Validation of remote-sensing precipitation products for Angola

Transcription

1 METEOROLOGICAL APPLICATIONS Meteorol. Appl. 22: (2015) Published online 4 August 2014 in Wiley Online Library (wileyonlinelibrary.com) DOI: /met.1467 Validation of remote-sensing precipitation products for Angola Sandra Pombo,* Rodrigo Proença de Oliveira and André Mendes CEHIDRO, Department of Civil Engineering, Architecture and Georesources, Instituto Superior Técnico, Universidade de Lisboa, Portugal ABSTRACT: In situ ground observation measurement of precipitation is difficult in sparsely populated areas with trying access conditions, as is the case in many countries in Africa. The use of remote sensors installed in satellites can be very useful in overcoming this challenge, enabling the improvement of the spatial variability description of this variable and the extension of data series. A number of standard products offering precipitation estimates on a regular basis is now available and may be used for water planning and management purposes. The present study examines the performance of four of these products in Angola, namely the Tropical Rainfall Measuring Mission (TRMM) 3B43 (version 6), Global Precipitation Climatology Project (GPCP) Combined Precipitation Data Set (version 2.2), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) and the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Centre (CPC) Morphing Technique (CMORPH), by comparing annual and monthly precipitation estimates with ground observation measurements. The data set of precipitation ground observation measurements was collected by the authors from different sources in Angola and Portugal, and is the result of an intense effort to gather hydrological records from Angola. It is believed to be one of the most complete data sets of monthly precipitation data from Angola. The four remote-sensing products are able to describe the main features of the spatial and temporal variability of annual and monthly precipitation in Angola. The results also show that the estimates from the TRMM are more accurate than the estimates offered by the other products, a conclusion which is in line with previous studies and which may be explained by the fact that this is the first product to incorporate measurements from precipitation radar. The estimation bias of TRMM is also more consistent which means that the results presented in the present study can be used in an operational environment to reduce the precipitation estimation error. KEY WORDS precipitation; remote-sensing rainfall products; water resources; Angola; ground stations Received 27 August 2013; Revised 23 December 2013; Accepted 22 May Introduction A sound water resources planning and management process requires reliable estimates of precipitation, which in turn require a large amount of measurements given the significant spatial and temporal variability of this variable. Traditionally these data are provided by networks of in situ monitoring stations, which are costly to maintain in vast and desert areas, such as the case of many African countries. In Angola, this problem was further aggravated by the civil war, which barred travelling within the country and prevented proper maintenance of most monitoring stations. Precipitation estimates can also be obtained from satellites equipped with remote sensors and several products are now being offered to the water resources technical community, such as the Tropical Rainfall Measuring Mission (TRMM), the Global Precipitation Climatology Project (GPCP), the Climate Prediction Centre (CPC) Morphing Technique (CMORPH) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). The estimation errors associated with these satellite-derived products remain significant and arise from many different sources, making it difficult to select a given product for use in all conditions. The most adequate * Correspondence: S. Pombo, CEHIDRO, Department of Civil Engineering, Architecture and Georesources, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal. sandra. m.pombo@gmail.com product for a given region depends on regional characteristics and the rainfall spatial and temporal pattern (Anagnostou, 2004). Several studies have evaluated these products by comparing precipitation estimates from satellite products with ground gauge data, a non-trivial task in areas with sparse and inadequate ground monitoring networks and where the measurement gauges may have been operating during quite distinct periods. Xie and Arkin (1995, 1996, 1997, 1998), Rudolf et al. (1996), Gruber et al. (2000), Adler et al. (2001), Sapiano and Arkin (2009), have performed these studies at a global scale. Adeyema and Nakamura (2003) and Hughes (2006a) have evaluated the performance of these products at a continental scale in Africa. Regional studies were performed for West and Equatorial Africa (McCollum et al., 2002; Nicholson et al., 2003a, 2003b; Nicholson, 2005), East Africa (Dinku et al., 2007) and Uganda (Maidment et al., 2012). Some authors carried out the evaluation of the precipitation estimates from satellite products using hydrological models to compare stream flow estimates (Grimes and Diop, 2003; Beighley et al., 2011), whereas others studied the impacts of precipitation uncertainty in runoff estimates (Hughes, 2006b; Ruelland et al., 2008; Sawunyama and Hughes, 2008; Wagner et al., 2009; Sahoo et al., 2011). None of these studies was specifically dedicated to Angola. The results from these studies show that, although remote-sensing based precipitation estimates are very useful to characterize the spatial and temporal variability of precipitation, significant estimation errors persist that need to be corrected for using them as inputs to hydrological models Royal Meteorological Society

