D. I. F. Grimes

Optimal areal rainfall estimation using raingauges and satellite data

Abstract The main aim of this paper is to present a new method of areal rainfall estimation using satellite and ground-based data. This method involves optimal merging of the estimates provided by satellite information and estimates obtained from raingauges. In the merging procedure, each estimate is weighted according to its uncertainty given by its estimation variance. The uncertainty attributed to the raingauge estimates is obtained using block kriging, while for the satellite uncertainties, a novel regression approach is developed. A standard error is also attached to the new merged estimates. In order to test the algorithm, a case study has been undertaken using the EPSAT dense raingauge network in Niger. The complete EPSAT raingauge network (94 gauges distributed over a 1×1° square) has been used to obtain a detailed picture of the rainfall pattern which is then used as a reference for comparing the estimation schemes. The schemes compared are: (1) estimates based on satellite data only; (2) kriged estimates from a randomly selected subset of four gauges; (3) kriging with external drift using both satellite data and the subset of gauges; and (4) the new merging algorithm. The merging process gives more reliable results both for the mean areal rainfall and its spatial distribution.

Toward a Combined Seasonal Weather and Crop Productivity Forecasting System: Determination of the Working Spatial Scale

Abstract 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 r2 = 0.62 (significance level p < 10–4) and a negative correlation with r2 = 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 (∼300 km), the first principal component (PC) of ...

The 30 year TAMSAT African Rainfall Climatology And Time series (TARCAT) data set

African societies are dependent on rainfall for agricultural and other water-dependent activities, yet rainfall is extremely variable in both space and time and reoccurring water shocks, such as drought, can have considerable social and economic impacts. To help improve our knowledge of the rainfall climate, we have constructed a 30 year (1983–2012), temporally consistent rainfall data set for Africa known as TARCAT (Tropical Applications of Meteorology using SATellite and ground-based observations (TAMSAT) African Rainfall Climatology And Time series) using archived Meteosat thermal infrared imagery, calibrated against rain gauge records collated from numerous African agencies. TARCAT has been produced at 10 day (dekad) scale at a spatial resolution of 0.0375°. An intercomparison of TARCAT from 1983 to 2010 with six long-term precipitation data sets indicates that TARCAT replicates the spatial and seasonal rainfall patterns and interannual variability well, with correlation coefficients of 0.85 and 0.70 with the Climate Research Unit and Global Precipitation Climatology Centre gridded-gauge analyses respectively in the interannual variability of the Africa-wide mean monthly rainfall. The design of the algorithm for drought monitoring leads to TARCAT underestimating the Africa-wide mean annual rainfall on average by −0.37 mm d−1 (21%) compared to other data sets. As the TARCAT rainfall estimates are historically calibrated across large climatically homogeneous regions, the data can provide users with robust estimates of climate related risk, even in regions where gauge records are inconsistent in time.

A Neural Network Approach to Real-Time Rainfall Estimation for Africa Using Satellite Data

Abstract Operational, real-time rainfall estimation on a daily timescale is potentially of great benefit for hydrological forecasting in African river basins. Sparseness of ground-based observations often means that only methodologies based predominantly on satellite data are feasible. An approach is presented here in which Cold Cloud Duration (CCD) imagery derived from Meteosat thermal infrared imagery is used in conjunction with numerical weather model analysis data as the input to an artificial neural network. Novel features of this approach are the use of principal component analysis to reduce the data requirements for the weather model analyses and the use of a pruning technique to identify redundant input data. The methodology has been tested using 4 yr of daily rain gauge data from Zambia in central Africa. Calibration and validation were carried out using pixel area rainfall estimates derived from daily rain gauge data. When compared with a standard CCD approach using the same dataset, the neural ...

Extension of the TAMSAT Satellite-Based Rainfall Monitoring over Africa and from 1983 to Present

AbstractTropical Applications of Meteorology Using Satellite Data and Ground-Based Observations (TAMSAT) rainfall monitoring products have been extended to provide spatially contiguous rainfall estimates across Africa. This has been achieved through a new, climatology-based calibration, which varies in both space and time. As a result, cumulative estimates of rainfall are now issued at the end of each 10-day period (dekad) at 4-km spatial resolution with pan-African coverage. The utility of the products for decision making is improved by the routine provision of validation reports, for which the 10-day (dekadal) TAMSAT rainfall estimates are compared with independent gauge observations. This paper describes the methodology by which the TAMSAT method has been applied to generate the pan-African rainfall monitoring products. It is demonstrated through comparison with gauge measurements that the method provides skillful estimates, although with a systematic dry bias. This study illustrates TAMSAT’s value as ...

Satellite-based rainfall estimation for river flow forecasting in Africa. I: Rainfall estimates and hydrological forecasts

Abstract Reliable, real-time river flow forecasting in Africa on a time scale of days can provide enormous humanitarian and economic benefits. This study investigates the feasibility of using daily rainfall estimates based on cold cloud duration (CCD) derived from Meteosat thermal infrared imagery as input to a simple rainfall—runoff model and also whether such estimates can be improved by the inclusion of information from numerical weather prediction (NWP) analysis models. The Bakoye catchment in Mali, West Africa has been used as a test area. The data available for the study covered the main months of the rainy season for three years. The rainfall estimates were initially validated against gauge data. Improvements in quality were observed when information relating to African Easterly Wave phase and storm type was included in a multiple linear regression (MR) algorithm. The estimates were also tested by using them as input to a rainfall—runoff model. When contemporaneous calibrations from raingauges were available for calibration, both CCD-only and MR rainfall estimates gave more accurate river flow forecasts than when using raingauge data alone. In the absence of contemporaneous calibrations, the performance was reduced but the MR did better than the CCDonly input in all years. The use of satellite-derived vegetation index did not improve the quality of the river flow forecasts.

