Validation of Satellite Rainfall Products for Western Uganda.

Central equatorial Africa is deficient in long-term, ground-based measurements of rainfall; therefore, the aim of this study is to assess the accuracy of three high-resolution, satellite-based rainfall products in western Uganda for the 2001–10 period. The three products are African Rainfall Climatology, version 2 (ARC2); African Rainfall Estimation Algorithm, version 2 (RFE2); and 3B42 from the Tropical Rainfall Measuring Mission, version 7 (i.e., 3B42v7). Daily rainfall totals from six gauges were used to assess the accuracy of satellite-based rainfall estimates of rainfall days, daily rainfall totals, 10-day rainfall totals, monthly rainfall totals, and seasonal rainfall totals. The northern stations had a mean annual rainfall total of 1390 mm, while the southern stations had a mean annual rainfall total of 900 mm. 3B42v7 was the only product that did not underestimate boreal-summer rainfall at the northern stations, which had ~3 times as much rainfall during boreal summer than did the southern stations. The three products tended to overestimate rainfall days at all stations and were borderline satisfactory at identifying rainfall days at the northern stations; the products did not perform satisfactorily at the southern stations. At the northern stations, 3B42v7 performed satisfactorily at estimating monthly and seasonal rainfall totals, ARC2 was only satisfactory at estimating seasonal rainfall totals, and RFE2 did not perform satisfactorily at any time step. The satellite products performed worst at the two stations located in rain shadows, and 3B42v7 had substantial overestimates at those stations

Stochastic Rainfall-runoff Model with Explicit Soil Moisture Dynamics

Stream runoff is perhaps the most poorly represented process in ecohydrological stochastic soil moisture models. Here we present a rainfall-runoff model with a new stochastic description of runoff linked to soil moisture dynamics. We describe the rainfall-runoff system as the joint probability density function (PDF) of rainfall, soil moisture and runoff forced by random, instantaneous jumps of rainfall. We develop a master equation for the soil moisture PDF that accounts explicitly for a general state-dependent rainfall-runoff transformation. This framework is then used to derive the joint rainfall-runoff and soil moisture-runoff PDFs. Runoff is initiated by a soil moisture threshold and a linear progressive partitioning of rainfall based on the soil moisture status. We explore the dependence of the PDFs on the rainfall occurrence PDF (homogeneous or state-dependent Poisson process) and the rainfall magnitude PDF (exponential or mixed-exponential distribution). We calibrate the model to 63 years of rainfall and runoff data from the Upper Little Tennessee watershed (USA) and show how the new model can reproduce the measured runoff PDF

Short-term rainfall nowcasting: using rainfall radar imaging

As one of the most useful sources of quantitative precipitation measurement, rainfall radar analysis can be a very useful focus for research into developing methods for rainfall prediction. Because radar can estimate rainfall distribution over a wide range, it is thus very attractive for weather prediction over a large area. Short lead time rainfall prediction is often needed in meteorological and hydrological applications where accurate prediction of rainfall can help with flood relief, with agriculture and with event planning. A system of short-term rainfall prediction over Ireland using rainfall radar image processing is presented in this paper. As the only input, consecutive rainfall radar images are processed to predict the development of rainfall by means of morphological methods and movement extrapolation. The results of a series of experimental evaluations demonstrate the ability and efficiency of using our rainfall radar imaging in a nowcasting system

Estimating rainfall and water balance over the Okavango River Basin for hydrological applications

A historical database for use in rainfall-runoff modeling of the Okavango River Basin in Southwest Africa is presented. The work has relevance for similar data-sparse regions. The parameters of main concern are rainfall and catchment water balance which are key variables for subsequent studies of the hydrological impacts of development and climate change. Rainfall estimates are based on a combination of in-situ gauges and satellite sources. Rain gauge measurements are most extensive from 1955 to 1972, after which they are drastically reduced due to the Angolan civil war. The sensitivity of the rainfall fields to spatial interpolation techniques and the density of gauges was evaluated. Satellite based rainfall estimates for the basin are developed for the period from 1991 onwards, based on the Tropical Rainfall Measuring Mission (TRMM) and Special Sensor Microwave Imager (SSM/I) data sets. The consistency between the gauges and satellite estimates was considered. A methodology was developed to allow calibration of the rainfall-runoff hydrological model against rain gauge data from 1960-1972, with the prerequisite that the model should be driven by satellite derived rainfall products for the 1990s onwards. With the rain gauge data, addition of a single rainfall station (Longa) in regions where stations earlier were lacking was more important than the chosen interpolation method. Comparison of satellite and gauge rainfall outside the basin indicated that the satellite overestimates rainfall by 20%. A non-linear correction was derived used by fitting the rainfall frequency characteristics to those of the historical rainfall data. This satellite rainfall dataset was found satisfactory when using the Pitman rainfall-runoff model (Hughes et al., this issue). Intensive monitoring in the region is recommended to increase accuracy of the comprehensive satellite rainfall estimate calibration procedur

