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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 improvement 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

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FIG. 1. (a) Elevation of Oromiya region of Ethiopia. Gauges used for RIANNAA training, testing, and validation are shown as black crosses; country boundary is in white. Elevation data used is U.S. National Imaging and Mapping Agency (NIMA) Digital Terrain and Elevation Data (DTED). (b) Location of gauge data used for training, testing, and validation of RIANNAA, and missing data for 1 Jul 2006. Black line shows country border.

networks such as the ones generally found in Africa b. Multispectral SEVIRI data (Creutin and Obled 1982; Lebel et al. 1987; Grimes and SEVIRI measures radiance values in a number of Pardo-Iguzquiza 2010). different spectral bands. Each spectral channel can be Separate variograms for July, August, and September used to give different information about the atmosphere (JAS) were computed, and the daily time series for JAS and in particular about clouds and indirectly about 2004–06 was then interpolated to SEVIRI pixel scale rainfall. The infrared window channels (10.8 and using ordinary kriging. The variogram for August is 12.0 mm) can be used to infer the temperature, and shown in Fig. 2, and the exponential model fitted to the therefore height of the emitting cloud top, and this has experimental variogram appears to reproduce well the been the basis of single-channel rainfall estimation spatial correlation of the data. Detailed investigation of techniques such as the Geostationary Operational En- any anisotropy of the variogram in this dataset was be- vironmental Satellite (GOES) precipitation index (GPI; yond the scope of this study. Arkin 1979) and TAMSAT (Grimes et al. 1999). To minimize the error in the comparison of satellite Radiance in the visible channels (0.6 and 0.8 mm) is data to kriged gauge estimates, only satellite pixels con- mainly a function of the physical thickness and geometry taining at least one gauge were used in this study (these are referred to as gauge pixels). As for the purposes of satellite RFE calibration and validation, we are only in- terested in these gauge pixels; it was considered a reasonable assumption that if a gauge registered zero rainfall for a day, the gauge-pixel estimate could also be taken to be zero. Following Grimes et al. (2003), all gauge-pixel kriged estimates where the corresponding gauge recorded zero rainfall were set to zero. When using this method, the variogram for nonzero rainfall is computed using only nonzero rainfall values. Figure 3 shows the distribution of daily 2006 JAS gauge data with and without interpolation to 4-km scale by kriging. It can be seen from Fig. 3d that the kriging procedure has the effect of increasing low gauge rainfall values and reducing high ones. This transformation is FIG. 2. Climatological variogram of Ethiopian daily rainfall (excluding zero rainfall values) in August, fitted with the expo- consistent with the findings of Balme et al. (2006) with nential model. The y axis shows the variogram value. Experimental regard to the distribution of point and areal rainfall time variogram is shown by line interspersed by dots and model vario- series in the Sahel. gram by smooth line.

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FIG. 3. Histograms of daily Oromiya rain gauge data for 2006 JAS: (a) raw gauge data, (b) gauge data kriged to 4- km Meteosat Second Generation (MSG) pixel scale, (c) gauge data kriged to 4-km and standardized by ECDF method, and (d) kriged data plotted against raw gauge estimates (excluding zero estimates), with least squares fit line. of a cloud together with the incident angle of the solar same as the 3.9-mm emitting temperature and subtracting beam, with effective particle size re of only secondary this from the total 3.9-mm radiance. The NIR channels are importance and cloud phase seemingly unimportant often used in combination with a visible channel to provide (Barrett and Martin 1981; King et al. 1995). As cloud information on both cloud depth and particle phase/size. thickness is an important constraint upon rainfall, with rain During the night, when visible and NIR channels are formation processes much more likely to occur for thicker unavailable, a combination of IR channels can be used clouds, visible data can give useful information about to provide information about cloud properties. The rainfall probability. This is particularly true when com- split-window technique of Inoue (1987) uses the 10.8– bined with IR information about cloud-top temperature. 12.0-mm brightness temperature difference (BTD) to As well as visible wavelengths, measurements of re- distinguish thin high-level cirrus cloud from thicker flectance in the near-IR (NIR) part of the spectrum are clouds. The 3.9–10.8-mm or 8.7–10.8-mm BTD can be available during the day in the 1.6- and 3.9-mmchannels. used for discrimination of deep clouds with large cloud These channels are useful for the determination of cloud- particles from thin clouds and clouds composed of small top properties, as the reflectance in these channels is de- particles (Lensky and Rosenfeld 2003; Thies et al. 2008). termined by optical depth (t), re, and particle phase. The The 8.7–10.8-mm BTD is more useful for ice clouds than 1.6-mm channel can be considered a purely solar channel, water clouds (Lutz et al. 2003). whereas the 3.9-mm channel has components from both solar reflectance and thermal emission. Therefore, the 3. TAMSAT thermal component of the received 3.9-mm radiance must be removed before the reflectance can be used. This is TAMSAT is similar in approach to the GPI, but with done following the method of Lindsey et al. (2006) by the adaptation that both the threshold temperature at assuming that the emitting temperature at 8.7 mmisthe which rain is assigned and the assigned rain rate vary

