Studies on rainfall variability and the influence of orography and land use on the climate of Peninsular India

The present study helped to understand the trend in rainfall patterns at smaller spatial scales and the large regional differences in the variability of rainfall. The effect of land use and orography on the diurnal variability is also understood. But a better understanding on the long term variation in rainfall is possible by using a longer dataset,which may provide insight into the rainfall variation over country during the past century. The basic mechanism behind the interannual rainfall variability would be possible with numerical studies using coupled Ocean-Atmosphere models. The regional difference in the active-break conditions points to the significance of regional studies than considering India as a single unit. The underlying dynamics of diurnal variability need to be studied by making use of a high resolution model as the present study could not simulate the local onshore circulation. Also the land use modification in this study, selected a region, which is surrounded by crop land. This implies the high possibility for the conversion of the remaining region to agricultural land. Therefore the study is useful than considering idealized conditions, but the adverse effect of irrigated crop is more than non-irrigated crop. Therefore, such studies would help to understand the climate changes occurred in the recent period. The large accumulation of rainfall between 300-600 m height of western Ghats has been found but the reason behind this need to be studied, which is possible by utilizing datasets that would better represent the orography and landuse over the region in high resolution model. Similarly a detailed analysis is needed to clearly identify the causative relations of the predictors identified with the predictant and the physical reasons behind them. New approaches that include nonlinear relationships and dynamical variables from model simulations can be included in the existing statistical models to improve the skill of the models. Also the statistical models for the forecasts of monsoon have to be continually updated.

Rainfall Climatology over Middle East Region and its Variability

A better understanding of the rainfall climatology of the Middle East region identifying the mechanisms responsible for the rain producing systems is essential for effective utilization of the water resources over the arid region. A comprehensive analysis on the rainfall climatology of the Middle East region is carried out to bring out the spatial and temporal variation of rainfall and mechanisms responsible for the rain events. The study was carried out utilizing rainfall, OLR, wind and humidity data sets procured from TRMM, NOAA and NCEP-NCAR. Climatology of annual rainfall brings out two areas of alarmingly low rainfall in the Middle East region: one in Egypt, Jordan and adjoining areas and the other in the southern part of Saudi Arabia. Daily rainfall analysis indicates that northern region gets rainfall mainly during winter and spring associated with the passage of Mediterranean low pressure systems whereas rain over the southern region is caused mainly by the monsoon organized convection, cross equatorial flow and remnants of low pressure systems associated with the monsoon during the summer season. Thermodynamic structure of the atmosphere reveals that the region does not have frequent local convection due to insufficient moisture content. The sinking motion associated with the sub tropic high pressure system and subsidence associated with the Walker circulation are responsible for maintaining warm and dry air over the region.

Spatial and temporal characteristics of rain intensity in the peninsular Malaysia using TRMM rain rate

The present study is focused on the intensity distribution of rainfall in different classes and their contribution to the total seasonal rainfall. In addition, we studied the spatial and diurnal variation of the rainfall in the study areas. For the present study, we retrieved data from TRMM (Tropical Rain Measuring Mission) rain rate available in every 3 h temporal and 25 km spatial resolutions. Moreover, station rainfall data is used to validate the TRMM rain rate and found significant correlation between them (linear correlation coefficients are 0.96, 0.85, 0.75 and 0.63 for the stations Kota Bharu, Senai, Cameron highlands and KLIA, respectively). We selected four areas in the Peninsular Malaysia and they are south coastal, east coastal, west coastal and highland regions. Diurnal variation of frequency of rain occurrence is different for different locations. We noticed bimodal variation in the coastal areas in most of the seasons and unimodal variation in the highland/inland area. During the southwest monsoon period in the west coastal stations, there is no distinct diurnal variation. The distribution of different intensity classes during different seasons are explained in detail in the results

Long term trends of seasonal and monthly rainfall in different intensity ranges over Indian subcontinent