Improving rainfall nowcasting and urban runoff forecasting through dynamic radar-raingauge rainfall adjustment

The insufficient accuracy of radar rainfall estimates is a major source of uncertainty in short-term quantitative precipitation forecasts (QPFs) and associated urban flood forecasts. This study looks at the possibility of improving QPFs and urban runoff forecasts through the dynamic adjustment of radar rainfall estimates based on raingauge measurements. Two commonly used techniques (Kriging with External Drift (KED) and mean field bias correction) were used to adjust radar rainfall estimates for a large area of the UK (250,000 km2) based on raingauge data. QPFs were produced using original radar and adjusted rainfall estimates as input to a nowcasting algorithm. Runoff forecasts were generated by feeding the different QPFs into the storm water drainage model of an urban catchment in London. The performance of the adjusted precipitation estimates and the associated forecasts was tested using local rainfall and flow records. The results show that adjustments done at too large scales cannot provide tangible improvements in rainfall estimates and associated QPFs and runoff forecasts at small scales, such as those of urban catchments. Moreover, the results suggest that the KED adjusted rainfall estimates may be unsuitable for generating QPFs, as this method damages the continuity of spatial structures between consecutive rainfall fields

Hydroclimate variability and its statistical links to the large-scale climate indices for the Upper Chao Phraya River Basin, Thailand

The local hydroclimates get impacts from the large-scale atmospheric variables via atmospheric circulation. The developing of their relationships could enhance the understanding of hydroclimate variability. This study focuses on the Upper Chao Phraya River Basin in Thailand in which rainfall is influenced by the Indian Ocean and tropical Pacific Ocean atmospheric circulation. The Southwest monsoon from the Indian Ocean to Thailand is strengthened by the temperature gradient between land and ocean. Thus, the anomalous sea surface temperature (SST) is systematically correlated with the monthly rainfall and identified as the best predictor based on the significant relation ships revealed by cross-correlation analysis. It is found that rainfall, especially during the monsoon season in the different zones of study basin, corresponds to the different SST indices. This suggests that the region over the ocean which develops the temperature gradient plays a role in strengthening the monsoon. The enhanced gradient with the SST over the South China Sea is related to rainfall in High Rainfall Zone (HRZ); however, the anomalous SST over the Indian Ocean and the equatorial Pacific Ocean are associated with rainfall in Normal and Low Rainfall Zone (NRZ and LRZ) in the study area. Moreover, the identified predictors are related to the rainfall with lead periods of 1-4 months for the pre-monsoon rainfall and 6-12 months for the monsoon and dry season rainfall. The study results are very useful in developing rainfall forecasting models and consequently in the management of water resources and extreme events. (Résumé d'auteur

Diurnal variability of tropical rainfall retrieved from combined GOES and TRMM satellite information

