Abstract

The paper presents a robust technique for cloud clearing of satellite imagery. The proposed algorithm combines mathematical morphological techniques with a conventional cloud clearing scheme to restore clear sky values. The derived equivalent clear sky brightness temperature plays a very important role in numerical weather prediction, climate research, and monitoring. The developed methodology uses distinct approaches for reconstruction of partially clouded domains and overcast regions. It is found that the algorithm is especially suitable for pre- or postmonsoon months, where there is a high percentage of partially cloudy and small overcast cloudy regions. The algorithm is tested for the Kalpana Very High Resolution Radiometer (VHRR) thermal infrared (TIR) band data acquired over the oceanic region adjoining India throughout the month of May 2009. It is found that the algorithm is able to clear 25% of cloudy pixels with an RMSE of 1.2 K for brightness temperature.

Abstract

Using the top-of-the-atmosphere radiative flux and cloud data from satellites, as well as atmospheric data from NCEP–NCAR reanalysis, this paper investigates the reason for the unusually large high-cloud amount in the Asian monsoon region during the summer monsoon season (June–September). Earlier studies attributed the large negative net cloud radiative forcing in the Asian monsoon region to the unusually large high-cloud amounts with high optical depth. Analysis during 1985–89 suggests that the unique upper-tropospheric easterly wind shear [tropical easterly jet (TEJ)], present over the Asian monsoon region during the summer monsoon season, may be responsible for the unusual increase in cloud amount. This strong wind shear sweeps the cloud tops and may be unfavorable for cloud growth beyond about 300 hPa. The spreading of cloud tops by wind may increase the high-cloud amount. A significant association is found between the high-cloud amount and the speed of the easterly jet. In addition, magnitudes of the shortwave, longwave, and net cloud radiative forcing also strongly depend upon the variations in the speed of TEJ.

Abstract

Assimilation experiments have been performed with the Weather Research and Forecasting (WRF) model’s three-dimensional variational data assimilation (3DVAR) scheme to assess the impacts of NASA’s Quick Scatterometer (QuikSCAT) near-surface winds, and Special Sensor Microwave Imager (SSM/I) wind speed and total precipitable water (TPW) on the analysis and on short-range forecasts over the Indian region. The control (without satellite data) as well as WRF 3DVAR sensitivity runs (which assimilated satellite data) were made for 48 h starting daily at 0000 UTC during July 2006. The impacts of assimilating the different satellite dataset were measured in comparison to the control run, which does not assimilate any satellite data. The spatial distribution of the forecast impacts (FIs) for wind, temperature, and humidity from 1-month assimilation experiments for July 2006 demonstrated that on an average, for 24- and 48-h forecasts, the satellite data provided useful information. Among the experiments, WRF wind speed prediction was improved by QuikSCAT surface wind and SSM/I TPW assimilation, while temperature and humidity prediction was improved due to the assimilation of SSM/I TPW. The rainfall prediction has also been improved significantly due to the assimilation of SSM/I TPW, with the largest improvement seen over the west coast of India. Through an improvement of the surface wind field, the QuikSCAT data also yielded a positive impact on the precipitation, particularly for day 1 forecasts. In contrast, the assimilation of SSM/I wind speed degraded the humidity and rainfall predictions.

Abstract

Monthly mean surface latent heat fluxes (LHFs) over the global oceans are estimated using bulk formula. LHFs are computed using wind speed (U) from the Special Sensor Microwave Imager (SSM/I), sea surface temperature (SST) from the Advanced Very High Resolution Radiometer (AVHRR), and near-surface specific humidity. Near-surface specific humidity (Q_a ) is estimated from SSM/I-observed precipitable water (W) and AVHRR-observed SST using a genetic algorithm (GA) approach. The GA-retrieved monthly mean Q_a has an accuracy of 0.80 ± 0.32 g kg⁻¹ as compared with surface marine observations based on the Comprehensive Ocean–Atmosphere Data Set (COADS). The GA approach improves upon the surface specific humidity retrieval based on regression, the EOF approach, and is comparable to the artificial neural network technique.

The satellite-derived LHFs are compared with globally distributed surface marine observations to monthly averages of 1° × 1° latitude–longitude bins, during 1988–93. When GA-retrieved Q_a is used in the computation of satellite-derived latent heat fluxes (LHF_GA) the global mean rmse, bias, and correlation are 22 ± 8 W m⁻², 5 W m⁻², and 0.85, respectively, for monthly mean latent heat fluxes. The rmses in LHF are larger when Q_a is retrieved using regression and EOF approaches.

