Assessing the Forecast Impact of a Geostationary Microwave Sounder using Regional and Global OSSEs

Forecast observing system simulation experiments (OSSEs) are conducted to assess the potential impact of geostationary microwave (GeoMW) sounder observations on numerical weather prediction forecasts. A regional OSSE is conducted using a tropical cyclone (TC) case that is very similar to hurricane Harvey (2017), as hurricanes are among the most devastating of weather-related natural disasters, and hurricane intensity continues to pose a significant challenge for numerical weather prediction. A global OSSE is conducted to assess the potential impact of a single GeoMW sounder centered over the continental United States versus two sounders positioned at the current locations of the National Oceanic and Atmospheric Administration Geostationary Operational Environmental Satellites (GOES) East and West. It is found that assimilation of GeoMW soundings result in better characterization of the TC environment, especially before and during intensification, which leads to significant improvements in forecasts of TC track and intensity. TC vertical structure (warm core thermal perturbation and horizontal wind distribution) is also substantially improved, as are the surface wind and precipitation extremes. In the global OSSE, assimilation of GeoMW soundings leads to slight improvement globally and significant improvement regionally, with regional impact equal to or greater than nearly all other observation types.

Temperature and Wind Distribution Effects on Wind Energy Production in Adrar Region (Southern Algeria)

The hottest transitions occur in the summer, as we notice during this period the peak of electricity consumption in Adrar, where the electricity network must use all kinds of energy, especially the wind energy produced by Cabertein wind farm. We evaluated the effect of temperature and wind distribution on the energy produced by one of Gamesa G52 wind turbines, and this was done by studying the wind distribution and determining the number of hours per year according to five cases. Finally, to estimate the monthly produced energy, we used a logical temperature equation, and then we determined the seasonal and annual energy. Low winds are the only reason why wind turbines are unable to produce electricity for a monthly period ranging from 152 An hour (May) to 274 hours (September), meaning that the seasonal production stop, for this reason, ranges between 590 hours (spring) and 779 hours (summer), with an average of 2736 hours per year, while temperatures did not constitute an obstacle to electricity production except. In three months for a short period of 2 hours (June and July) and 22 hours (August), affecting production in the summer season, with an estimated time of 26 hours.

Non-zonal structures of the midlatitude mesosphere/lower thermosphere dynamics studied by using atmospheric models and radar observations.

<p class="western" align="left">Radar observations from two SKiYMET radars at Collm (51°N, 13°E) and Kazan (56°N, 49°E) during 2016-2017 are used to investigate the longitudinal variability of the mesosphere/lower thermosphere (MLT) wind regime over western and eastern Europe. Both of the meteor radars have similar setups and apply the same analysis procedures to correctly compare MLT parameters and validate the simulated winds. The radar observations confirm the established seasonal variability of the wind distribution, but this distribution is not identical for the two stations. The results show good qualitative agreement with global circulations model predictions by the Middle and Upper Atmosphere Model (MUAM) and the Upper Atmosphere ICOsahedral Non-hydrostatic model (UA-ICON). The MUAM and UA-ICON models well reproduce the main dynamical features, namely the vertical and temporal distributions of the winds observed throughout the year. However, there are also some differences in the longitudinal wind variability of the models and radar observations. Numerical experiments with modified parameterization settings have also been carried out to study the response of the MLT wind circulation to the gravity waves originating from the lower atmosphere. The MUAM model results show that a decrease/increase in the gravity wave intensity at the lower atmosphere leads to an increase/decrease of the mesospheric zonal wind jet extension and the zonal wind reversal.</p>

Radial and vertical velocity field in a steady state symmetric tropical storm

ABSTRACT. A method to construct a consistent structure of steady state symmetric tropical storms from a few known values of temperature anomaly in the centre and around it has been developed. The role of kinematic eddy coefficient of viscosity in producing the transverse circulation in a tropical storm has been tested and discussed. The well known features and characteristics of a tropical storm, such as, eyewall, sinking motion, inside the eyewall, low-level radial inflow and high level outflow are well produced in the model. The computation shows that there is an increase of transverse circulation with increase of the magnitude of eddy coefficient. In the boundary layer, the vertical eddy coefficient plays more important role than the radial eddy coefficient; while in the upper layer the latter is much more important than the former. It has also been found that in absence of radial exchange coefficient, there can be no sinking motion in the central region of the storm. The magnitude of radial and vertical wind in the eye region is more sensitive to the variation of radial eddy coefficient. In addition to the eddy coefficients, transverse circulations also depend upon the tangential wind distribution above the boundary layer.

