The high limitation of rain gauges and weather radar systems, especially, in the tropics, reiterated the importance of satellite-based global rainfall data for weather and climate studies  , hence the operation of the Tropical Rainfall Measuring Mission (TRMM) and Famine Early Warning System (FEWS). TRMM’s launching has provided optimum understanding and synchronous measurements of rainfall and radiative fluxes in the tropics and subtropics  . FEWS Rainfall Estimate (FEWS-RFE), on the other hand, is operated by the United States Agency for International Development (USAID) to assist in drought monitoring efforts for Africa   . The FEWS-RFE, due to its high spatial resolution (), has the ability to blend gauge and satellite information on near-real time basis to provide rainfall estimates over Africa  .
Rigorous validation has been carried out by comparing in-situ measurements with TRMM satellite estimates, for example    . These studies have improved our understanding of spatio-temporal variations of rainfall in the tropics. Some studies have shown under/over estimation of TRMM estimates over gauge while using either version 6 (V6) or 7 (V7) with a spatial resolution of and a 3-hour temporal resolution      . Nicholson et al. (2003)  validated pre- TRMM and the blended rainfall products, Geostationary Operational Environmental Satellite (GOES) Precipitation Index (GPI), and the Global Precipitation Climatology Project (GPCP) version 1, and found inadequacies in pre-TRMM satellite rainfall analyses over Africa. Nonetheless, they indicated that the product proved very useful for climatological studies if tactfully interpreted. Young et al. (2014)  studied two standards of TRMM Multisatellite Precipitation Analysis (TMPA) products, 3B42 real time (3B42RT) and 3B42 version 6 (3B42V6), which were quantitatively evaluated in the Laohahe basin, China over a 6-year period (2000-2005). They observed that the spatiotemporal rainfall characteristics were well captured by TMPA 3B42V6 estimates while 3B42RT estimates showed unrealistic overestimation throughout the year. Moreover, both RT and V6 satellite rainfall estimates were found to have a geotopo- graphy-dependent pattern. In areas of lower latitudes and elevations, better performance was observed for both RT and V6. Maggioni et al. (2016)  also found that TRMM 3B42 rainfall products had lower false alarm rate relative to other Satellite products.
Tompkins and Adebeyi  studied the relationship between cloud and rainfall changes from three products: TRMM, FEWS-RFE and Climate Prediction Center (CPC) morphing technique (CMORPH) over West Africa. They found that the TRMM rainfall estimates had a distinct maximum centred over the deep convective zone at 5˚N, with a secondary maximum at 10˚ - 12˚N. In addition, they observed that CMORPH overesti- mated rainfall in the northern zone, whereas FEWS-RFE and TRMM underestimated rainfall from intense convection systems over the 5˚ - 12˚N zone. Similarly, slight disparities in heavy rainfall for FEWS-RFE estimates were observed by Symeonakis et al. (2008)  .
In a related study, Moazami et al. (2013)  compared the Precipitation Estimation from Remotely Sensed Information by using Artificial Neural Networks (PERSIANN) and TRMM Multisatellite Precipitation Analysis (TMPA) version 7 precipitation products with rain gauge data over Iran. They observed that although PERSIANN exhibited better results in detecting rainfall events, it had a false alarm ratio which was worse than that of TRMM 3B42RT and 3B42V7 products. As-Syakur et al. (2011)  compared daily, monthly, and seasonal rain rates derived from TMPA with rain gauge over the Bali islands and observed good and poor agreements on monthly and daily scales respectively. The TRMM satellite estimates therefore have good potential for hydrological applications  , intraseasonal studies  and consistency check of ground-based measurements      .