Abstract

In this study the simulation of a severe rainfall episode over Mumbai on 26 July 2005 has been attempted with two different mesoscale models. The numerical models used in this study are the Brazilian Regional Atmospheric Modeling System (BRAMS) developed originally by Colorado State University and the Advanced Research Weather Research Forecast (WRF-ARW) Model, version 2.0.1, developed at the National Center for Atmospheric Research. The simulations carried out in this study use the Grell–Devenyi Ensemble cumulus parameterization scheme. Apart from using climatological sea surface temperature (SST) for the control simulations, the impact of the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) SST on the simulation of rainfall is evaluated using these two models. The performances of the models are compared by examining the predicted parameters like upper- and lower-level circulations, moisture, temperature, and rainfall. The strength of convective instability is also derived by calculating the convective available potential energy. The intensity of maximum rainfall around Mumbai is significantly improved with TMI SST as the surface boundary condition in both the models. The large-scale circulation features, moisture, and temperature are compared with those in the National Centers for Environmental Prediction analyses. The rainfall prediction is assessed quantitatively by comparing the simulated rainfall with the rainfall from TRMM products and the observed station values reported in Indian Daily Weather Reports from the India Meteorological Department.

Abstract

A new approach is introduced for determining surface latent heat flux (LHF) and sensible heat flux (SHF) over the global oceans exclusively from satellite observations. Measurements of wind speed (U), sea surface temperature (SST), near surface specific humidity (Q_a ), and air–sea temperature difference (ΔT = SST − T_a ) are required for computing these fluxes by bulk formulas. To compute the heat fluxes exclusively from satellite data, U is obtained from Special Sensor Microwave Imager (SSM/I), SST is obtained from Advanced Very High Resolution Radiometer (AVHRR), empirical algorithm proposed earlier is used to compute ΔT, and a new one is developed to estimate Q_a . The developed empirical equation for Q_a estimations is an extension of the authors’ previous method. Compared to the Comprehensive Ocean–Atmosphere Data Set (COADS), the Q_a retrieved by the previous approach had a negative bias of the order of more than 2 g kg⁻¹ over the Gulf Stream and Kuroshio during winter but had a positive bias of more than 2 g kg⁻¹ over the Arabian Sea and the Bay of Bengal during summertime. The new empirical equation takes into account these seasonal biases over the Gulf Stream, Kuroshio, and the Arabian Sea. Compared to COADS observations, the Q_a retrieved from the developed empirical equation has global mean root mean square error (rmse), bias, and correlation of the order of 0.55, −0.007, and 0.98 g kg⁻¹, respectively.

Compared to COADS, the satellite-derived monthly mean LHF has global mean rmse, bias, and correlation of the order of 20, 6, and 0.97 W m⁻², respectively. Likewise, satellite-derived monthly mean SHF has global mean rmse, bias, and correlations of the order of 6, 0.4, and 0.98 W m⁻², respectively. The monthly fields show that the spatial patterns and seasonal variability of satellite-derived latent and sensible heat fluxes are generally good in agreement with those of the COADS and earlier satellite-derived fluxes.

Sixteen-year (January 1988–December 2003) datasets of surface heat fluxes and basic input parameters over the global oceans have been constructed using SSM/I and AVHRR data. This dataset has a spatial resolution of 1° × 1° latitude–longitude and temporal resolution of one month. This unique dataset is constructed exclusively from satellite observations, and it can be obtained from the Meteorology and Oceanography Group Space Applications Centre.

Abstract

The remotely sensed upper-tropospheric water vapor wind information has been of increasing interest for operational meteorology. A new tracer selection based on a local image anomaly and tracking procedure, itself based on Nash–Sutcliffe model efficiency, is demonstrated here for the estimation of upper-tropospheric water vapor winds both for cloudy and cloud-free regions from water vapor images. The pressure height of the selected water vapor tracers is calculated empirically using a height assignment technique based on a genetic algorithm. The new technique shows encouraging results when compared with Meteosat-5 water vapor winds over the Indian Ocean region. The water vapor winds produced by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) from Meteosat-5 and the present algorithm are compared with collocated radiosonde observations according to Coordination Group for Meteorological Satellites guidelines. The proposed algorithm shows better accuracy in terms of mean vector difference, rms vector difference, standard deviation, speed bias, number of collocations, and mean speed and mean direction differences. Also it is found that the sensitivity of the spatial consistency check in the quality indicator is not so significant for the improvement of statistics.

