The Spatiotemporal Evolution of the Diurnal Cycle in Two WRF Simulations of Tropical Cyclones

The properties of diurnal variability in tropical cyclones (TCs) and the mechanisms behind them remain an intriguing aspect of TC research. This study provides a comprehensive analysis of diurnal variability in two simulations of TCs to explore these mechanisms. One simulation is a well known Hurricane Nature Run, which is a realistic simulation of a TC produced using the Weather Research and Forecasting model (WRF). The other simulation is a realistic simulation produced using WRF of Hurricane Florence (2018) using hourly ERA5 reanalysis data as input. Empirical orthogonal functions and Fourier filtering are used to analyze diurnal variability in the TCs. In both simulations a diurnal squall forms at sunrise in the inner core and propagates radially outwards and intensifies until midday. At midday the upper-level outflow strengthens, surface inflow weakens, and the cirrus canopy reaches its maximum height and radial extent. At sunset and overnight, the surface inflow is stronger, and convection inside the RMW peaks. Therefore, two diurnal cycles of convection exist in the TCs with different phases of maxima: eyewall convection at sunset and at night, and rainband convection in the early morning. This study finds that the diurnal pulse in the cirrus canopy is not advectively-driven, nor can it be attributed to weaker inertial stability at night; rather, the results indicate direct solar heating as a mechanism for cirrus canopy lifting and enhanced daytime outflow. These results show a strong diurnal modulation of tropical cyclone structure, and are consistent with other recent observational and modeling studies of the TC diurnal cycle.

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 FUNCTIONS ASSOCIATED WITH PARAMETERS USED IN LONG RANGE FORECASTING OF INDIAN SUMMER MONSOON

EEmpirical Orthogonal Functions (EOF),. associated with the; parameters for long range forecasting of Indian summer monsoon onset and seasonal. rainfall have been discussed. It was found that the percentage of variance explained was 77 and 67 respectively through the first four EOF. The highest correlation coefficient with the onset date was found for the first function which showed the maximum influence of Cobar (Australia) and Darwin (Australia) zonal winds on the onset date. It was interesting to note that for rainfall prediction predominant effect on the first EOF was noticed of 50 hPa ridge over northern hemisphere, Eurasian snow cover, Argentina pressure (negatively correlated) and 500 hpa ridge, 10 hPa Balboa wind, north, central India and east coast minimum temperatures, and northern hemisphere temperature. However, the Influence of EI-Nino, equatorial pressure and Darwin pressure (Including Tahiti minus Darwin) and Himalayan snow cover was almost negligible. The eigen index for the onset date suggests a complementary method for its application In long range prediction of summer monsoon onset date.

Rainfall variability and meteorological drought in the Horn of Africa

Basic rainfall characteristics and drought over the Horn of Africa (HoA) is investigated, from 1901 to 2010. Standard Precipitation Index (SPI) is used to study drought variability, mainly focusing on 3-month SPI. The dominant mode of variability of seasonal rainfall was analyzed by performing Empirical orthogonal functions (EOF) analysis. Gridded data is sourced from Climate Research Unit (CRU), spanning from 1901 to 2010. The HoA experiences predominantly bimodal rainfall distribution in time; March to May (MAM) and October to December (OND). The spatial component of the first eigenvector (EOF1) shows that the MAM and OND seasonal rainfalls are dominated by negative and positive loadings, respectively. The EOF1 explain 34.5% and 58.9% variance of MAM and OND seasonal rainfall, respectively. The EOF2, 3 and 4 are predominantly positive, explaining less than 25% in total of the seasonal rainfall variance in the two seasons. The last two decades experienced the highest negative anomaly, with OND seasonal rainfall showing higher anomalies as compared to MAM season. The OND season recorded 9% more drought events as compared to MAM season. The frequency of occurrence of moderate, severe and extreme dryness was almost the same in the two seasons. These results give a good basis for regional model validation, as well as mapping out drought hotspots and projections studies in the HoA.