In-country validation of satellites with gauge data has only been a handful in Sub- Saharan Africa; among the few are the studies by Haque et al. (2013)  over Tanzania, Friesen and Diekkrüger, 2002  over Northern Ghana and Thiemig et al. (2012)  over Africa river basins including the Volta basin in Ghana and Burkina Faso. These studies found TRMM to perform quite well, but observed TRMM’s difficulties in identifying squall line formations that could be associated with measured events on the ground  and high intensity rainfall periods  . FEWS-RFE is also observed to predict the frequency of rainfall and is especially useful for the assessment of dry spells over other satellite products such as Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) and TAMSAT African Rainfall Climatology and Time-series (TARCAT). In Ghana, there is sparse network of rain gauges, making the acquisition of rainfall data in some remote areas quite difficult. In addition, there has been a few validation publications on FEWS-RFE  , hence its inclusion in this paper will be a valuable contribution.
The main objective of this paper is to validate TRMM and FEWS satellite estimates with rain gauge measurements over the Ashanti region of Ghana. This paper will form a prelude to the DACCIWA (Dynamics-aerosol-chemistry-cloud interactions in West Africa) Work Package 6 validation studies over the Ashanti Region  . The paper is structured as follows: Section 2 presents the study site and data source. Methodology is presented in Section 3, while results and discussions are given in Section 4. Finally, Section 5 presents the conclusions.
2. Study Site and Data
2.1. Study Site and Rain Gauge Dataset
Rain gauges provide the ground truth of rainfall data  which can be used as a benchmark for comparison with satellite rainfall estimates. Data from two different rain gauge types were used, the standard rain gauge (SRG) used at GMet stations and the automatic rain gauges (ARG) used at QWeCI project sites. The automatic rain gauge used, works on the “weighing” principle. Thus, rainfall collected in a container is measured by an electronic weighing cell and stored in a data logger. The data stored is then downloaded via a software called HYDRAS 3  . Based on its principle of operation and design, the instrument is able to measure rainfall intensity (rain rate), total rainfall amount and evaporation rate. The time resolution for the ARG measurements is 10 minutes.
Figure 1. Location of rain gauges (violet circles), grey-shaded polygon is DACCIWA project coverage area in Ashanti region, Ghana.
2.2. TRMM Dataset
2.3. FEWS-RFE Dataset
3.1. Rainfall Averaging
Daily rainfall measurements from nine rain gauge GMet stations and automatic rain gauges at four locations (Figure 1), were used for this study. Daily rainfall measure- ments were computed for each observational point using the averaging method which is given as;
where is the mean rainfall for the entire area, is the rainfall at the individual stations and N is the number of rain gauges.
The perfomance of the two gauge data products (ARG and SRG) used as the reference measurements for the validation was assessed by comparing these measure- ments from Kumasi Airport and KNUST Agromet.
Rain gauges present the most simple and direct way of measuring rainfall amounts and rates. Nonetheless, the amount of rainfall measured is most often less than the actual rainfall reaching the ground due to losses that vary depending on the type of rainfall, gauge and location. This introduces random and systematic errors, which include but are not limited to, the wind field deformation above the gauge orifice, errors due to wetting losses on the internal walls of the collector, evaporation errors from the container and sometimes mechanical errors  .
3.2. Statistical Approaches
For this paper, the performance of the gauge-satellite estimates was assessed using the bias, Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) given in Equations (2)-(4) (see Appendix). In addition, a validation toolbox described in Agha et al. (2011)  was used for a comprehensive assessment of the perfomance of both satellite products.
Quantification of rainfall products using a set of defined metrics is essential for comprehensive assessment of uncertainties. The metrics serve as important bench- marks for measuring how well satellite-based products compare with ground- truth observations (1). The probability tests described in Agha et al. (2011)   , which include Probability of Detection (POD), False Alarm Ratio (FAR), Critical Success Index (CSI), Volumetric Hit Index (VHI), Volumetric False Alarm Ratio (VFAR), and Volumetric Miss Index (VMI) were employed for comprehensive uncertainty assessment. Probability Of Detection (POD) measures the fraction of gauge observations that are correctly detected in the satellite estimates   . POD is sensitive to only missed events and not false alarms. Hence, it can be increased by giving out a large number of forecasts assuming that a greater number of them will be correct, usually at the expense of more false alarms. POD values range from 0 (no skill) to 1 (perfect skill).
