The comparison between Moderate Resolution Imaging Spectrometer (MODIS) Total Precipitable Water (TPW) and Global Positioning System (GPS) TPW showed that the standard deviation for differences between these two data sets was about 3.3 and 5.2 mm for near-infrared (nIR) and infrared (IR) TPW, respectively. The comparison also showed that there were biases for both retrieved nIR and IR TPW data. The MODIS nIR values were slightly underestimated in a dry atmosphere and overestimated in a moist atmosphere, and the overestimation increased as the column water vapor content increased. This makes it possible to correct the bias associated with these data. The bias correction and trend removal of MODIS nIR TPW reduced the standard deviation of differences from 3.3 mm to about 2 mm. A similar trend of differences between MODIS TPW and radiosonde TPW was also obtained, and a dry bias was found in the radisonde measurements. Two severe weather simulations, a severe thunderstorm (2004) over land and Hurricane Isidore (2002) over ocean, were used to assess the impact of assimilating MODIS nIR TPW data on severe weather simulations. The assimilation of conventional observations alone had a slightly positive impact on both weather simulations. The addition of assimilating original or bias-corrected MODIS TPW had no impact on simulated rainfall for the thunderstorm over the southern US. However, for Hurricane Isidore, MODIS nIR TPW with or without bias correction started influencing the simulated storm intensity positively after a one-day integration. There was almost no impact for the first day of simulation because almost no MODIS data were available due to cloudiness over the storm region and its vicinity. While this work is still too preliminary to draw conclusions on the impact of MODIS TPW on forecast improvement, it shows the type of results that may be expected. When assimilating MODIS TPW, severe weather simulations were improved over ocean but not over land since the quality of global analysis over land is usually better than over ocean. When over ocean, the assimilation of MODIS data can have a positive impact during the early simulation period if cloud-free data are available over the region of interest, while the impact can be delayed to a later simulation period if data are available only away from the region.

The lack of adequate observational information over the ocean, create a great difficulty in prediction of ocean state near the Indian coasts. Frequent satellite passes over this region provides valuable wind data resources that can be used to force regional models to evaluate ocean wave spectrum near coasts with a better accuracy. In this work both scatterometer wind from QuikSCAT as well as the ETA model wind from NCMRWF are used to force coastal wave model SWAN nested in open-ocean WAM model. The results indicate that the SWAN nested in WAM predicts the wind generated wave height with better accuracy when forced when forced with the QuikSCAT wind. But the swell height predominantly depends on the boundary conditions provided on the model.

For study of Asian monsoon, IR based estimates from Kalpana-1, Meteosat-5 and microwave based estimates from TRMM rainfall data are very useful, particularly for the oceanic regions. Satellite only estimates have biases, but are able to represent the large-scale monsoon rainfall features. IR estimates are unable to capture the heavy rain over the west coast of India. Even the TRMM values are underestimating the heavy rainfall over the west coast of India. Inclusion of gauge data (over land and island) improves the representation of daily rainfall. Gauge data corrects the biases in the satellite estimates. The number of gauge stations available in real time is less, and for a better analysis of the large-scale rainfall field at least the full set of 540 stations from India will be very useful. For meso-scale analysis a much denser network of gauge stations will be required. These gauge data will be very useful to correct the current biases in the satellite estimates.

A combination of current operational global atmospheric and oceanic data sets and those from ARGO oceanic floats provided a unique opportunity for data assimilation and seasonal forecasts. This study explores predictability of monsoonal component of the Madden Julian Oscillation (MJO) called the Intraseasonal Oscillation (ISO). The results of the inclusion versus non-inclusion of the ARGO temperature profile data sets are presented here where all other data sets are retained. Somewhat robust seasonal prediction of the ISO wave was possible from the inclusion of ARGO profile data sets. These results show very reasonable amplitudes and the meridional phase speed propagation for the monsoonal intraseasonal oscillation on seasonal time scale predictions. Such results were noted both in the atmospheric motion field (the 850 hPa level zonal winds) but also in the subsurface thermal fields of the Indian Ocean. These subsurface temperature structures were not known in previous studies that bring in a new aspect of the coupled nature of the ISO.

