Mean Monthly Rainfall Research Articles

Differing Estimates of Observed Bangladesh Summer Rainfall

Abstract The differences in boreal summer (June–August) monthly-mean rainfall estimates over the Indian Ocean region in five research-quality products are examined for the period 1979–2003. Two products derived from the merged satellite and surface observations are considered: the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) and the Global Precipitation Climatology Project (GPCP). In addition, three products derived solely from rain gauge observations are considered: the Chen et al. product; the Indian Meteorological Department (IMD) product; and a new, objectively analyzed product based on the Climate Anomaly Monitoring System (CAMS) dataset. Significant discrepancies have been found between the different products across the entire Indian Ocean region, with the greatest disagreement over Burma and neighboring Bangladesh. These differences appear to be primarily due to the absence of reported rain gauge data for Burma and differences in the algorithms used to merge the satellite microwave emission and scattering data in coastal regions. Representations of rainfall across much of the eastern Indian Ocean region would likely be improved by the identification and inclusion of reporting stations from Burma and a refinement of the techniques used for merging microwave data. The differences among the five products are sufficient to affect both quantitative and qualitative conclusions drawn about rainfall, particularly over Bangladesh and Burma. Consequently, the results of precipitation studies in this region will depend, in some cases, on the choice of the data product, including such basic questions as to whether a given summer was wet or dry. Of particular note is that the apparent relationship between rainfall and ENSO can depend on the choice of the data product.

Realizzazione di un sistema di calcolo e di spazializzazione dell’indice canadese di pericolo d’incendio boschivo FWI (Fire Weather Index) per la Regione Veneto

Abstract: Fire danger rating systems are essential tools for fire management activities, allowing optimal allocation of resources both before and during the fire danger periods. Veneto Region’s Forest Service is testing the Canadian Forest Fire Weather Index (FWI) System to assess fire intensity, accounting for the effect of wind and the moisture content of inflammable material. The following steps were taken to apply the FWI system: (a) selection of the smallest number (ideally 10 - 15) of weather stations to obtain input data. Principal Components Analysis was carried out on 62 time-series of 30 years (1960-1990), including mean monthly temperature (minimum and maximum) and rainfall. The results highlighted two principal directions of climatic variability that were interpolated by the co-kriging method, allowing to delineate 11 relatively homogeneous areas in the Veneto Region. One station representative of each area was chosen to provide daily data for computing the daily fire danger index by the Regional Rating Service; (b) automation of the FWI system. A SAS v.9.1® application runs the calculations and generates a regional map of daily fire danger for the Forest Service personnel. Graphics and tabular data are also available via intranet.

Stable isotope study of precipitation and cave drip water in Florida (USA): implications for speleothem-based paleoclimate studies†

Stable isotopes of hydrogen and oxygen were used to examine how the isotopic signal of meteoric water is modified as it travels through soil and epikarst into two caves in Florida. Surface and cave water samples were collected every week from February 2006 until March 2007. The isotopic composition of precipitation at the investigated sites is highly variable and shows little seasonal control. The δ18O vs. δ2H plot shows a mixing line having a slope of 5.63, suggesting evaporation effects dominate the isotopic composition of most rainfall events of less than 8 cm/day, as indicated by their low d-excess values. The δ18O values of the drip water show little variability (<0.6‰), which is loosely tied to local variations in the seasonal amount of precipitation. This is only seen during wintertime at the Florida Caverns site. The lag time of over two months and the lack of any relationship between rainfall amount and the increase in drip rate indicate a dominance of matrix flow relative to fracture/conduit flow at each site. The long residence time of the vadose seepage waters allows for an effective isotopic homogenisation of individual and seasonal rainfall events. We find no correlation between rainfall and drip water δ18O at any site. The isotopic composition of drip water in both caves consistently tends to resemble the amount-weighted monthly mean rainfall input. This implies that the δ18O of speleothems from these two caves in Florida cannot record seasonal cycle in rainfall δ18O, but are suitable for paleoclimate reconstructions at inter-annual time scales.† Revised version of a paper presented at the 9th, Symposium of the European Society for Isotope Research (ESIR), 23 to 28 June 2007, Cluj-Napoca, Romania.