In general Indian summer monsoon rainfall did not show any significant trend in all Indian summer monsoon rainfall series, however, it was reported that the ISMR is subjected to spatial trends. This paper made an attempt to bring out long term trends of different intensity classes of summer monsoon rainfall in different regions of Indian subcontinent. The long term trend of seasonal and monthly rainfall were also made using the India Meteorological Department gridded daily rainfall data with a spatial resolution of 1° × 1° latitude-longitude grid for the period from 1st January, 1901 to 31st December, 2003. The summer monsoon rainfall shows an increasing trend in southeast, northwest and northeast regions, whereas decreasing trend in the central and west coastal regions. In monthly scale, July rainfall shows decreasing trend over west coastal and central Indian regions and significant increasing trend over northeast region at 0.1% significant level. During the month August, decreasing trend is observed in the west coastal stations at 10% significant level. In most of the stations, mean daily rainfall shows an increasing trend for low and very high intense rainfall. For the moderate rainfall, the trend is different for different regions. In the central and southern regions the trend of moderate and moderately high classes show increasing trend. And for the high and very high intensity classes, the trend is decreasing significantly. In the northeastern regions, above 10 mm/day rainfall shows significantly increasing trend with 0.1% significant level.

Stochastic modelling of rainfall from satellite data

Satellite-based rainfall monitoring is widely used for climatological studies because of its full global coverage but it is also of great importance for operational purposes especially in areas such as Africa where there is a lack of ground-based rainfall data. Satellite rainfall estimates have enormous potential benefits as input to hydrological and agricultural models because of their real time availability, low cost and full spatial coverage. One issue that needs to be addressed is the uncertainty on these estimates. This is particularly important in assessing the likely errors on the output from non-linear models (rainfall-runoff or crop yield) which make use of the rainfall estimates, aggregated over an area, as input. Correct assessment of the uncertainty on the rainfall is non-trivial as it must take account of • the difference in spatial support of the satellite information and independent data used for calibration • uncertainties on the independent calibration data • the non-Gaussian distribution of rainfall amount • the spatial intermittency of rainfall • the spatial correlation of the rainfall field This paper describes a method for estimating the uncertainty on satellite-based rainfall values taking account of these factors. The method involves firstly a stochastic calibration which completely describes the probability of rainfall occurrence and the pdf of rainfall amount for a given satellite value, and secondly the generation of ensemble of rainfall fields based on the stochastic calibration but with the correct spatial correlation structure within each ensemble member. This is achieved by the use of geostatistical sequential simulation. The ensemble generated in this way may be used to estimate uncertainty at larger spatial scales. A case study of daily rainfall monitoring in the Gambia, west Africa for the purpose of crop yield forecasting is presented to illustrate the method.

Evaluation of reanalysis rainfall estimates over Ethiopia

There is a pressing need for good rainfall data for the African continent both for humanitarian and climatological purposes. Given the sparseness of ground-based observations, one source of rainfall information is Numerical Weather Prediction (NWP) model outputs. The aim of this article is to investigate the quality of two NWP products using Ethiopia as a test case. The two products evaluated are the ERA-40 and NCEP reanalysis rainfall products. Spatial, seasonal and interannual variability of rainfall have been evaluated for Kiremt (JJAS) and Belg (FMAM) seasons at a spatial scale that reflects the local variability of the rainfall climate using a method which makes optimum use of sparse gauge validation data. We found that the spatial pattern of the rainfall climatology is captured well by both models especially for the main rainy season Kiremt. However, both models tend to overestimate the mean rainfall in the northwest, west and central regions but underestimate in the south and east. The overestimation is greater for NCEP in Belg season and greater for ERA-40 in Kiremt Season. ERA-40 captures the annual cycle over most of the country better than NCEP, but strongly exaggerates the Kiremt peak in the northwest and west. The overestimation in Kiremt appears to have been reduced since the assimilation of satellite data increased around 1990. For both models the interannual variability is less well captured than the spatial and seasonal variability. Copyright © 2008 Royal Meteorological Society

Influence of south Atlantic sea surface temperatures on rainfall variability and extremes over southern Africa

