Rainfall Occurrence Research Articles

Comparison analysis of six purely satellite-derived global precipitation estimates

We executed a comprehensive evaluation and intercomparison between six purely satellite-derived precipitation estimates (i.e., IMERG-Late, IMERG-Early, GSMaP-NRT, GSMaP-MVK, TMPA-RT and PERSIANN-CCS) at global and regional scales for the period from February 2017 to January 2019. The results show that IMERG-Late exhibits the best performance among six evaluated products, while the worst performance was found in GSMaP-NRT and GSMaP-MVK. The root mean squared error (RMSE) has a power function to the logarithm of precipitation intensity in all six satellite products. On the basis of our findings, the RMSE of all products in rainfall events with intensity exceeding 32 mm/day (or 8 mm/h) accounts for beyond 30% of the corresponding precipitation intensity, which might result in a significant impact on the detectability and forecast of flash floods simulated by satellite precipitation. Additionally, both IMERG and GSMaP overestimate the proportions of light rainfall occurrences, and also display relatively larger errors in light precipitation (0.2–0.4 mm/h or 1–2 mm/day) with the RMSE values exceeding 0.5 mm (or 2 mm) at hourly (or daily) time scale. As for the error analysis, we decomposed the total bias of each product into hits, misses and false biases at hourly and 0.1° resolution over mainland China except for TMPA-RT. We found that the false bias is the dominated error sources for these five products in cold season over semi-humid areas despite that the hit bias accounts for a non-negligible proportion for GSMaP suite. The missed precipitation is the dominated error sources of PERSIANN-CCS both in two seasons over most of humid regions, and meanwhile is one of major error sources for other four products. We expect that the findings of this study not only provide some valuable feedbacks for algorithm developers to improve the GPM-based satellite precipitation retrievals, but also provide some guidance for data users across the world.

Evaluation and Application of Multi-Source Satellite Rainfall Product CHIRPS to Assess Spatio-Temporal Rainfall Variability on Data-Sparse Western Margins of Ethiopian Highlands

The spatio-temporal characteristic of rainfall in the Beles Basin of Ethiopia is poorly understood, mainly due to lack of data. With recent advances in remote sensing, satellite derived rainfall products have become alternative sources of rainfall data for such poorly gauged areas. The objectives of this study were: (i) to evaluate a multi-source rainfall product (Climate Hazards Group Infrared Precipitation with Stations: CHIRPS) for the Beles Basin using gauge measurements and (ii) to assess the spatial and temporal variability of rainfall across the basin using validated CHIRPS data for the period 1981–2017. Categorical and continuous validation statistics were used to evaluate the performance, and time-space variability of rainfall was analyzed using GIS operations and statistical methods. Results showed a slight overestimation of rainfall occurrence by CHIRPS for the lowland region and underestimation for the highland region. CHIRPS underestimated the proportion of light daily rainfall events and overestimated the proportion of high intensity daily rainfall events. CHIRPS rainfall amount estimates were better in highland regions than in lowland regions, and became more accurate as the duration of the integration time increases from days to months. The annual spatio-temporal analysis result using CHIRPS revealed: a mean annual rainfall of the basin is 1490 mm (1050–2090 mm), a 50 mm increase of mean annual rainfall per 100 m elevation rise, periodical and persistent drought occurrence every 8 to 10 years, a significant increasing trend of rainfall (~5 mm year−1), high rainfall variability observed at the lowland and drier parts of the basin and high coefficient of variation of monthly rainfall in March and April (revealing occurrence of bimodal rainfall characteristics). This study shows that the performance of CHIRPS product can vary spatially within a small basin level, and CHIRPS can help for better decision making in poorly gauged areas by giving an option to understand the space-time variability of rainfall characteristics.