Unauthenticated | Downloaded 10/04/21 11:09 AM UTC JUNE 2012 C HADWICK AND GRIMES 917 regionally and seasonally in the algorithm. This corresponds to the empirical relationship between IR cloud- top temperature and rainfall varying in both space and time, particularly over the diverse terrain and climate of Africa. TAMSAT is calibrated with historical rain gauge data in a two-stage process. The first stage is to determine a cloud-top threshold temperature, below which a satellite pixel is assigned as raining. This is done by comparing gauge observations against several different satellite IR brightness temperature (BT) thresholds. Once a threshold temperature has been established, rainfall estimates are produced using a linear fit between cold-cloud duration (CCD) and historical gauge values. FIG. 4. Schematic of the feed-forward multilayer perceptron with For a more detailed description of TAMSAT method- seven inputs and four layers used in RIANNAA IR108, showing ology, see Grimes et al. (1999). nodes and weighted connections between them. The final output While this simple approach has been shown to per- layer gives a rainfall estimate. form well in many regions of Africa (Jobard et al. 2011; Dinku et al. 2007), it has obvious limitations. High cirrus clouds with tops colder than the threshold temperature ‘‘test’’ dataset, and a third ‘‘validation’’ dataset. During will be identified as raining, while warm-cloud rainfall training, an error value E is periodically calculated for events will not be captured. both the training and test datasets. If E continues to fall or stabilizes for the training dataset but begins to rise for the test dataset, then overtraining is diagnosed and the 4. ANN methodology training process is stopped. The validation dataset is ANNs are a pattern recognition tool used to find reserved for independent validation of ANN output. empirical relationships between a set of ‘‘input’’ variables A multilayer perceptron with at least three layers and some corresponding ‘‘output’’ variables. ANNs con- should be able to reproduce any mapping between input sist of a number of ‘‘nodes’’ able to pass information be- and output variables (Hornick et al. 1989), but four layers tween one another in a similar way to neurons in the brain are used here, as it has been found previously that an (see Fig. 4 for a schematic of an ANN). For a full de- extra layer leads to more efficient ANN training for the scription of ANN theory, see Bishop (2000) or Picton kind of process modeled here (Grimes et al. 2003; Coppola (2000). Grimes et al. (2003) gives a description of the et al. 2006; Bellerby et al. 2000). The choice of number of particular type of ANN (a multilayer perceptron) used nodes in the hidden layers is more arbitrary, but was based for RIANNAA. The ANN back-propagation code used here on configurations of ANNs used for similar purposes here was based on a modified version of that developed by in the literature (Grimes et al. 2003; Coppola et al. 2006; Lo¨nnblad et al. (1991) for pattern recognition problems in Bellerby et al. 2000). The shape of the architecture used particle physics. for each ANN here can be seen from Table 1. A calibration, or ‘‘training,’’ process is used to prepare an ANN for a particular purpose. A large number of 5. ANN inputs training patterns are used where both the inputs and the corresponding output(s) are known. Each pattern con- For RIANNAA, the input data were various combi- sists of a set of input variables and the corresponding nations of SEVIRI radiances and the output data were ‘‘target’’ output variable(s). daily rainfall estimates, with kriged daily rain gauge totals To be useful, ANNs must be capable of generaliza- used for training. The main challenge when designing this tion. This means that after training they can process a set ANN was the mismatch in time scale between SEVIRI of inputs not used in the training process (and hence not radiance data (available for this project at 30-min in- previously ‘‘seen’’ by the network) into a reasonable tervals) and the daily rain gauge training data. As it was output value. If training continues for too long, an ANN not possible to accurately downscale the gauge data to can become ‘‘overtrained’’ to its training dataset and a smaller time scale, the SEVIRI data had to be upscaled loses the ability to generalize. in some way to daily scale. To avoid overtraining, the available input/output pat- One option would be to take the mean and variance terns can be separated into a training dataset, a smaller of each SEVIRI channel over each day. However, as

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TABLE 1. Channel combinations included, total number of inputs and number of nodes in each hidden layer for each version of RIANNAA.

ANN name Nighttime channels (mm) Daytime channels (mm) No. inputs Second layer Third layer IR108 10.8 10.8 7 5 2 IR120 10.8, 10.8–12.0 10.8, 10.8–12.0 10 5 2 IR087 10.8, 8.7–10.8 10.8, 8.7–10.8 10 5 2 IRMS 10.8, 10.8–12.0, 8.7–10.8 10.8, 10.8–12.0, 8.7–10.8 28 16 6 Vis008 10.8, 10.8–12.0, 8.7–10.8 10.8, 0.8 37 20 4 Vis016 10.8, 10.8–12.0, 8.7–10.8 10.8, 0.8, 1.6/0.8 55 24 6 Vis039 10.8, 10.8–12.0, 8.7–10.8 10.8, 0.8, 3.9/0.8 55 24 6 VisMS 10.8, 10.8–12.0, 8.7–10.8 10.8, 0.8, 1.6/0.8, 3.9/0.8 82 32 6