What parts of the US mainland are climatically suitable for invasive alien pythons spreading from Everglades National Park?

The Burmese Python (Python molurus bivittatus) is now well established in southern Florida and spreading northward. The factors likely to limit this spread are unknown, but presumably include climate or are correlated with climate. We compiled monthly rainfall and temperature statistics from 149 stations located near the edge of the python’s native range in Asia (Pakistan east to China and south to Indonesia). The southern and eastern native range limits extend to saltwater, leaving unresolved the species’ climatic tolerances in those areas. The northern and western limits are associated with cold and aridity respectively. We plotted mean monthly rainfall against mean monthly temperature for the 149 native range weather stations to identify the climate conditions inhabited by pythons in their native range, and mapped areas of the coterminous United States with the same climate today and projected for the year 2100. We accounted for both dry-season aestivation and winter hibernation (under two scenarios of hibernation duration). The potential distribution was relatively insensitive to choice of scenario for hibernation duration. US areas climatically matched at present ranged up the coasts and across the south from Delaware to Oregon, and included most of California, Texas, Oklahoma, Arkansas, Louisiana, Mississippi, Alabama, Florida, Georgia, and South and North Carolina. By the year 2100, projected areas of potential suitable climate extend northward beyond the current limit to include parts of the states of Washington, Colorado, Illinois, Indiana, Ohio, West Virginia, Pennsylvania, New Jersey, and New York. Thus a substantial portion of the mainland US is potentially vulnerable to this ostensibly tropical invader.

Simulated Physical Mechanisms Associated with Climate Variability over Lake Victoria Basin in East Africa

Abstract A fully coupled regional climate, 3D lake modeling system is used to investigate the physical mechanisms associated with the multiscale variability of the Lake Victoria basin climate. To examine the relative influence of different processes on the lake basin climate, a suite of model experiments were performed by smoothing topography around the lake basin, altering lake surface characteristics, and reducing or increasing the amount of large-scale moisture advected into the lake region through the four lateral boundaries of the model domain. Simulated monthly mean rainfall over the basin is comparable to the satellite (Tropical Rainfall Measuring Mission) estimates. Peaks between midnight and early morning hours characterize the simulated diurnal variability of rainfall over the four quadrants of the lake, consistent with satellite estimates, although the simulated peaks occur a little earlier. It is evident in the simulations with smoothed topography that the upslope/downslope flow generated by the mountains east of the lake and the land–lake breeze circulations play important roles in influencing the intensity, the location of lake/land breeze fronts, and the horizontal extent of the land–lake breeze circulation, as well as lake basin precipitation. When the lake surface is replaced with marsh (water hyacinth), the late night and early morning rainfall maximum located over the western sector of the lake is dramatically reduced. Our simulations also indicate that large-scale moisture transported via the prevailing easterly trades enhances lake basin precipitation significantly. This is in contrast to the notion advanced in some of the previous studies that Lake Victoria generates its own climate (rainfall) through precipitation–evaporation–reprecipitation recycling only.

Epidemiological studies of &lt;i&gt;Schistosoma mattheei&lt;/i&gt; infections in cattle in the highveld and lowveld communal grazing areas of Zimbabwe

