Mean Number Of Rainy Days Research Articles

Simulation and Evaluation of Statistical Downscaling of Regional Daily Precipitation over North China Based on Self-Organizing Maps

A statistical downscaling method based on Self-Organizing Maps (SOM), of which the SOM Precipitation Statistical Downscaling Method (SOM-SD) is named, has received increasing attention. Herein, its applicability of downscaling daily precipitation over North China is evaluated. Six indices (total season precipitation, daily precipitation intensity, mean number of precipitation days, percentage of rainfall from events beyond the 95th percentile value of overall precipitation, maximum consecutive wet days, and maximum consecutive dry days) are selected, which represent the statistics of daily precipitation with regards to both precipitation amount and frequency, as well as extreme event. The large-scale predictors were extracted from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) daily reanalysis data, while the prediction was the high resolution gridded daily observed precipitation. The results show that the method can establish certain conditional transformation relationships between large-scale atmospheric circulation and local-scale surface precipitation in a relatively simple way. This method exhibited a high skill in reproducing the climatologic statistical properties of the observed precipitation. The simulated daily precipitation probability distribution characteristics can be well matched with the observations. The values of Brier scores are between 0 and 1.5 × 10−4 and the significance scores are between 0.8 and 1 for all stations. The SOM-SD method, which is evaluated with the six selected indicators, shows a strong simulation capability. The deviations of the simulated daily precipitation are as follows: Total season precipitation (−7.4%), daily precipitation intensity (−11.6%), mean number of rainy days (−3.1 days), percentage of rainfall from events beyond the 95th percentile value of overall precipitation (+3.4%), maximum consecutive wet days (−1.1 days), and maximum consecutive dry days (+3.5 days). In addition, the frequency difference of wet-dry nodes is defined in the evaluation. It is confirmed that there was a significant positive correlation between frequency difference and precipitation. The findings of this paper imply that the SOM-SD method has a good ability to simulate the probability distribution of daily precipitation, especially the tail of the probability distribution curve. It is more capable of simulating extreme precipitation fields. Furthermore, it can provide some guidance for future climate projections over North China.

Rainfall Seasons Analysis as a Guiding Tool to Small Holder Farmers in The Face of Climate Change in Midlands in Zimbabwe

A rigorous analysis of trends in rainfall season of an area over a lengthy period of time can be effectively used as a tool to guide small holder farmers in their activities. Climate change adaptation is important to ensure sustainability and survival in rainfed crop farming. In this study two districts (Chirumhanzu and Zvishavane) in the Midlands Province of Zimbabwe were selected and rainfall data for the areas was provided by Zimbabwe Meteorological Services Department. Rainfall characteristics that include onset times, cessation times, length of seasons, total amount of rainfall, number of rainy days, dry spell and risk of water logging were analysed to provide farmers with an indicative picture of possibilities in their respective areas in a bid to reduce risks of crop failure due to uncertainties brought about by climate change and variability. Time series analysis method together with risk analysis were the statistical methods employed to understand better the risk and measures that can be taken by farmers. For Chirumhanzu it was concluded there is a 41% risk of replanting if farmers plant with the first rains received. However, over the period this risk seems to be decreasing. Farmers are encouraged to stagger the planting to minimise risk. Risk analysis showed that for Zvishavane the mean number of rainy days for Zvishavane is 34 with a possibility of 45 days in good seasons and 23 days in poor seasons The tool guides meant for farmers require proper packaging and systematic channels to promote buy-in by the end users of the information.

PREDICTING SOLAR RADIATION FOR TROPICAL ISLANDS FROM RAINFALL DATA

There are many correlations developed to predict incident solar radiation at a given location developed based on geographical and meteorological parameters. However, all correlations depend on accurate measurement and availability of weather data such as sunshine duration, cloud cover, relative humidity, maximum and minimum temperatures etc, which essentially is a costly exercise in terms of equipment and labour. Sri Lanka being a tropical island of latitudinal change of only 30 along the length of the country, the meteorological factors govern the amount of incident radiation. Considering the cloud formation and wind patterns over Sri Lanka as well as the seasonal rainfall patterns, it can be observed that the mean number of rainy days can be used to predict the monthly average daily global radiation which can be used for calculations in solar related activities conveniently.

PREDICTING SOLAR RADIATION FOR TROPICAL ISLANDS FROM RAINFALL DATA

There are many correlations developed to predict incident solar radiation at a given location developed based on geographical and meteorological parameters. However, all correlations depend on accurate measurement and availability of weather data such as sunshine duration, cloud cover, relative humidity, maximum and minimum temperatures etc, which essentially is a costly exercise in terms of equipment and labour. Sri Lanka being a tropical island of latitudinal change of only 30 along the length of the country, the meteorological factors govern the amount of incident radiation. Considering the cloud formation and wind patterns over Sri Lanka as well as the seasonal rainfall patterns, it can be observed that the mean number of rainy days can be used to predict the monthly average daily global radiation which can be used for calculations in solar related activities conveniently.

Rainfall trends in arid and semi-arid regions of Iran

Time-series of annual rainfall, number of rainy-days per year and monthly rainfall of 20 stations were analyzed to assess climate variability in arid and semi-arid regions of Iran. The results showed mixed trends of increasing and decreasing rainfall, which were statistically significant ( p<0.05 and p<0.01) only for Sabzevar and Zahedan stations by the Mann–Kendall test. Also, with the exception of Kashan and Torbat stations there was no statistically significant trend in the mean number of rainy-days per year. Increasing and decreasing monthly rainfall trends were found over large continuous areas in the study region. These trends were statistically significant mostly during the winter and spring seasons, suggesting a seasonal movement of rainfall concentration. Results also showed that there is no significant climate variability in the arid and semi-arid environments of Iran.

