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.

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.

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.

Deciphering organization of GOES-16 green cumulus through the empirical orthogonal function (EOF) lens

A subset of continental shallow convective cumulus (Cu) cloud fields has been shown to have distinct spatial properties and to form mostly over forests and vegetated areas, thus referred to as “green Cu” (Dror et al., 2020). Green Cu fields are known to form organized mesoscale patterns, yet the underlying mechanisms, as well as the time variability of these patterns, are still lacking understanding. Here, we characterize the organization of green Cu in space and time, by using data-driven organization metrics and by applying an empirical orthogonal function (EOF) analysis to a high-resolution GOES-16 dataset. We extract, quantify, and reveal modes of organization present in a green Cu field, during the course of a day. The EOF decomposition is able to show the field's key organization features such as cloud streets, and it also delineates the less visible ones, as the propagation of gravity waves (GWs) and the emergence of a highly organized grid on a spatial scale of hundreds of kilometers, over a time period that scales with the field's lifetime. Using cloud fields that were reconstructed from different subgroups of modes, we quantify the cloud street's wavelength and aspect ratio, as well as the GW-dominant period.