During the period between January 1999 and December 2000, the distribution and seasonal patterns of Schistosoma mattheei infections in cattle in the highveld and lowveld communal grazing areas of Zimbabwe were determined through monthly coprological examination. Faecal samples of cattle were collected from 12 and nine dipping sites in the highveld and lowveld communal grazing areas, respectively. Patterns of distribution and seasonal fluctuations of the intermediate host-snail populations and the climatic factors influencing the distribution were also determined at monthly intervals from November 1998 to October 2000, a period of 24 months, in six dams and six streams in the highveld and nine dams in the lowveld communal grazing areas. Monthly, each site was sampled for relative snail density, the vegetation cover and type, and physical and chemical properties of the water. Mean monthly rainfall and temperature were recorded. Snails collected at the same time were individually examined for shedding of cercariae of S. mattheei and Schistosoma haematobium. A total of 16264 (5418 calves, 5461 weaners and 5385 adults) faecal samples were collected during the entire period of study and 734 (4.5%) were positive for S. mattheei eggs. Significantly higher prevalences were found in the highveld compared to the lowveld (P < 0.001), calves compared to adult cattle (P < 0.01) and the wet season compared to the dry season (P < 0.01). Faecal egg output peaked from October/ November to March/April for both years of the study. Bulinus globosus, the snail intermediate host of S. mattheei was recorded from the study sites with the highveld having a significantly higher abundance of the snails than the lowveld (P < 0.01). Monthly densities of B. globosus did not show a clear-cut pattern although there were peaks between March/May and September/November. The mean number of snails collected was positively correlated with the water plants Nymphaea caerulea and Typha species. Overall, 2.5% of B. globosus were shedding Schistosoma cercariae. In the highveld, 2.8% of B. globosus were infected with schistosome cercariae and 1.5% in the lowveld, with the figures at individual sites ranging from 0-18.8% in the highveld and from 0-4.5% in the lowveld. The cercariae recorded here were a mixture of S. mattheei and S. haematobium since they share the same intermediate host. The transmission of Schistosoma cercariae exhibited a marked seasonal pattern, being more intensive during the hot, dry season (September/November).

A framework for predicting and visualizing the East African wildebeest migration‐route patterns in variable climatic conditions using geographic information system and remote sensing

AbstractIn the Serengeti–Mara ecosystem of East Africa, the migrating wildebeests’ (Connochaetes taurinus) response to food resource distribution and terrain complexity impacts their movement characteristics. This manifests in varying ways such as movement speed, direction, turning frequency, and moving distance. To investigate these characteristics, indices derived from vegetation quantity (normalized difference vegetation index, NDVI) and relief (slope) were used in our previous work to quantify the relationships between migration route location versus vegetation, relief complexity, and their combination. Least cost pathways determined using these indices were representative of approximate migration routes. The simulated routes were shown to be strongly influenced by vegetation during the dry season. However the impact of climatic change (rainfall) on route location was not investigated though known to influence vegetation recovery patterns. This paper specifically addresses the impact of climatic change on route location. The mean monthly rainfall data were used to classify the rainy and dry seasons in the Serengeti, the Western, and the Mara areas as normal, drier, or wetter than normal, over the 1986–1997 period. The classification is based on the mean monthly rainfall variability about the 11‐year seasonal mean. Regression analysis showed strong linear relationships between rainfall and mean NDVI for each one of the three areas. The subsequent seasonal classification based on the corresponding habitat vegetation characteristics (NDVI) revealed the relative variation of vegetation with rainfall. Using the derived general categories, migration routes are then predicted for the various categories using a “route attractivity index.” The seasonal migration routes were shown to change depending on the relative abundance of the rainfall during the dry season. Dry season migration routes tended toward areas with better vegetation activity, i.e., those characterized by higher NDVI gradients. Our results showed that during the western trek, wetter dry seasons have the effect of delaying the herds movement northwestward. During the northern trek, wetter dry seasons have the effect of delaying the tendency to move westward. However the variation in rainfall conditions during the rainy and dry season had no significant influence on the southern trek route location. We assume that predicted routes based on average general category conditions for different years are representative of main migration route patterns for similar seasons, therefore they are well suited for approximate route prediction, if the climatic characteristics of the year are known.