A Method to Characterize Climate, Earth or Environmental Vector Random Processes

We propose a methodology to characterize a multivariate non-stationary vector random process that can be used for simulating random realizations that keep the probabilistic behavior of the original time series. The marginal probability distribution of each component process is assumed to be a piecewise function defined by several weighted parametric probability models. The weights are obtained analytically by ensuring that the probability density function is well defined and that it is continuous at the common endpoints. The probability model is assumed to vary periodically in time over a predefined time period by defining the model parameters and the common endpoints as truncated generalized Fourier series. The coefficients of the expansions are obtained with the maximum likelihood method. Three different types of sets of orthogonal functions are tested. The method is applied to three time series with different particularities. Firstly, it is shown its good behavior to capture the highly variable freshwater discharges at a dam located in a semiarid zone in Andalucía (Spain) which is influenced not only by the climate variability but also by management decisions. Secondly, for the Wolf sunspot number time series, the Schwabe cycle and time variations close to the 7.5 and 17 years are analyzed along a 22-year cycle. Finally, the method is applied to a bivariate (velocity and direction) wind time series observed at a location of the Atlantic Ocean. For this case, the analysis, that was combined with a vectorial autoregresive model, focus on the assessment of the goodness of the methodology to replicate the statistical features of the original series. In particular, it is found that it reproduces the marginal and joint distributions, the wind rose, and the duration of sojourns above given thresholds.

Modes of the BiOS-driven Adriatic Sea thermohaline variability

In this study the impact of the Adriatic-Ionian Bimodal Oscillating System (BiOS) on the interannual to decadal variability of the Adriatic Sea thermohaline circulation is quantified during the 1987-2017 period with the numerical results of the Adriatic Sea and Coast (AdriSC) historical kilometer-scale climate simulation. The time series associated with the first five Empirical Orthogonal Functions (EOFs) computed from the salinity, temperature and current speed monthly detrended anomalies at 1-km resolution are correlated to the BiOS signal. First, it is found that the AdriSC climate model is capable to reproduce the BiOS-driven phases derived from in-situ observations along a long-term monitoring transect in the middle Adriatic. Then, for the entire Adriatic basin, high correlations to the 2-year delayed BiOS signal are obtained for the salinity and current speed first two EOF time series at 100 m depth and the sea-bottom Finally, the physical interpretation of the EOF spatial patterns reveals that Adriatic bottom temperatures are more influenced by the dense water circulation than the BiOS. These findings confirmed and generalized the known dynamics derived previously from observations, and the AdriSC climate model can thus be used to better understand the past and future BiOS-driven physical processes in the Adriatic Sea.

Spatio-Temporal Variations of Precipitable Water Vapor and Horizontal Tropospheric Gradients from GPS during Typhoon Lekima

GPS data during Typhoon Lekima at 700 stations in China were processed by the Precise Point Positioning (PPP) method. A refined regional Tm model was used to derive the precipitable water vapor (PWV) at these GPS stations. Spatio-temporal variations of PWV with the typhoon process were analyzed. As the typhoon approached, PWV at stations near the typhoon center increased sharply from about 50 mm to nearly 80 mm and then dropped back to about 40–50 mm as the typhoon left. Comparisons of GPS, radiosonde, the Global Data Assimilation System (GDAS) Global Forecast System (GFS) analysis products and ERA5 reanalysis products at four matched GPS-RS stations show overall overestimations of PWV from radiosonde, GFS and ERA5 compared with GPS in a statistical perspective. An empirical orthogonal functions (EOF) analysis of the PWV during the typhoon event revealed some different patterns of variability, with both the first EOF (~36.1% of variance) and second EOF (~30.3% of variance) showing distinctively large anomalies over the typhoon landing locations. The typhoon caused a large horizontal tropospheric gradient (HTG) with the magnitude reaching 5 mm and the direction pointing to the typhoon center when it made a landfall on mainland China. The magnitude and the consistency of the HTG direction decreased overall as the typhoon weakened.

Numerical method for solving problems of dynamics of marine complexes and ship auxiliary mechanism

The article describes the study of methods of hydrodynamic coefficients using finite volumes, as well as numerical methods based on the study of data from the air tube experiment.The methods of converting the data of air tube experiments when working out the elements of the fixed assets of the fleet in the bases of orthogonal functions (bursts) were investigated and described. The study is based on the obtained mathematical models of processes using the methods of multi-turn engineering design, methods for constructing computer models in a structure that provides for an open process of developing and approving proposed project elements.An urgent task is to develop methods for reducing costs associated with the design and creation of marine infrastructure facilities and fixed working capital. The relevance of the tasks follows from the expensive cycles of project development, the labor costs of highly qualified specialists.