Breaks in monsoon and related precursors

A comprehensive analysis of eleven break monsoon situations that occurred during the period 1987 to 1997 have been attempted in the study. The various features like daily rainfall departures, wind anomalies and the satellite derived Outgoing Long wave Radiation (OLR) associated with the commencement/cessation of the break monsoon condition are studied with a view to identifying the precursors associate the break situation. The results reveal that there is progressive decrease of below normal rainfall departures 5 days prior to the actual break day in the latitude belts south of 20° N. During the period of the revival of the monsoon, the time section of the daily rainfall departures shows that the daily rainfall departure first starts becoming above normal in the southern most latitudinal belt 5° N to 10°N from the second day onwards after the cessation of the break. Similarly, the easterly anomalies in the zonal wind are first noticed in the southern latitude even 5 days prior to the starting of the break in the lower and middle troposphere. The maximum easterly anomalies in the lower and the middle troposphere move northwards upto 20° N. The composite latitudinal time section of OLR anomaly show a large area of negative OLR anomaly extending from 20°S to 10°N. The area is defined as the Southern. Hemispheric Convective Zone ( SHCZ). The negative OLR anomaly (10 Wm-2 is noticed around 5° S to 0° N. It increases to 20 Wm-2 on the second day of the break on the same latitudinal belt. The daily OLR anomaly pattern shows that the area of the negative OLR anomaly around the equatorial region increases with the approach of a break epoch. The forecasting aspects of the commencement / cessation of the break have been also discussed.

Performance of the Global Forecast System's medium-range precipitation forecasts in the Niger river basin using multiple satellite-based products

Accurate weather forecast information has the potential to improve water resources management, energy, and agriculture. This study evaluates the accuracy of medium-range (1–15 d) precipitation forecasts from the Global Forecast System (GFS) over watersheds of eight major dams (Selingue Dam, Markala Dam, Goronyo Dam, Bakolori Dam, Kainji Dam, Jebba Dam, Dadin Kowa Dam, and Lagdo Dam) in the Niger river basin using NASA's Integrated Multi-satellitE Retrievals (IMERG) Final Run merged satellite gauge rainfall observations. The results indicate that the accuracy of GFS forecast varies depending on climatic regime, lead time, accumulation timescale, and spatial scale. The GFS forecast has large overestimation bias in the Guinea region of the basin (wet climatic regime), moderate overestimation bias in the Savannah region (moderately wet climatic regime), but has no bias in the Sahel region (dry climate). Averaging the forecasts at coarser spatial scales leads to increased forecast accuracy. For daily rainfall forecasts, the performance of GFS is very low for almost all watersheds, except for Markala and Kainji dams, both of which have much larger watershed areas compared to the other watersheds. Averaging the forecasts at longer timescales also leads to increased forecast accuracy. The GFS forecasts, at 15 d accumulation timescale, have better performance but tend to overestimate high rain rates. Additionally, the performance assessment of two other satellite products was conducted using IMERG Final estimates as reference. The Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) merged satellite gauge product has similar rainfall characteristics to IMERG Final, indicating the robustness of IMERG Final. The IMERG Early Run satellite-only rainfall product is biased in the dry Sahel region; however, in the wet Guinea and Savannah regions, IMERG Early Run outperforms GFS in terms of bias.

Improving daily rainfall extremes simulation using the generalized Pareto distribution: a case study in Western Iran

Prediction of the occurrence or non-occurrence of daily rainfall plays a significant role in agricultural planning and water resource management projects. In this study, gamma distribution function (GDF), kernel, and exponential (EXP) distributions were coupled (piecewise) with a generalized Pareto distribution. Thus, the gamma-generalized Pareto (GGP), kernel-generalized Pareto (KGP), and exponential-generalized Pareto (EGP) models were used. The aim of the present study was to introduce new methods to modify the simulated generation of extreme rainfall amounts of rainy seasons based on the preserved spatial correlation. The best approach was identified using the normalized root mean square error (NRMSE) criterion. For this purpose, the 30-yr daily rainfall datasets of 21 synoptic weather stations located in different climates of West Iran were analyzed. The first, second, and third-order Markov chain (MC) models were used to describe rainfall time series frequencies. The best MC model order was detected using the Akaike information criterion and Bayesian information criterion. Based on the best identified MC model order, the best piecewise distribution models, and the Wilks approach, rainfall events were modeled with regard to the spatial correlation among the study stations. The performance of the Wilks approach was verified using the coefficient of determination. The daily rainfall simulation resulted in a good agreement between the observed and the generated rainfall data. Hence, the proposed approach is capable of helping water resource managers in different contexts of agricultural planning.