A long term global atmospheric reanalysis Japanese 25-year Reanalysis (JRA-25) which covers from 1979 to 2004 was completed using the Japan Meteorological Agency (JMA) numerical assimilation and forecast system. This is the first long term reanalysis undertaken in Asia. JMA's latest numerical assimilation system, and observational data collected as much as possible, were used in JRA-25 to generate a consistent and high quality reanalysis dataset to contribute to climate research and operational work. One purpose of JRA-25 is to enhance to a high quality the analysis in the Asian region. 6-hourly data assimilation cycles were performed and produced 6-hourly atmospheric analysis and forecast fields with various kinds of physical variables. The global model used in JRA-25 has a spectral resolution of T106 (equivalent to a horizontal grid size of around 120km) and 40 vertical layers with the top level at 0.4hPa. For observational data, a great deal of satellite data was used in addition to conventional surface and upper air data. Atmospheric Motion Vector (AMV) data retrieved from geostationary satellites, brightness temperature (TBB) data from TIROS Operational Vertical Sounder (TOVS), precipitable water retrieved from radiance of microwave radiometer from orbital satellites and some other satellite data were assimilated with 3-dimensional variational method (3DVAR). Many advantages have been found in the JRA-25 reanalysis. Firstly, forecast 6-hour global total precipitation in JRA-25 performs well, distribution and amount are properly represented both in space and time. JRA-25 has the best performance compared to other reanalysis with respect to time series of global precipitation over many years, with few unrealistic variations caused by degraded quality of satellite data due to volcanic eruptions. Secondly, JRA-25 is the first reanalysis which assimilated wind profiles surrounding tropical cyclones retrieved from historical best track information; tropical cyclones were analyzed correctly in all the global regions. Additionally, low-level cloud along the subtropical western coast of continents is forecast very accurately, and snow depth analysis is also good.

Clouds play a major role in the radiation budget of the earth-atmosphere system. They contribute to a high amplitude of variation on the time scale of one day. This has significant impacts on the climate of the earth. Current cloud parameterization schemes have significant deficiency to predict the diurnal cycle of cloud cover a few days in advance. The present study addresses this issue utilizing a two fold approach. We used four versions of the Florida State University (FSU) global spectral model (GSM) including four different cloud parameterization schemes in order to construct ensemble/superensemble forecasts of cloud covers. The results show that it is possible to substantially reduce the 1-5 days forecast errors of phase and amplitude of the diurnal cycle of clouds with this methodology. Further, a unified cloud parameterization scheme is developed for climate models, which, when implemented in the FSU GSM, carries a higher skill compared to those of the individual cloud schemes. This study shows that while the multimodel superensemble is still the best product in forecasting the diurnal cycle of clouds, a unified cloud parameterization scheme, used in a single model, also provides higher skills compared to the individual cloud models. Moreover, since this unified scheme is an integral part of the model, the overall forecast skill improves both in terms of radiative fluxes and precipitation and thus has a greater impact on both weather and climate time scales.

In the present study, experiments have been performed to observe the impact of Southern Ocean Swell in the Indian Ocean region. For this purpose, wave model (WAM) runs have been made for two years using National Centre for Medium Range Weather Forecasting model derived analysed winds and QuikScat scatterometer derived surface winds. To observe the swell impact in Indian Ocean region, the wave models runs have been made in global as well as regional scale. The difference in the model derived wave heights have been compared with in-situ buoy data as well as satellite altimeter derived wave height data. The study clearly demonstrates that high swell waves from the Southern Ocean propagate towards the Bay of Bengal and Arabian Sea.

Cloud feedbacks are known to be the largest sources of uncertainty in the climate system. The cloud radiative forcing is strongly affected by microphysical properties of clouds which are closely related to precipitation as well as aerosol concentrations. We measured clouds and precipitation by a combined use of a radar and a passive solar/infrared radiometer onboard the Tropical Rainfall Measuring Mission (TRMM) satellite and examined how precipitation characteristics are linked to cloud properties. The radar is used to measure precipitation. Optical thickness and effective radius of clouds are estimated from radiances at two wavelengths measured with the radiometer. It is found that the sizes of cloud drops are closely related to precipitation: Cloud drop sizes are smaller for non-precipitating clouds than those for precipitating clouds in which clouds and precipitation coexist. Clouds with large drop size are almost precipitating. The processes that precipitation modifies cloud microphysics, therefore, the radiative properties are critical to evaluate the cloud feedbacks in the global warming prediction.