Study on south-west monsoon rainfall characteristics in arid zone of Haryana

The daily rainfall data of arid zone for a period of 50 years (1946-95) at three locations. Hisar, Bhiwani and Sirsa were analyzed to find out characteristics of south-west monsoon. Mean onset date was observed 4 July at Hisar and Bhiwani with standard deviation of 7 to 9 days respectively, whereas 7 July at Sirsa with standard deviation of 9 days. The onset date of monsoon was inversely related to amount of rainfall at all stations under study. The mean withdrawal dates of south-west monsoon were 15 September at Hisar and Bhiwani and 11 September for Sirsa with standard deviation of 9 days at all three locations. The withdrawal dates also showed inverse and significant correlation with rainfall amount. The mean number of rainy days at Hisar, Bhiwani and Sirsa were 18, 17 and 13 with standard deviation of 5, 6 and 6 days, respectively.

The effect of soil type and climate on hookworm (Necator americanus) distribution in KwaZulu-Natal, South Africa.

We investigated environmental factors influencing the distribution of hookworm infection in KwaZulu-Natal, South Africa. Prevalence data were sourced from previous studies and additional surveys carried out to supplement the database. When geo-referenced the data revealed that higher prevalences are limited to areas below 150 m above sea level, and low prevalences to areas above this altitude. Using univariate analysis we investigated the differences in environmental factors in the two areas. The relationship between hookworm prevalence, altitude and climate-derived variables was assessed using Pearson correlation coefficient, and that of soil type using the t-test. Multivariate analysis was used to determine environmental factors that combine best to provide favourable conditions for hookworm distribution. The results revealed that areas <or=150 m above sea level, i.e. the coastal plain, supported high prevalences (x = 45, n = 51), and were characterized by sandy soils with a clay content of less than 15%, warm temperatures and relatively high rainfall. Areas >150 m above sea level, i.e. inland, supported low mean hookworm prevalences (x = 6, n = 21), and were characterized by soils with a clay content of more than 45%, variable temperatures and moderate rainfall. Hookworm prevalence also decreased southwards as temperatures became slightly cooler, rainfall remained more-or-less constant and the coastal plain narrowed. In the multivariate model prevalence was most significantly correlated with the mean daily minimum temperature for January followed by the mean number of rainy days for January. This indicates the importance of summer conditions in the transmission of hookworm infection in KwaZulu-Natal and suggests that transmission may be seasonal.

Changes in the Southwest Monsoon mean daily rainfall intensity in Sri Lanka: relationship to the El Niño–Southern Oscillation

Daily rainfall data for 187 stations in Sri Lanka spanning the period 1960–1996 were analyzed to investigate the spatial and temporal characteristics of the mean rainfall intensity (MRI) through this time interval with special focus on the Southwest Monsoon (May–September). Particular emphasis was laid on temporal changes in the MRI series. The mean and standard deviation (SD) of the MRI data showed considerable spatial variation. Regression analysis expressing precipitation as a function of time at the various stations revealed distinct spatial trends; the results point to high MRI in lowland areas and low MRI in mountain areas. Principal Components Analysis of the temporal relationships among a reduced set of stations located in an equal-sized grid showed that the three dominant principal components (PCs) are characterized by the maximum and minimum mean and SD of the MRI series together with the mean number of rainy days. The first, second and third PC modes show significant patterns of the MRI data series over the northern half, southern half and southwestern coastal belt of Sri Lanka, respectively. The time series pattern of the dominant PC modes revealed distinct changes in MRI over time. A noticeable higher value in MRI was found from 1977 to 1996; this tendency is most pronounced for the first PC mode. The time series of the Southern Oscillation Index was found to be closely related to changes in the MRI patterns associated with the first PC mode. In addition, El Niño years coincide with low values of the first PC mode. Some La Niña years show a positive response for the first and third PC modes, while there is no clear response for the MRI pattern identified by the second PC.

Use of Climatic Parameters to Predict the Global Distribution of Ascochyta Blight on Chickpea

Climatic data of areas where chickpea (Cicer arietinum) is grown were analyzed by stepwise discriminant analysis to identify parameters that allow a discrimination of locations with and without occurrence of Ascochyta blight (caused by Ascochyta rabiei). A discriminant function was computed, based on mean daily temperature in month 1 of the vegetation, mean precipitation in month 2, average precipitation per rainy day in months 1 and 2, and mean number of rainy days in months 1 and 2 (.)

Dry and Wet Spells of Weather at Coimbatore

During the pre-monsoon period comprising the months from January to May, the dry spell prevails on an average on 90'1% of the days. The mean rainfall during this period is 6'2", received on 11 rainy days. A continuous dry spell has prevailed for 132 days in the year 1914. In 34 out of 54 years no wot spell occurred during the pre-monsoon period. In the monsoon period from June to December, the mean duration of dry spell is 168 days covering 78-5% of the period. The mean rainfall is 18'9", received on 34 rainy days. Dry spell may prevail continuously for 46 days, when rainfall is abnormal and for 61 days when the rainfall is sub-normal during this period. During the monsoon period the mean number of rainy days is 344 and wet spells of three or more days occur on 12-5 days. Wet spells may occur for 25 days when rainfall is abnormal and for upto 20 days when rainfall is sub-normal. Considering the year as a whole the mean duration of dry spells is 304 days. The average rainfall is 25'1 and the number of rainy days is 455. The rainfall and number of rainy days are more closely associated with the wet spells prevailing during the monsoon period than during the pre-monsoon period.