A synthetic view of rainfall intensification in the West African Sahel

The West African Sahel has been facing for more than 30 years an increase in extreme rainfalls with strong socio-economic impacts. This situation challenges decision-makers to define adaptation strategies in a rapidly changing climate. The present study proposes (i) a quantitative characterization of the trends in extreme rainfalls at the regional scale, (ii) the translation of the trends into metrics that can be used by hydrological risk managers, (iii) elements for understanding the link between the climatology of extreme and mean rainfall. Based on a regional non-stationary statistical model applied to in-situ daily rainfall data over the period 1983-2015, we show that the region-wide increasing trend in extreme rainfalls is highly significant. The change in extreme value distribution reflects an increase in both the mean and variability, producing a 5%/decade increase in extreme rainfall intensity whatever the return period. The statistical framework provides operational elements for revising the design methods of hydraulic structures which most often assume a stationary climate. Finally, the study shows that the increase in extreme rainfall is more attributable to an increase in the intensity of storms (80%) than to their occurrence (20%), reflecting a major disruption from the decadal variability of the rainfall regime documented in the region since 1950.

Time-series analysis and statistical forecasting of daily rainfall in Kupang, East Nusa Tenggara, Indonesia: a pilot study

This pilot study presents a novel statistical time-series approach for analyzing daily rainfall data in Kupang, East Nusa Tenggara, Indonesia. By using the piecewise cubic hermite interpolation algorithm, we succeeded in filling in the null values in the daily rainfall time series. We then analyzed the monthly average and its pattern using the continuous wavelet transform (CWT) algorithm, which shows the strong annual pattern of rainfall in this region. In addition, we use the rainfall anomaly index (RAI) function to standardize daily rainfall as an indicator of dry/wet conditions in this region. Then we also use the daily RAI time-series objects from 1978 to 2020 for modeling and predicting daily RAI over the next year. The result is the root mean squared error (RMSE) of 0.8424041040593219. This Prophet model is also able to capture the linear trend of increasing drought throughout the study time period and the annual pattern of wet/dry conditions which is in accordance with previous study by Aldrian and Susanto (2003).

A Markov chain model for daily rainfall occurrences at east Thanjavur district

The occurrences and non-occurrences of the rainfall can be described by a two-state Markov chain. A dry date is denoted by state 0 and wet date is denoted by state 1. We have taken the sample which follows a Poisson process with known parameter. Using this Poisson sample we have given a new approach to affect statistical inference for the law of the Markov chain and state estimation concerning un-observed past values or not yet observed future values. The paper aims at comparing the earlier fit of the data with the new approach.

Daily rainfall persistence over Sri Lanka

The spatial distribution of daily rainfall persistence is examined adopting Besson's persistence coefficient and using daily rainfall data for 15 consecutive years (1971-1985). The daily rainfall persistence coefficients have been studied separately for all the twelve months individually and for the whole year. Where January and February indicate the lowest rainfall persistence coefficients the period from October to December indicates the highest coefficients over Sri Lanka. Besides the monsoonal atmospheric conditions, the topography has a strong influence on the rainfall persistence distribution over space and time. The daily rainfall persistence coefficients record higher values in the wet zone then in the dry zone of Sri Lanka. Regression analysis shows a better linear relationship between mean length of wet spell~ and the daily rainfall persistence coefficients and the resultant final equation is y.4' =0.1093+0.1600+X M having the correlation coefficient of 0.721 which is significant at the 0.01% level.

1 to 10 days extreme rainfall studies for Kerala state

The daily rainfall data for 80 years from 98 stations in Kerala region have been analysed to arrive at the Probable Maximum Precipitation (PMP) estimates for rainfall durations or 1 to 10 days. Hershfield's statistical technique has been adopted for the estimation of PMP from annual maximum data. The study will be useful in the estimation of extreme precipitation for computation of design floods, required for design of spillways of dams and other major hydraulic structures in the Kerala state.

Spatial and temporal variations of rainy days and mean daily rainfall intensity in northern Nigeria.

The spatial and temporal variations of rainy Gays arid daily rainfall intensity for northern Nigeria for using 54 years data are analysed, The extent and nature of non-random changes, such as trend and fluctuations are Investigated. In general, both, the rainy day frequency and mean daily rainfall intensity decreases northwards except for localized orographic effect in the north central Part of the region. There is statistical evidence or decreasing trend in the, number of rainy days over the period of study, but the trend analysis showed no significance or the mean daily rainfall intensity. This suggests that the recent decreasing rainfall trend In the region particularly In the Sahellan zone, In the result of decrease In the frequency of rainy days and not due to any significant change In the rainfall intensity.