Radiative transfer simulation based study was carried for developing sea surface temperature algorithms for ISRO's next geostationary satellite INSAT-3D that will be similar to GOES-9 configuration. Characterization of Indian tropical marine atmosphere was done by utilising the surface and atmospheric parameters like temperature, pressure and humidity observed onboard research vessels, covering entire Indian oceans. These parameters were further perturbed in order to achieve the full temporal and spatial variability in the Indian region. 1392 such atmospheric profiles were generated as input to the radiative transfer model. Brightness temperatures for INSAT-3D imager and sounder channels were simulated for these profiles. Various combinations of the channels suitable for sea surface temperature and total water vapor estimation were considered and depending on the statistical parameters and retrieval errors, daytime and nighttime SST retrieval equations were finalised. These equations were applied to GOES-9 data over eastern pacific and the retrieved SST fields were validated with insitu ship observations. The rms error achieved was ~ 0.68 K. Finally SST retrieval equations were suggested for INSAT-3D. The advantage of frequent sampling by geostationary satellites was also demonstrated by studying the diurnal variability of SST and improving the cloud free SST fields using INSAT-3A data. It was found that cloud free fields can be increased to ~ 25% in a day by compositing eight images for that day.

The Baikal lake region is studied by the remote sensing methods, using fractal approach. It is shown that the using of fractal dimensionalities give possibility to describe the natural communities by exact quantitative way and make the classification of earth covers. There are classified three big classes with different values of fractal dimensionalities: smooth homogeneous areas, rough fragments with a small bushes, and forests.

One of the most important problems in non-convection-resolving atmospheric circulation models is cumulus parametrization, i.e. the problem of determining, from the model's coarse-scale state variables, the triggering, vertical distribution and time scale of convective kinetic energy dissipation. One way to derive an empirically-verified parametrization scheme is to use intensive four-dimensional observations of tropical precipitation to compile the statistics of the joint behavior of the convective available potential energy (CAPE) on one hand and the resulting four-dimensional latent heating on the other hand. While tracking CAPE requires frequent soundings, tracking the latent heating is much harder because it requires frequent estimates of the vertical structure of precipitation: the latter can only be estimated from microwave remote sensing, which is only available in the form of infrequent (at best six-hourly) snapshots from low-Earth-orbiting satellites. The approach proposed in this paper seeks to remedy this problem by combining geo-stationary IR observations with a simplified vertical evolution model to fill-in the vast gaps in the microwave observations. The results will help in developing empirically verified convective parametrization schemes for use in large-scale atmospheric models, and in producing fine temporal-scale precipitation estimates which can be directly assimilated.

This investigation focuses on developing a better understanding of the assorted mechanisms controlling the global distribution of diurnal rainfall variability. The horizontal distributions of precipitation's diurnal cycle, based on eight years of TRMM Microwave Imager (TMI) and TRMM Precipitation Radar (PR) measurements involving three TRMM standard algorithms, are analyzed in detail at various spatiotemporal scales. Results demonstrate the prominence of the late-evening to early-morning precipitation maxima over oceans and the mid- to late-afternoon maxima over continents, but also reveal a widespread distribution of secondary maxima occurring over both oceans and continents, maxima which generally mirror their counterpart regime's behavior. That is, many ocean regions exhibit clear-cut secondary afternoon precipitation maxima while many continental areas exhibit just as evident secondary morning maxima. Notably, this investigation represents the first comprehensive study of these secondary maxima and their widespread nature when analyzed using a global precipitation dataset. The characteristics of the secondary maxima are thoroughly mapped and described on a global grid. In addition, a Fourier harmonic decomposition scheme is used to examine detailed amplitude and phase properties of the primary and secondary maxima -- as well as tertiary and quartern modes. Accordingly, the advantages, ambiguities, and pitfalls resulting from using harmonic analysis are also examined.

In a broad sense, 'Data Assimilation' refers to a technique, whereby the realistic observational datasets are injected to a model simulation for bringing accurate forecasts. There are several schemes available for insertion of observational datasets in the model. In this piece of research, we present one of the simplest, yet powerful data assimilation techniques - known as nudging through optimal interpolation in the ARPS (Advanced Regional Prediction System) model. Through this technique, we firstly identify the assimilation window in space and time over which the observational datasets need to be inserted and the model products require to be adjusted. Appropriate model variables are then adjusted for the realistic observational datasets with a proper weightage being given to the observations. Incorporation of such a subroutine in the model that takes care of the assimilation in the model provides a powerful tool for improving the forecast parameters. Such a technique can be very useful in cases, where observational datasets are available at regular intervals. In this article, we demonstrate the effectiveness of this technique for simulation of profiles of Atmospheric Boundary Layer parameters for a tiny island of Kaashidhoo in the Republic of Maldives, where regular GPS Loran Atmospheric Soundings were carried out during the Intensive Field Phase of Indian Ocean Experiment (INDOEX, IFP-99).

