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Downloaded 10/04/21 11:09 AM UTC 914 JOURNAL of HYDROMETEOROLOGY VOLUME 13 Which Can Then Be Combined with a Hydrological Model JUNE 2012 C HADWICK AND GRIMES 913 An Artificial Neural Network Approach to Multispectral Rainfall Estimation over Africa ROBIN CHADWICK Met Office Hadley Centre, Exeter, United Kingdom DAVID GRIMES* Department of Meteorology, University of Reading, United Kingdom (Manuscript received 22 June 2011, in final form 8 December 2011) ABSTRACT Multispectral Spinning Enhanced Visible and IR Interferometer (SEVIRI) data, calibrated with daily rain gauge estimates, were used to produce daily high-resolution rainfall estimates over Africa. An artificial neural network (ANN) approach was used, producing an output of satellite pixel–scale daily rainfall totals. This product, known as the Rainfall Intensity Artificial Neural Network African Algorithm (RIANNAA), was calibrated and validated using gauge data from the highland Oromiya region of Ethiopia. Validation was performed at a variety of spatial and temporal scales, and results were also compared against Tropical Ap- plications of Meteorology Using Satellite Data (TAMSAT) single-channel IR-based rainfall estimates. Several versions of RIANNAA, with different combinations of SEVIRI channels as inputs, were developed. RIANNAA was an improvement over TAMSAT at all validation scales, for all versions of RIANNAA. However, the addition of multispectral data to RIANNAA only provided a statistically significant im- provement over the single-channel RIANNAA at the highest spatial and temporal-resolution validation scale. It appears that multispectral data add more value to rainfall estimates at high-resolution scales than at averaged time scales, where the cloud microphysical information that they provide may be less important for determining rainfall totals than larger-scale processes such as total moisture advection aloft. 1. Introduction provided a high enough temporal resolution to monitor the availability of water for crops during the growing The African continent spans a vast array of different season. Recently there has been interest in providing landscapes and climates, but factors common to the daily rainfall estimates, as it is not only the total dekadal whole region are the huge importance and frequent rainfall that affects crop growth, but also the daily dis- unreliability of rainfall. Because of the scarcity of rain tribution of rainfall within a dekad (Teo and Grimes gauge data available in real time over Africa, rainfall 2007). However, this requires satellite RFEs to be ac- estimates are usually taken from satellite-based algo- curate at a daily scale. rithms or from a combination of gauge and satellite es- As well as being used for famine early warning systems, timates (e.g., Grimes et al. 1999). rainfall estimates have more general potential applica- For drought monitoring, 10-day (dekadal) rainfall tions in agriculture in the region. Numerical crop-yield accumulations have traditionally been used. This is be- models such as the General Large-Area Model (GLAM; cause satellite rainfall estimates (RFEs) are more ac- Challinor et al. 2004) require rainfall inputs at a daily curate when averaged over longer time scales, and scale over a growing season. In this case a seasonal because it was considered that 10-day rainfall estimates rainfall forecast updated throughout the season with daily satellite rainfall observations would seem to provide one possible method of producing accurate crop-yield fore- * Deceased. casts. Short-term river flow and flood forecasting is another Corresponding author address: Robin Chadwick, Met Office potential application of rainfall estimates (Grimes and Hadley Centre, Fitzroy Rd., Devon EX1 3PB, United Kingdom. Diop 2003). The requirement here is for short timescale E-mail: robin.chadwick@metoffice.gov.uk (daily or shorter) rainfall estimates over a river basin, DOI: 10.1175/JHM-D-11-081.1 Unauthenticated | Downloaded 10/04/21 11:09 AM UTC 914 JOURNAL OF HYDROMETEOROLOGY VOLUME 13 which can then be combined with a hydrological model. with historical gauge data has the potential to be used This technique has so far proved impractical for opera- over much more of Africa (with regional calibration) tional purposes because of the limited accuracy of avail- than one calibrated with radar. able rainfall estimates at short time scales over Africa. The relationship between multispectral SEVIRI data Various satellite RFE products are commonly used to and surface rainfall is complex, nonlinear, and not well provide rainfall estimates over Africa, and these nor- understood. Therefore, any algorithm relating the two mally use infrared (IR) and/or microwave data from must currently be largely empirical in nature. TAMORA a variety of satellite platforms, often combined with uses a contingency