S. K. Deb

Impact of TMI SST on the Simulation of a Heavy Rainfall Episode over Mumbai on 26 July 2005

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 ca...

Height Assignment Improvement in Kalpana-1 Atmospheric Motion Vectors

The real-time operational use of atmospheric motion vectors (AMVs) at numerical weather prediction (NWP) centers in India are being adversely affected due to inaccurate height assignment of cloud tracers, especially in thin semi-transparent clouds. In India, the operational derivation of AMVs from the Indian geostationary satellite Kalpana-1 began few years ago. A statistical empirical method (SEM) of height assignment, based on a genetic algorithm, is currently used to estimate the height of the retrieved vectors from Kalpana-1. This method has many limitations. In this paper, attempts have been made to implement the widely used and well tested height assignment methods such as the infrared window (WIN) technique, the H2O intercept, and the cloud base method in the Kalpana-1 AMV retrieval algorithm. The new height assignment algorithm significantly improves the statistics of the retrieved winds when compared to radiosondes, especially in high and mid levels winds.

Assimilation of INSAT data in the simulation of the recent tropical Cyclone Aila

The atmospheric motion vectors (AMVs) from the operational geostationary Indian National Satellite Kalpana-1 are now regularly available at the Space Applications Centre, Indian Space Research Organization (ISRO). ISRO also provides a large number of near real-time surface observations, such as winds, temperature, relative humidity, pressure, etc., from automatic weather stations (AWS) at various locations in India under the Prediction of Regional Weather with Observational Meso-Network and Atmospheric Modeling (PRWONAM) project. A series of experimental forecasts are attempted here to evaluate the impact of AMVs derived from Kalpana-1 and AWS surface observations for the track and intensity prediction of the recent Bay of Bengal Cyclone Aila using the Advanced Research Weather Research Forecast model (ARW-WRF). The insertion of AMVs using Cressman objective analysis techniques has had some positive, though not significant, impact in the initial position errors and track forecasts when compared with the corresponding control experiments. However, no significant improvement is noticed in the simulations of cyclone intensities, that is, minimum sea-level pressure and maximum surface winds forecasts when satellite winds are used for assimilation. Moreover, the model performance is also evaluated by repeating the same sets of experiments using AMV, AWS surface observations and upper-air radiosonde data together for assimilation. The simulation of initial position errors, track and intensity forecasts from all experiments are comparable. Though these results are preliminary with respect to the Kalpana-1 AMV, the present study can provide some insight for WRF model users over the Indian Ocean region.

Intercomparison of OSCAT Winds With Numerical-Model-Generated Winds

Subsequent to the launch of an ocean scatterometer onboard the Oceansat-2 satellite, hereby referred to as OSCAT, on September 23, 2009, the nine-month period from November 2009 to July 2010 was the validation phase for the retrieved ocean surface winds. This letter focuses on one section of the validation campaign, where the validation study for OSCAT winds with European Centre for Medium-Range Weather Forecasts (ECMWF) and National Centers for Environmental Prediction (NCEP) analyses is carried out. It is found that, in the 4-24-m/s range of wind speed, the rms error in OSCAT wind speed with ECMWF analysis is 1.4 m/s while that with NCEP analysis is 1.7 m/s. In the case of retrieved wind direction, the rms error is 17.2° with ECMWF analyses, while that for NCEP is 18.8°. These statistics are well within the mission goal of 2-m/s accuracy in wind speed and 20 ° in wind directions. This letter discusses the comparison results for different geographical regions and wind speed ranges.

Layer average relative humidity from SAPHIR on board Megha-Tropiques: validation with numerical model analyses

Subsequent to the launch of the Sondeur Atmosphérique du Profil d’Humidité Intertropicale par Radiométrie (SAPHIR) sensor on board the Megha-Tropiques satellite on 12 October 2011 from Satish Dhawan Space Centre, Indian Space Research Organization (ISRO), Shriharikota, India, the validation of layer averaged relative humidity (LARH) retrieved from SAPHIR has been initiated in different phases along with other retrieved parameters. The Megha-Tropiques is a joint satellite mission executed by the Indian Space Research Organization (ISRO) and the Centre National d’Etudes Spatiales (CNES), and is primarily devoted to study the tropical atmospheric processes influencing both weather and climate. The present study focuses on a validation campaign, where the validation of LARH derived from SAPHIR is carried out with three different numerical model analyses: the European Centre for Medium Range Weather Forecasts (ECMWF) model, the National Centers for Environmental Prediction (NCEP) model, and the National Centre for Medium Range Weather Forecasting (NCMRWF) model, over a period of six months from January 2013 to June 2013. It is observed that the root mean square difference (RMSD) of LARH has improved considerably for layers 1, 2, 3, and 6, and some marginal changes for layers 4 and 5, when a bias correction is applied to the data. The RMSD of SAPHIR LARH after correcting for bias is well within the range of the mission goal of 20% accuracy.

Inter-Comparison of Numerical Model Generated Surface Winds with QuikSCAT Winds over the Indian Ocean

The accurate surface wind in the equatorial Indian Ocean is crucial for modeling ocean circulation over this region. In this study, the surface wind analysis generated at the European Center for Medium Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction (NCEP) are compared with NASA QuikSCAT satellite derived Level2B (swath level) and Level3 (gridded) surface winds for the year 2005. It is observed that the ECMWF winds exhibit speed bias of 1.5 m/s with respect to QuikSCAT Level3 in the southern equatorial Indian Ocean. The NCEP winds are found to exhibit speed bias (1.0–1.5 m/s) in the southern equatorial Indian Ocean specifically during January–February 2005. The biases are also observed in the analysis when compared with Level2B product as well; however, it is less in comparison to Level3 products. The amplitude of daily variations of both ECMWF and NCEP wind speed in Bay of Bengal and parts of the Arabian Sea is about 80% of that in QuikSCAT, while in the equatorial Indian Ocean it is about 60% of that of QuikSCAT.