Quantitative assessment of annual runoff in sub‐catchments using GIS: a case study of the Tapi River Basin, India

The Tapi is a major interstate, westerly flowing river with 14 tributaries, of which the left bank tributaries support large settlements in Maharashtra State. It has been identified as one of the water scarce basins of India. There are many dams and barrages in the basin to store and divert water for agricultural and industrial purposes. This results in low flow and deterioration of water quality in the region particularly in non‐monsoon months. This paper presents the details of a GIS‐based rainfall‐runoff modelling study carried out to assess the runoff flow in the sub‐catchments and at the outlet of the Tapi Basin. Using mean monthly rainfall data at seven gauging stations in the basin, and land use and soil characteristics as reported by the Central Pollution Control Board (CPCB), the annual runoff flow at the watershed outlet was estimated to be 18,814 MCM. A comparison of runoff generated in the sub‐catchments and the observed runoff at Central Water Commission (CWC) monitoring stations is presented. Results of the simulation indicate that the reduced river flows caused by the number of dams/reservoirs constructed upstream are useful as a guideline for the development of environmental flow regulations for rivers and streams in the Tapi Basin.

Sea surface salinity constrains rainfall estimates over tropical oceans

[1] Uncertainties in the monthly mean estimates of precipitation are the largest over oceanic regions with the heaviest rainfall. Using the adjoint sensitivity analysis and 4d variational assimilation of sea surface salinity (SSS) into an ocean model, we show that rainfall estimation errors in the regions of heavy precipitation could be reduced by taking SSS observations into account. Inverse analysis of SSS in the Bay of Bengal also indicates that the monthly mean rainfall of the Global Precipitation Climatology Project (GPCP) is more consistent with SSS and river runoff data than other precipitation climatologies.

Influence of the Eastern Indian Ocean Warm Pool variability on the spring precipitation in China

The relationship between the variability of the Eastern India Ocean Warm Pool (EIWP) and the spring precipitation in China is studied in the paper based on an analysis of the Simple Ocean Data Assimilation (SODA) Sea Surface Temperature (SST) data, the reanalysis data of monthly grid wind field at 925 hPa with a resolution of 2.5° latitude and longitude from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR), and the monthly mean rainfall data from 160 observational stations in China. The results show that there is a strong correlation between the EIWP variability and the spring precipitation in China. The area, volume and intensity indices of the EIWP are negatively correlated with the spring precipitation in southwestern China, while they are positively correlated with the spring precipitation in the rest of China, especially in the northeast. For this correlation between the EIWP variability and the spring precipitation in China, it is found that the correlative relationship is mainly connected with the variations of the moisture transport by the warm air flow, which is under the influence of the EIWP variability, into the inland of China in spring. Two causative factors may influence this transport. One is the variation of the moisture transport carried by the warm air flow from the Arabian Sea influenced by the EIWP variability. The other is the variation of the equator-crossing flow (70°–90°E) influenced by the EIWP anomaly in the previous winter which exerts its effect on the moist warm air transported from the Southern Hemisphere. The position and intensity of the Western North Pacific Subtropical High (WNPSH) variability caused by EIWP variation also influence the spring precipitation in China.

The Eastern Pacific ITCZ during the Boreal Spring

Abstract The 6-yr (1998–2003) rainfall products from the Tropical Rainfall Measuring Mission (TRMM) are used to quantify the intertropical convergence zone (ITCZ) in the eastern Pacific (defined by longitudinal averages over 90°–130°W) during boreal spring (March–April). The double-ITCZ phenomenon, represented by the occurrence of two maxima with respect to latitude in monthly mean rainfall, is observed in most but not all of the years studied. The relative spatial locations of maxima in sea surface temperature (SST), rainfall, and surface pressure are examined. Interannual and weekly variability are characterized in SST, rainfall, surface convergence, total column water vapor, and cloud water. There appears to be a competition for rainfall between the two hemispheres during this season. When one of the two rainfall maxima is particularly strong, the other tends to be weak, with the total rainfall integrated over the two varying less than does the difference between the rainfall integrated over each separately. There is some evidence for a similar competition between the SST maxima in the two hemispheres, but this is more ambiguous, and there is evidence that some variations in the relative strengths of the two rainfall maxima may be independent of SST. Using a 25-yr (1979–2003) monthly rainfall dataset from the Global Precipitation Climatology Project (GPCP), four distinct ITCZ types during March–April are defined, based on the relative strengths of rainfall peaks north and south of, and right over, the equator. Composite meridional profiles and spatial distributions of rainfall and SST are documented for each type. Consistent with previous studies, an equatorial cold tongue is essential to the existence of the double ITCZs. However, too strong a cold tongue may dampen either the southern or northern rainfall maximum, depending on the magnitude of SST north of the equator.