2 396 S. Pombo et al. Table 1. Number of monitoring stations as a function of the record length. Number of hydrological years with complete records Number of monitoring stations >40 11 >30 and >20 and >10 and >5 and The present study reviews the annual and monthly precipitation estimates for Angola from four remote-sensing products and compares them with values derived from ground measurements. The data set of precipitation ground observation measurements was collected from different sources and is the result of an intense effort to gather hydrological data in Angola. 2. Angola precipitation monitoring network The Republic of Angola is the fifth largest African country with an area of km 2 and a western ocean front of 1650 km. The country shares borders with the Democratic Republic of Congo (in the north), Zambia (in the east) and Namibia (in the south). Around 60% of the territory is a sequence of plateaus in the centre of the country, with an altitude ranging between 1000 and 2000 m (Figure 1). The highest peak, reaching 2620 m, is Monte Môco, in the Huambo province. Tributaries of the main African rivers have their origin in this central plateau, with water courses flowing north towards the Zaire (or Congo), east towards the Zambezi, southeast towards the Okavango and southwest towards the Cunene. Angola s climate is determined largely by: (1) the presence of a high pressure centre in the south Atlantic Ocean and a low-pressure centre over the continent; (2) the cold Benguela current that flows west east, and (3) the land topography with the dominating central plateau where the altitude is >1000 m (Figure 1). The climate varies considerably from the coast to the central plateau and from the north to the south along the coast. The northern, eastern and central regions have a humid tropical climate with a rainy season longer than the dry season. South of Luanda and along the coast towards the Namib Desert, in the southwest, the climate varies from dry tropical climate, with a dry season longer than the rainy season, to a hot desert climate, where rainfall is scarce. The average annual precipitation over Angola is about 1100 mm, but varies significantly over the territory, closely related to the altitude. Along the coast, below 100 m, the mean annual precipitation is generally <500 mm, but it reaches values above 800 mm in the interior where the altitude is >800 m (Figure 3). Namibe in the southwest records a mean annual precipitation of 50 mm, whereas Dundo in the northeast registers 1600 mm. On average, over 95% of annual rainfall occurs in the wet season. The two clearly distinctive rainfall seasons are controlled by the movement of the Inter-Tropical Conversion Zone and the wet season occurs usually from October to April (Lars, 2004). The oldest monitoring station in Angola is João Capelo, installed before 1900 in the capital city of Luanda. From 1940 onwards, a significant effort was made to create a structured rainfall monitoring network that reached 145 stations in the late 1940s and 371 stations in the late 1960s. In 1974, there were 465 stations with >5 years of complete records, but the civil war that followed the country s independence weakened this effort and only 18 stations located in the main cities remained in operation. Figure 1 shows the location and the co-ordinates of the monitoring stations operational after the civil war. At the onset of the new millennium only 14 stations were in operation, a clear insufficient number for this vast country, but the ending of the civil war provided the opportunity to increase the investment in monitoring activities. Figure 1. Monitoring stations used in the study. Details of the gauges are given in Table 2.

3 Validation of remote-sensing precipitation products for Angola 397 Table 2. Localization of the monitoring stations used in the study. Coordinates Gauges Lat( S) Long( E) Alt(m) Luanda ,8 Namibe Luena Cabinda Huambo Lubango Malange Kuito Bié Dundo Menongue Porto Amboim Benguela N Dalatando Uige Saurimo Waco kungo Mavinga Nzeto For the current research, a large effort was made to gather precipitation data from distinct sources in Angola and Portugal. Most of the data were collected from the printed network record of books available at Instituto Infante D. Luiz (Lisbon), the Portuguese National Library, the Portuguese Water Institute and the Angolan National Water Resources Institute. Table 1 summarizes the result of this effort, which covers 465 rainfall stations including 11 stations with >40 years of records and 26 stations with records between 30 and 40 years. Figure 1 presents the location of the stations with the longest records. Although it is hard to fully justify the claim, the collected data set is probably one of the most complete data sets of monthly precipitation data from Angola. It appears to exceed the Global Historical Climatology Network (GHCN) data set, from National Oceanic and Atmospheric Administration (NOAA), arguably the most complete global data set of climatologic data. This latter data set includes 104 rainfall stations in Angola, with only 8 stations with records longer than 40 complete years, 16 stations with records between 30 and 40 years, 42 stations between 20 and 30 years and 38 stations with <20 years of records (ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/v2/). Quality control checks were carried out on the rain gauge records. The homogeneity of the data series with >20 years of records was checked using double mass plots and tests on partial means. One data series was rejected with a confidence interval of 95%, based on the results of the partial means test. Figure 2 presents the stations used to calculate the mean annual rainfall maps for four periods, assuming a hydrological year starting in October and ending in September that is presented in Figure 3. The periods are P1 (1941/ /1974), P2 (1946/ /1974), P3 (1954/ /1974) and P4 (1961/ /1974). Although there are 465 stations in the country with >5 years of complete records, the number of stations with complete and simultaneous records in each of these periods is 36 (P1), 73 (P2), 