Severe thunderstorms form over the Eastern and Northeastern parts of India, i.e., Gangetic West Bengal, Jharkhand, Orissa, Assam and parts of Bihar during the pre-monsoon months (April-May). These storms are known as "Nor'wester" as they move from Northwest to Southeast. In this study we have made numerical simulations of 10 thunderstorms that formed over the West Bengal region during April-May of 2005 and 2006. Numerical simulations have been carried out using MM5 mesoscale model (at 10 km resolution) using conventional and non-conventional observations from Doppler Weather Radar (DWR) and satellites. Composite characteristics of the Nor'wester have been made based upon the simulations. Results indicate that the Nor'westers occur generally when the CAPE increases above 1500 J Kg^-1. They have updraft speeds up to 3-4 m s^-1, while the downdrafts have magnitudes of about 0.4 - 0.5 m s^-1. The updrafts can extend up to 8-9 km altitudes. The total amount of hydrometeors simulated inside the Nor'westers is up to 600-800 mg kg^-1. Large amount of ice and snow exist at upper levels, while liquid water is present in the lower levels. The magnitudes of the ice, snow and liquid water depend on the stage of their life cycle.

We upgrade the East Asia rain-gauge-based daily analysis of precipitation (Xie et al., 2006) for 1998 by utilizing daily rain-gauge precipitation data over Southeast and South Asia those are archived in GAME-T data center. This GAME enhanced version shows significant improvements in precipitation amounts in those regions where we input additional data, especially along Himalayas. We compare TRMM/PR monthly product with the GAME Enhanced version for future improvement of the orographic rainfall patterns in our rain gauge analysis. We found that TRMM/PR underestimates wet (summer) season of monsoon rainfall ~100 mm/month. Then we validate precipitation derived from JRA-25, the ongoing Japanese 25-year reanalysis project, with the new gauge-based data set for 1998. JRA-25 reproduce precipitation pattern well in time and space, but it tends to overestimate precipitation in most of the Asian monsoon region. The simulated precipitation along Himalayas shifts southward. JRA-25 reproduces the trend of extreme events that leads a flood of the Yangtze River (July 1998), but it overestimates at extreme events. The change of the precipitation amount due to re-gridding (T106 to 2.5 degree) is sometimes comparable with the difference between simulation and observation. We need to be careful about the bias caused by the regridding in extreme events. APHRODITE's (Asian Precipitation - Highly Resolved Observational Data Integration Towards Evaluation of the) water resources, a project to develop a long-term rain-gauge-based daily precipitation dataset over Asia n started, and it consolidates the data-model interfaces. We welcome a wide spectrum of collaborations, particularly for collection of rain gauge data.

The skill of short-range forecasts produced using the PSU-NCAR Mesoscale Model (MM5) during the July 1998 episode of Indian summer monsoon is evaluated statistically. The spatial and temporal variations in the forecast error is analysed by computing bias and root mean square error (rmse) in the model predicted wind, temperature, and relative humidity. The model forecasted rainfall is evaluated against observation by computing statistical skill scores. It is observed that model simulated upper-tropospheric anticyclone from both 24- and 48-h forecast is slightly east of its observed position. The strength of tropical easterly jet (TEJ) is underestimated in model forecast. It is seen that the rmse in forecasted wind at 850 hPa is higher in case of Peninsular India (PI) as compared to other regions studied. Over Indian subcontinent the model forecast under predicts moisture at 850 hPa, which is consistent with the previous studies. The rainfall distribution from both 24- and 48-h predictions shows an underestimation of monthly rainfall over Indian land mass. The rain shadow region observed in the eastern coast of southern Peninsular India is reproduced in model forecast. It is evident from the threat scores obtained that MM5 shows moderate skill in predicting rainfall and model skill does not vary significantly with rainfall threshold.