GEOSTATISTICAL METHODS FOR PREDICTION OF SPATIAL VARIABILITY OF RAINFALL IN A MOUNTAINOUS REGION

Reliable estimation of rainfall distribution in mountainous regions poses a great challenge not only due to highlyundulating surface terrain and complex relationships between land elevation and precipitation, but also due tonon-availability of abundant rainfall measurement points. Prediction of rainfall variability over mountainous islands is alogical step towards meaningful land use planning and water resources zoning. In this context, geostatistical techniques weredeveloped for mapping the rainfall variability over the island of St. Lucia in the Caribbean, using the elevation informationextracted from a Digital Elevation Model (DEM) and long-term mean monthly rainfall (MMR) data of 40 raingauge stationsspread over 616 km2. The ordinary co-kriging (OCK) and collocated co-kriging (CCK) methods of interpolation were appliedfor the standardized rainfall depths associated with elevation, as the primary variate, and the surface elevation values as thesecondary variate. The best semivariogram model algorithm generated, using either of the above co-kriging (CK) methods,was used to predict standardized values for the elevation points extracted from the DEM for which the rainfall depths werenot known. The predicted values were further destandardized to generate the rainfall depth at the unmeasured locations.Ordinary kriging (OK) was then performed for the destandardized and observed rainfall depths to generate the predictionmap of MMR over the entire island. These sequential steps were repeated for the MMR data of all twelve months to generaterainfall prediction maps over the island. The spherical semivariogram model fit well (0.84 < R2 < 0.98) for both the OCKand OK methods. The cross-validation error statistics of OCK presented in terms of coefficient of determination (R2), krigedroot mean square error (KRMSE), and kriged average error (KAE) were within the acceptable limits (KAE close to zero, R2close to one, and KRMSE from 0.55 to 1.45 for 40 raingauge locations) for most of the months. The exploratory data analysis,variogram model fitting, and generation of MMR prediction map through kriging were accomplished through use of ArcGISand GS+ software.

Influencia de la climatología, según la clasificación de Koppën e índice de Lang, sobre la tasa bruta de mortalidad asociada al cáncer de próstata en el área geográfica de España.

ObjectiveTo evaluate if the climatic factors of temperature and rainfall influence mortality associated with prostate cancer for a period of five years. Material and methodsThe mortality trends associated with prostate cancer will be evaluated in the period ranging from 1st january 1998 to 31st december 2002, in the geographical area of Spain. The demographic and mortality data were obtained from the National Institute of Statistics and the climatologically data on temperature and rainfall were obtained from the National Meteorology Institute applying the values registered in 2002. Based on the meteorological data, the different provinces are classified in accordance with Koppën climate classification and climatic index of Lang that defines them from the mean annual and monthly temperature and rainfall data. Mortality rates will be expressed by 100.000 men year, applying for their study the Kruskall-Wallis test for “n” independent samples through the SPSS v12.0 Windows software. ResultsThree main climatic areas are obtained, one area with type B climate, which accounts for 13% of the total population, a Csa climate, and a Csb, accounting for 67 and 20% respectively. The mortality found is higher in the Csb type for prostate cancer (p= 0.007), as compared to the other two climatic areas. ConclusionsMortality associated with prostate cancer is significantly higher in the regions with a lower sun exposure. However, randomized prospective studies confirming these findings are warranted.