142 (P3) and 224 (P4). The number of stations used in each map decreases as the length of the period increases. The sets of ground stations used in the longer periods are included in the sets of shorter periods. The inverse-distance-weighting (IDW) algorithm was used for the spatial interpolation of the mean annual precipitation values from each ground station (Figure 3). The overall mean annual rainfall over Angola is similar for the four periods and very close to 1100 mm. The spatial distribution of the mean annual rainfall is also similar for all periods; however, as the number of stations increases for a shorter period a more detailed picture of the rainfall distribution is obtained. All maps show a clear decreasing trend in the northeast southwest direction, from values exceeding 1600 mm in the northwest to values lower that 50 mm in the coastal southwest. Periods P1, P2 and P3 exhibit a similar maximum value of around 1600 mm, but the local maximum of the shorter P4 period exceeds 1880 mm. In addition, the minimum value for the periods P2, P3 and P4 is close to 20 mm, but the value corresponding to the larger P1 period is 56 mm. 3. Remote-sensing rainfall products Today, active (i.e. with their own source of electromagnetic radiation, such as radar) and passive instruments are used to estimate precipitation from the electromagnetic radiation reflected by raindrops. These remote sensors work in the microwave (MW), infrared (IR) and visible (VIS) bands. In the VIS and IR bands, the techniques for precipitation measurement are indirect. In the VIS band, estimated precipitation is based on the fact that the brightness of the Sun, reflected by the clouds, provides an indication of their thickness and, consequently, of their water content. In the IR band, the measured radiant energy can be converted through the Stefan Boltzmann law into temperature, known as brightness temperature, which in turn is related to the rainfall Figure 2. Location of ground monitoring stations in operation in four periods: P1 (1941/ /1974), P2 (1946/ /1974), P3 (1954/ /1974) and P4 (1961/ /1974) and of the transect lines adopted (Figure 5).

4 398 S. Pombo et al. Figure 3. Mean annual precipitation maps obtained by inverse-distance-weighting (IDW) interpolation from ground observation measurements for four distinct periods (values in mm). intensity. In contrast to VIS and IR, MW sensors can obtain a direct estimate of precipitation by identifying clouds with drops big enough to produce rain. MW sensors often provide more accurate estimates of rain rate (Huffman et al., 2007). The sensors can be installed in satellites, planes or portable devices, the former two being the preferable option for routine monitoring of large areas. Several agencies maintain constellations of meteorological satellites, the most important being NASA (National Aeronautics and Space Administration), NOAA, ESA (European Space Agency) and EUMETSAT (European Organization for the Exploitation of Meteorological Satellites). Standard products with specific estimation algorithms using measurements from different instruments are available. The TRMM satellite was initially a trial mission conceived mainly for the study of tropical and subtropical precipitation and to verify its influence on global climate, but it quickly became a reference for the study of precipitation (Kummerow et al., 1998). The instruments aboard the TRMM are the TRMM microwave imager (TMI), the precipitation radar (PR), the visible infrared scanner (VIRS), the cloud system and Earth radiant energy sensor (CERES) and the imaging lightning sensor (LIS). The TRMM products incorporate data from TRMM satellite instruments but also measurements made by a variety of other low Earth orbit platforms and geostationary satellites (Huffman et al., 1995, 2007). These include passive MW data from the DMSP (Defense Meteorological Satellite Program),

5 Validation of remote-sensing precipitation products for Angola 399 the Aqua satellite and the NOAA satellite series, as well as the IR data from the international constellation of geosynchronous satellites. To calibrate and improve the reliability of estimates and minimize the differences between satellite estimates and ground measurements, there is a parallel programme of ground validation that uses weather radar and rain gauges in various stations along the inter-tropical track. Nicholson et al. (2003a, 2003b) concluded that the TRMM product provides good precipitation estimates for West Africa, when compared with gauge data at monthly time step. The great advantage of the TRMM products is the high temporal (3 h) and spatial (0.25 latitude/longitude) resolutions, in the range between 50 Sand 50 N. The two principal products are 3B42 and 3B43, offering a temporal resolution of 3 h and of 1 month, respectively. The data set used in the present research was downloaded from A map of TRMM mean annual rainfall estimates for the period from 1998 to 2011 is presented in Figure 4(a). The GPCP uses data from the geostationary and polar satellites and from surface observations to provide a monthly global precipitation estimate (Schneider et al., 2010). Version 1 of this product, which includes estimates from 1986 to the present with a spatial resolution of 2.5 latitude/longitude, is a combination of precipitation estimates from MW and IR sensors in polar orbit satellites and geostationary satellites, as well as surface observations. The method uses data with greater precision from MW sensors in low-orbit satellites to calibrate observations obtained in the IR band. Version 2 uses an algorithm by Figure 4. Mean annual precipitation maps obtained from four remote sensing products estimates (a) Tropical Rainfall Measuring Mission (TRMM), (b) Global Precipitation Climatology Project (GPCP), (c) Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) and (d) Climate Prediction Centre (CPC) Morphing Technique (CMORPH) (values in mm).

