Modelling Ionospheric vertical drifts over the African low latitude region

Low/equatorial latitudes vertical plasma drifts and electric fields govern the formation and changes of ionospheric density structures which affect space-based systems such as communications, navigation and positioning. Dynamical and electrodynamical processes play important roles in plasma distribution at different altitudes. Because of the high variability of E × B drift in low latitude regions, coupled with various processes that sometimes originate from high latitudes especially during geomagnetic storm conditions, it is challenging to develop accurate vertical drift models. This is despite the fact that there are very few instruments dedicated to provide electric field and hence E × B drift data in low/equatorial latitude regions. To this effect, there exists no ground-based instrument for direct measurements of E×B drift data in the African sector. This study presents the first time investigation aimed at modelling the long-term variability of low latitude vertical E × B drift over the African sector using a combination of Communication and Navigation Outage Forecasting Systems (C/NOFS) and ground-based magnetometer observations/measurements during 2008-2013. Because the approach is based on the estimation of equatorial electrojet from ground-based magnetometer observations, the developed models are only valid for local daytime. Three modelling techniques have been considered. The application of Empirical Orthogonal Functions and partial least squares has been performed on vertical E × B drift modelling for the first time. The artificial neural networks that have the advantage of learning underlying changes between a set of inputs and known output were also used in vertical E × B drift modelling. Due to lack of E×B drift data over the African sector, the developed models were validated using satellite data and the climatological Scherliess-Fejer model incorporated within the International Reference Ionosphere model. Maximum correlation coefficient of ∼ 0.8 was achieved when validating the developed models with C/NOFS E × B drift observations that were not used in any model development. For most of the time, the climatological model overestimates the local daytime vertical E × B drift velocities. The methods and approach presented in this study provide a background for constructing vertical E ×B drift databases in longitude sectors that do not have radar instrumentation. This will in turn make it possible to study day-to-day variability of vertical E×B drift and hopefully lead to the development of regional and global models that will incorporate local time information in different longitude sectors

Forecasting Economic and Financial Variables with Global VARs

This paper considers the problem of forecasting real and financial macroeconomic variables across a large number of countries in the global economy. To this end a global vector autoregressive (GVAR) model previously estimated over the 1979Q1-2003Q4 period by Dees, di Mauro, Pesaran, and Smith (2007), is used to generate out-of-sample one quarter and four quarters ahead forecasts of real output, inflation, real equity prices, exchange rates and interest rates over the period 2004Q1-2005Q4. Forecasts are obtained for 134 variables from 26 regions made up of 33 countries covering about 90 % of world output. The forecasts are compared to typical benchmarks: univariate autoregressive and random walk models. Building on the forecast combination literature, the effects of model and estimation uncertainty on forecast outcomes are examined by pooling forecasts obtained from different GVAR models estimated over alternative sample periods. Given the size of the modelling problem, and the heterogeneity of economies considered — industrialised, emerging, and less developed countries — as well as the very real likelihood of possibly multiple structural breaks, averaging forecasts across both models and windows makes a significant difference. Indeed the double-averaged GVAR forecasts perform bette

ENSEMBLES: a new multi-model ensemble for seasonal-to-annual predictions: Skill and progress beyond DEMETER in forecasting tropical Pacific SSTs

A new 46-year hindcast dataset for seasonal-to-annual ensemble predictions has been created using a multi-model ensemble of 5 state-of-the-art coupled atmosphere-ocean circulation models. The multi-model outperforms any of the single-models in forecasting tropical Pacific SSTs because of reduced RMS errors and enhanced ensemble dispersion at all lead-times. Systematic errors are considerably reduced over the previous generation (DEMETER). Probabilistic skill scores show higher skill for the new multi-model ensemble than for DEMETER in the 4–6 month forecast range. However, substantially improved models would be required to achieve strongly statistical significant skill increases. The combination of ENSEMBLES and DEMETER into a grand multi-model ensemble does not improve the forecast skill further. Annual-range hindcasts show anomaly correlation skill of ∼0.5 up to 14 months ahead. A wide range of output from the multi-model simulations is becoming publicly available and the international community is invited to explore the full scientific potential of these data

Pembangunan modul pembelajaran autocad dan kajian penerimaan pelajar. Satu kajian kes di Politeknik Kota Bharu

Modul Pengajaran dan Pembelajaran AutoCAD (MPP) merupakan satu media pengajaran yang mengandungi asas-asas mengenai komputer, perisian AutoCAD 2000 dan langkah-langkah berperingkat membuat lukisan teknikal menggunakan AutoCAD 2000. Kajian ini adalah bertujuan untuk menilai sejauh mana MPP ini boleh digunakan dalam proses pengajaran dan pembelajaran dalam aspek kesesuaian isi kandungan, sifat mesra pengguna dan kebolehlaksanaannya. Respondan untuk kajian ini ialah seramai 42 orang pelajar Diploma Kejuruteraan Elektrik Politeknik Kota Bharu. Untuk kajian ini instrumen yang digunakan ialah borang soal selidik di mana penilaian dilakukan berdasarkan persepsi responden terhadap MPP. Data-data yang dikumpulkan dianalisis menggunakan SPSS VI1.0 yang melibatkan skor min. Hasil kajian melaporkan dapatan yang diperolehi berkenaan penerimaan terhadap MPP. Hasil dapatan kajian menunjukkan penerimaan yang positif terhadap MPP oleh pelajar dan ianya mempimyai kebolehlaksanaan yang tinggi (skor min = 3.96) untuk diaplikasikan dalam proses pengajaran dan pembelajaran. Walaubagaimanapun pengkaji percaya MPP ini mempunyai ruang untuk penambahbaikan seperti saranan oleh penilai yang mengesahkan MPP ini agar ia lebih menarik dan sesuai digunakan pada masa depan

Improving rainfall nowcasting and urban runoff forecasting through dynamic radar-raingauge rainfall adjustment

The insufficient accuracy of radar rainfall estimates is a major source of uncertainty in short-term quantitative precipitation forecasts (QPFs) and associated urban flood forecasts. This study looks at the possibility of improving QPFs and urban runoff forecasts through the dynamic adjustment of radar rainfall estimates based on raingauge measurements. Two commonly used techniques (Kriging with External Drift (KED) and mean field bias correction) were used to adjust radar rainfall estimates for a large area of the UK (250,000 km2) based on raingauge data. QPFs were produced using original radar and adjusted rainfall estimates as input to a nowcasting algorithm. Runoff forecasts were generated by feeding the different QPFs into the storm water drainage model of an urban catchment in London. The performance of the adjusted precipitation estimates and the associated forecasts was tested using local rainfall and flow records. The results show that adjustments done at too large scales cannot provide tangible improvements in rainfall estimates and associated QPFs and runoff forecasts at small scales, such as those of urban catchments. Moreover, the results suggest that the KED adjusted rainfall estimates may be unsuitable for generating QPFs, as this method damages the continuity of spatial structures between consecutive rainfall fields