Long range forecasting parameters vis-a-vis sub-divisional summer rainfall over India

Interannual variability of Empirical Orthogonal Functions (EOF) based upon regional/global parameters, associated with the summer monsoon rainfall over different meteorological sub-divisions of the country have been discussed, based upon the data during the years 1958 to 1990 enabling us to identify three broad sub-divisions of the country. It was interesting to note that the first empirical orthogonal function did not show significant correlation with monsoon rainfall over most SUB-DIVISIONS of the NE and SE parts of the country. However, this EOF was found to be significantly correlated with the rainfall over the remaining meteorological sub-divisions of the country.

Principal component analysis of monthly mean Area surface temperature over the Arabian Sea, Bay of Bengal and north Indian Ocean for two Contrasting sets of monsoon years

The spatial distribution and temporal variation of the monthly mean SSTA over the Arabian Sea, Bay of Bengal and the north Indian Ocean were investigated for a set of contrasting years of monsoon over the period 1961-80 for months April through July using Empirical Orthogonal Function (EOF) technique with a view to identify regions that are significantly related to the monsoon rainfall. Over 75% of the total variance is, explained by the first mode EOF. SSTA over the north and northeast Arabian Sea during pre-monsoon months were found to be possible indicators of the ensuing monsoon activity. The higher eigen vectors in May over northeast Arabian Sea may signal good monsoon and vice versa. In June there is a marked contrast in the distribution of SST over the Arabian Sea between the two sets of the years the eastern Arabian Sea IS warmer for the deficient monsoon years while the entire Arabian Sea except over the extreme north Arabian Sea is cool during good monsoon years. There is formation of SSTA over the equatorial Indian Ocean area close to Indonesian island commencing from May which is more marked in June and is positively correlated with seasonal rainfall activity over India.

Unraveling the spatiotemporal spread of COVID-19 in Brazil through spatial network connectivity

Background: Describing and understanding the process of diffusion can allow local managers better plan emergence scenarios. Thus, the main aim of this study was to describe and unveil the spatiotemporal patterns of diffusion of the COVID-19 in Brazil from February 2020 until April 2021. Methods: This is a retrospective purely observational ecologic study including all notified cases and deaths. We used satellite-derived night light imagery and spatiotemporal Empirical Orthogonal Function analysis to quantify the spatial network structure of lighted development and the spatiotemporal transmission of the pathogen through the network. Results: The more populous state capitals within the largest network components presented higher frequency of deaths and earlier onset compared to the increasing numbers of smaller, less populous municipalities trending toward lower frequency of deaths and later onset. By week 48 2020, the full network was almost completely affected. Cases and deaths showed a distinct second wave of wider geographic expansion beginning in early November 2020. Conclusions: The spatiotemporal diffusion in Brazil was characterized by an intertwined process of overseas relocation, hierarchical network transmission and contagious effects. A rapid response as the immediate control of all ports, airports and borders combined with mandatory quarantine are critical to retard disease diffusion.

Empirical Orthogonal Function Analysis and Modeling of Global Tropospheric Delay Spherical Harmonic Coefficients

Based on the ERA-5 meteorological data from 2015 to 2019, we establish the global tropospheric delay spherical harmonic (SH) coefficients set called the SH_set and develop the global tropospheric delay SH coefficients empirical model called EGtrop using the empirical orthogonal function (EOF) method and periodic functions. We apply tropospheric delay derived from IGS stations not involved in modeling as reference data for validating the dataset, and statistical results indicate that the global mean Bias of the SH_set is 0.08 cm, while the average global root mean square error (RMSE) is 2.61 cm, which meets the requirements of the tropospheric delay model applied in the wide-area augmentation system (WAAS), indicating the feasibility of the product strategy. The tropospheric delay calculated with global sounding station and tropospheric delay products of IGS stations in 2020 are employed to validate the new product model. It is verified that the EGtrop model has high accuracy with Bias and RMSE of −0.25 cm and 3.79 cm, respectively, with respect to the sounding station, and with Bias and RMSE of 0.42 cm and 3.65 cm, respectively, with respect to IGS products. The EGtrop model is applicable not only at the global scale but also at the regional scale and exhibits the advantage of local enhancement.

Research on Micro-terrain Classification of Transmission Line Galloping

Micro-terrain and micro-weather have an important impact on transmission line galloping. In order to carry out galloping prediction of micro-terrain, the classification of galloping micro-terrain is studied in this work. Firstly, we collect historical data of 1537 galloping points from the State Grid Corporation of China, and select 208 galloping points located in the micro-terrain area by analyzing the altitude and the topographic relief characteristics around each galloping point. Then the galloping micro-terrain types are extracted by Empirical Orthogonal Function method, the first four spatial modes of galloping micro-terrain are the windward slope of east-west mountain area, the windward slope of north-south mountain area, the independent hill, and the saddle back of mountain/hill. Finally, the regional characteristics of typical micro-terrain are analyzed according to the actual lines.

MODIS-derived fire spatial and temporal distribution during haze season in Southeast Asia using empirical orthogonal function

One of the efforts to control the forest and land fire disasters which affect on the biomass burning haze is fire hotspots monitoring. Biomass burning haze in Southeast Asia (SEA) has become a recurring annual issue. This study aims to determine the spatial and temporal distribution of fire hotspots along SEA, so that it can serve as guidance for efforts to control them. The hotspot data used is derived from NASA’s Fire Information for Resource Management System (FIRMS) MODIS sensors which is collected from 2001-2020. Spatial analysis of the re-gridded data shows the highest burning activities over SEA occurred in Feb-Apr, with >2000 fire events in the Indo-China area and >1000 fire events in Sumatra and Borneo. Empirical Orthogonal Function (EOF) was performed on monthly total hotspot data for 228 months for determining dominant patterns spatially and temporally. Based on the EOF analysis results, the three major modes have achieved a total variance of 71 %. The first mode (EOF1) explains 65 % of the total variance. The second (EOF2) and third (EOF3) modes account for 3.60 % and 2.97 % of the total variance respectively. The first and the third principal component identified high loadings over the Indo-China and Sumatra-Borneo regions respectively. Whereas the second principal component separates the fire areas into China and Indo-China region. Inter-annual pattern is dominant in the EOF1, while the inter-seasonal pattern is dominant in EOF2 and EOF3. ENSO, IOD, and MJO are factors that influence the pattern of the determined principal components. The result of this study provides general understanding on how the fire events varied over the past two decades in SEA.