6 400 S. Pombo et al. Xie and Arkin (1996) to extend the estimation period backwards to 1979, based on the records of about gauge stations existing in the NOAA database. Measurements from the IR band and information, mostly synoptic, of the monthly weather reports from around the world compiled by the Global Telecommunication System were also used. The data set used in this research was downloaded from gov/daac-bin/g3/gui.cgi?instance_id=gpcp_monthly. A map of GPCP mean annual rainfall estimates for the period between 1979 and 2010 is presented in Figure 4(b). PERSIANN uses neural networks to compute rainfall rate estimates, with a latitude/longitude spatial resolution (Sorooshian et al., 2000). The estimatesare basedonthe combination of IR sensor measurements, provided by images from long wave IR geostationary satellites (GOES-8, GOES-10, GMS-5, Metsat-6 and Metsat-7), updated using the higher quality rainfall estimates from low-orbit passive MW sensors, such as TMI instantaneous rainfall estimates from TRMM and DMSP satellites. PERSIANN data sets cover a region between 50 Nand 50 S and have a temporal resolution of 30 min (Hsu et al., 1999). Full monthly archives, from March 2000 through the present, are available at a 0.25 spatial resolution and a 3 h temporal resolution. The data set used in this research was downloaded from A map of PERSIANN annual mean rainfall estimates for the period from 2000 to 2007 is presented in Figure 4(c). The CMORPH uses precipitation estimates provided by passive MW sensors exclusively from low orbit satellites (DMSP satellites F13 to F15, NOAA-15 to 18, Aqua and TRMM) combined with IR data from geostationary satellites (Joyce et al., 2004). The CMORPH data sets cover a region between 60 N and 60 S, with a latitude/longitude (8 km at the equator) spatial resolution and a 30 min temporal resolution. The data are available from December 2002 through the present. The data set used in this research was downloaded from ftp://ftp.cpc.ncep.noaa.gov/precip/global_cmorph/3-hourly_ 025deg/. A map of CMORPH mean annual rainfall estimates for the period from 2003 to 2011 is presented in Figure 4(d). 4. Methodology The monthly precipitation estimates from four remote-sensing products, TRMM 3B43 (version 6), GPCP Combined Precipitation Data Set (version 2.2), PERSIANN and NOAA CMORPH, were evaluated by comparing them with measurements from ground stations. When comparing rainfall estimates from satellite products against ground station measurements, the different spatial scales of the two data sets have to be considered carefully. Satellite-based estimates are offered in a grid format, whereas ground measurements comprise precipitation values at specific points. Comparison of precipitation values at a given measurement station requires the computation of satellite estimates at that specific location, which can be obtained by interpolating the values at adjacent cells within the satellite product grid. Alternatively, the comparison of precipitation maps requires the computation of ground measurement maps by interpolating ground measurements at specific points and satellite estimate maps by interpolating the gridded values. In this research the IDW method was applied whenever interpolation was required. The satellite-based estimates at each station site were computed by interpolating the estimates of the four adjacent grid points, assuming that each value refers to the middle point of the cell. The precipitation maps obtained from ground measurements and from the gridded satellite product estimates were computed using the precipitation values from the nearest 12 points. The short period of concurrent ground measurements and satellite estimates from the different data sources adds another difficulty when comparing the data sets. The civil war in Angola that started in 1974 compromised the country s meteorological observation effort during the period when the satellite-based products were launched. Consequently, the number of simultaneous estimates from ground stations and remote-sensing products is small and a direct comparison of specific location measurements from these two types of data sources offers a limited insight. When data from distinct periods are compared through averages, it is assumed that the averaging periods are long enough to ensure that the observed differences between the products are dominated by the respective algorithms rather than the long-term meteorological variability. To overcome these challenges, the comparison between the estimates from remote-sensing products and measurements from ground stations was based on different analysis techniques. Although subjective in nature, simple visual comparison of mean annual precipitation maps is one way to verify the precipitation spatial distribution patterns in a territory. The maps of mean annual precipitation obtained from rain gauge measurements (Figure 3) and the satellite-based estimates (Figure 4) offer a first generic overview of the values magnitude, even if the periods referred in each map are not coincident. The period used in each map is the longest possible period for each data set. Figure 5 compares annual precipitation maps for a common, but shorter, period, the 2003/2004 hydrological year. Figure 6 shows the correspondent north south transects along three longitude values. A second type of analysis used scatter plots