John P George
Articles written in Journal of Earth System Science
Volume 125 Issue 5 July 2016 pp 935-944
Surface level soil moisture from two gridded datasets over India are evaluated in this study. The firstone is the UK Met Office (UKMO) soil moisture analysis produced by a land data assimilation systembased on Extended Kalman Filter method (EKF), which make use of satellite observation of AdvancedScatterometer (ASCAT) soil wetness index as well as the screen level meteorological observations. Seconddataset is a satellite soil moisture product, produced by National Remote Sensing Centre (NRSC) usingpassive microwave Advanced Microwave Scanning Radiometer 2 measurements. In-situ observations ofsoil moisture from India Meteorological Department (IMD) are used for the validation of the gridded soilmoisture products. The difference between these datasets over India is minimum in the non-monsoonmonths and over agricultural regions. It is seen that the NRSC data is slightly drier (0.05%) and UKMOsoil moisture analysis is relatively wet during southwest monsoon season. Standard AMSR-2 satellitesoil moisture product is used to compare the NRSC and UKMO products. The standard AMSR-2 andUKMO values are closer in monsoon season and AMSR-2 soil moisture is higher than UKMO in allseasons. NRSC and AMSR-2 showed a correlation of 0.83 (significant at 0.01 level). The probabilitydistribution of IMD soil moisture observation peaks at 0.25 m^3/m^3, NRSC at 0.15 m^3/m^3, AMSR-2 at0.25 m3/m3 and UKMO at 0.35 m^3/m^3 during June–September period. Validation results show UKMOanalysis has better correlation with in-situ observations compared to the NRSC and AMSR-2 datasets.The seasonal variation in soil moisture is better represented in UKMO analysis. Underestimation of soilmoisture during monsoon season over India in NRSC data suggests the necessity of incorporating theactual vegetation for a better soil moisture retrieval using passive microwave sensors. Both productshave good agreement over bare soil, shrubs and grassland compared to needle leaf tree, broad leaf treeand urban land cover types.
Volume 126 Issue 2 March 2017 Article ID 0024
Incorporation of cloud- and precipitation-affected radiances from microwave satellite sensors in data assimilation system has a great potential in improving the accuracy of numerical model forecasts over the regions of high impact weather. By employing the multiple scattering radiative transfer model RTTOVSCATT,all-sky radiance (clear sky and cloudy sky) simulation has been performed for six channel microwave SAPHIR (Sounder for Atmospheric Profiling of Humidity in the Inter-tropics by Radiometry) sensors of Megha-Tropiques (MT) satellite. To investigate the importance of cloud-affected radiance data in severe weather conditions, all-sky radiance simulation is carried out for the severe cyclonic storm
‘Hudhud’ formed over Bay of Bengal. Hydrometeors from NCMRWF unified model (NCUM) forecasts are used as input to the RTTOV model to simulate cloud-affected SAPHIR radiances. Horizontal and vertical distribution of all-sky simulated radiances agrees reasonably well with the SAPHIR observed radiancesover cloudy regions during different stages of cyclone development. Simulated brightness temperatures of six SAPHIR channels indicate that the three dimensional humidity structure of tropical cyclone is well represented in all-sky computations. Improved correlation and reduced bias and root mean squareerror against SAPHIR observations are apparent. Probability distribution functions reveal that all-sky simulations are able to produce the cloud-affected lower brightness temperatures associated with cloudy regions. The density scatter plots infer that all-sky radiances are more consistent with observed radiances.Correlation between different types of hydrometeors and simulated brightness temperatures at respective atmospheric levels highlights the significance of inclusion of scattering effects from different hydrometeors in simulating the cloud-affected radiances in all-sky simulations. The results are promisingand suggest that the inclusion of multiple scattering radiative transfer models into data assimilation system can simulate the cloud-affected microwave radiance data which provide detailed information on three dimensional humidity structure of the atmosphere in the presence of cloud hydrometeors.
Volume 127 Issue 2 March 2018 Article ID 0026
Frequent occurrence of fog in different parts of northern India is common during the winter months of December and January. Low visibility conditions due to fog disrupt normal public life. Visibility conditions heavily affect both surface and air transport. A number of flights are either diverted or cancelled every year during the winter season due to low visibility conditions, experienced at differentairports of north India. Thus, fog and visibility forecasts over plains of north India become very important during winter months. This study aims to understand the ability of a NWP model (NCMRWF, Unified Model, NCUM) with a diagnostic visibility scheme to forecast visibility over plains of north India. Thepresent study verifies visibility forecasts obtained from NCUM against the INSAT-3D fog images and visibility observations from the METAR reports of different stations in the plains of north India. The study shows that the visibility forecast obtained from NCUM can provide reasonably good indication ofthe spatial extent of fog in advance of one day. The fog intensity is also predicted fairly well. The study also verifies the simple diagnostic model for fog which is driven by NWP model forecast of surface relative humidity and wind speed. The performance of NWP model forecast of visibility is found comparable tothat from simple fog model driven by NWP forecast of relative humidity and wind speed.