Optimal amounts of water and nitrogen applied to sugar beet when crop price depends on its sugar content

Aim of study: To derive mathematical formulas to determine the optimum amounts of applied water and N at variable crop prices and rainfall conditions for sugar beet.Area of study: Karaj Research Center, Alborz Province, IranMaterial and methods: At first, mathematical formulas were derived to determine optimum applied water and nitrogen for sugar beet under rainfall occurrence, land limited (in cases that arable land area is limited and the farmer can not put more land area under irrigation) and water limited conditions when crop price depends on sugar content. Second, this theory was applied to analyze the relevant experimental data. The experiment was a split-plot design with irrigation treatments as the main plots (40%, 80%, 120% and 160% of evaporation from the surface of class A evaporation pan) and N fertilizer rates (0, 90, 180 and 270 kg N/ha) as subplots.Main results: Under land and water limiting conditions, deficit irrigation of 27% and 48% led to 6.4% and 25.4% decrease in yield and 21.4% and 96.2% increase in total net income, respectively, compared with full irrigation. Under water limiting conditions, cultivated land area increased by 93.7, 108 and 128% for 0, 60 and 120 mm rainfall, respectively. Under land limiting conditions, amounts of optimum irrigation water were 12381.2, 11781.2 and 11181.2 m3/ha, for 0, 60 and 120 mm rainfalls, respectively. The corresponding values for N were 262.5 kg/ha in all three rainfall quantities. Besides, under water limiting conditions, optimum amounts of irrigation water were 8708.1, 7828.8 and 6882.1 m3/ha for 0, 60 and 120 mm rainfalls, respectively. The corresponding values for N were 301.1, 299.5 and 295.5 kg/ha, respectively. Optimum amounts of irrigation water and N decreased by increase in rainfall amount.Research highlights: Under limited irrigation water conditions, if the rainfall, residual N, water cost and base crop price increases, the value of optimum applied water should be decreased.

Assessing coincidence probability for extreme precipitation events in the Jinsha River basin

Global analyses show that climate change has already increased the frequency and strength of extreme weather events. The observed and remote-sensed precipitation productions have been deeply studied considering the stationary and non-stationary characteristics of extreme series. However, as these studies largely addressed the evolution of precipitation extremes at a single station, the consistency risk of extremes at multiple stations within close days has been little researched. In this paper, a multivariate frequency analysis method is proposed to reveal the exceeding and coincidence probabilities of the most dependent extreme precipitation series. First, a generalized extreme distribution, which has proved better at describing the tail behavior of extreme series, is developed to simulate the marginal distribution of rainfall extremes. Then, a mixed von Mises distribution is introduced to fit the occurrence time interval of the extreme events. Taking advantage of its flexibility in choosing arbitrary marginals, a copula is constructed to implement a joint probability analysis of extreme precipitation events at multiple stations. The upper reaches of the Yangtze River, where frequent heavy rains are the main source of the regional floods, were selected as a typical research area. Five hundred twenty-eight copula models were finally constructed by arbitrarily choosing two precipitation extreme series among the total 33 meteorological stations. The results revealed the risk of extreme rainfall occurrence once in 2, 5, 10, 20, 50, or 100 years, within a time interval of 1–90 days. The research has great significance for understanding complicated extreme events and is helpful for the disaster emergency response and management.

Regional Differences in the Response of Rainfall to Convectively Coupled Kelvin Waves over Tropical Africa

AbstractThe representation of convection remains one of the most important sources of bias in global models, and evaluation methods are needed that show that models provide the correct mean state and variability, both for the correct reasons. Here we develop a novel approach for evaluating rainfall variability due to convectively coupled Kelvin waves (CCKWs) in this region. A phase cycle was defined for the CCKW cycle in OLR and used to composite rainfall anomalies. We characterize the observed (TRMM) rainfall response to CCKWs over tropical Africa in April and evaluate the performance of regional climate model (RCM) simulations: a parameterized convection simulation (P25) and the first pan-Africa convection-permitting simulation (CP4). TRMM mean rainfall is enhanced and suppressed by CCKW activity, and the occurrence of extreme rainfall and dry days is coupled with CCKW activity. Focusing on regional differences, we show for the first time that there is a dipole between West Africa and the Gulf of Guinea involving onshore/offshore shifts in rainfall, and the transition to enhanced rainfall over west equatorial Africa occurs one phase before the transition over east equatorial Africa. The global model used to drive the RCMs simulated CCKWs with mean amplitudes of 75%–82% of observations. The RCMs simulated coherent responses to the CCKWs and captured the large-scale spatial patterns and phase relationships in rainfall although the simulated rainfall response is weaker than observations and there are regional biases that are bigger away from the equator. P25 produced a closer match to TRMM mean rainfall anomalies than CP4 although the response in dry days was more closely simulated by CP4.