to compare monthly precipitation values obtained from remote-sensing products with concurrent ground measurements at specific meteorological stations. The comparison was made for 15 ground stations in the case of TRMM (Figure 7), PERSIANN (Figure 9) and CMORPH (Figure 10) and for 18 ground stations in the case of GPCP (Figure 8). The number of points in each plot ranged from 24 to 372. The number of ground stations used in GPCP is larger because this product offers precipitation estimates from an earlier date (1979), when a larger number of ground stations were still operating. From each scatter plot the following performance indicators were computed: (1) slope of the linear regression line relating the gauge observations and the remote-sensing product estimates, without intercept; (2) mean error (ME); (3) bias; (4) the rootmean-square error (RMSE); (5) the Nash Sutcliffe Efficiency (NSE); (6) Pearson correlation co-efficient (r); and (7) the co-efficient of determination (R 2 ) associated with the previous linear regression. The slope of the linear regression is an indicator of the remote-sensing products systematic deviation from the observed values, i.e. its tendency to overestimate or underestimate them. Values >1 indicate a tendency to underestimate ground measurements, whereas values <1 indicate the opposite trend. The ME and the bias are also indicators of the estimation error trend and can be computed by: ME = 1 N N ( ) Si G i i=1 N i=1 bias = S i N i=1 G i (1) (2)

7 Validation of remote-sensing precipitation products for Angola 401 Figure 5. Annual precipitation maps for the 2003/2004 hydrological year obtained by inverse-distance-weighting (IDW) interpolation. where S i is the satellite estimate and G i is the ground measurement. ME measures the systematic deviation from the observed values in absolute terms and is expressed in millimetres, whereas the bias expresses it in relative terms and does not have units. The two statistics are related through the following equation: ME = (bias 1).G. Values of bias >1 and positive ME values indicate an overestimation trend. The RMSE and the NSE (Nash and Sutcliffe, 1970) can be computed by: RMSE = 100 NSE = 1 1 N ( ) 2 N i=1 Si G i G N i=1 ( Si G i ) 2 N i=1 (3) (G G i ) 2 (4) The RMSE is an indicator of the mean absolute error for any given estimate and is expressed as a percentage. A satellite product can provide unbiased estimates with significant RMSE. The NSE has no units and indicates the skill of the estimate relative to a reference, in this case the gauge mean. It varies from to 1, one being the perfect skill. Negative values of NSE indicate that the observed value is a better predictor than the estimated value, zero signifies that the observed mean is as good as the estimate and positive values show good skill. The Pearson correlation co-efficient measures the linear correlation between ground measurements and satellite estimates and ranges between 100 and 100%. The co-efficient of determination describes the proportion of the variance of ground measurements explained by a linear function of satellite estimates with a null intercept. It can assume negative values, meaning that the mean of the ground measurements is a better fit than the linear

8 402 S. Pombo et al. Annual precipitation (mm) Long ' E ' 6 50 ' 7 50 ' 8 50 ' 9 50 ' ' ' ' ' ' ' ' ' TRMM PERSIANN CMORPH GPCP In situ Lat ( S) Annual precipitation (mm) Long ' E ' 9 00 ' ' ' ' ' ' ' ' ' TRMM PERSIANN CMORPH GPCP In situ Lat ( S) Annual precipitation (mm) Long ' E ' 7 55 ' 8 55 ' 9 55 ' ' ' ' ' ' ' ' ' ' TRMM PERSIANN CMORPH GPCP In situ Lat ( S) Figure 6. North south transect of annual precipitation from the 2003/2004 hydrological year for ground observation measurements and for all four products at longitudes E, E and E. model estimates. If G is the gauge measurement, Ĝ the estimation of G from satellite estimates by the linear model with null intercept, G the mean of G and N the number of data pairs, it can be computed by: ) 2 (G i Ĝ i R 2 = 1 N i=1 N i=1 ( ) 2 (5) G i G Higher values of r and R 2 indicate that it is possible to establish a linear relationship between the satellite product estimate and the ground measurement, which can be used to correct the estimate. Together, these seven indicators allow the overall evaluation of the satellite product s ability to provide consistently accurate estimates of ground measurements. A third type of analysis of month-by-month plots was performed to verify if the accuracy of satellite product monthly precipitation estimates vary significantly along the year, from month to month and from the rainy season to the dry season. Given the short concurrent operational period of each remote-sensing product and the ground monitoring network, the plots and correspondent statistics were not computed for the CMORPH estimates and neither for any month where the number of concurrent pairs were <3. The remaining monthly plots include 3 31 pairs of values. Owing to space restrictions, the monthly scatter plots are not presented in the present study. Tables S1 and S2 in the Supporting information present the statistics for the two best performing products, the TRMM product (15 distinct gauge locations) and the GPCP product (18 gauge locations). Figures S1 and S2 in the Supporting information present the spatial distribution of TRMM and GPCP estimates bias, for each month.

