for the seasonal biases.

Simultaneous MSMR and SSM/I observations and analysis of sea‐ice characteristics over the Antarctic region

Polar sea ice has been monitored quasi‐continuously over the last 30 years using passive microwave radiometers onboard three satellites in polar orbit, namely Nimbus‐5, Nimbus‐7 and Defense Meteorological Satellite Program (DMSP) series. A short overlap between Scanning Multichannel Microwave Radiometer (SMMR) on Nimbus‐7 and Special Sensor Microwave Imager (SSM/I) onboard DMSP allowed inter‐calibration of the two sensors leading to a consistent series of long‐term sea‐ice measurements since 1978. With the launch of Multifrequency Scanning Microwave Radiometer (MSMR) onboard OCEANSAT‐1 in the polar sun‐synchronous orbit during 1999, India developed the capability to monitor the polar sea ice on a regular basis. The concurrent availability of SSM/I and MSMR over the last few years presents a valuable opportunity to attempt an inter‐comparison of MSMR with SSM/I measurements and derived sea‐ice parameters. In this paper, we present an indirect validation of the brightness temperatures (T b) observed by MSMR with near‐simultaneous measurements from SSM/I over the Antarctic and Southern Polar Ocean regions. Simultaneous MSMR and SSM/I data from two contrasting seasons—summer and winter—for the 1999–2000 period have been used. Analysis includes a comparison of T b scatterograms to achieve confidence in the quantitative use of the T b data to derive various geophysical parameters, e.g. sea‐ice concentration and extent. Additionally, the T b images produced by the two sensors are compared to establish the capability of MSMR in reliable two‐dimensional portrayal of all the sea and continental ice features over the Antarctic Region. Based on a regression analysis between MSMR observed T b in different frequency channels and polarizations, and SSM/I‐derived sea‐ice concentration (SIC) values, we have developed algorithms to estimate SIC over the Southern Polar Ocean from MSMR data. The MSMR algorithms allow estimation of SIC with better than 10% rms error. MSMR SIC images faithfully capture the observed distribution of sea ice in all the sectors of the Southern Ocean both during summer and winter periods. Using the MSMR‐derived SIC, we have also derived monthly sea‐ice extent (SIE) estimates for a period extending for about 20 months from the beginning of the launch of MSMR. These estimates show excellent agreement with values derived from SSM/I. These analyses bring out the very high level of compatibility in the measurements and derived sea‐ice parameters produced by the two sensors.

Development of a Time Series–Based Methodology for Estimation of Large-Area Soil Wetness over India Using IRS-P4 Microwave Radiometer Data

AbstractSoil moisture is a very important boundary parameter in numerical weather prediction at different spatial and temporal scales. Satellite-based microwave radiometric observations are considered to be the best because of their high sensitivity to soil moisture, apart from possessing all-weather and day–night observation capabilities with high repetitousness. In the present study, 6.6-GHz horizontal-polarization brightness temperature data from the Multifrequency Scanning Microwave Radiometer (MSMR) onboard the Indian Remote Sensing Satellite IRS-P4 have been used for the estimation of large-area-averaged soil wetness. A methodology has been developed for the estimation of soil wetness for the period of June–July from the time series of MSMR brightness temperatures over India. Maximum and minimum brightness temperatures for each pixel are assigned to the driest and wettest periods, respectively. A daily soil wetness index over each pixel is computed by normalizing brightness temperature observations from these extreme values. This algorithm has the advantage that it takes into account the effect of time-invariant factors, such as vegetation, surface roughness, and soil characteristics, on soil wetness estimation. Weekly soil wetness maps compare well to corresponding weekly rainfall maps depicting clearly the regions of dry and wet soil conditions. Comparisons of MSMR-derived soil wetness with in situ observations show a high correlation (R > 0.75), with a standard error of the soil moisture estimate of less than 7% (volumetric unit) for the surface (0–5 cm) and subsurface (5–10 cm) soil moisture.

Impact of Oceansat-I MSMR data on analyzed oceanic winds and wave predictions

The present study makes an assessment of the impact of satellite data on ocean surface wind analysis and predicted wave heights. The surface wind analysis data utilized in this study were generated by assimilation of satellite data in numerical weather prediction models. The impact of these winds on the wave heights predicted by a third generation ocean wave model (WAM) was also studied. Results of several numerical experiments involving analysis products generated with a multi-frequency scanning microwave radiometer (MSMR) on board the Oceansat-I satellite and their comparison with those generated by the SSM/I radiometer onboard the DMSP satellites, as well as without these satellite products, have been presented. Extensive comparison results of MSMR ingested surface wind analyses (and corresponding model predicted wave heights) with ocean buoy data and co-located and concurrent measurements of the Topex/Poseidon altimeter over the Indian Ocean are presented in this work. The impact of satellite derived winds was also seen through time series analysis. The results of the experiments carried out show that ingestion of MSMR data produces significant improvement in the surface wind analysis and predicted wave heights for the corresponding winds compared to those generated without ingestion of satellite winds. The experiments also show that the surface wind analysis with MSMR ingested surface wind speed is almost as good as that obtained with SSM/I.