convective rain events typically last only a few hours to Table 2 for each additional channel, resulting in or less, this method would probably not be effective at a correspondingly larger number of ANN inputs. capturing and distinguishing between different types of The physical reasoning behind creating the inputs in cloud and rainfall amounts. this way is to produce an implicit cloud-classiﬁcation A second option is to separate each channel into system. Each input corresponds to a speciﬁc combina- radiance bins, and then to record the number of half tion of SEVIRI radiances, which is the signature of hours that the channel falls within each bin during a particular cloud type. Certain cloud types (and there- a particular day. This method seems more likely to be fore ANN inputs) are more likely to produce rain than able to distinguish between different cloud types than others, and some are more likely to produce heavy or simply taking the mean radiance over the day. An ad- light rain. Therefore, if input 1 is equal to nine half hours ditional complexity is that it would be preferable to re- over a day, this is on average likely to correspond to tain the synchronicity of the information from different a different amount of rainfall from the case where input channels. For example, if the channels are binned and 5 is equal to nine half hours over a day. recorded independently for each day, it may be found The advantage of using a nonlinear calibration such as that on a particular day the 10.8-mm channel showed an ANN is that any nonlinear relationships between cold brightness temperature for 6 h and the 0.8-mm cloud types and rainfall should also be recognized by the channel showed high optical depth for 4 h. However, to algorithm. For example, a squall line with deep convec- identify better the cloud types present (and estimate tive cloud followed by a stratiform layer and a cirrus anvil rainfall), it is also preferable to know for how long these might produce more rain than a local convective event. two time periods overlapped. It can be seen that a three- or four-channel ANN would Therefore, the method chosen to upscale SEVIRI have a large number of inputs, even for a small number of radiances to daily scale was to bin combinations of ra- radiance bins per channel. As the maximum number of diances together. A 2D example of this, for the case inputs that an ANN can support is constrained by the where only the 10.8-mm and 10.8–12.0-mm channels are used, is shown in Table 2. In this case, each channel BT TABLE 2. Composition of ANN inputs for a two-channel ANN (or BTD difference) is divided into three bins, pro- (referred to later as IR120), with both channels divided into three ducing nine ANN inputs (plus one for elevation, which radiance bins. The actual value of each input is the number of half will be explained later in this section). The actual value hours during a day for which both channels are simultaneously in the required radiance bins for that particular input. The last input of each of the nine ANN inputs for a particular day is the for all ANNs used in this study is elevation above sea level. number of half hours for which the input channel–bin combination is recorded. So, for example, ANN input number 10.8-mm bin 10.8–12.0-mm bin 111 Input1 5 Number of ½ hours for which simultaneously: 212 313 (IR10.8-mmBTis inbin1)and (IR12.0-mmBTisinbin1), 421 Input2 5 Numberof ½ hours for which simultaneously: 522 (IR10.8-mmBTis inbin1)and(IR12.0-mmBTisinbin2), 623 731 832 and so on. For ANNs with more than two channels the 933 same method is used, with an extra column being added 10 Elevation Elevation

Unauthenticated | Downloaded 10/04/21 11:09 AM UTC JUNE 2012 C HADWICK AND GRIMES 919 number of training patterns available (in this case the two periods. However, the same problem does not arise number of gauge days in the training dataset), it was for RIANNA where daily total rainfall is estimated as desirable to minimize the number of radiance bins for a single value. RIANNAA does not use nighttime and each channel while still retaining as much information daytime channels separately to produce nighttime and as possible. daytime rainfall estimates that are then aggregated to Another issue raised by upscaling the SEVIRI radi- produce a daily total. Instead, both daytime and night- ances to a daily time scale is the unavailability of solar time channels are used together to produce a single daily channels at nighttime and the unavailability of the 3.9-mm estimate. The ANN will learn to recognize and replicate BT channel during the day (because of the solar com- the pattern that best fits the combination of inputs (in- ponent of the 3.9-mm channel). To try to understand the cluding both daytime and nighttime channels) to the importance of each of the channels with regard to rainfall daily kriged gauge totals. estimation, several versions of RIANNAA were created, For RIANNAA, it was necessary to choose the radi- each with different combinations of inputs. These are ance bins into which the channels were partitioned into shown in Table 1. carefully and sparingly because of the restriction on the These channels were chosen based on the analysis of number of inputs caused by limited training data. This Capacci and Conway (2005), who used an ANN approach choice of bins was made by analyzing the probability of to identify the combination of channels that produced the rainfall [P(R)] resulting from combinations of different most skillful rainfall estimates. Ideally, it would also have channel radiances examined for the TAMORA algo- been possible to assess the potential of the water vapor rithm (Chadwick et al. 2010). channels to improve rainfall estimates, but unfortunately For the IR channels, more useful information about the SEVIRI data available was that which had been ac- cloud properties can be obtained from the 10.8–12.0-mm quired for TAMORA (Chadwick et al. 2010) and did not and 8.7–10.8-mm BTDs than from the 12.0- and 8.7-mm include the water vapor (WV) channels. channels alone; therefore, these BTDs were used as in- The 8.7-, 10.8-, and 12.0-mm channels were each avail- puts in conjunction with the 10.8-mm BT. For the day- able for the full 24-h period, so day–night separation was time 1.6- and 3.9-mm channels, the channel ratios 1.6/0.8 unnecessary for the ANNs that used only combinations of and 3.9/0.8 were used in order to try to reduce the de- these channels. For the versions of RIANNAA that used pendence of these channels on optical depth and pro- any of the 0.8-, 1.6-, or 3.9-mm channels, it was necessary duce variables that are mainly dependent on cloud to have inputs for day and night separated but together in microphysical properties. The SEVIRI channel binnings the same ANN. The value of each of the nighttime inputs used in RIANNAA are shown in Table 3. is equal to the number of half hours during the night for For the IR108 version of Riannaa, the 10.8-mmchan- which the channel-bin combination of that input is met, nel was separated into six bins, with one bin every 108C and similarly for the daytime inputs during the day. So for from 2208 to 2708C. A sensitivity test of the IR108 ANN example, for the Vis008 RIANNAA there were three showed that using three or six 10.8-mm bins did not sig- channels at night, each divided into three radiance bins, nificantly affect the ANN output. Therefore only three leading to 27 inputs. During the day there were two 10.8-mmbinswereusedforthemultispectralANNsin channels, again divided into three radiance bins each, order to reduce the number of ANN inputs needed. leading to 9 inputs. Including elevation (see later), the Similarly, the 3.9/0.8 ratio input was binned into fewer total number of Vis008 inputs is 37. segments for the Visible Multispectral (VisMS) ANN For the purposes of this algorithm, ‘‘day’’ was defined than for Vis039 to limit the already large number of de- as 0400–1400 UTC, as this was the time period for which grees of freedom in VisMS. solar zenith angle (SZA) was below 808 for the whole Here, 10.8-mmdatawarmerthan2208Cwerenot Ethiopian Oromiya region throughout the period JAS, included in the ANN inputs, as clouds with tops and so the solar channels could be considered reliable. warmer than this were considered unlikely to be rain- Time slots outside this period were defined as ‘‘night.’’ ing in this region. As will be described later in this section, Although the actual period of daylight for this region a 10.8-mm thresholding approach was used to separate varies continuously over the 3 months, it was necessary to RIANNAA rain/no-rain estimates, so warmer 10.8-mm define a fixed day/night boundary so that the ANN inputs data would not have given extra information to the final could be standardized in a consistent way (see section 6). estimates in any case. For satellite RFEs that use visible channels to produce As well as the multispectral SEVIRI inputs, pixel ter- estimates at subdaily time scales, one problem is an in- rain elevation was also included as one of the RIANNAA consistency between daytime and nighttime estimates inputs (for all versions of RIANNAA). The Oromiya due to different channel combinations being used for the region is mountainous (see Fig. 1), and orographic effects