Monsoons are generally considered to be developed by land-ocean differential heating. However, it is revealed from the one to two months time lag between the maximum lightning activity/surface temperature and the monsoon rain onset that the diabatic heating generated by deep cumulus convection is another driving mechanism for monsoon development. The TRMM LIS observations, rainfall measurements, and the ERA-40 reanalyses were analyzed to demonstrate this monsoon development.

The Huaihe River basin has dry winter and wet summer seasons, and the structure of the atmospheric boundary layer shows the seasonal march. The change was continuously observed using a flux tower, a Doppler sodar, and a boundary layer profiler for more than two years. The mixed layer development was strongly affected by the surface conditions, that is, the wheat field, bare soil, and paddy field. When the surface is wet, the development of the mixed layer is weak and vice versa. Generally, the development was controlled by the surface sensible heat flux. But the development of the mixed was also affected by large scale up/downdraft. When a large scale subsidence exists, the development of the mixed layer is suppressed. A so-called LES simulation was performed in order to study the vertical profiles of the sensible heat and latent heat fluxes. It was found that the profile over paddy field is in between those over dry land and over ocean. The importance of the buoyancy due to the water vapor was also confirmed.

We present in this work composite relationships among Barrier Layer (BL) depth, and various other parameters either directly responsible for its formation or the sequence of events which follow once it is formed. Underlying mechanisms responsible for the development of the BL depth, its sustenance and annihilation are examined in the southeastern Arabian Sea (SEAS) in the north Indian Ocean using primarily ARGO floats observations along with ancillary data from various satellites and surface currents from ocean model. All the available Argo floats observations of temperature and salinity as of December 2005 have been analyzed to evaluate the seasonal characteristics of barrier layer (BL) in this warm pool region of Arabian Sea. The annual average BL thickness in this region varies from 20 to 70 m, with larger values towards coast. The standard deviation is also high (15-30 m) in this region showing a strong seasonal variation. In a complete seasonal characteristic studied with the use of observations, BL thickness shows a primary peak (~ 50 m) in January and a secondary peak in September (~ 35 m). While the former is remotely forced, the later owes its generation to the local forcing via precipitation. TMI observations show a lag of 3 months in the SST warming with respect to the maximum BL thickness observed during January. Peak warming in SST during April immediately follows by rise in integrated water vapour. Interestingly, following the secondary maxima of BL, SST does not show any warming signature, possibly due to the overcast condition, preventing the surface from heating up.

A detailed analysis and validation of ocean thermodynamic variables, simulated, using an Ocean general circulation Model (OGCM) forced by QuikSCAT scatterometer (QS-R) has been carried out in this study. The results are discussed in terms of comparison of vertical profiles of temperature against TRITON observations for the year 2004. Root mean square error (RMSE) of Sea Surface Temperature (SST) simulated by QS-R showed an error of the order of 0.6°C. QS-R SST also has a cooler bias. Another OGCM simulation was carried out in which the National Center for Environmental Prediction (NCEP) reanalysis winds (NCEP-R) were used to force the OGCM. The simulated SST resulted in lesser RMSE (~0.5°C) and there was no bias. However, variabilities in SST were captured more realistically in scatterometer forced solution. However the RMSE in SST was higher which could be partly due to physical inconsistency between scatterometer winds and other air-sea exchange parameters used from NCEP. A detailed statistics of the model simulated temperatures at different depths show excellent performance of scatterometer forced simulations as compared to NCEP-R. Maximum error in the temperature profile was in the thermocline region (~1.5°C) in QS-R as against 3.5°C in NCEP-R. Possible causes of these errors in relation to air-sea exchange parameters used in forcing the OGCM are described in this work.

