A. K. Varma

Small-Scale Horizontal Rain-Rate Variability Observed by Satellite

Abstract The horizontal distribution of rain rates within an area comparable to the pixel size of satellite microwave radiometers and the grid size of numerical weather prediction models has been studied over the global Tropics using three years of the Tropical Rainfall Measuring Mission satellite precipitation radar (PR) data. The global distribution of rain-rate standard deviation derived from the PR data suggests that the horizontal variability of rain rates is largely influenced by two factors: surface type (land or ocean) and latitudinal location (tropical or extratropical). Except for light stratiform rain, the land–ocean contrast seems to be the dominant feature for the differences in conditional probability density functions (PDFs) of rain rate. That is, oceanic rain-rate distribution is narrower when the rain rate is low, but becomes broader when the rain rate is high. For light stratiform rain, there is no clear difference among the rain-rate PDFs for rain events over land and ocean. The latitud...

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.

Potential of simultaneous dual-frequency radar altimeter measurements from TOPEX/Poseidon for rainfall estimation over oceans

Abstract TOPEX/Poseidon — a joint U.S. (NASA) and French (CNES) mission called Ocean Topography Experiment — carries onboard a dual frequency (5.3 GHz and 13.6 GHz) radar altimeter providing simultaneous measurements of radar backscatter coefficient over the global oceans since August 1992. In the present work, we have used the concept of differential attenuation of the radar signal due to rain at two widely separated frequencies to estimate rainfall. Simultaneously available passive microwave radiometric measurements from TOPEX/Poseidon itself have also been used to delineate and quantify rain over oceanic regions surrounding India. Based on a reasonably good correlation between rainfall inferred from radiometric measurements and the difference δσ° between radar backscatter coefficients at 5 GHz and 13 GHz, significant rain events are isolated during the course of the 1993 South-West (SW) monsoon season over the Indian region. Monthly maps of these rain events from altimeter based analysis clearly bring out the nature of rainfall activity associated with SW monsoon. The results from δσ° also compare very well with the operational quantitative precipitation estimates available from INSAT-VHRR analyses. The advantages and limitations of radar altimeter data are discussed in terms of those of the other current and future rain measurement systems. Synergistic application of the present technique with visible/IR and microwave techniques hold promise for more precise rainfall measurements from space.

Auto-correlation analysis of ocean surface wind vectors

The nature of the inherent temporal variability of surface winds is analyzed by comparison of winds obtained through different measurement methods. In this work, an auto-correlation analysis of a time series data of surface winds measuredin situ by a deep water buoy in the Indian Ocean has been carried out. Hourly time series data available for 240 hours in the month of May, 1999 were subjected to an auto-correlation analysis. The analysis indicates an exponential fall of the autocorrelation in the first few hours with a decorrelation time scale of about 6 hours. For a meaningful comparison between satellite derived products andin situ data, satellite data acquired at different time intervals should be used with appropriate ‘weights’, rather than treating the data as concurrent in time. This paper presents a scheme for temporal weighting using the auto-correlation analysis. These temporal ‘weights’ can potentially improve the root mean square (rms) deviation between satellite andin situ measurements. A case study using the TRMM Microwave Imager (TMI) and Indian Ocean buoy wind speed data resulted in an improvement of about 10%.

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.

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.

Use of TOPEX altimeter for the study of diurnal and spatial distribution of southwest monsoon rainfall over the Bay of Bengal and the Arabian Sea

In this paper, the rain detection capability of the dual-frequency (Ku and C band) radar altimeter onboard, the non-sun-synchronous TOPEX/Poseidon (T/P) satellite is exploited to study the diurnal variability of rainfall over Indian oceanic regions during the southwest monsoon season. The study is done using three consecutive years (1993, 1994 and 1995) of T/P altimeter data. Based on the difference of normalized backscattered coefficient, Δσo (C–Ku band), the T/P satellite observations are categorized into three different classes of “no rain,” “low rain,” and “high rain.” The data is further divided into 12 time intervals of 2 h each, starting from 0000 to 2400 Z. The uneven distribution of observations in each time interval due to orbit characteristics of T/P satellite is removed. The histograms of rain events are plotted for the Bay of Bengal and the Arabian Sea to study diurnal and regional variability of rain events. The variation of rainfall intensity, within “high-rain” category, over the two regions is also studied. The results showed that there is no consistent diurnal variability of rainfall exist over the Arabian Sea and the Bay of Bengal regions from year to year. However, the 3-year composite data shows more rain events over the Arabian Sea at early morning hours between 0000 and 0200 GMT. This is verified by concurrently available TOPEX Microwave Radiometer (TMR) observations of rain events. The intensity of rain rate also does not show any marked diurnal variability. The probability of rain events is significantly high over the Bay of Bengal region compared to the Arabian Sea region. This is also verified with TMR-based analysis. Further, interannual variability of rain events and amount over the two regions from Δσo-based analysis is also discussed in association with interannual variations in the monsoon activities over these two regions.

Rain rate estimation from nadir-looking TOPEX/POSEIDON microwave radiometer (TMR) for correction of radar altimetric measurements

Atmospheric liquid water, particularly in the form of rain, produces anomalies in the radar altimetric range measurements. Such features are observed as sudden large changes in radar backscatter as a means of identification. To quantify the rain that radar altimetric pulses encounter, the instantaneous rain estimation capability of the nadir looking multichannel microwave radiometer onboard the TOPEX/POSEIDON satellite is explored. The three frequency (18, 21, and 37 GHz) nadir looking TOPEX microwave radiometer (TMR) brightness temperature data are colocated with the special sensor microwave/imager (SSM/I) rain rates to find a new rain rate algorithm by regression over the Indian Sea. Among the colocated data on different spatial and temporal scales, the most restrictive criteria (<0.1/spl deg/, <1 h apart) produce the best correlations between the SSM/I estimated rain rates and the TMR brightness temperatures. The TMR measurements, colocated with SSM/I, thus presents a nontraditional usage of nadir viewing microwave radiometer data for estimation of instantaneous rainfall for correction of the radar altimetric measurements over the oceans. This equation is further used to generate monthwise-averaged global rain rate maps for the year 1993. Typical rain rate maps for two contrasting seasons for the months of January and July 1993, during the northeast and southwest monsoon, respectively, are compared with similar maps of the SSM/I rain rate. It is found that all the major features of global rainfall are picked up accurately and reproduced by the TMR-based algorithm. The mean rainfall rate thus derived (totaling a month) also is analyzed with some simultaneous atmospheric and oceanic processes in mind, which couple each other through rainfall.

Estimation of large scale monthly rainfall over Indian region using minimal INSAT-VHRR data

Abstract In this paper, we have examined the possibility of minimizing the number of geostationary Very High Resolution Radiometer (VHRR) images required for the estimation of rainfall on large time and space scales using the Arkins approach. In the selection of appropriate images we are guided by our knowledge of the pattern of diurnal variability of cloudiness/rainfall over the region of interest. For the present work, INSAT-VHRR thermal band images over the Indian region for the month of June 1986 are utilized. Monthly average brightness temperatures (Tb) over 2·5° by 2·5° regions, derived from afternoon (0900 UTC) and post-midnight (2100UTC) INSAT-VHRR thermal infrared band images, separately and in conjunction, have been compared with the Arkin et at. monthly average rainfall based on 3–hourly INSAT images, as well as with ground based measurements.. The analysis indicates that even one image taken at 0900 UTC daily is able to locate the regions of high convection almost as well as depicted by Arkin...

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.