Lower ambient temperature was associated with an increased risk of hospitalization for stroke and acute myocardial infarction in young women

Objective Results of investigations into the impact of seasonal variation on the incidence of cardiovascular diseases (CVD) have been inconsistent. Using the WHO Collaborative Study of CVD and Steroid Hormone Contraception database, we attempted to examine the relationship between variation in three climatic variables and risk of hospitalization for venous thromboembolism (VTE), arterial stroke, and acute myocardial infarction (AMI). Study design and setting We compared the monthly mean temperature, rainfall, and humidity with rates of hospitalized VTE, stroke, and AMI among young women aged 15–49 from 17 different countries in Africa, Asia, Europe, Latin America, and the Caribbean by using a negative binomial regression model. Results The study included 1146, 2,269, and 369 cases of VTE, stroke, and AMI, respectively. Significant associations between temperature and hospital admission rates of stroke and AMI, but not VTE, were apparent. Lagging the effects of temperature suggested that these effects were relatively acute, within a period of a month. Conclusion These data may help in understanding the mechanisms whereby stroke and AMI events are triggered.

A high‐resolution simulation of a West African rainy season using a regional climate model

The regional climate model Modèle Atmosphérique Régional (MAR) is applied to West Africa and the year 1992 is simulated. MAR reproduces the observed intraseasonal variations of rainfall. It is suggested that such a phenomenon is associated with oscillations between a weak and a strong regime of the Hadley cell. The later is correlated with a stronger meridional gradient of moist static energy in the planetary boundary layer and is responsible for an enhanced convergence of this quantity and a subsequent increase of convection and rain. An enhanced consumption of moist static energy and finally a weakening of the meridional circulation result. The meridional gradient of the moist static energy is restored by surface processes. The model also simulates the observed abrupt northward shift of the rainband in the first half of July. The spatial variability of the simulated monthly mean rainfall is in good agreement with the observations, although the model overestimates rainfall in some places from the beginning of August. Time series of daily mean rainfall are averaged over two 2.5° × 2.5° grid meshes in the Niamey region and in the Ouémé high valley. Maxima reaching up to 40 mm/day are found in both areas, as in the observations. Atmospheric variables such as temperature and wind are briefly compared with the European Center for Medium‐Range Weather Forecasting reanalyses. The main (cold) biases are located where the hydrological cycle simulated by MAR is too strong.

Modelling the spatial and temporal distribution of rainfall: a case study in the wet and dry tropics of North East Australia

Rainfall regimes with strong spatial and temporal variation are characteristic of many coastal regions of north and eastern Australia. In coastal regions of north eastern Australia, regimes vary considerably over short distances. This occurs because of changes in local topography, including the height and orientation of mountain ranges and the direction of the coastline with respect to the prevailing moist south east air stream. Northern Australia experiences a tropical monsoon climate with rainfall occurring predominantly during the summer months. Areas with a closer proximity to the coast typically experience the heavier rainfalls. While networks of rainfall gauges have been established and continuous records are available for most of these stations from the 1890s, their low distribution density relative to the complexity of rainfall pattern they are required to represent means that there remains a poor understanding of the spatial and temporal distribution of rainfall in the wet tropics. An enhanced knowledge of rainfall distribution in both space and time has the potential to deliver significant economic and environmental benefits to managers of natural resources. This paper reports on the application of a technique for estimating mean annual and mean monthly rainfall across the Herbert River catchment of north east Australia's dry and wet tropics. The technique utilises thin plate smoothing splines to incorporate both location and elevation into estimates of rainfall distribution. We demonstrate that the method can be applied successfully at the meso scale and within the domain of routinely available data. As such, the method has broad relevance for decision making.

Evaluation of Regional Climate Simulations of the 1998 and 1999 East Asian Summer Monsoon using the GAME/HUBEX Observational Data

