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Distributions of amount of rain with rainfall rate and of duration with rate are determined for all rain of 1963 and for the heavy rain of 1958 to 1962. The rainfall rates are obtained from the rain chart records of the McGill Cbservatory tipping-bucket recording rain gauge. Separate distributions for winter and summer rain are evaluated. The distributions of amount with rate for both winter and summer rain are log-normal; they have logarithmic means of 2.5 mm hr⁻¹ and 12.6 mm hr⁻¹ respectively and logarithmic standard deviations of 0.4 and 0.7 respectively. A comparison of the distributions of amount with rate for winter rain and for snow shows that the two distributions are similar in form. [...]

Radar cells from the GATE experiment, from Spain and from the Montreal area were followed and measurements of their total rain flux and area were extracted. The values of the flux and area, for each one of the cells, for initial time increments T(,0) were used to evaluate various extrapolation schemes for forecasting purposes. The extrapolation procedure that gave the smallest error in forecasting the changes in flux and area, was found to be the linear one and the optimum increment T(,0) was about 30 min. However, even though these techniques have the advantage of establishing a trend in the behavior of the flux and area with time, a comparison of the forecast errors from the linear extrapolation scheme with those from the assumption of no change in cell area and rain flux shows insignificant improvements. A technique including both cell motion and internal changes in flux and area of the rain cells was developed to evaluate the accuracy of rain accumulation forecasts. Again, it was found that the errors were similar with those generated by the assumption of no change in rain flux and area from the moving cell. / The preceding results were used as a possible input into the design and evaluation of cloud seeding experiments. Based on this, a method is developed which gives the necessary number of experiments (i.e., seeded cases) in order for a seeding factor to be statistically significant at specified confidence levels. As a conclusion, it can be stated that the use of short term rain predictions (which are translated as differences of the rain flux in time) is very promising. In comparison to other techniques described in the literature this method appears to be superior, in the sense that fewer experiments are needed to detect seeding factors at specified confidence levels.

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In order to address rain attenuation scattering of millimetric waves and microwave sin Botswana, we have employed a comparison technique to determine the Ro.o1 at fourteen diverse locations in Botswana. In addition we have identified two rain climatic zones for Botswana. We note that Matzler employs Mie Scattering technique to determine the specific attenuation due to rain in Central Europe. Both Matzler and Olsen use the exponential distribution of N(D) to calculate y. In this dissertation we use the Mie scattering approach, but assume several distributions, including the log-normal distribution of N(D) as expounded by Ajayi et aI., to determine y for tropical and subtropical regions of Africa. The results show that the extinction coefficients depend more strongly on temperature at lower frequencies than at higher frequencies for lognormal distribution: at selected frequencies, we record high attenuation values at rising SHF bands: at 300 GHz, tropical showers take on values of 12, 12.5, 11.9 and 14 dB/km for Gaborone, Francistown, Kasane and Selebi-Phikwe, respectively. The absorption coefficient is significant but decreases exponentially with rain temperature at lower microwave frequencies. The application of the proposed model (Continental Thunderstorm is shown using practical results from Durban) is corroborated using practical results from Durban. Further, based on attenuation measurements, it is found that the lognormal distribution is suitable for Durban at rain rates greater than or equal to 21 mm/h. At rain rates below this, the loss-Thunderstorm is the better fit. Finally in this dissertation the results show that for rainfall intensity below about 10 mm/h for Marshall-Palmer (MP), Joss-Drizzle (JD), Joss-Thunderstorm (JT) and Law-Parson (LP) distributions, and below about 4 mm/h for Continental-Showers (CS), Tropical Showers (TS), Continental Thunderstorms (CT) and Tropical Thunderstorm (TT) distributions, the specific rain backscattering follows Rayleigh scattering law where the rain drops are small with respect to the wavelength when the frequency is 19.5 GHz. At rain rates above 10 mm/h for exponential distribution, and above 4 mm/h for lognormal distribution, the specific backscattering follows Mie scattering law. When the received echo power from rain becomes significant, it contributes to the rise in the noise floor and the radar receiver can lose its target. In addition, the result shows that Mie backscattering efficiency is highest at a raindrop diameter of 4.7mm. / Thesis (M.Sc.Eng.)-University of KwaZulu-Natal, Durban, 2008.