Recent progress in satellite remote-sensing techniques for precipitation estimation, along with more accurate tropical rainfall measurements from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and precipitation radar (PR) instruments, have made it possible to monitor tropical rainfall diurnal patterns and their intensities from satellite information. One year (August 1998-July 1999) of tropical rainfall estimates from the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) systems were used to produce monthly means of rainfall diurnal cycles at hourly and 1° × 1° scales over a domain (30°S-30°N, 80°E-10°W) from the Americas across the Pacific Ocean to Australia and eastern Asia. The results demonstrate pronounced diurnal variability of tropical rainfall intensity at synoptic and regional scales. Seasonal signals of diurnal rainfall are presented over the large domain of the tropical Pacific Ocean, especially over the ITCZ and South Pacific convergence zone (SPCZ) and neighboring continents. The regional patterns of tropical rainfall diurnal cycles are specified in the Amazon, Mexico, the Caribbean Sea, Calcutta, Bay of Bengal, Malaysia, and northern Australia. Limited validations for the results include comparisons of 1) the PERSIANN-derived diurnal cycle of rainfall at Rondonia, Brazil, with that derived from the Tropical Ocean Global Atmosphere Coupled Ocean-Atmosphere Response Experiment (TOGA COARE) radar data; 2) the PERSIANN diurnal cycle of rainfall over the western Pacific Ocean with that derived from the data of the optical rain gauges mounted on the TOGA-moored buoys: and 3) the monthly accumulations of rainfall samples from the orbital TMI and PR surface rainfall with the accumulations of concurrent PERSIANN estimates. These comparisons indicate that the PERSIANN-derived diurnal patterns at the selected resolutions produce estimates that are similar in magnitude and phase

Impacts of the Madden-Julian oscillation on Australian rainfall and circulation

Impacts of the Madden¿Julian oscillation (MJO) on Australian rainfall and circulation are examined during all four seasons. The authors examine circulation anomalies and a number of different rainfall metrics, each composited contemporaneously for eight MJO phases derived from the real-time multivariate MJO index. Multiple rainfall metrics are examined to allow for greater relevance of the information for applications. The greatest rainfall impact of the MJO occurs in northern Australia in (austral) summer, although in every season rainfall impacts of various magnitude are found in most locations, associated with corresponding circulation anomalies. In northern Australia in all seasons except winter, the rainfall impact is explained by the direct influence of the MJO's tropical convective anomalies, while in winter a weaker and more localized signal in northern Australia appears to result from the modulation of the trade winds as they impinge upon the eastern coasts, especially in the northeast. In extratropical Australia, on the other hand, the occurrence of enhanced (suppressed) rainfall appears to result from induced upward (downward) motion within remotely forced extratropical lows (highs), and from anomalous low-level northerly (southerly) winds that transport moisture from the tropics. Induction of extratropical rainfall anomalies by remotely forced lows and highs appears to operate mostly in winter, whereas anomalous meridional moisture transport appears to operate mainly in the summer, autumn, and to some extent in the sprin

Bias adjustment of infrared-based rainfall estimation using Passive Microwave satellite rainfall data

This study explores using Passive Microwave (PMW) rainfall estimation for spatial and temporal adjustment of Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System(PERSIANN-CCS). The PERSIANN-CCS algorithm collects information from infrared images to estimate rainfall. PERSIANN-CCS is one of the algorithms used in the IntegratedMultisatellite Retrievals for GPM (Global Precipitation Mission) estimation for the time period PMW rainfall estimations are limited or not available. Continued improvement of PERSIANN-CCS will support Integrated Multisatellite Retrievals for GPM for current as well as retrospective estimations of global precipitation. This study takes advantage of the high spatial and temporal resolution of GEO-based PERSIANN-CCS estimation and the more effective, but lower sample frequency, PMW estimation. The Probability Matching Method (PMM) was used to adjust the rainfall distribution of GEO-based PERSIANN-CCS toward that of PMW rainfall estimation. The results show that a significant improvement of global PERSIANN-CCS rainfall estimation is obtained

Multifractal analyses of daily rainfall time series in Pearl River basin of China

The multifractal properties of daily rainfall time series at the stations in Pearl River basin of China over periods of up to 45 years are examined using the universal multifractal approach based on the multiplicative cascade model and the multifractal detrended fluctuation analysis (MF-DFA). The results from these two kinds of multifractal analyses show that the daily rainfall time series in this basin have multifractal behavior in two different time scale ranges. It is found that the empirical multifractal moment function $K(q)$ of the daily rainfall time series can be fitted very well by the universal mulitifractal model (UMM). The estimated values of the conservation parameter $H$ from UMM for these daily rainfall data are close to zero indicating that they correspond to conserved fields. After removing the seasonal trend in the rainfall data, the estimated values of the exponent $h(2)$ from MF-DFA indicate that the daily rainfall time series in Pearl River basin exhibit no long-term correlations. It is also found that $K(2)$ and elevation series are negatively correlated. It shows a relationship between topography and rainfall variability.Comment: 16 pages, 7 figures, 1 table, accepted by Physica