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TABLE 3. SEVIRI channel bins used in RIANNAA. All BT and BTD values are in 8C; 10.8-mm BT V1 binning is used for the IR108 RIANNAA; V2 is used for all others. Here 3.9/0.8 ratio V1 binning is used for Vis039 RIANNAA; V2 is used for VisMS.

Channel Bin 1 Bin 2 Bin 3 Bin 4 Bin 5 Bin 6 10.8-mmBTV1 220 to 230 230 to 240 240 to 250 250 to 260 260 to 270 #270 10.8-mmBTV2 220 to 230 230 to 250 #250 NA NA NA 10.8212.0-mm BTD #1 1 to 3 .3NANANA 8.7–10.8-mm BTD #0 0 to 2 2 NA NA NA 0.8-mm #0.8 0.8 to 0.9 .0.9 NA NA NA 1.6/0.8 ratio #0.3 0.3 to 0.5 .0.5 NA NA NA 3.9/0.8 ratio V1 #0.04 0.04 to 0.08 .0.08 NA NA NA 3.9/0.8 ratio V2 #0.06 .0.06 NA NA NA NA would be expected to play a large part in rainfall forma- Zero rainfall values were dealt with separately to tion processes; therefore, including elevation in the inputs nonzero values throughout the ANN process, so only non- might allow the ANN to produce more accurate esti- zero values were standardized using an ECDF: mates. Some weight is lent to this argument by the fact ð that the original version of the Climate Prediction Center R (CPC) RFE satellite rainfall product performs better over f (R) dR R Ethiopia than the updated version (Dinku et al. 2007). R95 ð min , (2) Rmax The most likely reason for this is that the original version f (R) dR R includes an orographic enhancement correction that was min not included in the updated version. where R is the kriged gauge value, f(R) is the kriged rainfall frequency distribution, R9 is the standardized

6. Standardization of input and training data rainfall value, and Rmin and Rmax are the minimum and maximum values of R in the dataset. Here, R9 is a value To improve the efficiency of ANN training by giving between [0, 1]. Figure 3c shows the distribution of JAS all inputs equal weight in the ANN input layer, inputs 2006 daily Oromiya kriged rainfall standardized in this should be standardized to values in the range [0, 1]. The way, and it can be seen that the nonzero rainfall distri- simplest way to achieve this is to use the function bution is approximately uniform. As the rainfall training data for RIANNAA is stan- x 2 xmin xfinal 5 , (1) dardized in this way, the RIANNAA output will have the xmax 2 xmin same form. Output values have to be destandardized by inverting Eq. (2) to produce actual rainfall estimates. where xmax and xmin are the mininum/maximum values One side effect of using an ECDF standardization is that for a particular input and xfinal is the final standardized input for use in the ANN. The final values of the inputs RIANNAA rainfall outputs can never be higher than the to RIANNAA were standardized in this way. maximum rainfall value in the training dataset. The training data should also be standardized to Figure3showsthatthereisalargenumberofzeros the same range, but in this case the method described present in the daily rainfall dataset. Coppola et al. (2006) above was considered inappropriate for standardiza- found that using all zeros during the training process re- tion of daily rainfall values. Daily rainfall values, even duced the ability of an ANN to correctly discriminate low after interpolation to satellite pixel scale by kriging, rain rates. They found that removing 90% of zero rainfall have a highly nonnormal distribution. This can be seen values from ANN training gave a more stable calibration, from Fig. 3a, which shows the histogram of daily and this same procedure was followed for RIANNAA. kriged rainfall for the Oromiya gauge dataset for JAS 2006. As well as the obvious bimodal zero/nonzero 7. Training and validation procedure distribution, the nonzero rainfall distribution itself is highly skewed. Kriged gauge data from the Oromiya gauge data- Coppola et al. (2006) found that standardizing daily set described in section 2a were used to train, test, and rainfall values using an empirical cumulative distribu- validate RIANNAA. Data were available for three rainy tion function (ECDF) approach (Wilks 1995) gave seasons of JAS for 2004–06, and the corresponding improvements to ANN rainfall estimation over using SEVIRI multispectral data were also obtained for this the maximum/minimum standardization of Eq. (1). time period. Only SEVIRI pixels that contained at least