It is hypothesized that the South China Sea (SCS) monsoon and the Indian monsoon intensify each other in the boreal summer. The Indian monsoon westerlies strengthen the SCS monsoon through the WE (Wind- Evaporation feedback) mechanism, but weaken it through the reduction in SCS SST. The SCS monsoon may have a positive feedback to enhance the Indian monsoon westerlies through the Walker circulation. Further question is how the convective activity over the Western North Pacific (WNP) interacts the Indian monsoon westerlies. The WNP SST may affect the Indian monsoon through the WNP precipitation. In this study, we investigate the relationships among the Indian monsoon, SCS monsoon and WNP activities from various observational data. The regression coefficient analyses are made. The data are NCEP reanalysis data, the CMAP precipitation data and ICOADS SST data averaging over June-July-August (JJA) for 21 years (1982-2002). First, the precipitation anomalies are regressed to the Indian monsoon westerly anomaly. The result shows that the Indian monsoon westerlies are positively correlated with the SCS precipitation. It indicates that the surface heating exchange over the SCS region is largely induced by the Indian monsoon westerlies and/or the SCS monsoon enhances the Indian monsoon westerlies. Another map of the regression of the 850hPa wind field anomalies to the WNP precipitation anomaly also shows that the Indian monsoon westerlies are positively correlated with the WNP precipitation. Completing this result, we confirm that the WNP precipitation and WNP SST have a positive correlation. Thus, the strengthening of the Indian monsoon westerlies and SCS westerlies may be also induced by the precipitation increase on the WNP region through the stronger east-west circulation associated with the warmer WNP SST. From the above results, it is found that the Indian monsoon westerlies are strongly correlated with the SCS monsoon activity and the WNP precipitation. Probably, the SCS monsoon is affected by the Indian monsoon through the WE, and affects the Indian monsoon through the diabatic heating. Further link is found to the precipitation over the WNP. To confirm the mechanism of correlation, the model study is underway.

We present here algorithms to determine the inherent optical properties of water, backscattering probability and single scattering albedo at 490 and 676 nm from the apparent optical property, remote sensing reflectance. We have used the measured scattering and backscattering coefficients and the remote sensing reflectance to obtain a relationship for the backscattering ratio, which is defined as the ratio of the total backscattering to the total scattering in terms of the remote sensing reflectance of two bands. Using the empirical relationship for the total backscattering ratios, we have also computed single scattering albedo, which is defined as the ratio of the scattering to the beam attenuation coefficient. The values of single scattering albedo obtained from measured values and those obtained from the empirical method are found to be comparable. The values of single scattering albedo derived using the algorithm are found to be comparable to the measured values obtained from the eastern Arabian Sea, with the root mean squared error of 0.078 and the mean percentage error of 9.5% for the 490 nm and root mean squared error of 0.043 and the mean percentage error of 7.5% for the 676 nm.

The realism of the impact of penetrative solar radiation, an effect we refer to as biological heating, on the upper ocean thermodynamics has been studied using an Ocean General Circulation Model (OGCM). Daily fields of winds, air temperature, specific humidity, net long-wave and shortwave radiation from NCEP were used to force the model. In the control run (cntl-R), diffuse attenuation coefficient (K_d) which signifies the visible radiation penetration is parameterized for clear water condition. In the experimental run (exp-R), attenuation coefficient for blue-green wavelength (K_d_490) obtained from SeaWiFS sensor was used to determine the penetrative depth of solar radiation. Use of satellite derived K_d_490 alters the upper ocean thermodynamics quite significantly. Model simulated parameters sea surface temperature (SST), current and mixed layer depth (MLD) were found to be sensitive to the choice of diffuse attenuation coefficients that limit the penetration of solar radiation into the ocean. The SST cools and MLD deepens in clear water regions (large attenuation depths) due to heat penetration in deeper layers, while the surface gets heated and MLD shoals in regions of high turbidity (low attenuation depths) due to heat trapping.

The marine waters of the Yellow Sea and East China Sea cover a large area and represent an important fishing ground. Monitoring changes in their biological productivity is an important goal that can hopefully be reached using satellite remote-sensing. Operational ocean color satellite algorithms are generally successful over the open ocean. These algorithms are based upon simple band ratios, assuming that the only optically active component of any significance in the water is phytoplankton. While this assumption generally holds for the open ocean waters, it may not always be the case for coastal ecosystems (in what are commonly referred to as Case II waters). This problem is particularly evident in the study site. For the high concentration of SPM, it make the failure of the atmospheric correction algorithm and decrease the accuracy of the products of ocean color remote sensing, such as the SeaWiFS and MODIS. In order to develop the high accuracy regional algorithm, the inherent optical properties in Yellow Sea and East China Sea must study previously. In this paper, the absorption and scattering properties of the optically significant biogeochemical variables and the composition of SPM are studied use the in situ dataset of the inherent optical properties in the Yellow Sea and East China Sea.