A Data-Driven Approach for Accurate Rainfall Prediction

In recent years, there has been growing interest in using Precipitable Water Vapor (PWV) derived from Global Positioning System (GPS) signal delays to predict rainfall. However, the occurrence of rainfall is dependent on a myriad of atmospheric parameters. This paper proposes a systematic approach to analyze various parameters that affect precipitation in the atmosphere. Different ground-based weather features like Temperature, Relative Humidity, Dew Point, Solar Radiation, PWV along with Seasonal and Diurnal variables are identified, and a detailed feature correlation study is presented. While all features play a significant role in rainfall classification, only a few of them, such as PWV, Solar Radiation, Seasonal and Diurnal features, stand out for rainfall prediction. Based on these findings, an optimum set of features are used in a data-driven machine learning algorithm for rainfall prediction. The experimental evaluation using a four-year (2012-2015) database shows a true detection rate of 80.4%, a false alarm rate of 20.3%, and an overall accuracy of 79.6%. Compared to the existing literature, our method significantly reduces the false alarm rates.

Contrasting frontal and warm-sector heavy rainfalls over South China during the early-summer rainy season

Heavy rainfalls occur frequently during early summer (April–June) over South China, causing severe floods. Using 12 years of hourly rain-gauge data, we identify a large number of regional heavy rainfall events and categorize them into two major types based on the strength of synoptic forcing. It is shown that frontal heavy rainfalls associated with fronts or shear lines mainly occurs over inland regions, whereas warm-sector heavy rainfall under weakly forced synoptic environment is observed in coastal areas. The rainfall maxima of both types tend to form over the low-lying plains or sea surfaces adjacent to windward mountains. Frontal heavy rainfall usually propagates southwards with a cold front, while warm-sector events move relatively slowly and produce coherent patterns of rainfall. The occurrence of warm-sector rainfall increases markedly from April to June in a close association with the onset of summer monsoon, in contrast to frontal rainfall with less monthly variation. Warm-sector rainfall also exhibits pronounced diurnal variation, with a peak in the early morning, as a result of intensified convergence between nocturnal low-level jets in southerly monsoon and land breezes. In contrast, frontal rainfall has an afternoon peak, due to the arrival of eastward-propagating rain systems and daytime heating on land. As frontal (warm-sector) heavy rainfall has relatively high (low) predictability in numerical models, understanding their occurrence and formation is of benefit to operational forecasting and decision-making for disaster prevention.

Hydrological Modelling of Deo River Sub-Basin using SWAT Model and Performance Evaluation using SWAT-CUP

The current study analyses the runoff response using Soil and Water Assessment Tool (SWAT) during rainfall incidents over the sub-basin of Deo River, Panch Mahal, Gujarat, India. The SWAT model is developed for the Deo river sub-basin having catchment area of 194.36 km2, with 7 sub-basins comprising of 94 Hydrological Response Units (HRUs). Two rain gauge stations present in the study area (viz., Deo dam and Shivrajpur) werechosen to evaluate the efficiency of the SWAT model. To conduct SWAT model Calibration and Validation, the Soil and Water Assessment Tool-Calibration Uncertainty Program (SWAT-CUP) with Sequential Uncertainty Fitting (SUFI-2) algorithm has been used. The model was run for the period from 2000 to 2017 considering 2 years (2000-2001) warm up period with a calibration period of 2002 to 2012 and a validation period of 2013 to 2017. The sensitivity of the basin parameters was evaluated and found Curve Number as the most sensitive parameter, hence, it can be considered to improve the model's runoff simulation efficiency. The study found that the model performed good with a Coefficient of Determination (R2) and Nash–Sutcliffe Efficiency (NSE) as 0.89 and 0.87 during calibration and 0.88 and 0.81 during validation respectively giving data at daily scale. The findings of this study revealed that SWAT model is helpful for runoff prediction and flood forecasting for extreme rainfall occurrences in Deo river basin.