Unauthenticated | Downloaded 10/04/21 11:09 AM UTC JUNE 2012 C HADWICK AND GRIMES 921 one rain gauge were used in order to minimize error due to gauge uncertainty. The gauge data (215 gauges) were randomly separated into 70% training, 10% test, and 20% validation datasets. These are shown in Fig. 1. A cross-validation procedure was then performed, using two of the three years for training and the third for validation in all three possible permutations of years. The validation was therefore independent of the ANN training in both time and space, making it a stringent test of an operational rainfall estimation scenario. Days where more than three half hours of SEVIRI data were missing were excluded from the process, as were gauge days where the gauge data was missing. Training was carried out separately for each month, giving approximately 7000 training patterns for each calibration. FIG. 5. Boxplots of daily kriged gauge rainfall for each value of Grimes et al. (2003) used an ANN approach to pro- CCD (with a threshold of 2308C) for Oromiya calibration gauges, duce daily rainfall estimates over Africa and found that July 2004 and 2005. The least squares fit line is computed by using the median of each distribution (excluding zero CCD), weighted by it was difficult to generate zero rainfall values, instead the amount of data in that distribution. This line provides the producing estimates of very low rainfall (less than 1 mm) TAMSAT calibration parameters used to compute estimates for over large areas. To resolve this, they used a non-ANN July 2006. approach to separate estimates into rain/no-rain, then used an ANN to produce rainfall estimates only for the An example of a TAMSAT calibration plot using data areas assigned as rainy. The chosen rain/no-rain discrim- from July 2004 and 2005 is shown in Fig. 5. It can be seen ination technique was to use a CCD threshold, as in the from the spread of the rainfall distribution for each value TAMSAT algorithm (see section 3). Pixel days without of CCD that the TAMSAT approach will clearly not be any cloud colder than a certain threshold (2308Cinthe able to accurately estimate all high values of rainfall. case of Grimes et al. 2003) were taken to have zero rainfall. The same approach was used for RIANNAA, with 9. Validation of ANN output and comparison with the CCD threshold determined during the TAMSAT TAMSAT estimates calibration described below. Examples of RIANNAA IR108, RIANNAA VisMS, and TAMSAT daily rainfall fields, together with the cor- 8. Daily calibration of TAMSAT over Ethiopia responding kriged gauge estimates, are shown in Fig. 6. In order that RIANNAA estimates could be com- It can be seen that the rainfall delineation appears to be pared with an operational rainfall estimation method, reasonably accurate when compared to the gauges. None TAMSAT daily estimates were produced for the Oro- of the satellite estimates capture the higher gauge values miya region. To make the comparison as fair as possible, on this day. RIANNAA IR108 produces the highest TAMSAT was calibrated and validated with exactly rainfall values of the three satellite images shown here the same gauge data as RIANNAA, using the cross- in the north of the image. However, this area lies out- validation approach described above for 2004206. The side the Oromiya calibration zone, and it is possible that TAMSAT calibration method is described in detail in RIANNAA IR108 is extrapolating its calibration to section 3. Here, a separate calibration was determined a region containing a different cloud regime (possibly for each combination of calibration years and month, with generally higher cloud tops). It is not known what then applied to the validation year and month. the true rainfall is for this day in this northern region. The best CCD threshold for rain/no-rain discrimina- A cross validation of RIANNAA was performed as tion was found to be 2308C, and this was also used for described in section 7, and the three validation years of rain/no-rain discrimination for the RIANNAA algo- data for the validation gauges were then collected to- rithm. As the two methods use the same technique for gether and analyzed at various temporal and spatial this, any difference in skill between them (and between scales. This was done for each version of RIANNAA different versions of RIANNAA) can only be due to the shown in Table 1. The three validation years of TAMSAT ability to correctly estimate rainfall amount, not occur- data were accumulated in the same way and compared rence. with the RIANNAA estimates. All figures and validation

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FIG. 6. Daily (a) TAMSAT, (b) RIANNAA IR108, (c) RIANNAA VisMS, and (d) kriged gauge estimates for 21 Aug 2006 over Ethiopia. (a)–(c) Circles show validation gauge locations for this day; satellite zero rainfall estimates are shown as white. (d) Kriged gauge rainfall estimates shown as colored circles; gauge zeros shown as empty circles. statistics in this section show the three years of cross- A 1 B A B Bias 5 , POD 5 , FAR 5 , validation data collected in this way. A 1 C A 1 C A 1 B Four different spatial and temporal degrees of aver- (3) aging were used for assessing the estimates. The first was at the basic scale of the RIANNAA and TAMSAT es- where A, B, C, and D are defined in Table 4, and N 5 timates, daily SEVIRI-pixel (around 4 km) scale. The A 1 B 1 C 1 D. Note that the bias used for rain/no-rain second was at daily time scale but averaged over all val- discrimination (and defined above) is different to the idation pixels for each day (around 36 pixels, depending standard definition of bias, which is used later for rain- on amount of missing data). The third was at pixel scale fall amount validations of RIANNAA and TAMSAT but aggregated to dekadal time scale, and the fourth was against gauge data. for dekadal aggregates averaged over all validation pixels for each dekad. It was considered worthwhile to perform the analysis at a range of scales, as different applications of satellite rainfall products require estimates at different TABLE 4. Contingency table used for determination of satellite spatial and temporal scales. skill scores. As both RIANNAA and TAMSAT use the same Gauge rain No rain method of rain/no-rain discrimination, the skill for this will be the same for both products. Skill scores used for Satellite Rain AB this are defined as follows: No rain CD

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TABLE 5. Skill scores for rain identiﬁcation at SEVIRI daily pixel Histograms of kriged gauge rainfall, RIANNAA scale over Ethiopia using a 2308C threshold for JAS 2004–06. and TAMSAT daily pixel estimates are shown in Fig. 7. No. gauge No. gauge Probability of False-alarm The median values of the satellite RFE distributions days rainy days nonrainy detection (POD) rate (FAR) Bias and kriged gauge distribution are similar, but neither 6265 2741 0.85 0.22 1.08 RIANNAA nor TAMSAT are able to correctly replicate the spread of the kriged distribution. In particular, the IR108 RIANNAA and TAMSAT have a very high Daily pixel rain/no-rain skill scores are shown in Table 5. proportion of their estimates close to the median value, It can be seen that this threshold produces a minor suggesting that the 10.8-mm channel alone is not a good overestimation of the number of rainy pixels, as shown discriminator of daily pixel rainfall amount. The VisMS by the bias. RIANNAA is able to produce a distribution with tails

FIG. 7. Histograms of daily (a) kriged gauge, (b) TAMSAT, (c) RIANNAA IR108, (d) RIANNAA IRMS, and (e) RIANNAA VisMS estimates at pixel scale.