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.

Artificial neural network approach for estimation of surface specific humidity and air temperature using multifrequency scanning microwave radiometer

Microwave sensor MSMR (Multifrequency Scanning Microwave Radiometer) data onboard Oceansat-1 was used for retrieval of monthly averages of near surface specific humidity (Q a) and air temperature (T a) by means of Artificial Neural Network (ANN). The MSMR measures the microwave radiances in 8 channels at frequencies of 6.6, 10.7, 18 and 21 GHz for both vertical and horizontal polarizations.The artificial neural networks (ANN) technique is employed to find the transfer function relating the input MSMR observed brightness temperatures and output (Q a andT a) parameters. Input data consist of nearly 28 months (June 1999 – September 2001) of monthly averages of MSMR observed brightness temperature and surface marine observations ofQ a andT a from Comprehensive Ocean-Atmosphere Data Set (COADS).The performance of the algorithm is assessed with independent surface marine observations. The results indicate that the combination of MSMR observed brightness temperatures as input parameters provides reasonable estimates of monthly averaged surface parameters. The global root mean square (rms) differences are 1.0‡C and 1.1 g kg−1 for air temperature and surface specific humidity respectively.

Distribution and intraseasonal variability of rain over Indian Oceanic region from Indian Remote Sensing Satellite IRS‐P4 Multifrequency Scanning Microwave Radiometer

Multifrequency Scanning Microwave Radiometer (MSMR) onboard Indian Remote Sensing Satellite (IRS‐P4) has been utilized for measurement of precipitation over Indian Oceanic region. MSMR‐derived monthly rain rates for the month of August for three consecutive years are compared with that from TMI and PR. A reasonably good agreement is found. Rain distributions are presented for three seasons, namely, premonsoon (February–May), monsoon (June–September), and postmonsoon (October–January) for the entire period of MSMR from June 1999 to September 2001. The year‐to‐year difference in the three seasons is found in agreement with the weather reports. Latitudinal distribution of the MSMR‐derived rain rate for the Arabian Sea and the Bay of Bengal and for the entire study area are analyzed and are found to be in good agreement with climatology. Furthermore, starting from 60°E longitude up to 95°E, at every 5° interval, latitudinal distribution of rain rate is plotted for the three monsoon seasons. It is found that the vigor of monsoon rain begins building up between 65° and 70°E and goes beyond 95°E. A time series and wavelet analysis of daily rain rate is presented for monsoon seasons of three consecutive years. Wavelet analysis clearly shows the presence of long waves of 30–60 and 10–20 day oscillations and also of synoptic scale of 3–6 day oscillations in the monsoon rain. Furthermore, Hovmoller diagrams of daily rain over the northern Bay of Bengal are analyzed for monsoon seasons of three years, and some northward propagation of rain is reported.

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.

A Technique for Direct Retrieval of Surface Specific Humidity Over Open Oceans from Irs-P4/Msmr Satellite Data

The Multi frequency Scanning Microwave Radiometer (MSMR) onboard Oceasat-1 was used to develop a retrieval method fornear-surface specific humidity by means of multivariate regressiontechnique. The MSMR measures the microwaveradiances in 8 channels at the frequencies of 6.6, 10.7, 18 and 21 GHzfor both vertical and horizontal polarizations. Regression coefficients were derived using the ship reports of the Comprehensive Ocean-Atmosphere Data Set (COADS) for the months of July, October and December, in 1999. Daily near-surface specific humidity data from COADS in 2° × 2° latitude/longitude bins and collocated brightness temperature data from MSMR were used to derive the coefficients. The derived coefficients werevalidated with humidity given in COADS.A linear relationship is established to determine the near-surface specifichumidity from MSMR brightness temperature (Tb) with an rms error of 1.2 g kg-1 for individual situations and an rms errorof 0.84 g kg-1 for monthly time scales over global oceans.The retrieval algorithm is validonly for the open sea regions.