In this study global temperature profiles available from Argo have been assimilated into an Ocean General Circulation Model (OGCM) to study its impact on ocean temperatures. In the control run the model was forced with daily QuickSCAT derived scatterometer winds for the period Jan-June 2004 and air temperature, specific humidity, net shortwave and net long wave radiation from NCEP reanalysis. Two assimilation experiments were performed for Jan-Jun 2004; one in which the monthly averaged Argo profiles were assimilated in the OGCM using nudging technique (Exp-1) and another experiment (Exp-2) in which daily data from Argo was assimilated into the OGCM using Cressman technique. Temperature outputs from all the three runs (control and assimilation runs) were first inter-compared and then compared with independent observations from one of the Indian Ocean TRITON buoys. Errors in surface temperature at the TRITON buoy location are reduced by 37% and 16% in Exp-1 and Exp-2 respectively. However, the variance explained in surface temperature with respect to observations is reduced in the assimilated runs as compared to the control run. Subsurface features like ILD and D20 show significant improvement in terms of error reduction in both the experiments implying improvement in the mixed layer and the thermocline region. Exp-2 scores over Exp-1 in terms of the explained variance of ILD and D20. This is so because in exp-1 monthly averaged data is assimilated which constraints the high frequency variability of the parameter.

It is shown that to describe the wave propagation in disordered systems - in natural random fractals the fractional calculus is necessary to apply. The analogy between wave equations of fractional temporal order and diffusion equation of fractional temporal order is preceded. The new generalized wave equations of fractional order are deduced from microscopic models as Comb model. The solutions of these equations are obtained and the physical sense of these fractional equations is discussed.

The present study utilized the Penn State/NCAR mesoscale model (MM5), to assimilate the INSAT-CMV (Indian National Satellite System-Cloud Motion Vector) wind observations using analysis nudging to improve the prediction of a monsoon depression which occurred over the Arabian Sea, India during 14 September 2005 to 17 September 2005. NCEP-FNL analysis has been utilized as the initial and lateral boundary conditions and two sets of numerical experiments were designed to reveal the impact of assimilation of satellite-derived winds. The model was integrated from 14 September 2005 00 UTC to 17 September 2005 00 UTC, with just the NCEP FNL analysis in the NOFDDA run. In the FDDA run, the NCEP FNL analysis fields were improved by assimilating the INSAT-CMV (wind speed and wind direction) as well as QuickSCAT sea surface winds during the 24 hour pre-forecast period (14 September 2005 00 UTC to 15 September 2005 00 UTC) using analysis nudging. The model was subsequently run in the free forecast mode from 15 September 2005 00 UTC to 17 September 2005 12 UTC. The simulated sea level pressure field from the NOFDDA run reveals a relatively stronger system as compared to the FDDA run. However, the sea level pressure fields corresponding to the FDDA run are closer to the analysis. The simulated lower tropospheric winds from both experiments reveal a well-developed cyclonic circulation as compared to the analysis.

The role of aerosols in weather and climate processes over the Asian Region is one of the important scientific problems. Space based observations are the main source of quantitative information on aerosols and their variability over large areas. In the present study 18 years of TOMS Monthly Mean Data on Aerosol Index, and 6 years of MODIS Optical Depth data at 550nm were analyzed. The spatial and temporal distribution was examined for all the months over the Indian Sub-continent and adjacent Indian Ocean. Mean monthly aerosol distribution is described in detail. Temporal variations are described using separate monthly charts for the North West, North East, Western Equatorial and Eastern Equatorial Indian Ocean for all the years.

We present here the cross calibration of ocean color satellite sensor, IRS-P4 OCM using the radiative transfer code, with SeaWiFS as a reference. Since the bands of IRS-P4 OCM are identical to those of SeaWiFS and SeaWiFS has been continuously and rigorously calibrated, SeaWiFS is used as a reference for the cross calibration of IRS-P4 OCM. Calibrations coefficients for each band of IRS-P4 OCM are derived by comparing the actual radiances detected by the satellites at top of the atmosphere and those obtained from the radiative transfer simulations of IRS-P4 OCM and SeaWiFS. The chlorophyll a values derived using the calibrated IRS-P4 OCM are found to be comparable with those derived from SeaWiFS and in close agreement with the measured values. The relative root mean square error (RRMSE) between measured chlorophyll a and those derived from the satellites are found to be 0.28 and 0.26 for SeaWiFS and IRS-P4 OCM respectively.