A New Approach to Assess Wind Potential

<p>Weibull Cumulative Distribution Function (C.D.F.) has been employed to assess and compare wind potentials of two wind stations Europlatform and Stavenisse of The Netherland. Weibull distribution has been used for accurate estimation of wind energy potential for a long time. The Weibull distribution with two parameters is suitable for modeling wind data if wind distribution is unimodal. Whereas wind distribution is generally unimodal, random weather changes can make the distribution bimodal. It is always desirable to find a method that accurately represents actual statistical data. Some well-known statistical methods are Method of Moment (MoM), Linear Least Square Method (LLSM), Maximum Likelihood Method (M.L.M.), Modified Maximum Likelihood Method (MMLM), Energy Pattern Factor Method (EPFM), and Empirical Method (E.M.), etc. All these methods employ Probability Density Function (PDF) of Weibull distribution, except LLSM, which uses Cumulative Distribution Function (C.D.F.). In this communication, we are presenting a newly proposed method of evaluating Weibull parameters. Unlike most methods, this new method employs a cumulative distribution function. A MATLAB® GUI-based simulation is developed to estimate Weibull parameters using the C.D.F. approach. It is found that the Mean Square Error (M.S.E.) is the lowest when using the new method. The new method, therefore, estimates wind power density with reasonable accuracy. Wind Power (W.P.) is estimated by considering four different Wind Turbine (W.T.) models for two sites, and maximum W.P. is found using Evance R9000.</p>

Analyzing Sea Surface Wind Distribution Characteristics of Tropical Cyclone Based on Sentinel-1 SAR Images

The spaceborne synthetic aperture radar (SAR) cross-polarization signal remains sensitive to sea surface wind speed with high signal-to-noise ratio under tropical cyclone (TC) conditions. It has the capability of observing TC intensity and size information over the ocean with large coverage and high spatial resolution. In this paper, TC wind distribution characteristics were studied based on SAR images. We collected 41 Sentinel-1A/B cross-polarization images covering TC eye, which were acquired between 2016 and 2020. For each case, sea surface wind speeds were retrieved by the modified MS1A model in a spatial resolution of 1 km. After deriving the value and location of maximum wind speed, wind fields were simulated symmetrically within a 200 km radius. Two new methodologies were proposed to calculate the decay index and the symmetry index based on the retrieved and simulated wind fields. Characteristics of the two indices were analyzed with respect to maximum wind. In addition, the maximum and averaged wind speeds of the right, back and left side of the motion direction were compared with TC intensity and storm motion speed. Statistical results indicate that right-side wind speed is the strongest for maximum and average, the wind difference between the left and right side is dependent on storm motion speed.

A python program to model and analyze wind speed data

A python program has been developed to analyze wind distributions using the Weibull density function. A two-parameter Weibull function is frequently used to model and assess wind potential and wind distribution. This python program finds first Weibull parameters from the recorded wind data by five different methods, namely, Empirical Method(EPM), Method of Moment (MoM), Energy Pattern Factor Method (EPFM), Maximum Likelihood Method (MLM), Modified Maximum Likelihood Method (MMLM), the parameters are then used to find theoretically fitted pdfs. The program is implemented on wind distribution of two cities of Pakistan (Chakri and Sadiq Abad). The program-generated pdfs were plotted with the histogram of recorded data, the fitting was excellent. To check the validity of the fitted pdfs, statistical errors Root Mean Square (RMSE), MeanAbsolute Percent Error (MAPE), Mean Absolute Error (MABE), and Chi-square statistic are calculated. In all cases,these statistical errors are well below the acceptance range. Both pictorial results and numerical values of statistical errors indicate the performance of the python program to analyze wind speed data

Analysis of wind speed characteristics using different distribution models in Medan City, Indonesia

<span lang="EN-CA">Estimating wind speed characteristics plays an essential role in designing a wind power plant at a selected location. In this study, the Weibull, gamma, and exponential distribution models were proposed to estimate and analyze the wind speed parameters and distribution functions. Real measured data were collected from Medan City, Indonesia. The scale and shape factors of wind distribution for three years data were calculated. The observed cumulative probability of the three models was compared to predicted wind characteristics. The probability density function (PDF) and the cumulative density function (CDF) of wind speed were also analyzed. The results showed that the Weibull model was the best model to determine PDF, while the exponential model was the best model to determine CDF for the Medan City wind site.</span>