9 Validation of remote-sensing precipitation products for Angola 403 Figure 7. Performance analysis of Tropical Rainfall Measuring Mission (TRMM) to estimate monthly precipitation at ground monitoring stations between January 1998 and December Details of the gauges are given in Table 3. Table 3. Performance analysis of TRMM to estimate monthly precipitation at ground monitoring stations between January 1998 and December Gauge Altitude (m) N Slope ME (mm) Bias NSE RMSE (%) r (%) R 2 (%) Cabinda Benguela Namibe João Capelo (Luanda) Porto Amboim N Dalatando Uíge Saurimo Malange Mavinga Luena Menongue Kuito Bié Huambo Lubango Median values (all stations) Median values (stations below 1000m) Median values (stations above 1000m) N, number of data pairs; ME, mean error; NSE, Nash Sutcliffe efficiency; RMSE, root-mean-square error; r, Pearson correlation coefficient; R 2, coefficient of determination of the linear regression with a null intercept. 5. Results 5.1. Analysis of annual precipitation The analysis of the mean annual precipitation maps presented in Figures 3 and 4 shows that the rainfall distribution in all data sets is very similar, with a clear decreasing trend from the northeast to the southwest. CMORPH provides the lowest average annual rainfall estimate (507 mm), whereas PERSIANN provides the highest (1594 mm). The maps obtained from TRMM and GPCP estimates (Figure 4(a) and (b)) are particularly similar to the maps obtained from ground measurements (Figure 3). The areal mean of the TRMM map (1077 mm) falls within the range of values from the ground measurement maps, between 1073 and 1103 mm, depending on the period under analysis. Figure 5 presents the mean annual precipitation maps for a period common to all data sets, the 2003/2004 hydrologic year. The ground observations map was obtained from measurements in 15 monitoring stations as shown in Figure 1. To facilitate the comparison between these maps, Figure 6 presents the transect of each product estimate of the annual precipitation along three north south lines, at longitudes E, E and E (mapped in Figure 2), and compares them with the transects obtained from observed measurements. The smoothness of transects from the in situ observations is due to the significantly smaller number of measurement stations which hinders the description of the spatial variability of the annual precipitation, especially in the south of the country. All maps show a decreasing trend from the northeast to the

10 404 S. Pombo et al. Figure 8. Performance analysis of Global Precipitation Climatology Project (GPCP) to estimate monthly precipitation at ground monitoring stations between January 1979 and December Details of the gauges are given in Table 4. Table 4. Performance analysis of GPCP to estimate monthly precipitation at ground monitoring stations between January 1979 and December Gauge Altitude (m) N Slope ME (mm) Bias NSE RMSE (%) r (%) R 2 (%) Nzeto Cabinda Benguela Namibe João Capelo (Luanda) Porto Amboim Dundo N Dalatando Uíge Saurimo Malange Mavinga Waco Kungo Luena Menongue Kuito Bié Huambo Lubango Median values (all stations) Median values (stations below 1000m) Median values (stations above 1000m) N, number of data pairs; ME, mean error; NSE, Nash Sutcliffe efficiency; RMSE, root-mean-square error; r, Pearson correlation coefficient; R 2, coefficient of determination of the linear regression with a null intercept. southwest, with the TRMM offering a surface reasonably close to the one obtained from ground observations. The largest discrepancies between satellite estimates and ground measurements are found in the southeast of the country and may be explained by the lack of ground stations in that region that leads to a poor description of the precipitation spatial distribution. The TRMM mean annual precipitation distribution is reasonably close to the ground measurement distribution and presents the same areal mean of 995 mm. The GPCP mean annual precipitation distribution, with a narrower range of estimates, does not reach the lower values occurring along the coast, especially in the southwest. The CMORPH underestimates the observed values, whereas the PERSIANN overestimates them. The analysis of the transects presented in Figure 6 confirm these evidences. The apparent anomaly observed in the E transect is due to the large precipitation values at Saurimo monitoring station Aggregated analysis of monthly precipitation The scatter plots of all data of monthly precipitation values for each gauge are shown in Figure 7 (TRMM), Figure 8 (GPCP), Figure 9 (PERSIANN) and Figure 10 (CMORPH). Each figure compares the monthly precipitation values observed at several monitoring stations with the corresponding product estimates

11 Validation of remote-sensing precipitation products for Angola 405 Figure 9. Performance analysis of Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) to estimate monthly precipitation at ground monitoring stations between March 2000 and December Details of the gauges are given in Table 5. Table 5. Performance analysis of PERSIANN to estimate monthly precipitation at ground monitoring stations between March 2000 and December Gauge Altitude (m) N Slope ME (mm) Bias NSE RMSE (%) r (%) R 2 (%) Cabinda Benguela Namibe João Capelo (Luanda) Porto Amboim N Dalatando Uíge Saurimo Malange Mavinga Luena Menongue Kuito Bié Huambo Lubango Median values (all stations) Median values (stations below 1000m) Median values (stations above 1000m) N, number of data pairs; ME, mean error; NSE, Nash Sutcliffe efficiency; RMSE, root-mean-square error; r, Pearson correlation coefficient; R 2, coefficient of determination of the linear regression with a null intercept. and