Diurnal Variation of Rainfall in a Tropical Coastal Region with Complex Orography

We examined the diurnal cycle of the rainfall in a coastal tropical mountainous region in central Veracruz State, Mexico (18°–21° N, 95.5°–98.5° W), featuring a striking topographic gradient running from sea level at the Gulf of Mexico coast to 5000 m above sea level (m.a.s.l.) in less than 100 km horizontal distance. During the summer, this unique location leads to regular the interaction between the easterly moisture inflow and the mountainous barrier. Over the complex terrain, forced ascent leads the occurrence of maximum rainfall during the afternoon (16–19 local time, LT ≈ 1½ hours ahead of solar time in summer), first along the slope and later over the coast. Along the coastal plain, the precipitation continues until the early morning consistent with there being convergence between land breezes and the trade winds. Observations obtained during a measurement campaign from 28 June to 3 July 2015, indicate that during the early evening downslope winds move against easterly flow, likely due to katabatic outflows previously observed over the region. These features are confirmed using spatial (0.88°) and temporal (30 min) resolution CMORPH rainfall estimates, since we observed evening episodes initiating along the slope during the afternoon (14–17 LT) moving later towards the coast.

Modeling of Extreme Rainfall Recurrence Using Conditional Probability Approach

Forecasts for the occurrence of extreme rainfall are related to time lapse estimates until the next extreme rain event. The accuracy of these estimates depends on the choice of interevent time distributions of extreme rain events. The aim of this research is to estimate the probability of time recurrence of extreme rainfall. In this research, we observed the time between extreme rain events distributed by Weibull and Pareto. Let T be the time interval for the next extreme rain and it is assumed that there has been no extreme rain at time interval (0, t0]. To analyze the occurrence of the next extreme rain event, the concept of conditional probability P(Δt0|t0) is used. P(Δt0|t0) is the probability of extreme rain occurrence during the time interval Δt0. The results show that by using three models, we found that the next extreme rain event is not depending on the time interval Δt0 since the last extreme rain event. Other results show that for case studies of extremerain events in South Sulawesi it was found that the time of occurrence of extreme rainfall was influenced by parameter value for each model.

Analysis of Rainfall Variability for Mekelle Meteorological Station, Northern Ethiopia (1960-2009)

Nowadays rainfall variability is threatening the world. Despite this, studies related to the determination of variations, trends and fluctuations of rainfall have little local and regional specificity. Hence, this study aimed to analyze the trend and variability of the annual and seasonal rainfall occurrence at Mekelle Airport meteorological station, Northern Ethiopia. Historical rainfall data for the period 1960-2009 of the rain gauge station were obtained from National Meteorological Service Agency. Annual, seasonal and monthly rainfall trends; onset and cessation date of kiremt rainfall; growing period length and the number of rainy days and dry spell length were considered. Standardize anomaly index (SAI) and coefficient of variance was employed to analyze the rainfall data variability using INSTAT plus, SPSS version 20 and Excel. Analysis of the historical data showed extremely severe drought happened in the area in 1984 which resulted in the death of one million people, 8 million people highly affected, and 2.3 million people food in secured. 1961 was an extremely wet year. Generally, 70%, 18% and 12% years were normal, dry and wet respectively for the last 50 years. High annual, seasonal and monthly rainfall variability was observed. Though the station showed both increasing (1984-1988) and decreasing trends (1961-1968) of annual rainfall totals a declining trend was more pronounced. Besides, one to two weeks variability on the onset and cessation date was observed. The minimum and maximum kiremt season rainy days recorded in 1987 (27days) and 1986 (86 days) respectively while the maximum dry spell days recorded in the 50 years was in 1969. Thus, devising agronomic practices that retain moisture at the plant root zone and reduce crop failure due to variability of rainfall including extended dry spell could be important. Keywords Rainfall variability, trends, standard anomaly index, Mekelle DOI : 10.7176/CER/11-9-02 Publication date :October 31 st 2019