Abstract

The estimation of atmospheric motion vectors from infrared and water vapor channels on the geostationary operational Indian National Satellite System Kalpana-1 has been attempted here. An empirical height assignment technique based on a genetic algorithm is used to determine the height of cloud and water vapor tracers. The cloud-motion-vector (CMV) winds at high and midlevels and water vapor winds (WVW) derived from Kalpana-1 show a very close resemblance to the corresponding Meteosat-7 winds derived at the European Organisation for the Exploitation of Meteorological Satellites when both are compared separately with radiosonde data. The 3-month mean vector difference (MVD) of high- and midlevel CMV and WVW winds derived from Kalpana-1 is very close to that of Meteosat-7 winds, when both are compared with radiosonde. When comparing with radiosonde, the low-level CMVs from Kalpana-1 have a higher MVD value than that of Meteosat-7. This may be due to the difference in spatial resolutions of Kalpana-1 and Meteosat-7.

Abstract

In this study, the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) with three-dimensional variational data assimilation (3DVAR) is utilized to investigate the influence of Special Sensor Microwave Imager (SSM/I) and Quick Scatterometer (QuikSCAT) observations on the prediction of an Indian Ocean tropical cyclone. The 3DVAR sensitivity runs were conducted separately with QuikSCAT wind vectors, SSM/I wind speeds, and total precipitable water (TPW) to investigate their individual impact on cyclone intensity and track. The Orissa supercyclone over the Bay of Bengal during October 1999 was used for simulation and assimilation experiments.

Assimilation of the QuikSCAT wind vector improves the initial position of the cyclone’s center with a position error of 33 km, which was 163 km in the background analysis. Incorporation of QuikSCAT winds was found to increase the air–sea heat fluxes over the cyclonic region, which resulted in the improved simulated intensity when compared with the simulation made without QuikSCAT winds in the initial conditions. The cyclone track improved significantly with assimilation of QuikSCAT wind vectors. The track improvement resulted from relocation of the initial cyclonic vortex after assimilation of QuikSCAT wind vectors.

Like QuikSCAT, assimilation of SSM/I wind speeds strengthened the cyclonic circulation in the initial conditions. This increase in the low-level wind speeds enhanced the air–sea exchange processes and improved the simulated intensity of the cyclone. The lack of information about the wind direction from SSM/I prevented it from making much of an impact on track prediction. As compared to the first guess, assimilation of the SSM/I TPW shows a moistening of the lower troposphere over most of the Bay of Bengal except over the central region of the cyclone, where the assimilation of SSM/I TPW reduces the lower-tropospheric moisture. This decrease of moisture in the TPW assimilation experiment resulted in a weak cyclone intensity.

Abstract

In this paper, the three-dimensional variational data assimilation scheme (3DVAR) in the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (Penn State–NCAR) Mesoscale Model (MM5) is used to study the impact of assimilating Atmospheric Infrared Sounder (AIRS) retrieved temperature and moisture profiles on board Aqua, a satellite that is part of NASA’s Earth Observing System. A record-breaking heavy rain event that occurred over Mumbai, India, on 26 July 2005 with 24-h rainfall exceeding 94 cm was used for the simulation.

By analyzing the data from the NCEP–NCAR reanalysis, possible causes of this heavy rainfall event were investigated. The temporal evolution of meteorological fields clearly indicates the formation of midtropospheric mesoscale vortices over Mumbai that exactly coincides with the duration of the intense rainfall. Analysis also indicated the midlevel dryness with higher temperature and moisture in the lower levels. This midlevel dryness with high temperature and moisture in the lower levels increases the conditional instability, which was conducive for the development of very severe local thunderstorms. The midtropospheric mesoscale vortices existed over Mumbai together with lower-level instability and the active monsoon conditions over the west coast resulted in intense rainfall, on the order of 94 cm in 24 h.

Numerical experiments were conducted, with two nested domains (45- and 15-km grid spacing). The assimilation of the AIRS-retrieved temperature and moisture profiles produced significant impacts on the location and intensity of the simulated rainfall. It is seen from the numerical experiments that the assimilation of AIRS data could produce the structure of mesoscale vortices, and lower-level thermodynamics and convergence much more realistically compared with the control simulation. The spatial distribution of the rainfall from the simulation using AIRS data was more realistic than that without AIRS data. To make the quantitative comparison of the predicted rainfall with the observed one, the equitable threat score and bias were calculated for different threshold values of rainfall. Inclusion of AIRS data significantly improved the precipitation as indicated by the equitable threat scores and biases for almost all of the threshold rainfall categories.