Deterministic Seasonal Quantitative Precipitation Forecasts: Benchmark Skill with a GCM
by K. C. Gouda, S. Nahak & P. Goswami
Many applications, from agricultural planning (such as crop choice) to estimation of hydro-power and surface water availability require quantitative precipitation forecasts (QPF). While a large number of studies have addressed the problem of forecasting seasonal Indian summer monsoon (ISM) rainfall over the decades, the emphasis generally has been on simulation and forecasting of rainfall anomalies from long-period mean. However, given the trends in monsoon rainfall, the procedure of considering anomalies has inherent errors; thus reconstruction of actual rainfall from the anomalies does not necessarily provide accurate information. Most QPF have been attempted at short-range forecasts, with a variety of techniques like model output statistics. This work represents evaluation of an atmospheric Variable Resolution General Circulation Model (VRGCM) for QPF during monsoon season. The VRGCM, with variable grid resolution provides relatively high (~ 50 km) resolution over the ISM region, has been validated over all India as well as in different regions like Central and North India, South India, North East India for seasonal forecast of monsoon rainfall. VRGCM simulated QPF appears to provide comparable information as that of anomaly forecasts of monsoon over different regions in India. The forecast skill is appreciable, with significant correlation at all India, South India and North East India regions. The Root Mean Square Error of VRGCM in forecasting quantitative rainfall overall India and Central North India is very low and bit high over South and North East region of India. The performance of VRGCM in forecasting the rainfall in extreme (deficit or excess) monsoon years is also very high with phase synchronisation of 89% in the inter annual variability of rainfall during monsoon at all India scale while the value is about 57%, 76% and 55% over Central North, South and North East India region, respectively.
Source: https://link.springer.com/article/10.1007%2Fs00024-020-02463-7
Detecting ionospheric disturbances using GPS without aliasing caused by non-uniform spatial sampling: Algorithm, validation and illustration
by K.Shimna and M. Sithartha Muthu Vijayan
Abstract
Ionospheric disturbance (ID) detected using GPS based Total Electron Content (TEC) measurements are widely used to study the morphology and dynamics of the ionosphere, and its impact on radio communication and satellite based navigation. The IDs normally derived as rate of change of TEC (ROT) between consecutive ionospheric pierce points at uniform time interval implicitly results in non-uniform spatial sampling along the GPS satellite tracks. The non-uniform spatial sampling introduces aliasing in ROT. These aliasing corrupt amplitude and Signal-to-Noise Ratio (SNR) of the detected IDs. In this study, we propose a Spatio-Periodic Leveling Algorithm (SPLA) to remove such aliasing. Efficiency of the proposed algorithm was tested by simulating the IDs along a satellite track and validated with GPS observations carried out during 2015 St. Patrick's day geomagnetic storm. Spatiotemporal, and SNR analyses of simulated and observed IDs reveal that the SPLA is (i) efficient in removing the aliases, (ii) increases the SNR on an average of 99.5% compared to ROT, (iii) removes the need of applying elevation cut-off, and (iv) expands the area of coverage up to 65%.
Source: https://www.sciencedirect.com/science/article/abs/pii/S1364682620302091?via%3Dihub
Detecting aliasing and artifact free co-seismic and tsunamigenic ionospheric perturbations using GPS
by M.Sithartha Muthu Vijayan and K.Shimna
Ionospheric perturbations induced by tsunamis and earthquakes can be used for tsunami early warning and remote sensing of earthquakes, provided the perturbations are characterized properly to distinguish them from the ones caused by other sources. The ionospheric perturbations are increasingly being obtained from Global Positioning System (GPS) based Total Electron Content (TEC) measurements sampled at uniform time intervals. However, the sampling is not uniform in space. The nonuniform spatial sampling along the GPS satellite tracks introduces aliasing if it is not accounted while computing the ionospheric perturbations. All the methods hitherto used to detect the co-seismic and tsunamigenic ionospheric perturbations did not account the nonuniform spatial sampling while computing these perturbations. In addition, the residual approach used to obtain the perturbations by detrending the TEC time series using high-order polynomial fit introduces artifacts. These aliasing and artifacts corrupt amplitude, Signal-to-Noise Ratio (SNR), phase, and frequency of ionospheric perturbations which are vital to distinguish the perturbations induced by tsunamis and earthquakes from the rest. We show that Spatio-Periodic Leveling Algorithm (SPLA) successfully removes such aliasing and artifacts. The efficiency of SPLA in removing the aliases and artifacts is validated under two simulated scenarios, and using GPS observations carried out during two natural disasters – the 2004 Indian Ocean tsunami and the 2015 Nepal-Gorkha earthquake. We, further, studied the severity of aliasing and artifacts on co-seismic and tsunamigenic perturbations by analyzing its characteristics employing SNR, spatiotemporal, and wavelet analyses. The results reveal that removal of aliasing and artifacts using SPLA i) increases the SNR up to ∼149% compared to the residual method and ∼39% compared to the differential method, ii) distinctly resolves signals from sharp static variations, and iii) detects 50% more co-seismic ionospheric perturbations and 25% more tsunami-induced ionospheric perturbations in the two events studied. Cross-correlation of the perturbation time series obtained using the residual method and SPLA reveals that aliasing and artifacts shift the time of occurrence by −7.64 minutes to +4.21 minutes. Further, the results show that the SPLA efficiently detects the ionospheric perturbations at low elevation angles, thereby removes the need of applying elevation cut-off and increases the area of ionospheric exploration of a GPS receiver.
Dengue situation in India: Suitability and transmission potential model for present and projected climate change scenarios
by Satya Ganesh Kakarla, Kantha Rao Bhimala, Madhusudhan Rao Kadiri, Sriram Kumaraswamy and Srinivasa Rao Mutheneni
Abstract: Dengue fever is mosquito borne viral disease caused by dengue virus and transmitted by Aedes mosquitoes. In recent years the dengue has spread rapidly to several regions and it becomes a major public health menace globally. Dengue transmission is strongly influenced by environmental factors such as temperature and rainfall. In the present study, a climate driven dengue model was developed and predicted areas vulnerable for dengue transmission under the present and future climate change scenarios in India. The study also projected the dengue distribution risk map using representative concentration pathways (RCP4.5 and RCP8.5) in India in 2018–2030 (forthcoming period), 2031–2050 (intermediate period) and 2051–2080 (long period).
The dengue cases assessed in India from 1998 to 2018 and found that the dengue transmission is gradually increasing year over year. The temperature data from 1980 to 2017 shows that, the mean temperatures are raising in the Southern region of India. During 2000–2017 periods the dengue transmission is steadily increasing across the India in compare with 1980–1999 periods. The dengue distribution risk is predicted and it is revealed that the coastal states have yearlong transmission possibility, but the high transmission potential is observed throughout the monsoon period. Due to the climate change, the expansion two more months of dengue transmission risk occurs in many regions of India. Both RCP4.5 and RCP8.5 scenarios revealed that dengue outbreaks might occur at larger volume in Southern, Eastern, and Central regions of India. Furthermore a sensitivity analysis was performed to explore the impact of climate change on dengue transmission. These results helps to suggest appropriate control measures should be implemented to limit the spread in future warmer climates. Besides these, a proper plan is required to mitigate greenhouse gas emissions to reduce the epidemic potential of dengue in India.
Source: https://www.sciencedirect.com/science/article/pii/S0048969720338584
Page 24 of 33