It is generally agreed that changing climate variability, and the associated change in climate extremes, may have a greater impact on environmentally vulnerable regions than a changing mean. This research investigates rainfall variability, rainfall extremes, and their associations with atmospheric and oceanic circulations over southern Africa, a region that is considered particularly vulnerable to extreme events because of numerous environmental, social, and economic pressures. Because rainfall variability is a function of scale, high-resolution data are needed to identify extreme events. Thus, this research uses remotely sensed rainfall data and climate model experiments at high spatial and temporal resolution, with the overall aim being to investigate the ways in which sea surface temperature (SST) anomalies influence rainfall extremes over southern Africa. Extreme rainfall identification is achieved by the high-resolution microwave/infrared rainfall algorithm dataset. This comprises satellite-derived daily rainfall from 1993 to 2002 and covers southern Africa at a spatial resolution of 0.1° latitude–longitude. Extremes are extracted and used with reanalysis data to study possible circulation anomalies associated with extreme rainfall. Anomalously cold SSTs in the central South Atlantic and warm SSTs off the coast of southwestern Africa seem to be statistically related to rainfall extremes. Further, through a number of idealized climate model experiments, it would appear that both decreasing SSTs in the central South Atlantic and increasing SSTs off the coast of southwestern Africa lead to a demonstrable increase in daily rainfall and rainfall extremes over southern Africa, via local effects such as increased convection and remote effects such as an adjustment of the Walker-type circulation.

A Bayesian approach for multi-model downscaling: Seasonal forecasting of regional rainfall and river flows in South America

This study addresses three issues: spatial downscaling, calibration, and combination of seasonal predictions produced by different coupled ocean-atmosphere climate models. It examines the feasibility Of using a Bayesian procedure for producing combined, well-calibrated downscaled seasonal rainfall forecasts for two regions in South America and river flow forecasts for the Parana river in the south of Brazil and the Tocantins river in the north of Brazil. These forecasts are important for national electricity generation management and planning. A Bayesian procedure, referred to here as forecast assimilation, is used to combine and calibrate the rainfall predictions produced by three climate models. Forecast assimilation is able to improve the skill of 3-month lead November-December-January multi-model rainfall predictions over the two South American regions. Improvements are noted in forecast seasonal mean values and uncertainty estimates. River flow forecasts are less skilful than rainfall forecasts. This is partially because natural river flow is a derived quantity that is sensitive to hydrological as well as meteorological processes, and to human intervention in the form of reservoir management.

Toward a combined seasonal weather and crop productivity forecasting system: Determination of the working spatial scale

A methodology is presented for the development of a combined seasonal weather and crop productivity forecasting system. The first stage of the methodology is the determination of the spatial scale(s) on which the system could operate; this determination has been made for the case of groundnut production in India. Rainfall is a dominant climatic determinant of groundnut yield in India. The relationship between yield and rainfall has been explored using data from 1966 to 1995. On the all-India scale, seasonal rainfall explains 52% of the variance in yield. On the subdivisional scale, correlations vary between variance r(2) = 0.62 (significance level p < 10(-4)) and a negative correlation with r(2) = 0.1 (p = 0.13). The spatial structure of the relationship between rainfall and groundnut yield has been explored using empirical orthogonal function (EOF) analysis. A coherent, large-scale pattern emerges for both rainfall and yield. On the subdivisional scale (similar to 300 km), the first principal component (PC) of rainfall is correlated well with the first PC of yield (r(2) = 0.53, p < 10(-4)), demonstrating that the large-scale patterns picked out by the EOFs are related. The physical significance of this result is demonstrated. Use of larger averaging areas for the EOF analysis resulted in lower and (over time) less robust correlations. Because of this loss of detail when using larger spatial scales, the subdivisional scale is suggested as an upper limit on the spatial scale for the proposed forecasting system. Further, district-level EOFs of the yield data demonstrate the validity of upscaling these data to the subdivisional scale. Similar patterns have been produced using data on both of these scales, and the first PCs are very highly correlated (r(2) = 0.96). Hence, a working spatial scale has been identified, typical of that used in seasonal weather forecasting, that can form the basis of crop modeling work for the case of groundnut production in India. Last, the change in correlation between yield and seasonal rainfall during the study period has been examined using seasonal totals and monthly EOFs. A further link between yield and subseasonal variability is demonstrated via analysis of dynamical data.

