P. K. Pal

The Impact of Variational Assimilation of SSM/I and QuikSCAT Satellite Observations on the Numerical Simulation of Indian Ocean Tropical Cyclones

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 ...

Impact of Atmospheric Infrared Sounder Data on the Numerical Simulation of a Historical Mumbai Rain Event

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...

Impacts of Satellite-Observed Winds and Total Precipitable Water on WRF Short-Range Forecasts over the Indian Region during the 2006 Summer Monsoon

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, WR...

Surface Heat Fluxes over Global Oceans Exclusively from Satellite Observations

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 (Qa), and air–sea temperature difference (T SST Ta) 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 T, and a new one is developed to estimate Qa. The developed empirical equation for Qa estimations is an extension of the authors’ previous method. Compared to the Comprehensive Ocean– Atmosphere Data Set (COADS), the Qa retrieved by the previous approach had a negative bias of the order of more tha n2gk g 1 over the Gulf Stream and Kuroshio during winter but had a positive bias of more than 2gk g 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 Qa 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.

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...

Skill of regional and global model forecast over Indian region

The global model analysis and forecast have a significant impact on the regional model predictions, as global model provides the initial and lateral boundary condition to regional model. This study addresses an important question whether the regional model can improve the short-range weather forecast as compared to the global model. The National Centers for Environmental Prediction (NCEP) Global Forecasting System (GFS) and the Weather Research and Forecasting (WRF) model are used in this study to evaluate the performance of global and regional models over the Indian region. A 24-h temperature and specific humidity forecast from the NCEP GFS model show less error compared to WRF model forecast. Rainfall prediction is improved over the Indian landmass when WRF model is used for rainfall forecast. Moreover, the results showed that high-resolution global model analysis (GFS4) improved the regional model forecast as compared to low-resolution global model analysis (GFS3).

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 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.