Effects of Topographic Perturbation on the Precipitation Distribution in Sichuan

Terrain characteristics can be accurately represented in spectrum space. Terrain spectra can quantitatively reflect effects of topographic dynamic forcing on the atmosphere. The one-dimensional weighted-average spatial spectral analysis method is used to explore topographic forcing on precipitation distribution in Sichuan. Results indicate that spectral distributions of terrain and winter precipitation in zonal direction present a typical resonance coupling pattern, while that of terrain and precipitation in other seasons drifts toward the smaller scale. In meridional direction, spectral distributions of terrain and precipitation in each season present the large-scale drift pattern. Different patterns are probably relevant to the change of circulations. In winter, due to strong zonal circulation and weak meridional circulation, atmospheric fluctuations caused by zonal topographic forcing show the most significant impact on precipitation. After that season, the zonal circulation weakens gradually in agreement with the decrease of zonal topographic forcing while the meridional flow enhances, leading to the increase of the damping of the zonal wind disturbance caused by terrain, and the pattern transforms from resonance to drift. Summer rainfall is produced by interaction among different scale systems, and terrain is one of the most important factors. The maximum topographic spectral energy in zonal direction is about an order of magnitude larger than that in meridional direction, implying that effects of topographic dynamic forcing are zonally stronger than that in meridional direction. Values of meridional and zonal topographic characteristic scales are 296.8 km and 475.8 km, respectively, which reflects the characteristic of the mesoscale topographic forcing coincident with the frequent mesoscale systems in Sichuan. The peak of the precipitation spectral energy in summer is about two orders of magnitude larger than that in winter and one order of magnitude larger than that in spring or autumn, and the characteristic scale in summer is about 150 km smaller than that in winter. It illustrates that the intensity of the zonal topographic dynamic forcing in summer is significantly increased when the scale of precipitation systems decreases, which explains the high frequency of mesoscale convective precipitation, and implies the significant impact of topographic dynamic forcing on atmosphere as well. The strongest summer precipitation in Sichuan is located at Ya'an, where larger-scale topographic perturbation is more significant than other region in Sichuan. The terrain spectra and summer precipitation spectra in meridional direction are phase-locked in identical wavelength (37.1 km), implying the critical role of terrain on the occurrence of heavy rainfall, and the effect of topographic dynamic forcing in meridional direction is dominant.

Meridionally Extending Anomalous Wave Train over Asia During Breaks in the Indian Summer Monsoon

Anomalous interactions between the Indian summer monsoon (ISM) circulation and subtropical westerlies are known to trigger breaks in the ISM on subseasonal time-scales, characterised by a pattern of suppressed rainfall over central-north India, and enhanced rainfall over the foothills of the central–eastern Himalayas (CEH). An intriguing feature during ISM breaks is the formation of a mid-tropospheric cyclonic circulation anomaly extending over the subtropical and mid-latitude areas of the Asian continent. This study investigates the mechanism of the aforesaid Asian continental mid-tropospheric cyclonic circulation (ACMCC) anomaly using observations and simplified model experiments. The results of our study indicate that the ACMCC during ISM breaks is part of a larger meridional wave train comprising of alternating anticyclonic and cyclonic anomalies that extend poleward from the monsoon region to the Arctic. A lead–lag analysis of mid-tropospheric circulation anomalies suggests that the meridional wave-train generation is linked to latent heating (LH) anomalies over the CEH foothills, Indo-China, and the Indian landmass during ISM breaks. By conducting sensitivity experiments using a simplified global atmospheric general circulation model forced with satellite-derived three-dimensional LH, it is demonstrated that the combined effects of the enhanced LH over the CEH foothills and Indo-China and decreased LH over the Indian landmass during ISM breaks are pivotal for generating the poleward extending meridional wave train and the ACMCC anomaly. At the same time, the spatial extent of the mid-latitude cyclonic anomaly over Far-East Asia is also influenced by the anomalous LH over central–eastern China. While the present findings provide interesting insights into the role of LH anomalies during ISM breaks on the poleward extending meridional wave train, the ACMCC anomaly is found to have important ramifications on the daily rainfall extremes over the Indo-China region. It is revealed from the present analysis that the frequency of extreme rainfall occurrences over Indo-China shows a twofold increase during ISM break periods as compared to active ISM conditions.

Investigating Precipitation Extreme and Ardebil Temperature Behavior by Statistical Tests

One of the natural disasters that threatens our country and every year billions of Rials damage to agricultural land and areas of natural resources and infrastructures, such as bridges, canals, dams, etc. makes, floods that usually associated with the occurrence of heavy rainfall. The aim of this study was to investigate the behavior of precipitation extreme precipitation and temperature with the help of statistical tests is Ardabil. Two measures atmospheric temperature and precipitation are the most important elements for identification and analysis of climate change identified by research centers and researchers used climate change. For this purpose, the daily data of precipitation, temperature minimum (night) and the maximum daily synoptic stations during the period 1965 to 2015 is Ardabil. The research team identified profiles are presented of climate change and climate change profiles (ETCCDI) was used to analyze the events and climate change. As impressive 12 profiles 20 profile 22 profiles of the precipitation extreme temperature and precipitation extreme of precipitation. For information about events, climate change, statistical analysis has been used by the World Meteorological Organization.