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FIG. 8. Daily (a) TAMSAT, (b) RIANNAA IR108, (c) RIANNAA IRMS, and (d) RIANNAA VisMS estimates against gauge data at pixel scale. more similar to the kriged gauge distribution than the effect of eliminating the double distribution seen in the other algorithms, suggesting that the inclusion of the histograms in Fig. 7. Also, as the averaging was meant to extra SEVIRI channels may be improving the estimates recreate situations that might occur in an operational at this scale. However, the VisMS RIANNAA is still satellite RFE scenario, it was more realistic to include incapable of estimating extreme high rainfall values. the zero estimates in the validation skill scores. RIANNAA Figure 8 shows daily pixel-scale estimates from RIANNAA and TAMSAT against kriged gauge estimates. To conserve space, only three versions of RIANNAA are TABLE 6. Validation statistics of RIANNAA and TAMSAT shown. It was considered inappropriate to calculate skill estimates for daily pixel (excluding zeros) and daily mean (including zeros) over all validation pixels. Best skill scores are shown scores for the data as shown in this ﬁgure, as the correlation in bold. coefﬁcient and RMSE would be highly affected by the rain/ no-rain discrimination and the values may not be simple Daily mean 2 2 to interpret. As the rain/no-rain method is the same for Algorithm Bias Daily RMSE R Bias RMSE R RIANNAA and TAMSAT, any difference in skill be- IR108 21.57 5.15 0.24 20.32 2.26 0.43 tween the two methods will be in the estimation of IR120 21.56 5.11 0.26 20.32 2.18 0.47 IR087 21.57 5.11 0.26 20.33 2.19 0.47 rainfall amount. To account for this, daily pixel estimates IRMS 21.55 5.11 0.26 20.32 2.17 0.48 were validated but only for the case where both satellite Vis008 21.47 5.02 0.30 20.27 2.12 0.51 RFE and gauge are nonzero. Validation statistics for this Vis016 21.36 5.01 0.29 20.20 2.13 0.51 case are shown in Table 6. Vis039 21.44 5.04 0.29 20.25 2.11 0.51 For the other validation scales zeros were not re- VisMS 21.30 5.10 0.25 20.17 2.15 0.50 TAMSAT 22.65 5.55 0.25 21.13 2.47 0.43 moved, as the temporal or spatial averaging had the

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FIG. 9. Daily (a) TAMSAT, (b) RIANNAA IR108, (c) RIANNAA IRMS, and (d) RIANNAA VisMS estimates against gauge data, averaged over all validation pixels for each day, including least squares ﬁt line.

and TAMSAT estimates plotted against kriged gauge es- extremes, they are similarly unable to do so at dekadal timates at daily averaged, dekadal pixel, and dekadal- scale. averaged scales are shown in Figs. 9–11. Daily skill scores The validation statistics show an improvement in skill are shown in Table 6 and dekadal skill scores in Table 7. at all scales for RIANNAA as more input channels are When accumulated to dekadal time scale, the satellite included, particularly in the bias. All versions of histograms peak at a much higher rainfall value than the RIANNAA also show improvement over TAMSAT at gauge histogram (see Fig. 12). This appears to be be- all scales, with TAMSAT having a larger negative bias. cause the satellite dekadal estimates saturate at a value The negative bias of all algorithms at daily pixel scale below the gauge maximum, and the high satellite esti- can be largely explained by the failure to correctly es- mates are concentrated into a much narrower range of timate high rainfall amounts. The extra SEVIRI chan- values than the corresponding gauge estimates. nels used in the multispectral versions of RIANNAA Dekadal rainfall is determined by the number of rain appear to help in estimating high rainfall values and days combined with the amount of rain on each rain day. hence reduce this bias. As both RIANNAA and TAMSAT use the same daily As the differences in skill scores between the algo- rain/no-rain delineation method, the fact that the dek- rithms were small, statistical hypothesis tests were per- adal RIANNAA peaks at a higher rainfall value than formed to determine whether these differences were TAMSAT must be explained by the greater occurrence statistically signiﬁcant. These tests were performed on of high rainfall values in the RIANNAA daily pixel dis- the residuals r 5 ps 2 pg, where ps is the satellite esti- tribution (see Fig. 7). However, as neither RIANNAA mate and pg is the kriged gauge estimate. The tests were nor TAMSAT are able to capture the range of daily performed at each validation scale, with only estimates