Geostatistical analysis of rainfall

Rainfall can be modeled as a spatially correlated random field superimposed on a background mean value; therefore, geostatistical methods are appropriate for the analysis of rain gauge data. Nevertheless, there are certain typical features of these data that must be taken into account to produce useful results, including the generally non-Gaussian mixed distribution, the inhomogeneity and low density of observations, and the temporal and spatial variability of spatial correlation patterns. Many studies show that rigorous geostatistical analysis performs better than other available interpolation techniques for rain gauge data. Important elements are the use of climatological variograms and the appropriate treatment of rainy and nonrainy areas. Benefits of geostatistical analysis for rainfall include ease of estimating areal averages, estimation of uncertainties, and the possibility of using secondary information (e.g., topography). Geostatistical analysis also facilitates the generation of ensembles of rainfall fields that are consistent with a given set of observations, allowing for a more realistic exploration of errors and their propagation in downstream models, such as those used for agricultural or hydrological forecasting. This article provides a review of geostatistical methods used for kriging, exemplified where appropriate by daily rain gauge data from Ethiopia.

Climatic and oceanic associations with daily rainfall extremes over southern Africa

Changes in climate variability and, in particular, changes in extreme climate events are likely to be of far more significance for environmentally vulnerable regions than changes in the mean state. It is generally accepted that sea-surface temperatures (SSTs) play an important role in modulating rainfall variability. Consequently, SSTs can be prescribed in global and regional climate modelling in order to study the physical mechanisms behind rainfall and its extremes. Using a satellite-based daily rainfall historical data set, this paper describes the main patterns of rainfall variability over southern Africa, identifies the dates when extreme rainfall occurs within these patterns, and shows the effect of resolution in trying to identify the location and intensity of SST anomalies associated with these extremes in the Atlantic and southwest Indian Ocean. Derived from a Principal Component Analysis (PCA), the results also suggest that, for the spatial pattern accounting for the highest amount of variability, extremes extracted at a higher spatial resolution do give a clearer indication regarding the location and intensity of anomalous SST regions. As the amount of variability explained by each spatial pattern defined by the PCA decreases, it would appear that extremes extracted at a lower resolution give a clearer indication of anomalous SST regions.

Assessment of a climate model to reproduce rainfall variability and extremes over Southern Africa

It is increasingly accepted that any possible climate change will not only have an influence on mean climate but may also significantly alter climatic variability. A change in the distribution and magnitude of extreme rainfall events (associated with changing variability), such as droughts or flooding, may have a far greater impact on human and natural systems than a changing mean. This issue is of particular importance for environmentally vulnerable regions such as southern Africa. The sub-continent is considered especially vulnerable to and ill-equipped (in terms of adaptation) for extreme events, due to a number of factors including extensive poverty, famine, disease and political instability. Rainfall variability and the identification of rainfall extremes is a function of scale, so high spatial and temporal resolution data are preferred to identify extreme events and accurately predict future variability. The majority of previous climate model verification studies have compared model output with observational data at monthly timescales. In this research, the assessment of ability of a state of the art climate model to simulate climate at daily timescales is carried out using satellite-derived rainfall data from the Microwave Infrared Rainfall Algorithm (MIRA). This dataset covers the period from 1993 to 2002 and the whole of southern Africa at a spatial resolution of 0.1° longitude/latitude. This paper concentrates primarily on the ability of the model to simulate the spatial and temporal patterns of present-day rainfall variability over southern Africa and is not intended to discuss possible future changes in climate as these have been documented elsewhere. Simulations of current climate from the UKMeteorological Office Hadley Centre’s climate model, in both regional and global mode, are firstly compared to the MIRA dataset at daily timescales. Secondly, the ability of the model to reproduce daily rainfall extremes is assessed, again by a comparison with extremes from the MIRA dataset. The results suggest that the model reproduces the number and spatial distribution of rainfall extremes with some accuracy, but that mean rainfall and rainfall variability is underestimated (over-estimated) over wet (dry) regions of southern Africa.