A quick and low‐cost technique to identify layers associated with heavy rainfall in sediment archives during the Anthropocene

AbstractLong‐term records are needed to investigate the impact of extreme events in the current framework of global change. Sedimentary reconstruction with a high resolution remains difficult without conducting expensive, destructive and/or time‐consuming analyses. In this study, high resolution CT‐scan profiles (0·6 mm resolution) were used to investigate their potential for detecting flood deposits induced by heavy rainfall events. This method was applied to a sediment core dated with fallout radionuclides – covering a 120 year period – collected in a pond draining a small forested catchment (French Massif Central – Central France). Between 1960 and 2017, 28 layers were deposited. Seventy‐six percent of these deposits were correlated to the occurrence of heavy rainfall (>50 mm) recorded during one or two consecutive days. The remainder of the deposits detected with the Computer Tomography (CT) scanner (n = 5) were not correlated to weather events. They mainly occurred in response to landscape management operations (for example, afforestation works as a result of the major 1999 storm). This period was indeed characterized by an increase in the delivery of137Cs‐enriched sediment, demonstrating a greater topsoil contribution to sediment during major forest management operations. The intensity of detrital layers has significantly decreased throughout time after a major land use change that took place in 1948 and land abandonment. The frequency of heavy rainfall and associated detrital deposits has nevertheless increased by 60% and 75%, respectively, between the years 1960 and 2017. These results outline the potential of CT‐scan for reconstructing long‐term flood deposits associated with heavy precipitation.

Socioeconomic Determinants of Banana Farmers’ Perception to Climate Change in Nyeri County, Kenya.

This study examined the perception of banana farmers in Mt. Kenya region of Kenya (Nyeri County) on climate change and assessed the socio-economic factors that determines their perceptions. Triangulation research design was used to guide the study by integrating both qualitative and quantitative methods in data collection and analysis. The sites were purposively selected to include Mukurweini sub-county where banana production has been practiced since 1980. A sample size of 130 respondents were sampled. Simple random sampling was used to select the participating respondents. Field survey techniques were used in the collection of data which was analysed using the SPSS v21. Results showed that about 78.2% of the respondents perceived unpredictable rainfall and increased temperature, while 21.5% perceived no change in rainfall and temperature over the last 20 to 30 years period. Majority (79%), of the farmers highly perceived climate to have changed over the years while 53.5% of the respondents in the study area understood climate change as occurrence of unpredictable rainfall. Perceived effect of high rainfall on banana marketing and trading was reported as low market prices (48.5%), while during low rains (dry seasons) was high market demands with (56.2%). Logit model was used to determine the socioeconomic factors influencing farmers perception and was significant at p<0.001 and correctly predicted 83.5% of both those who perceived and those who never perceived changes in rainfall and temperature in the last 20 to 30 years. Results of the logit model analysis showed that the gender of household head, farming system, type of farming and access to weather information influenced banana farmers perception towards climate change. About 47.7% recommended provision of weather information on the onset of the rainfall period while 49.2% preferred information on rainfall distribution within the seasons in order to respond to climate change issues. This has implications on policy makers in the formulation and integration of the policies on development of climate information.

Selecting the best model to fit the Rainfall Count data Using Some Zero Type models with application

&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; 28&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; Counts data models cope with the response variable counts, where the number of times that a certain event occurs in a fixed point is called count data, its observations consists of non-negative integers values {0,1,2,…}. Because of the nature of count data, the response variables are usually considered doing not follow normal distribution. Therefore, linear regression is not an appropriate method to analysis count data due to the skewed distribution. Hence, using linear regression model to analysis count data is likely to bias the results, under these limitations, Poisson regression model and “Negative binomial regression” are likely the appropriate models to analysis count data. Sometimes researchers may Counts more zeros than the expected. Count data with many Zeros leads to a concept called “Zero-inflation”. Data with abundant zeros are especially popular in health, marketing, finance, econometric, ecology, statistics quality control, geographical, and environmental fields when counting the occurrence of certain behavioral and natural events, such as frequency of alcohol use, take drugs, number of cigarettes smoked, the occurrence of earthquakes, rainfall, and etc. Some models have been used to analyzing count data such as the “zero- altered Poisson” (ZAP) model and the “negative binomial” model. In this paper, the models, Poisson, Negative Binomial, ZAP, and ZANB were been used to analyze rainfall data.