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FIG. 10. Dekadal (a) TAMSAT, (b) RIANNAA IR108, (c) RIANNAA IRMS, and (d) RIANNAA VisMS estimates against gauge data, at pixel scale. Solid line shows linear least squares fit. where both satellite and gauge were nonzero included at improvement in bias of VisMS at daily pixel scale. For daily pixel scale. other validation scales there is no significant improve- Hypothesis tests were performed on each possible ment in any validation statistics. pair of three algorithms: TAMSAT, RIANNAA IR108, The improvement in bias of RIANNAA over TAMSAT and RIANNAA VisMS. The residuals were tested for is consistent with the result of Coppola et al. (2006) that equality of mean (Student’s t test), equality of variance a nonlinear ANN approach can often provide more ac- (F test), and equality of R2 (Pearson Z test). These tests curate satellite rainfall estimates than a linear method. rely on an assumption of normality of the residuals, This is probably because the relationship between which was not generally met. Therefore, nonparametric cloud-top variables and surface rainfall is dependent on hypothesis tests, which do not require an assumption many complex interactions within cloud microphysics of normality, were also performed. These were the and is almost certainly nonlinear. Therefore, a nonlinear Wilcoxon rank sum test (mean) and the Ansari–Bradley ANN approach may be better equipped to model this test (variance). The tests were performed at the 95% relationship than a linear one such as TAMSAT. confidence level, and results are shown in Tables 8 and 9. The addition of multispectral SEVIRI channels does The improvement in bias of both versions of RIANNAA show some statistically significant improvement in the bias over TAMSAT is shown to be statistically significant at at a daily pixel scale. This provides evidence that the in- all four validation scales, with the IR108 also showing clusion of multispectral data, and the implicit information a significant improvement in the RMSE. VisMS shows about cloud properties contained within, can improve a significant improvement in RMSE over TAMSAT for surface rainfall estimates at a daily time scale. However, the nonparametric test but not for the parametric one. when averaging is performed there is no longer a statisti- For the comparison, between RIANNAA IR108 and cally significant improvementofthemultispectralANN VisMS, the only statistically significant result is the over the IR108 ANN.

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FIG. 11. Dekadal (a) TAMSAT, (b) RIANNAA IR108, (c) RIANNAA IRMS, and (d) RIANNAA VisMS estimates against gauge data, averaged over all validation pixels for each dekad, including least squares ﬁt line.

This is partly explained by the inclusion of gauge and this may be due to cloud microphysical information being satellite zero rainfall amounts at averaged scales. As less important for rainfall estimation than total vertical these are determined in the same way for all RIANNAA advection of moisture when averaged over time. The and TAMSAT methods, their inclusion will serve to re- same may also be true of spatial averaging, and this would duce the differences between the algorithm outputs. In explain why RIANNAA VisMS with its multispectral fact, Fig. 7 shows that the range of output of the satellite algorithms is relatively small, with a large proportion of output values clustered around the output median for all TABLE 7. Validation statistics of RIANNAA and TAMSAT algorithms. Therefore, the assignment of rain/no-rain at estimates for dekadal pixel and dekadal mean over all validation pixels. Best skill scores are shown in bold. daily pixel scale would be expected to represent a large proportion of the variance of the estimates when averaged Dekadal to coarser scales. The lack of extreme values in the satellite Dekadal mean 2 2 rainfall outputs will also lead to similarities between them Algorithm Bias RMSE R Bias RMSE R when averaging is applied. IR108 23.61 27.22 0.69 23.11 13.03 0.38 A physical explanation of the lack of statistical signiﬁ- IR120 23.61 26.96 0.69 23.12 12.41 0.43 2 2 cance in the difference in bias between the multispectral IR087 3.69 27.07 0.69 3.22 12.52 0.43 IRMS 23.58 26.91 0.69 23.14 12.43 0.43 and IR108 versions of RIANNAA at temporally and Vis008 23.07 26.59 0.70 22.50 12.02 0.44 spatially averaged scales may be found in cloud physics. Vis016 22.37 26.33 0.71 21.89 11.49 0.48 Chadwick et al. (2010) showed that the TAMORA al- Vis039 22.90 26.46 0.70 22.31 11.73 0.46 gorithm did not appear to be an improvement over VisMS 22.05 26.44 0.71 21.59 11.66 0.47 2 2 TAMSAT at dekadal time scales. It was theorized that TAMSAT 11.73 29.31 0.68 11.81 17.40 0.34

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FIG. 12. Histograms of dekadal (a) kriged gauge, (b) TAMSAT, (c) RIANNAA IR108, (d) RIANNAA IRMS, and (e) RIANNAA VisMS estimates at pixel scale.

data shows a significant improvement over RIANNAA used the same calibration and validation data from IR108 at daily pixel scale but not at other scales. Ethiopia, and the same IR thresholding method of rain/ no-rain delineation. Validation was performed at four different levels of 10. Discussion and conclusions spatial and temporal averaging. RIANNAA was found to An artificial neural network (RIANNAA) was used to have a statistically significant improvement in bias over produce daily rainfall estimates from SEVIRI input TAMSAT at all 4 scales, and this may be because of the data. Various different combinations of channel inputs nonlinear nature of ANNs. Previous work (Grimes et al. were used, and these were also compared with daily 2003; Coppola et al. 2006) has shown that an ANN ap- TAMSAT estimates. Both RIANNAA and TAMSAT proach can provide rainfall estimates superior to those

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TABLE 8. Standard and nonparametric hypothesis tests for comparison of daily validation statistics between algorithms; Y indicates difference is signiﬁcant at the 95% conﬁdence level; N indicates it is not. No nonparametric test for R2 was performed.