Atmospheric motion vector retrieval using improved tracer selection algorithm

Tracer selection is the fundamental step in the retrieval of atmospheric motion vectors (AMVs). In this study, a new technique for tracer selection based on extracting the corner points in an infrared (IR) image of a geostationary satellite for the retrieval of AMVs is developed. Corner points are frequently used in computer vision to identify the important features of an image. These points are usually characterized by high gradient values of the image intensity in all directions and lie at the junctions of different brightness regions in the image. Corner points find application in computer vision for motion tracking, stereo vision, mosaics, etc., but this is the first time that the information from corners is used for tracer selection in AMV retrieval. In the present study, a commonly used Harris corner (HC) detection algorithm is followed to extract corners from the image intensity of an IR image. The tracers selected using the HC method are then passed on to the other steps of the retrieval algorithm, viz., tracking, height assignment, and quality control procedures for the retrieval of AMVs. For the initial development of the HC, Meteosat-7 IR images are used to derive AMVs for July and December 2010. The AMVs retrieved using HC are validated against collocated radiosonde observations, and the results are compared with the local anomaly (LA) method as reference. LA is used for tracer selection in operational AMV retrieval algorithm from the Indian geostationary satellite Kalpana-1. AMVs retrieved using HC have shown considerable improvement in the AMV accuracy over the AMVs derived using LA.

Impact of Kalpana-1 retrieved atmospheric motion vectors on mesoscale model forecast during summer monsoon 2011

The atmospheric motion vectors (AMVs) retrieved from multi-spectral geostationary satellites form a very crucial input to improve the initial conditions of numerical weather prediction (NWP) models at all operational agencies throughout the globe. With the recent update of operational AMV retrieval algorithm using infrared, water vapor, and visible channels of Indian geostationary meteorological satellite Kalpana-1, an attempt has been made to assess the impact of AMVs in the NWP models. In this study, the impact of Kalpana-1 AMVs is assessed by assimilating them in the Weather Research and Forecasting (WRF) model using three-dimensional variational data assimilation method during the entire month of July 2011 over the Indian Ocean region. Apart from Kalpana-1 AMVs, the other AMVs available from Global Telecommunications System (GTS) are also assimilated to generate the WRF model analyses. After the initial verification of WRF model analyses, the 12-h wind forecasts from the WRF model are compared with National Centers for Environmental Prediction Global Data Assimilation System final analyses. The assimilation of Kalpana-1 AMVs shows positive impact in 12-h wind forecast over the tropical region in the upper troposphere. Similar results are obtained when other AMVs available through GTS are used for assimilation, though the magnitude of positive impact of Kalpana-1 AMVs is slightly higher over tropical region. The 24-h rainfall forecasts are also improved over the Western India and the Bay of Bengal region, when Kalpana-1 AMVs are used for assimilation against control experiments.

Impact of Kalpana-1 retrieved multispectral AMVs on Mahasen tropical cyclone forecast

The atmospheric motion vectors (AMVs) retrieved from geostationary satellites are recognized as one of the important inputs for numerical weather prediction models to improve the tropical cyclone (TC) forecast. In this study, the weather research and forecasting (WRF) model, WRF three-dimensional variational (3D-Var) data assimilation system and WRF tangent linear and adjoint model are used to investigate the impact of multispectral Kalpana-1 AMVs on the simulation of Mahasen tropical cyclone (now known as cyclonic storm Viyaru) over the Indian Ocean. Three different sets of experiments are performed to evaluate the impact of Kalpana-1 AMVs. First, the impacts of Kalpana-1 AMVs are evaluated for different forecast lengths. The assimilation of Kalpana-1 AMVs improves the cyclone track prediction compared to control experiment. However, all the experiments are unable to capture the deep re-curvature of the TC. The next set of experiments is performed to evaluate the impact of Kalpana-1 AMVs derived from different multispectral channels (viz. visible, infrared and water vapor channels). More improvement is observed in TC track forecast when AMVs from water vapor channel are used for assimilation compared to infrared channel. Results also show degradation in short-range forecast when less-strict quality control is used for AMVs assimilation, but a considerable improvement is observed in long-range forecasts. Finally, the WRF tangent linear and adjoint model is used to compute the forecast sensitivity to Kalpana-1 AMVs observations. Upper- and lower-level circulation information provided by the Kalpana-1 AMVs influences the TC steering flow, and a positive impact on the track prediction is observed.

Temporal Scales of Satellite-Derived Atmospheric Winds Over the Asian Monsoon Region

The autocorrelation function of Meteosat-7-derived atmospheric motion vectors (AMVs) has been calculated over the Indian Ocean region for the Asian summer and winter monsoon seasons. The time where the autocorrelation function dropped to 0.5 was defined as the decorrelation time. It was observed that seasonal forcing caused the circulation patterns to be highly stable with decorrelation timescales on the order of 24 h or more at some regions. The analysis was done for the vector wind (u + iv), the zonal component u, and the meridional component v. The nature of the autocorrelation function clearly followed the changing wind circulations with season and pressure levels.