A regional climate model based on the Penn State/NCAR Mesoscale Model (MM5) was used to simulate the 1998 and 1999 East Asian summer monsoon conditions. Simulations were performed for 1 April-31 August of each year, with initial and lateral boundary conditions provided by the ECMWF analysis. Observations from the 1998 and 1999 GAME/HUBEX experiments were used to evaluate the regional climate simulations. Based on observations, large differences can be found between the 1998 and 1999 meteorological conditions and surface energy budgets at the Shouxian station during the IOPs, with much higher rain intensity but only slightly higher rain frequency in 1998 than 1999. For 1998, although the regional climate model was able to reproduce the general spatial distribution of monthly mean rainfall quite well during the summer monsoon season, large discrepancies can be found in comparing the observed and simulated surface climate and energy fluxes in the HUBEX region. By using Four Dimensional Data Assimilation (FDDA) technique, which constrains the simulated large-scale circulation with observations from 21 soundings in the HUBEX α-scale region, both the root mean square error and mean bias in rainfall were greatly reduced. The improvements in simulating rainfall were related to both reduction in errors of precipitation amount and timing. In the control simulation, a mean bias of −63 W/m² (−36%) was found in the simulated surface net radiation at Shouxian, which suggest large errors in simulating clouds in the region. With FDDA, the bias was significantly reduced to −23 W/m² (−13%), with corresponding reduction of bias in the latent heat flux. This suggests that at least part of the model bias in simulating net radiation is related to errors in simulating the large-scale circulation, which can affect cloud amount and vertical distribution.Comparing the 1998 and 1999 simulations, both without FDDA, smaller biases were found in the surface fluxes during 1999. Percentage biases in the net radiation and latent heat flux were −18% and −33% in 1999 and −36% and −50% in 1998 respectively. Based on observations, large differences in the net surface radiation, and small differences in cloud fraction between the two years suggest that cloud optical depth and/or vertical distribution were very different, with more cloudy conditions observed during 1999. Although the 1999 simulations were sensitive to the cumulus convective parameterizations (Grell scheme versus Kain-Fritsch scheme) as shown by the sensitivity experiments, the large differences in simulation skill between the 1998 and 1999 cases, regardless of the convection schemes used, suggest possible dependence of model errors on cloud properties that deserve further investigations.

Forecasting summer rainfall over India using genetic algorithm

In this study we have assessed the feasibility of a nonlinear technique based on genetic algorithm for the prediction of summer rainfall over India. The genetic algorithm finds the equations that best describe the temporal variations of the seasonal rainfall over India, and therefore enables the forecasting of the future rainfall. The forecast equation developed in the present study uses the monthly mean rainfall during June, July, and August for past years over five rainfall homogeneous zones of India to predict the seasonal rainfall (JJA combined) over the Indian landmass.

Mapping the potential distribution of five species of Passiflora in Andean countries

Geographic location data for 383 accessions of five Passiflora species from five Andean countries (P. cumbalensis, P. mixta, P. tripartita var. mollissima, P. natistipula, and P. manicata) were linked to interpolated continental surfaces of monthly mean rainfall, monthly mean temperature, and monthly mean diurnal temperature range. This permitted us to identify the potential distribution of each species and document its climatic adaptation. Maps are presented showing regions where the climate is similar to that in areas where each species has been found, but from which no collections have yet been made, thus representing possible geographical gaps in collections. Some species showed evidence of intra-specific variation in their climatic adaptation. The study of Passiflora diversity in Andean countries continues and these maps based on passport data are expected to be useful tools for the planning of both ex situ and in situ conservation activities.

Climatic variables and transmission of malaria: a 12-year data analysis in Shuchen County, China.

The objective of this study was to explore the impact of climate variability on the transmission of malaria, a vector-borne disease, in a county of China and provide suggestions to similar regions for disease prevention. A time-series analysis was conducted using data on monthly climatic variables and monthly incidence of malaria in Shuchen County, China, for the period 1980-1991. Spearman's correlation analysis showed that monthly mean maximum and minimum temperatures, two measures of monthly mean relative humidity, and monthly amount of precipitation were positively correlated with the monthly incidence of malaria in the county. Regression analysis suggested that monthly mean minimum temperature and total monthly rainfall, with a one-month lagged effect, were significant climatic variables in the transmission of malaria in Shuchen County. Seasonality was also significant in the regression model and there was a declining secular trend in the incidence of malaria. The results indicate that climatic variables should be considered as possible predictors for regions with similar geographic, climatic, and socioeconomic conditions to those of Shuchen County.