Souvik Majumder

Bio: Souvik Majumder is an academic researcher from University of Calcutta. The author has contributed to research in topic(s): Disdrometer & Precipitation. The author has an hindex of 3, co-authored 8 publication(s) receiving 25 citation(s).

Multi-technique observations of convective rain events at a tropical location

Abstract: The effects of the convective rain on various atmospheric parameters have been investigated at Kolkata (22.57°N, 88.37°E), India, during pre-monsoon and monsoon period of 2013. Various parameters like cloud base height and depth, liquid water content, rain rate and rain drop size distribution (DSD) are observed concurrently. The atmospheric electric field measured with an electric field monitor and the attenuation and depolarisation of satellite signals measured by a Ku-band receiving system are also studied during rain events. The instability indices obtained from radiometric measurements and the rain height profiles from micro rain radar are used to classify rain into two types, namely, convective and stratiform. The signatures of rain events on multi-technique observations are studied to indicate the various aspects of convective processes at a tropical location.

Study of tropospheric delay over Indian region from MODIS, NCEP/NCAR data and ground based water vapor measurements at Kolkata

Abstract: A priori estimation of delays with error variance of atmospheric radio wave propagation is important for precise position estimation and integrity of the system like Global Positioning System (GPS). Tropospheric effects are less severe than the ionospheric counterpart, but behave in a complex manner, making it difficult to isolate them. In this present paper, a ground based radiometer has been utilized to study the characteristics of tropospheric delay and compared it with the MODIS satellite observations over Kolkata (22.57°N, 88.37°E). Results indicate a good agreement between radiometer and MODIS data except the monsoon months. A climatology of spatial and temporal variation of dry and wet tropospheric delay over the Indian subcontinent have also been estimated using MODIS data and NCEP–NCAR reanalysis data for 2008–2012. The spatial variations of dry delay over Indian region are observed to be in the range of 120–250 cm with limited seasonal variability. However, the wet delay varies from 20 cm in winter months to 45 cm during monsoon period in the coastal and central Indian region. Further analysis reveals that the contribution of wet delays in total delay is significant only along the Indo-Gangetic plain. This indicates that extra precaution is needed in handling tropospheric delay for this region due to fast varying nature of water vapor.

Simplistic approach for water vapour sensing using a standalone global positioning system receiver

Abstract: Precipitable water vapour (PWV) is an important input for numerical weather prediction model, meteorology and high-precision navigational applications. Conventional methods for the determination of PWV using radiosonde are not sufficient owing to poor temporal resolution, whereas radiometer-derived PWV is reliable only in fair weather conditions. Global positioning system (GPS) is a very useful and cost-effective tool to determine PWV continuously in all weather conditions. The processing of GPS data to extract the PWV information is, however, very complicated due to very small effect of the PWV (~0.5% of total delay) on GPS frequencies than other sources of delay and errors and requires a network of GPS in differential configuration for such purpose. The authors show how the problem can be handled in a standalone dual-frequency GPS receiver in a relatively less complicated manner with reasonable accuracy. The performances of different dry tropospheric delay models are also investigated. The methodology is tested with GPS measurements at Kolkata (22.57°N, 88.37°E) and Bangalore (13.01°N, 77.5°E). The results indicate that the proposed methodology can be implemented for PWV estimation using single GPS receiver with satisfactory performance.

Seasonal Variation of Rain and Cloud Microstructure at a Tropical Location

Abstract: Precipitation is a key element of the Earth’s hydrology cycle and needs to be carefully monitored due to the rapid growth of satellite and terrestrial link based telecommunication services, using higher frequency band particularly above 10 GHz. The presence of raindrops which absorbs and scatters radio wave energy can produce degradation of the reliability and performance of the communication links. Thus, rain drop size distribution (DSD) is one of the most extensively used parameters for improved an accurate description of any rain event. Several DSD models, namely Lognormal, Gamma, Weibull, Marshall and Palmer are employed to characterize the DSD. Our present work employs three parameter Gamma distributions utilizing Method of Moments (MOM) technique to investigate the physical characteristics and microstructure of rain DSD in a tropical region based on three year long disdrometer observations. Characteristics of precipitation and clouds have been analysed in the present study over the tropical station Kolkata $(22.57^{\circ}\mathrm{N}, 88.36^{\circ}\mathrm{E})$, during the pre-monsoon (March, April and May), monsoon (June, July, August and September) and post-monsoon (October and November) months. Three year long measurements of the rain DSD have been made using a ground based impact type JW (Joss and Waldvogel) disdrometer located at the Institute of Radio Physics and Electronics of University of Calcutta during 2014-2016. Cloud microphysics is also observed during the same period as to investigate its role in determining rain microstructure. To depict the cloud microphysics of the above mentioned location, $1^{\circ} \mathrm{X}1^{\circ}$ cloud optical depth (COD) and cloud effective radius (CER) data are collected from Moderate Resolution Imaging Spectroradiometer (MODIS). Rain DSD analysis reveals that larger drops are more dominant in the pre-monsoon season compared to any other seasons (Monsoon and Post-monsoon) whereas comparatively smaller drops are more dominant in the monsoon season than the other seasons (pre-monsoon and post-monsoon) at same rain rate bins. This phenomenon occurs because of the pre-monsoon rain droplets are associated with well-built vertical updraft. Distinct variation of cloud effective radius (CER) value is noticed during different seasons of the year. A combined analysis of cloud drop size and rain drop size indicates that in the pre-monsoon months low CER values are associated with the dominance of large raindrops, and during the monsoon period large cloud droplets effect smaller rain drops to dominate the precipitation at the present location.

