An appraisal of rainfall estimation over India using remote sensing and in situ measurements

167-177The most important meteorological parameter Rainfall, shows high variability in space and time, particularly over Tropics / Monsoon region. Many new observational and analysis methods to observe / analyse them by remote sensing techniques (Satellites, Doppler Weather Radars) have emerged over the decades, besides the dense network of in situ rain gauges, Automatic Weather Stations (AWS) etc on ground. The scales of observations being vastly different for in situ and remote sensing methods, large discrepancies between different techniques are inherent. These problems have been brought out through various validation studies by many groups in the country. Even on the daily all India spatial scale, basically only the peaks and troughs from satellite estimates match reasonably well with in situ data. Results of a case study during an intense and long-lasting rain event over Chennai, from DWR, with different satellite products and ground truth are presented. The importance of DWR rainfall data in significantly improving the integrated products is emphasised. A simple two-way approach to establish Z – R relationship for the DWRs in the country is also suggested. A well-coordinated integrated programme to study the inter comparability of precipitation at various spatio- temporal scales in the context of our water resources, model validation, extreme rainfall events, Climate change, etc., is called for. The desired accuracies from satellite data vis a vis IMD gridded data for different applications have been summarised

Noaa tovs-derived moisture fields over the Arabian Sea and the Bay of Bengal and their association with the South-West monsoon

Moisture fields derived from the Tiros-N Operational Vertical Sounder (TOVS) data onboard previous National Oceanic and Atmospheric Administration (NOAA) series of satellites have been used to study monthly moisture fields over the Arabian Sea and the Bay of Bengal for the years 1979-1981. It is observed that monthly averaged moisture fields over the Bay of Bengal are always more than those over the Arabian Sea at a given latitudinal belt. Further, the monthly mean moisture flux divergence has been calculated using monthly mean winds from ship observations over the same areas of the Arabian Sea and the Bay of Bengal. It is found that net moisture flux divergence over the Arabian Sea is consistently higher than that over the Bay of Bengal for the years, 1979, 1980, and 1981, which are deficient, and excess monsoon rainfall years in terms of departure from long-term climatological rainfall values. Moisture variability over the Arabian Sea and the Bay of Bengal on a monthly scale has also been studied for the months of June, July, and August for the years 1979-1981. A plot of the zonally averaged (at every 2° latitude) differential moisture signal (DMS) by latitude indicates the approximate position of the low-level westerly jet. The overall differential signal between zonally averaged moisture fields over the Arabian Sea and the Bay of Bengal has also been found to be an indicator of total Indian rainfall excluding orographically influenced regions, although more data is needed to firmly establish the relationship quantitatively

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

Surface-level moisture transport over the Indian Ocean during Southwest monsoon months using NOAA/HIRS data

Surface-level moisture transport over the Indian Ocean has been computed using NOAA/HIRS data for the years 1980, 1981 and 1984. The global relation between monthly mean surface-level humidity and precipitable water (Liu, 1986) has been applied for the computation of surface-level humidity using monthly mean satellite-derived water vapour. The monthly mean surface wind fields over the Indian ocean provided by Florida State University have been used for the surface-level moisture flux computations. Our analysis indicates net positive surface-level moisture flux divergence over the Arabian Sea and negative moisture flux divergence over the Bay of Bengal. It has also been found that evaporation over the Arabian Sea is a variable quantity and forms a significant part of the net moisture budget over the Arabian Sea. The relative contribution of cross-equatorial flux and evaporation from the Arabian Sea has been studied for all three years

A critical assessment of the Q-W relation and a parametrization relation for computing latent heat fluxes over the Indian Ocean

Ten years (1982–1991) of upper air and ocean surface observations over the Indian Ocean from Sagar Kanya and Monex–79 data have been used to examine the relationship between the precipitable water (W) and surface level humidity (Q) on an instantaneous scale. Our analysis of Q and W over the Indian Ocean shows that Q is correlated with Won an instantaneous scale with r=O·44, which is a statistically significant correlation. A regression between Q and W has been fitted and it has been found that a fifth order polynomial yields a lowest root-mean-square (rms) error of I-4gkg-1 when compared with observed Q using an independent observation. The validity of earlier derived global relation between Q and W has been examined over the Indian Ocean. It has been found that Liu's global Q-W relation gave a large rms error of 4·1 gkg-1 when compared with the observed instantaneous Q values over the Indian Ocean. The usefulness of the above derived Q-W relation and an earlier derived relation between the monthly mean Q and W has been examined for the estimation of latent heat fluxes (LHF) over the Indian Ocean using an independent observation. The LHF estimated from the bulk aerodynamic method using all quantities available from ship observations, called the direct method (M1), has been compared with the LHF computed by using a derived Q-W relation (M2). The rms error between MI versus M2 is found to be 56 Wm-2. The LHF estimated by Liu's Q -W relation, when compared with MI gave an rms error of 155Wm -2>, which is suggestive of its unsuitability for the estimation of LHF over the Indian Ocean on an instantaneous basis. The difference between the sea surface humidity (Q,) and surface level humidity (Q) has been parametrized in terms of sea surface temperature (SST) and W, both obtainable from satellite sensors. This relation has then been used to compute LHF (M3) and was compared with MI, where it was found that M1 versus M3 gave an rms error of 58Wm-2>. Thus, this study indicates that methods M2 and M3 are found to be more consistent and accurate in nature, and also suggests that it can be further applied to the LHF estimation using satellite based microwave/IR measurements for Wand SST on an instantaneous basis

Forecasting non-stationary financial time series through genetic algorithm

We utilize a recently developed genetic algorithm, in conjunction with discrete wavelets, for carrying out successful forecasts of the trend in financial time series, that includes the NASDAQ composite index. Discrete wavelets isolate the local, small scale variations in these non-stationary time series, after which the genetic algorithm's predictions are found to be quite accurate. The power law behavior in Fourier domain reveals an underlying self-affine dynamical behavior, well captured by the algorithm, in the form of an analytic equation. Remarkably, the same equation captures the trend of the Bombay stock exchange composite index quite well.

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°, <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

An appraisal of rainfall estimation over India using remote sensing and in situ measurements

The most important meteorological parameter Rainfall, shows high variability in space and time, particularly over Tropics / Monsoon region. Many new observational and analysis methods to observe / analyse them by remote sensing techniques (Satellites, Doppler Weather Radars) have emerged over the decades, besides the dense network of in situ rain gauges, Automatic Weather Stations (AWS) etc on ground. The scales of observations being vastly different for in situ and remote sensing methods, large discrepancies between different techniques are inherent. These problems have been brought out through various validation studies by many groups in the country. Even on the daily all India spatial scale, basically only the peaks and troughs from satellite estimates match reasonably well with in situ data. Results of a case study during an intense and long-lasting rain event over Chennai, from DWR, with different satellite products and ground truth are presented. The importance of DWR rainfall data in significantly improving the integrated products is emphasised. A simple two-way approach to establish Z – R relationship for the DWRs in the country is also suggested. A well-coordinated integrated programme to study the inter comparability of precipitation at various spatio- temporal scales in the context of our water resources, model validation, extreme rainfall events, Climate change, etc., is called for. The desired accuracies from satellite data vis a vis IMD gridded data for different applications have been summarised