Potential of IRS-P4 microwave radiometer data for soil moisture estimation over India

Soil moisture at different temporal and spatial scales is very important for various applications. At smaller spatial scales it has importance for the agro-meteorological applications, whereas at large spatial scales it is an important boundary parameter in the numerical prediction models of atmosphere for monthly to seasonal time-scale integrations. Frequent in situ global measurements of soil moisture at these spatial scales are virtually impossible because large heterogeneity of soil types makes these observations highly expensive and time consuming. Satellite based microwave radiometers can provide indirect estimates of soil moisture at resolutions compatible to that of climate models (50-100 km). In this paper the potential of Multi-frequency Scanning Microwave Radiometer (MSMR) onboard Indian satellite IRS-P4 is assessed for large area averaged soil moisture estimation. These are compared with the weekly-observed in situ soil moisture data over a few observatories of India Meteorological Department (IMD).

Assimilation of IRS-P4 (MSMR) meteorological data in the NCMRWF global data assimilation system

Oceansat-1 was successfully launched by India in 1999, with two payloads, namely Multi-frequency Scanning Microwave Radiometer (MSMR) and Ocean Color Monitor (OCM) to study the biological and physical parameters of the ocean. The MSMR sensor is configured as an eight-channel radiometer using four frequencies with dual polarization. The MSMR data at 75 km resolution from the Oceansat-I have been assimilated in the National Centre for Medium Range Weather Forecasting (NCMRWF) data assimilation forecast system. The operational analysis and forecast system at NCMRWF is based on a T80L18 global spectral model and Spectral Statistical Interpolation (SSI) scheme for data analysis. The impact of the MSMR data is seen globally, however it is significant over the oceanic region where conventional data are rare. The dry-nature of the control analyses have been removed by utilizing the MSMR data. Therefore, the total precipitable water data from MSMR has been identified as a very crucial parameter in this study. The impact of surface wind speed from MSMR is to increase easterlies over the tropical Indian Ocean. Shifting of the positions of westerly troughs and ridges in the south Indian Ocean has contributed to reduction of temperature to around 30‡S.

Retrieval of sea surface velocities using sequential Ocean Colour Monitor (OCM) data

The Indian remote sensing satellite, IRS-P4 (Oceansat-I) launched on May 26th, 1999 carried two sensors on board, i.e., the Ocean Colour Monitor (OCM) and the Multi-frequency Scanning Microwave Radiometer (MSMR) dedicated for oceanographic research. Sequential data of IRS-P4 OCM has been analysed over parts of both east and west coast of India and a methodology to retrieve sea surface current velocities has been applied. The method is based on matching suspended sediment dispersion patterns, in sequential two time lapsed images. The pattern matching is performed on a pair of atmospherically corrected and geo-referenced sequential images by Maximum Cross-Correlation (MCC) technique. The MCC technique involves computing matrices of cross-correlation coefficients and identifying correlation peaks. The movement of the pattern can be calculated knowing the displacement of windows required to match patterns in successive images. The technique provides actual flow during a specified period by integrating both tidal and wind influences. The current velocities retrieved were compared with synchronous data collected along the east coast during the GSI cruise ST-133 of R.V. Samudra Kaustubh in January 2000. The current data were measured using the ocean current meter supplied by the Environmental Measurement and CONtrol (EMCON), Kochi available with the Geological Survey of India, Marine Wing. This current meter can measure direction and magnitude with an accuracy of ±5‡ and 2% respectively. The measurement accuracies with coefficient of determination (R 2) of 0.99, for both magnitude (cm.s-1) and direction (deg.) were achieved.

Identification of Large-Scale Atmospheric and Oceanic Features fromIRS-P4Multifrequency Scanning Microwave Radiometer: Preliminary Results

Abstract Large-scale features of sea surface temperature, wind speed, water vapor, and cloud liquid water, derived from multifrequency scanning microwave radiometer (MSMR) on board Indian oceanographic satellite IRS-P4 could be identified during 15 June–23 August 1999. This is the period during which extensive validation was carried out. MSMR is the only sensor in orbit operating at 6.6 GHz. Average distribution of these parameters brings out large-scale atmospheric and oceanographic features. Zonal averages of these parameters were also studied to examine the consistency of MSMR data over larger spatial scales. Linear correlations between all parameters were also computed to check for the interconsistency of these parameters. The present analysis shows the potential use of MSMR products in studying the oceanographic and atmospheric phenomena.

Impact of MSMR data on NCMRWF Global Data Assimilation System

¶It is very essential to make use of non-conventional remotely sensed data mainly from various satellites for numerical weather prediction. IRS series of Indian satellites have been found very useful for various types of studies. Recently IRS-P4 was successfully launched and its Multi-frequency Scanning Microwave Radiometer (MSMR) sensor is giving near surface wind speed and total precipitable water content over oceanic region. An attempt has been made to assimilate directly these two geophysical parameters derived at 150 km resolution, along with other global meteorological data, in the National Center for Medium Range Weather Forecasting (NCMRWF), New Delhi Global Data Assimilation System. (GDAS).