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Asian climate varies on various spatial and temporal scales and has a wide spectrum of climatic characteristics. Climate variability, especially decadal to inter-annual scale rainfall variability across Asia has gained considerable attention of climatologists over the last century due to the fact that rainfall variability is known to have caused considerable damage to southern Asian nations. Until recent, much of the existing literature on southern Asian climate focused on India and it is only recently that studies have focused on countries other than India. Although the Maldives is a nation within southern Asia (lying in the Indian Ocean southwest of India), literature on precipitation patterns over the Maldives and its connection to the Asian monsoon is lacking. This thesis examines the variability of precipitation over the Maldives in relation to the Asian monsoon, since proper knowledge of the spatial and temporal variations of precipitation is essential for managing the water resources and agricultural sector of the Maldives. Yearly and monthly rainfall across the Maldives indicates that the rainfall varies temporally and spatially. Despite spatial variability of mean annual rainfall (January-December total) showing rainfall increasing from north to south, it was found that on average the northern and southern parts of the Maldives have received less rainfall during the monsoon season (May-November). This suggests that the mean annual rainfall maximum for the Maldives occurs between central and southern parts of the Maldives during the monsoon season. The Maldives monsoon rainfall is characterised by inter-decadal and inter-annual periodicities with a frequency of 12.9 and 2.5-4 years, and intra-seasonal periodicities (10-20 days and 30-60 day) in daily time series of monsoon rainfall for different regions of Asia. The fact that no objective criteria previously existed to identify monsoon onset and withdrawal dates in the Maldives, the criteria developed here for defining the monsoon season objectively for this region indicates that on average the rainy season or monsoon commences between 4 May and 13 May (mean onset dates based on outward longwave radiation (OLR) index and rain and wind criteria, respectively) and terminates in late November (21 and 23 November: mean withdrawal dates based on rain and wind, and OLR index criteria, respectively) for the Maldives. The mean length of the rainy season (LRS) based on the OLR index is 204 days, the mean LRS based on rain and wind is 11 days shorter (193 days). Results also demonstrate that the earliest monsoon onset for the Asian region occurs in the south of the Maldives in April. Correlation coefficient maps generated between Maldives monsoon rainfall and meteorological parameters suggest that the most significant parameters that influence the interannual variability of the Maldives monsoon rainfall (MMR) are mean sea level pressure, surface air temperature, OLR, sea surface temperature (SST), and the zonal wind and relative humidity at various levels. Temporal consistency checks carried out for these parameters with the MMR led to the elimination of some of these predictors (which have less influence in the variance of MMR). The predictors which explained a significant amount of variance in the MMR were retained, including surface relative humidity during April (SRHAPR), 850 hPa level relative humidity during May (850RHMAY) and 500 hPa relative humidity for May (500RHMAY). These parameters were then used to formulate a regression model (using backward regression) for the prediction of Maldives monsoon rainfall. The predictors included in the model account for a significant part of the variance (76.6%, with a correlation coefficient, CC = 0.9) in MMR, indicating the usefulness of the model for medium-range prediction of MMR before the core monsoon season commences. Global scale processes such as the El Niño-Southern Oscillation (ENSO) phenomenon influence the weather and climate around the globe, with ENSO considered to be one of the strongest natural phenomena influencing the climate of Asia on inter-annual time-scales. The association between the Maldives monsoon rainfall and ENSO events demonstrates that deficient/excess monsoon rainfall over the Maldives and India region is linked to the strong/moderate El Niño and La Niña events, respectively. During strong/moderate El Niño events, about 71.4% of the time the Maldives/India region experiences deficiencies in monsoon rainfall, while the Maldives/India region experiences excessive monsoon rainfall about 75% of the time during strong/moderate La Niña events. One of the regional scale processes that influence the climate of Asia is Eurasian snow cover. No previous studies have directly examined possible relationships between Eurasian snow and Maldives monsoon rainfall. The possible relationship between Eurasian snow cover (ESC) and the Maldives monsoon rainfall, explored in this research for the first time, appears to be only very weak. The results also demonstrate that the inverse relationship between the ESC and the Indian monsoon has weakened over recent decades. The correlation coefficient (-0.34) between Indian monsoon rainfall and ESC obtained for the 1973-94 period dropped to -0.18 for the 1979-2007 period. The inter-annual variability of the Indian and Australian monsoon rainfall experiences a remarkable biennial oscillation, which has been referred to as the tropospheric biennial oscillation (TBO). It is believed that the land and ocean surface conditions in March-May (MAM) over the Indo-Pacific region play an important role in monsoon transitions. The Maldives monsoon rainfall transition from relatively strong/weak to relatively weak/strong in consecutive years demonstrates a TBO connection (via a biennial tendency in Maldives monsoon rainfall). In relation to the Maldives monsoon rainfall, TBO strong years occur about 47.1% of the time, while weak TBO years occur about 52.9% of the time. Only some of the El Niño and La Niña onset years correspond to strong TBO years, with El Niño onset years (1982, 1987 and 2002) corresponding to weak TBO years, while La Niña onset years (1988 and 2000) corresponding to strong TBO years. Variability (spatial and temporal) in Maldives precipitation associated with global and regional scale processes results in flood and drought events that have downstream impacts, such as on water resources and the agricultural sector of the Maldives. Excess (wet) or deficient rainfall years identified for the period 1992-2008 indicate that the central region is most vulnerable to flooding (5 years with excess rainfall: 27.8% of the time), while the southern region is least vulnerable to both flooding (2 years with excess rainfall: 11.1% of the time) and drought (2 years with deficit rainfall: 11.1% of the time). The northern and central regions show an equal number of years with deficit rainfall (3 years: 16.7% of the time), indicating that they are equally prone to drought events. Furthermore, field survey results demonstrate that about 23, 31 and 37% households (respondents) from the northern, central and southern regions experienced flood events. About 79, 58 and 77% of the farmers from the northern, central and southern areas also experienced floods on their farms. On the other hand, field survey results also suggest that the 49-63% of the households in outer islands of the Maldives and 48-62% of farmers experience shortage of rainwater.

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