is presented together with the corresponding performance indicator values. The comparison period and the number of available simultaneous estimates vary from product to product. In each figure, the stations are assembled in two groups depending on their altitude, which is related to precipitation. Together, Figures 7 10 show that, on average, the TRMM and GPCP products produce better estimates than the PER- SIANN and CMORPH products, with TRMM presenting some slightly better statistics than GPCP. TRMM presents the following median values of the slope, ME and bias indicators for all ground stations: 1.01, 2.50mm and 0.98, respectively;whereas GPCP median values are 0.86, 14.6 mm and 1.18, respectively. These values show a slight trend of TRMM to underestimate monthly precipitation and a larger trend of GPCP to overestimate it. The median values of these indicators for PERSIANN indicate a tendency to overestimate monthly precipitation by 50% and that for CMORPH to underestimate it by 50%. The medians of the NSE values for the TRMM and GPCP products are both 0.56, which is generally considered as a good fit (Moriasi et al., 2007). CMORPH and PERSIANN show much lower values of this statistic, with the latter presenting a negative value, a sign of the product s poor estimation skill in this region. The medians of the RMSE for the TRMM and GPCP products are 83.1 and 71.9%, respectively, indicating that although the

12 406 S. Pombo et al. Figure 10. Performance analysis of Climate Prediction Centre (CPC) Morphing Technique (CMORPH) to estimate monthly precipitation at ground monitoring stations between January 2003 and December Details of the gauges are given in Table 6. Table 6. Performance analysis of CMORPH to estimate monthly precipitation at ground monitoring stations between January 2003 and December Gauge Altitude (m) N Slope ME (mm) Bias NSE RMSE (%) r (%) R 2 (%) Cabinda Benguela Namibe João Capelo (Luanda) Porto Amboim N Dalatando Uíge Saurimo Malange Mavinga Luena Menongue Kuito Bié Huambo Lubango Median values (all stations) Median values (stations below 1000m) Median values (stations above 1000m) N, number of data pairs; ME, mean error; NSE, Nash Sutcliffe efficiency; RMSE, root-mean-square error; r, Pearson correlation coefficient; R 2, coefficient of determination of the linear regression with a null intercept. average estimation error may be considered low, the error at each ground station is significant and varies considerably. All products show similar values for the r and R 2 indicators, >80% and >60%, respectively. CMORPH presents some slightly better values of these two indicators which suggests that, although its average estimation error is significant, the regression equation may be used to substantially improve the product precipitation estimates. Analysing each figure individually, one concludes that the TRMM product tends to overestimate precipitation at altitudes <1000 m and underestimate it at higher altitudes (Figure 7). The exceptions are Benguela and Namibe, below 1000 m, and Huambo and Malange, above that level. The precipitation estimates for stations located below 1000 m are, on average, reasonably accurate, with the exception of João Capelo (Luanda), as the regression slope values between and show. The bias and ME values computed for these stations are also good (bias between 0.94 and 1.28 and ME between 2.5 and 10.1 mm). The estimates at Porto Amboim and João Capelo are associated with largest bias, 1.28 and 1.21, respectively. Above the altitude of 1000 m the estimates are generally less accurate, with a bias ranging from 0.66 to 0.87, with the exception of Malange and Luena, having biases of 1.12 and 1.15, respectively. ME values range from 10.1 to 78.1 mm, with the exception of Malange (9.3 mm). Saurimo presents the biggest ME ( 78.1 mm) because, from January 1998 to December 2011, the station registered high precipitation values which accumulated to a mean annual precipitation of 2366 mm, a record that was not reached by TRMM estimates. Although all stations present positive NSE values, ranging between 0.27 and 0.86, most monitoring stations above 1000 m

13 Validation of remote-sensing precipitation products for Angola 407 assume an NSE value <0.5, showing a lower skill of the TRMM product to estimate ground measurements at this altitude range. The r and R 2 values for these monitoring stations are also generally <80% and <60%, respectively, when the stations below 1000 m present r and R 2 values usually >83% and >70%, respectively. These r and R 2 values show a better fit of the linear regression model for monitoring stations below 1000 m. The GPCP product tends to overestimate precipitation at all altitudes (Figure 8). The exceptions are Uíge, below 1000 m, and Saurimo and Kuito Bié, above that level. Below 800 m, GPCP significantly overestimates precipitation at many ground stations with the largest error occurring at Namibe monitoring station, in the Namib Desert (regression slope of 0.10 and bias of 12.04). The precipitation estimates for stations located higher than 1000 m are, on average, more accurate, as shown by regression slope values, ranging between and shows, and the bias values (ranging between 0.88 and 1.28). The exception is Saurimo (slope of and bias of 0.44), for the reason explained above. All stations above 750 m present positive NSE values >0.60, with the exception of Saurimo (0.01), Malange (0.39) and Waco Kungo (0.41). Below 750 m, with the exception of Cabinda, the NSE assumes negative values meaning that the observed average is a better predictor than the estimated value. The r and R 2 values for the monitoring stations below 750 m are also generally <74% and <54%, respectively. Above 750 m, r and R 2 values show a better fit of linear regression model for monitoring stations below 1000 m, as r and R 2 values are usually >80% and >64%, respectively. The PERSIANN product overestimates the precipitation over the whole country (bias between 1.26 and 7.54 and ME between 19.0 and 67.8 mm), except in the northeast near Saurimo where the computed bias and the ME are 0.84 and 36.2 mm, respectively (Figure 9). The overestimation tendency is generally larger at higher altitudes as the bias, ME and slopes values show, although no significant trend was found from the RMSE values. Namibe presents higher values of bias (7.54) and RMSE (1806.5%) due to the small amount precipitation that occurs in this desert region. The co-efficient of determination is bigger than 60% for most stations, the exceptions being Cabinda (35%) and João Capelo (46%), both at altitudes below 1000 m, as well as Kuito Bié (53%) and Mavinga (52%), above 1000 m. The poor performance of this product is also clear from the NSE values which are negative or close to zero in all stations. The CMORPH product underestimates the ground observation measurements of precipitation in all stations, except in the Namibe desert (Figure 10). Like the GPCP and PERSIANN products, CMORPH underestimation propensity is generally larger at higher altitudes as the bias, ME and slope values show, although the RMSE is generally larger below 1000 m. The co-efficient of determination ranges between a maximum of 84% (Huambo) and a minimum of 26% (Namibe), but only three stations have a value <50%. The NSE is positive in all stations with values ranging , with the exception of Namibe ( 0.18) and Saurimo ( 0.60) Month-by-month and seasonal analysis of monthly precipitation A month-by-month analysis of the monthly precipitation estimates was done to evaluate the performance of each product in different months. The split of the available data lead to monthly data sets with 8 12 pairs of values in the case of TRMM and GPCP, between 2 and 8 pairs of values in the case of PERSIANN and below 3 in the case of CMORPH, the most recent product offered from 2003 onwards. Tables S1 and S2 in the Supporting information present the monthly statistics of the best performing products, TRMM and GPCP. For some months, when no precipitation was recorded, it is not possible to compute the statistics. Aggregate values for the rainy season (October to April) and for the dry season (May to September) are also included in the tables. Maps with the spatial distribution of bias are presented in Figures S1 (TRMM) and S2 (GPCP) in the Supporting information. Overall, the split of each data set into monthly or seasonal data sets leads to lower NSE, r and R 2 values. In particularly, the lower NSE values, most of them negative, show that the average values observed in specific situations, defined by a given month or season and by a selected group of monitoring stations, are usually better estimates than the satellite product estimates. When the analysis is restricted to a specific range of elevation and to a specific season or month, two of the most important components of precipitation variability are removed from the analysis and the satellite products struggle to identify other factors that condition monthly precipitation, leaving the average observed values a decent alternative estimate. TRMM is able to perform better than the average at estimating monthly precipitation during the rainy season (October to April), at gauges below 1000 m (Table S1). The NSE values for most stations below 1000 m and rainy season months are mostly positive, leading to median value of The r and R 2 values are also consistently >65% and >40%, respectively. Moreover, the medians of bias, ME and RMSE assume low values, respectively, 1.05, mm and 21%. The NSE values for GPCP are generally negative, as are for TRMM for the dry season months or for stations above 1000 m. The distinct results between the rainy season and the dry season are evident for both products and all statistics. From October to April, the statistics monthly values fall within a reasonably uniform range and it is hard to detect any significant trend, whereas the monthly statistics for the dry season vary considerably due to the small amount of rain. Generally, TRMM overestimates monthly precipitation below 1000 m and above 1000 m during the dry season (bias values >1 and positive ME values) and overestimates it in the wet season at stations above 1000 m. GPCP systematically underestimates the ground measurements. The RMSE values obtained for the rainy season months are generally lower than the overall average but the bias is higher. ME values are all positive for the monitoring stations below 1000 m and mostly positive for stations above 1130 m, with the exception of Kuito Bié. Figures S1 and S2 in the Supporting information show the monthly spatial distribution of the bias for TRMM and GPCP, respectively. TRMM bias values >1, from May to August, confirm the tendency to overestimate monthly precipitation over most of the country during these months. From September to April, the overestimation trend is mainly located on the coast and in the northwest, whereas underestimation errors are likely in the precipitation in the south and east of the country. GPCP tends to overestimate precipitation, except in the northeast from October to May Synthesis and discussion of the results All products evaluated in the present study were able to capture the main characteristics of the precipitation regime in Angola, namely the strong decreasing trend from the higher elevations in the interior northeast region to the southwestern desert region