Rain microphysical features as observed from ground based radar and disdrometer at a tropical location

Abstract: Tropical region experiences a variety of rainfall types throughout the year. The rain can vary from stratiform to convective even within a single event. The rain parameters have been separately investigated for stratiform and convective cases. The rain phases have been classified into stratiform and convective on the basis of bright band signature in the rain rate profile of the micro rain radar. The rain drop size distributions exhibit discernible difference for stratiform and convective cases. The drop size distribution parameters consequently demonstrate distinguishable variations for the two types of rain.

Blended wind fields for wave modeling of tropical cyclones in the South China Sea and East China Sea

Abstract: Accurate tropical cyclone (TC) wind fields are crucial for modeling TC waves. Usually, reanalysis wind data, such as the ERA-Interim dataset, and parametric TC models, such as the Holland model, are widely used to generate TC wind fields. In the present work, 29 tropical cyclones (TCs) in the South China Sea (SCS) and East China Sea (ECS) of 4 years (2011–2014) are analyzed at 10 buoy locations. Among them, 9 TCs are selected as study cases due to buoys experience their whole processes of the TC passing. For the 9 selected TCs, data of the ERA-Interim and Holland model are compared with observation data of 10 buoys in the SCS and ECS. Results show that the ERA-Interim largely under-predicts wind speeds near the TC center, where the Holland model performs generally well. However, the Holland model fails to reproduce wind speeds in outer-region of the TC. After analyzing character of two sets of data, applicable ranges for the ERA-Interim and Holland model are identified with critical boundary limits, which are associated with the TC size. A formula for blended TC wind fields combining two datasets is proposed, which shows good capacity of the TC wind simulation. Then, the blended wind model is applied in TC wave simulations in the SCS and ECS, and shows a better performance than both the ERA-Interim and the Holland model. Thus, the proposed blending formula can be used to generate more accurate TC wind fields which are required in TC wave hindcasts.

Prediction of convective events using multi-frequency radiometric observations at Kolkata

Abstract: In the present study, the effectiveness of nowcasting convective activities using a microwave radiometer has been examined for Kolkata (22.65° N, 88.45° E), a tropical location. It has been found that the standard deviation of brightness temperature (BT) at 22 GHz and instability indices like Lifting Index (LI), K Index (KI) and Humidity Index (HI) has shown definite changes before convective events. It is also seen that combination of standard deviation of BT at 22 GHz and LI can be most effective in predicting convection. A nowcasting algorithm is prepared using 18 isolated convective events of 2011 and in all cases, a marked variation of these parameters has been seen an hour before the event. Accordingly, a prediction model is developed and tested on convective events of 2012 and 2013. It is seen that the model gives reasonable success in predicting convective rain about 7075 min in advance with a prediction efficiency of 80%.

A random forest algorithm for nowcasting of intense precipitation events

Abstract: Automatic nowcasting of convective initiation and thunderstorms has potential applications in several sectors including aviation planning and disaster management. In this paper, random forest based machine learning algorithm is tested for nowcasting of convective rain with a ground based radiometer. Brightness temperatures measured at 14 frequencies (7 frequencies in 22–31 GHz band and 7 frequencies in 51–58 GHz bands) are utilized as the inputs of the model. The lower frequency band is associated to the water vapor absorption whereas the upper frequency band relates to the oxygen absorption and hence, provide information on the temperature and humidity of the atmosphere. Synthetic minority over-sampling technique is used to balance the data set and 10-fold cross validation is used to assess the performance of the model. Results indicate that random forest algorithm with fixed alarm generation time of 30 min and 60 min performs quite well (probability of detection of all types of weather condition ∼90%) with low false alarms. It is, however, also observed that reducing the alarm generation time improves the threat score significantly and also decreases false alarms. The proposed model is found to be very sensitive to the boundary layer instability as indicated by the variable importance measure. The study shows the suitability of a random forest algorithm for nowcasting application utilizing a large number of input parameters from diverse sources and can be utilized in other forecasting problems.

Retrieval of atmospheric properties with radiometric measurements using neural network

Abstract: Microwave radiometer is an effective instrument to monitor the atmosphere continuously in different weather conditions. It measures brightness temperatures at different frequency bands which are subjected to standard retrieval methods to obtain real time profiles of various atmospheric parameters such as temperature and humidity. But the retrieval techniques used by radiometer have to be adaptive to changing weather condition and location. In the present study, three retrieval techniques have been used to obtain the temperature and relative humidity profiles from brightness temperatures, namely; piecewise linear regression, feed forward neural network and neural back propagation network. The simulated results are compared with radiosonde observations using correlation analysis and error distribution. The analysis reveals that neural network with back propagation is the most accurate technique amongst the three retrieval methods utilized in this study.

Retrieval of total water vapour in the Arctic using microwave humidity sounders

Abstract: . Quantitative retrievals of atmospheric water vapour in the Arctic present numerous challenges because of the particular climate characteristics of this area. Here, we attempt to build upon the work of Melsheimer and Heygster (2008) to retrieve total atmospheric water vapour (TWV) in the Arctic from satellite microwave radiometers. While the above-mentioned algorithm deals primarily with the ice-covered central Arctic, with this work we aim to extend the coverage to partially ice-covered and ice-free areas. By using modelled values for the microwave emissivity of the ice-free sea surface, we develop two sub-algorithms using different sets of channels that deal solely with open-ocean areas. The new algorithm extends the spatial coverage of the retrieval throughout the year but especially in the warmer months when higher TWV values are frequent. The high TWV measurements over both sea-ice and open-water surfaces are, however, connected to larger uncertainties as the retrieval values are close to the instrument saturation limits. This approach allows us to apply the algorithm to regions where previously no data were available and ensures a more consistent physical analysis of the satellite measurements by taking into account the contribution of the surface emissivity to the measured signal.