Algorithms Bias Daily RMSE R2 Bias Daily mean RMSE R2 Standard IR108, VisMS Y N N N N N IR108, TAMSAT Y Y N Y N N VisMS, TAMSAT Y N N Y N N Nonparametric IR108, VisMS Y N NA N N NA IR108, TAMSAT Y Y NA Y N NA VisMS, TAMSAT Y Y NA Y N NA from a linear method using the same inputs. However, it time and space the results necessarily become more was not the aim of this paper to show this specifically, and similar to each other as zeros are included. As no version as TAMSAT does not have exactly the same input data as of RIANNAA has the capability of correctly estimating any version of RIANNAA then it cannot be stated cate- high rainfall values, the different RIANNAA estimates gorically that the improvement of RIANNAA over are likely to become smoothed and similar as averaging TAMSAT is because of the use of an ANN. The aim here is applied. was to compare RIANNAA against an operational sat- From a physical perspective, the lack of improvement ellite RFE, and it has been shown that RIANNAA of RIANNAA at averaged scales may be simply due to offers a small but statistically significant improvement. the effect of averaging. The cloud microphysical and One caveat here is that for operational 10-day estimates optical depth properties of clouds appear to be impor- TAMSAT would normally be calibrated on a 10-day scale, tant in determining rain rates at high temporal and whereas here it has been calibrated at a daily scale and spatial resolutions, as shown by the improvement of accumulated. A 10-day TAMSAT calibration might be RIANNAA VisMS over RIANNAA IR108 at daily expected to reduce the bias of dekadal estimates (as might pixel scale. However, these properties but may not be so a version of RIANNAA calibrated at a 10-day scale). influential on determining accumulated rainfall at more The inclusion of multispectral data in RIANNAA spatially or temporally averaged scales. For these larger leads to an improvement at the highest resolution here, scales, the effects of cloud microphysics on rainfall may suggesting that multispectral data do have the capacity be averaged out, with large-scale processes such as the to enhance high-resolution rainfall estimates. However, total vertical advection of moisture becoming more there was no statistically significant improvement seen important. As this large-scale vertical moisture trans- from including the multispectral data at averaged scales. port is the principle on which IR-only area–time integral This is consistent with the results from the validation of methods such as TAMSAT are based, the extra cloud TAMORA (Chadwick et al. 2010), which showed an microphysical information provided by multispectral improvement in accuracy for high-resolution estimates, data may not provide enhanced rainfall estimates at but not averaged ones. averaged scales. There is some evidence of this from The explanation for this could be methodological, the work of Mathon et al. (2002), who found that the physical, or a combination of both. All versions of total rain yield of organized convective systems in the RIANNAA use the same threshold method of rain/ Sahel is primarily linked to their duration, not mean no-rain identification, so when estimates are averaged in rain rate.

TABLE 9. Standard and nonparametric hypothesis tests for comparison of dekadal validation statistics between algorithms; Y indicates that the difference is signiﬁcant at the 95% conﬁdence level; N indicates it is not. No nonparametric test for R2 was performed.

Algorithms Bias Dekadal RMSE R2 Bias Dekadal mean RMSE R2 Standard IR108, VisMS N N N N N N IR108, TAMSAT Y N N Y N N VisMS, TAMSAT Y N N Y N N Nonparametric IR108, VisMS N N NA N N NA IR108, TAMSAT Y N NA Y N NA VisMS, TAMSAT Y N NA Y N NA

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The failure to capture daily extremes may be a prob- Chadwick, R., D. Grimes, R. Saunders, P. Francis, and T. Blackmore, lem with the ANN method, or else it may be due to the 2010: The TAMORA algorithm: Satellite rainfall estimates limitations of using multispectral cloud-top data to over West Africa using multi-spectral SEVIRI data. Adv. Geo- sci., 25, 3–9. estimate daily surface rainfall. Even for RIANNAA Challinor, A., T. Wheeler, J. Slingo, P. Craufurd, and D. Grimes, VisMS, the solar channel inputs are used for less than 2004: Design and optimisation of a large-area process-based half of the day, so RIANNAA is highly dependent on model for annual crops. Agric. For. Meteor., 135 (1–4), 180– the ability of a combination of IR channels to provide 189. rainfall amount information. Coppola, E., D. Grimes, M. Verdecchia, and G. Visconti, 2006: Validation of improved TAMANN neural network for oper- Future improvements to RIANNAA could include ational satellite-derived rainfall estimation in Africa. J. Appl. the use of a second ANN as a rain/no-rain classifier Meteor. Climatol., 45, 1557–1572. instead of the current temperature threshold method. Creutin, J., and C. Obled, 1982: Objective analysis and mapping It is possible that a combination of two multispectral techniques for rainfall fields: An objective comparison. Water ANNs, one for rainfall delineation and one for esti- Resour. Res., 18, 413–431. Dinku, T., P. Ceccato, E. Grover-Kopec, M. Lemma, S. Connor, mating rainfall amount, could provide an improvement and C. Ropelewski, 2007: Validation of satellite rainfall over single-channel estimates for averaged estimates. To products over East Africa’s complex topography. Int. J. Re- be applied outside of the Oromiya region and in seasons mote Sens., 28, 1503–1526. other than JAS, the calibration of RIANNAA would ——, S. Chidzambwa, P. Ceccato, S. Connor, and C. Ropelewski, need to be extended, and it would be interesting to see 2008: Validation of high-resolution satellite rainfall products over complex terrain. Int. J. Remote Sens., 4097– whether the results shown here apply in other regions of 29, 4110. the world. Grimes, D., and M. Diop, 2003: Satellite-based rainfall estimation for river flow forecasting in Africa. I: Rainfall estimates and Acknowledgments. The authors thank the National hydrological forecasts. Hydrol. Sci., 48, 567–584. Meteorological Agency of Ethiopia for providing the ——, and E. Pardo-Iguzquiza, 2010: Geostatistical analysis of rain gauge data used in this study, and EUMETSAT and rainfall. Geogr. Anal., 42, 136–160. the Met Office for providing the SEVIRI data. RC ——, ——, and R. 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