In this article, we describe the variation of air-sea exchange coefficients and air-sea interface fluxes over the East Asian marginal seas surrounding the Korean peninsula and compare them with the similar estimates reported for the tropical Indian Ocean. Surface layer meteorological observations for a period of about five years obtained from five oceanic buoys in the adjoining seas of Korean peninsula form the database for this study. Depending on the stability of the atmosphere, buoy data is classified into three categories - unstable, neutral and stable data. For unstable conditions, sensible and latent heat flux show good correlation with the wind speed, whereas it is not so for the neutral and stable condition. Irrespective of the stability of the atmosphere, momentum flux always shows a steady dependence on the varying wind speed. Sensible and latent heat fluxes also show good correlation with the difference between sea surface temperature and air temperature. Unlike the linear regression between the exchange coefficients and wind speeds reported for the Indian Ocean, we suggest second order and exponential fits for these exchange coefficients, which give better representation of their wind speed dependence. The results presented in this article form very useful input to the coupled ocean atmospheric models and the oceanic wave models, hence significant.

A case study of sea breeze circulation at a coastal region Thumba (8.5°N, 76.9°E) was carried out using Doppler Sodar, surface wind, temperature, humidity measurements and radiosonde ascents. The analysis of surface meteorological data showed that the onset of sea breeze on 12th April 2006 was at 0945 hrs. GPS sonde observation over sea at 1425 hrs and Radiosonde observation over land at 1730 showed a well developed sea breeze circulation over Thumba coast by afternoon hours. The vertical extent of sea breeze circulation was ~1000m over sea as well as on land. The Thermal Internal Boundary Layer (TIBL) depth associated with sea breeze circulation was about 400m at 8 km away from coast. The marine mixed layer height was ~500m about 12 km away from the coast. Numerical simulation of sea breeze was made using HRM (High Resolution Model) and compared the results with the observations.

Accurate retrieval of air-sea exchange parameters from satellite is highly desirable as it contributes immensely to the understanding of climate change. Due to the inherent difficulties in the direct flux measurement, indirect approaches to flux estimation have been widely adopted in spite of the large uncertainties associated with them. More recently Fairall et al (2003) have reported the final version of the bulk flux algorithm that emerged out of a carefully planned Coupled Ocean-Atmosphere Response Experiment (COARE) where more than 7216 hours of direct flux measurements were used to validate the bulk algorithm. They have succeeded in narrowing down the uncertainty from the then existing 20-30% level to 5% for wind speeds of 0-10 m/s and to 10% for 10-20 m/s wind speed.

In the present study we have carried out few experiments to see the impact of sea surface temperature (SST) field obtained from TRMM Microwave Imager on the simulation of tropical cyclone MALA, formed over the Bay of Bengal on 24th April 2006 and made landfall on 29 April 0600 UTC at Arakan coast in Myanmar (17.7 N, 94.5 E). We have used NCEP global analysis data for this study. In this global analysis the Reynolds SST field is used as ocean boundary condition, which is having coarser resolution. Some experiments were carried out in past where people found that the wind stress fields in the ECMWF model improved dramatically after implementation of improved SST boundary condition. Also, satellite microwave measurement of SST by the TMI and Advance Microwave Scanning Radiometer (AMSR-E) have revealed that SST in regions of strong SST fronts associated with ocean currents exert a strong influence on the marine atmospheric boundary layer, resulting in a remarkably high positive correlation between surface winds and SST on scales smaller than few thousand kilometers. Here we have replaced coarser resolution SST in the control runs with high-resolution microwave based SST, obtained from TMI. Results are sowing that, the use TMI SST as ocean boundary condition has positive impact on the simulation of tropical cyclone.

In this paper, we attempted to estimate the upper ocean heat content from Sea Surface Height Anomaly (SSHA), Sea Surface Temperature (SST) and Wind Stress Curl (WSC) obtained from satellite observations through Artificial Neural Networks approach. For this purpose, we analyzed the monthly heat content derived from 3D-model, SSHA, SST and WSC during 2000-2003. Due to non availability of Argo profiles at all locations during all the seasons, estimation of heat content is not possible at different time scales. 3D-model derived heat content is validated with Argo heat content during 2003. We have developed a model for the Arabian Sea to estimate upper ocean heat content, with a standard deviation error of 0.05E+09 J/m². In estimating the Heat Content (HC) we also studied the effect of each parameter through ANN model.