Evaluation of satellite-based and model re-analysis rainfall estimates for Uganda

The dependence of much of Africa on rain fed agriculture leads to a high vulnerability to fluctuations in rainfall amount. Hence, accurate monitoring of near-real time rainfall is particularly useful, for example in forewarning possible crop shortfalls in drought-prone areas. Unfortunately, ground based observations are often inadequate. Rainfall estimates from satellite-based algorithms and numerical model outputs can fill this data gap, however rigorous assessment of such estimates is required. In this case, three satellite based products (NOAA-RFE 2.0, GPCP-1DD and TAMSAT) and two numerical model outputs (ERA-40 and ERA-Interim) have been evaluated for Uganda in East Africa using a network of 27 rain gauges. The study focuses on the years 2001 to 2005 and considers the main rainy season (February to June). All data sets were converted to the same temporal and spatial scales. Kriging was used for the spatial interpolation of the gauge data. All three satellite products showed similar characteristics and had a high level of skill that exceeded both model outputs. ERA-Interim had a tendency to overestimate whilst ERA-40 consistently underestimated the Ugandan rainfall.

Cloud bands over southern Africa: seasonality, contribution to rainfall variability, and modulation by the MJO

Tropical-extratropical cloud band systems over southern Africa, known as tropical temperate troughs (TTTs), are known to contribute substantially to South African summer rainfall. This study performs a comprehensive assessment of the seasonal cycle and rainfall contribution of TTTs by using a novel object-based strategy that explicitly tracks these systems for their full life cycle. The methodology incorporates a simple assignment of station rainfall data to each event, thereby creating a database containing detailed rainfall characteristics for each TTT. This is used to explore the importance of TTTs for rain days and climatological rainfall totals in October–March. Average contributions range from 30 to 60 % with substantial spatial heterogeneity observed. TTT rainfall contributions over the Highveld and eastern escarpment are lower than expected. A short analysis of TTT rainfall variability indicates TTTs provide substantial, but not dominant, intraseasonal and interannual variability in station rainfall totals. TTTs are however responsible for a high proportion of heavy rainfall days. Of 52 extreme rainfall events in the 1979–1999 period, 30 are associated with these tropical-extratropical interactions. Cut-off lows were included in the evolution of 6 of these TTTs. The study concludes with an analysis of the question: does the Madden-Julian Oscillation influence the intensity of TTT rainfall over South Africa? Results suggest a weak but significant suppression (enhancement) of intensity during phase 1(6).

Scale-selective verification of rainfall accumulations from high-resolution forecasts of convective events

The development of NWP models with grid spacing down to 1 km should produce more realistic forecasts of convective storms. However, greater realism does not necessarily mean more accurate precipitation forecasts. The rapid growth of errors on small scales in conjunction with preexisting errors on larger scales may limit the usefulness of such models. The purpose of this paper is to examine whether improved model resolution alone is able to produce more skillful precipitation forecasts on useful scales, and how the skill varies with spatial scale. A verification method will be described in which skill is determined from a comparison of rainfall forecasts with radar using fractional coverage over different sized areas. The Met Office Unified Model was run with grid spacings of 12, 4, and 1 km for 10 days in which convection occurred during the summers of 2003 and 2004. All forecasts were run from 12-km initial states for a clean comparison. The results show that the 1-km model was the most skillful over all but the smallest scales (approximately <10–15 km). A measure of acceptable skill was defined; this was attained by the 1-km model at scales around 40–70 km, some 10–20 km less than that of the 12-km model. The biggest improvement occurred for heavier, more localized rain, despite it being more difficult to predict. The 4-km model did not improve much on the 12-km model because of the difficulties of representing convection at that resolution, which was accentuated by the spinup from 12-km fields.