Modeling the Rainfall Count data Using Some Zero Type models with application

Count data, including zero counts arise in a wide variety of application, hence models for counts have become widely popular in many fields. In the statistics field, one may define the count data as that type of observation which takes only the non-negative integers value. Sometimes researchers may Counts more zeros than the expected. Excess zero can be defined as Zero-Inflation. Data with abundant zeros are especially popular in health, marketing, finance, econometric, ecology, statistics quality control, geographical, and environmental fields when counting the occurrence of certain behavioral and natural events, such as frequency of alcohol use, take drugs, number of cigarettes smoked, the occurrence of earthquakes, rainfall, and etc. Some models have been used to analyzing count data such as the zero-inflated Poisson (ZIP) model and the negative binomial model. In this paper, the models, Poisson, Negative Binomial, ZIP, and ZINB were been used to analyze rainfall data.

Dominant large-scale parameters responsible for diverse extreme rainfall events over vulnerable Odisha state in India

The exorable climate changes leads to changes in the frequency and intensity of extreme rainfall events worldwide; however, the amendment in extreme rainfall events is not uniform over space, precisely it is more localized and a great threat to the society. Thus, study of rainfall extremes at a finer spatial scale is essential and identifying the large-scale parameters that are responsible factors is highly needed. Odisha state in India is one of the most vulnerable to weather extremes and considered as a study region. The present study is bi-fold, firstly examining the changes in the extreme rainfall (≥ 204.5 mm/day) over Odisha and exploring the foremost large-scale meteorological parameters responsible for heterogeneous characteristics of extreme rainfall within Odisha during 1980–2017 summer monsoon period. India Meteorological Department gridded high-resolution (0.25° × 0.25°) rainfall analysis and ERA-Interim (0.25° × 0.25°) reanalysis data at daily scale are used for the analysis. The study region has an increasing trend in extreme rainfall events and it is evident that the Indian Ocean is warmer during extreme rainfall events compared to the dry events, particularly near the seashore of Odisha. The stronger (weak) and cyclonic (anti-cyclonic) flows at 850-hPa exhibit during extreme rainfall (dry) events. The moisture flux is convergent during extreme rainfall events, while it is reverse during dry events. The monsoon trough has been shifted to south (north) from its normal position during extreme rainfall (dry) events. A detailed investigation is carried out for extreme rainfall events over five different regions in Odisha. It reveals that the wind at 850 hPa, omega at 500 hPa, and SST play the important role for Region I, while OLR and omega at 500 hPa are dominating for the Region IV in the occurrence of extreme rainfall. Moreover, the role of the dominant climatic parameters for the extreme rainfall occurrence varies for the other three regions. Analysis confirms that the role of main meteorological parameters is statistically significant for the extreme rainfall events over the respective region. Although the Odisha is a small state in India, not only the long-term trend in extreme rainfall varies region-to-region but also responsible factors associated with the climatic conditions differ for the occurrence of the extreme rainfall.

The effect of Zagros Mountains on rainfall changes of Sudanese low pressure system in western Iran

Zagros Mountains, as the most important topography faces in western Iran, have an effect on the activity of rainfall systems. The main objective of this paper is to analyze the amplifier effect of the Sudanese precipitation system on the Zagros Mountains. For this purpose, using factor analysis method, daily precipitation pattern of December 2, 1999 was selected as a case study from the set of Sudanese 1-day precipitation in the time period 1996–2017. The method of studying the system in this research was by a synoptic analysis of atmospheric elements and precipitation interpolation model in GIS. This study has indicated that with the conveyance of warm and humid air of Sudanese into the Zagros western slopes, a vast field of positive vorticity and convergence is formed in the lower atmosphere of the region. As a result, suitable thermodynamic conditions have provided for the growth and strengthening of the convection clouds and severe precipitation on the surface of the region topography. Thus the conveyance of humidity and instabilities has occurrence inward the region. These conditions increased the intensity of the upward movement of the air, the growth of the cloud and the occurrence of heavy rainfall in the steep slopes and the southern and southeast slopes of Zagros. Also, it determined which rainfall variations on the Zagros steep slopes has been more than other levels.