table method to establish a probabi- available gauge data (see Kidd et al. 2009 or Kidd 2001 listic relationship between the SEVIRI data and rainfall for a review of satellite RFE methods). However, esti- rain rate. The alternative method chosen here is to em- mates from the current generation of satellite RFEs show ploy an artificial neural network (ANN) to find the pat- relatively low accuracy when validated at a daily time tern between satellite data and rainfall. Neural networks scale over Africa (Dinku et al. 2008; Laws et al. 2004). have previously been used on many occasions in the field This paper investigates whether the use of multi- of satellite rainfall estimation (Sorooshian et al. 2000; spectral visible and IR data from the Spinning Enhanced Bellerby et al. 2000; Hong et al. 2004) with the Estima- Visible and IR Interferometer (SEVIRI) instrument can tion of Precipitation by Satellites-Second Generation lead to improved satellite rainfall estimates over Africa (EPSAT-SG) method of Berge`s et al. (2010) using an compared to a single-channel IR product, particularly at ANN to produce rainfall estimates over Africa from daily time scales. No current operational satellite RFE multispectral SEVIRI data. However, rain gauges have uses geostationary multispectral data to produce rainfall rarely been used for calibration (the exception being the estimates over Africa for use in food-security applica- TAMANN algorithm of Coppola et al. 2006), and as far tions. The advantage of using only SEVIRI data, as op- as the authors are aware this paper describes the first posed to a multisatellite product, is that many African instance of gauge calibration combined with multispec- Met services are equipped to receive SEVIRI data, and tral input data. The ANN used here will be referred could therefore apply and adapt rainfall estimates pro- to as the Rainfall Intensity Artificial Neural Network duced from SEVIRI themselves. Estimates were pro- African Algorithm (RIANNAA). duced and validated at several spatial and temporal scales, as applications of RFEs over Africa require products at 2. Data a variety of different scales. a. Ethiopian rain gauge dataset The Tropical Applications of Meteorology Using Sat- ellite Data (TAMSAT) Met Office Rainfall for Africa A relatively dense rain gauge dataset for the Oromiya (TAMORA) algorithm (Chadwick et al. 2010) used data region of Ethiopia was provided by the National Mete- from a mobile precipitation radar to calibrate SEVIRI orological Agency of Ethiopia, comprising 278 stations data and produce precipitation estimates over West Af- with daily data from 2002 to 2006. After quality control rica. A validation against gridded dekadal gauge data procedures, this number was reduced to 215 as a number showed that TAMORA produced accurate estimates in of stations containing large amounts of missing, dupli- the region close to the calibration radar, but that this cated, or questionable data were excluded. Oromiya accuracy was reduced for other areas of West Africa. This gauge locations are shown in Fig. 1. suggests the need for local calibration of multispectral To calibrate and assess satellite rainfall estimates over satellite rainfall products, which is a result also found by Africa, it is usually necessary to compare them with rain Ba and Gruber (2001) when using a multispectral satellite gauge data. Satellite rainfall products produce pixel RFE over North America. rainfall estimates with a resolution of 3 km at the equator Because of the lack of precipitation radar networks for SEVIRI-based algorithms (Schmetz et al. 2002). over most of Africa, regional calibration of satellite However, rain gauge data by their nature consist of RFEs by radar is currently impractical. One alternative point estimates, which in Africa are often sparse and is to use rain gauge data for calibration of a satellite RFE unevenly distributed. A comparison of SEVIRI pixel- algorithm. Although gauges are comparatively sparse in scale radiances with gauge point rainfall estimates would Africa compared with other continents, there is far not be comparing like with like. Therefore, it is neces- greater gauge coverage than radar coverage. This is sary to interpolate the gauge data to satellite pixel scale. particularly true if real-time gauge data are not needed, The interpolation method used here is kriging, which as relatively dense rain gauge data are often collected by has been shown to perform better than other inter- African Met agencies but not distributed internationally polation methods (e.g., Thiessen polygons and spline in real time. Therefore an algorithm that is calibrated surface fitting) for medium- and low-density gauge Unauthenticated | Downloaded 10/04/21 11:09 AM UTC JUNE 2012 C HADWICK AND GRIMES 915 FIG. 1. (a) Elevation of Oromiya region of Ethiopia. Gauges used
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