Comparison of TAMSAT and CPC rainfall estimates with raingauges, for southern Africa

Two different TAMSAT (Tropical Applications of Meteorological Satellites) methods of rainfall estimation were developed for northern and southern Africa, based on Meteosat images. These two methods were used to make rainfall estimates for the southern rainy season from October 1995 to April 1996. Estimates produced by both TAMSAT methods and estimates produced by the CPC (Climate Prediction Center) method were then compared with kriged data from over 800 raingauges in southern Africa. This shows that operational TAMSAT estimates are better over plateau regions, with 59% of estimates within one standard error (s.e.) of the kriged rainfall. Over mountainous regions the CPC approach is generally better, although all methods underestimate and give only 40% of estimates within 1 s.e. The two TAMSAT methods show little difference across a whole season, but when looked at in detail the northern method gives unsatisfactory calibrations. The CPC method does have significant overall improvements by building in real-time raingauge data, but only where sufficient raingauges are available.

Satellite observations of the microwave emissivity of a semi-arid land surface

Microwave emissivity is an important parameter for rainfall estimation over land, as well as for atmospheric temperature and humidity retrievals. However, over land surfaces, it varies over a considerable range depending principally on vegetation cover and soil moisture. This study examines the feasibility of estimating emissivity from satellite-based vegetation and moisture indicators for a semiarid region in the African Sahel. Microwave emissivity was calculated from SSM/I observations at 19, 37, and 85 GHz horizontal (H) and vertical (V) polarisation. The technique was validated by comparing the measured emissivity of a sea surface area with the theoretically predicted emissivity. For a dry atmosphere, there was good agreement between theory and measurement. However, the discrepancy was considerably higher in an area where the atmosphere was humid, particularly at 85 GHz. This is attributable to increased uncertainty in atmospheric correction. The land surface emissivity over a 5� square area, which included the Hapex Sahel site, was studied from August to October 1992. The horizontally polarised emissivity eH and polarisation difference measured over dry land areas were found to be well-correlated with Normalised Difference Vegetation Index (NDVI) such that NDVI can be used to estimate pixel eH to within ±0.02. For a wet land surface, there is a general trend for the emissivity to increase with increasing NDVI and for the polarisation difference to decrease. However, the trend is much less well defined than in the dry case. Aweak relationship was observed between areal averages of previous day’s rainfall (PDR) and emissivity for various vegetation cover classes. A similar relationship was observed with ground-based soil moisture measurements. The results show that emissivity can be estimated with a S.E.<0.015 at 19 GHz from a combination of NDVI and rainfall or soil moisture information. D 2001 Elsevier Science Inc. All rights reserved.

Validation of Improved TAMANN Neural Network for Operational Satellite-Derived Rainfall Estimation in Africa

Real-time rainfall monitoring in Africa is of great practical importance for operational applications in hydrology and agriculture. Satellite data have been used in this context for many years because of the lack of surface observations. This paper describes an improved artificial neural network algorithm for operational applications. The algorithm combines numerical weather model information with the satellite data. Using this algorithm, daily rainfall estimates were derived for 4 yr of the Ethiopian and Zambian main rainy seasons and were compared with two other algorithms—a multiple linear regression making use of the same information as that of the neural network and a satellite-only method. All algorithms were validated against rain gauge data. Overall, the neural network performs best, but the extent to which it does so depends on the calibration/validation protocol. The advantages of the neural network are most evident when calibration data are numerous and close in space and time to the validation data. This result emphasizes the importance of a real-time calibration system.

The Estimation of Land Surface Emissivities at 24 GHz to 157 GHz Using Remotely Sensed Aircraft Data

Abstract Rainfall estimation from passive microwave satellite data has been used widely over oceans but has been less successful over land. This is because over land surfaces, the high spatial and temporal variability in emissivity, coupled with relatively low contrast between surface and rain cloud microwave emissions, make the rainfall signal more difficult to extract. The variability of emissivity is mainly due to variations in vegetation cover and soil moisture. Major improvements in reliability of rainfall estimates are possible if emissivities could be measured routinely at appropriate scales. The possibility of estimating emissivity at the frequencies relevant to rainfall from vegetation and soil moisture measurements is explored in this paper using data from airborne sensors over a semiarid area in Spain. Results show a good correlation between vegetation cover (represented by Normalized Difference Vegetation Index) and emissivity in dry conditions. This relationship is not significantly affected by vegetation type. Under wet conditions, the correlation is greatly reduced possibly due to the difficulty in accounting for cloud effects at higher frequencies. Attempts to quantify the effect of soil moisture using the Antecedent Precipitation Index were partially successful but more accurate measurements would be needed for reliable retrieval of emissivities. The use of a soil-adjusted vegetation index produced a higher correlation with emissivity than did the nonadjusted Normalized Difference Vegetation Index.