The False Alarm Ratio (FAR) is the fraction of events observed by satellite but not confirmed by gauge observations   . It is sensitive to false predictions only and not to missed events and ranges from 0 (perfect skill) to 1 (no skill). Critical Success Index (CSI) is also a relative measure of accuracy which is sensitive to both false alarms and missed events and ranges from 0 (no skill) to 1 (perfect skill). The Volumetric Hit Index (VHI) gives information on the fraction of the volume of rainfall that is detected correctly whereas Volumetric Miss Index (VMI) is the ratio of the volume of missed satellite estimates relative to the ground observations. They both range from 0 (no skill) to 1 (perfect skill). Volumetric False Alarm Ratio (VFAR) shows the volume of false satellite estimates above the threshold relative to the sum of the gauge observations, and also ranges from 0 (perfect skill) to 1 (no skill). Volumetric Critical Success Index (VCSI) is also a general measure of volumetric performance, ranging from 0 (no skill) to 1 (perfect score). Equations for the defined metrics are shown in the Appendix.
4. Results and Discussion
4.1. Gauge-Gauge Inter-Comparison
Error analysis carried out for the ARG found negligible systematic and random errors which were mostly less than 0.4 mm at both stations for the four years. As a first step performance assessment of the new ARGs, a gauge-gauge inter-comparison was carried out using Pearson’s correlation   , which showed a very good agreement with coefficient of 0.99 (see Figure 2). The SRG and ARG were at the same location, which explains the high correlation found in the gauge-gauge inter-comparison.
4.2. Automatic Rain Gauge, TRMM and FEWS-RFE Comparison
Figure 3 and Figure 4 show the monthly time series comparison of rainfall for gauge, TRMM and FEWS. In most cases, TRMM underestimated rainfall and FEWS-RFE sometimes overestimates the gauge rainfall values. The seasonal and annual average rainfall are shown in Figure 5 and Figure 6. In general, both wet and dry spells were clearly captured in gauge and satellite estimates as shown in Figure 5 and Figure 6. Also, the onset and cessation of the rainy season in March and November respectively  were well captured. Figure 7 and Figure 8 display the scatter plots of rainfall for the QWeCI and DACCIWA sites respectively. In Figure 7 it was clearly shown that there is a consistent agreement between gauge and the satellite estimates. On the other hand, Figure 8 sometimes shows a high variability between gauge and satellite rainfall measurements which could be due to the limited number of rain gauges within the study area. In addition, the difference in rainfall detection capabilities of satellites, for
Figure 2. 4-year daily correlation plots for Kumasi Airport and KNUST Agromet.
Figure 3. Monthly time series of rainfall for the various data used from 2011 to 2014: TRMM (red), FEWS-RFE (green) and gauge (black).
Figure 4. Monthly time series of rainfall for the various data used from 2001 to 2010: TRMM (green), FEWS-RFE (red) and gauge (black) over the region.
Figure 5. Seasonal and annual rainfall from 2011 to 2014: TRMM (left), FEWS-RFE (right) and gauge (center).
Figure 6. Seasonal and annual rainfall from 2001 to 2010: TRMM (Right), FEWS-RFE (center) and gauge (left).
Figure 7. Scatter plot for satellite TRMM (red) and FEWS-RFE (green) measurement respect to gauge for 2011 to 2014.
Figure 8. Scatter plot for satellite measurement TRMM (red) and FEWS-RFE(green) with respect to Gauge for 2001 to 2014.
The correlation coefficients, which measure the linear relationship between satellite products and gauge observations were found to be 0.6 and 0.7 for FEWS-RFE and TRMM with SRG, and 0.87 and 0.86 for TRMM and FEWS with ARG respectively. The biases were less than 1.3 and 0.9 for TRMM and FEWS-RFE respectively. The RMSEs were less 10 mm and MAEs less than 1.0 mm for both satellite products (Table 1 and Table 2).
Table 1. Summary of statistics indicating the performance of TRMM comparison with Gauge for the entire study region.
Table 2. Summary of statistics indicating the performance of FEWS-RFE comparison with Gauge for the entire study region.
The performance of satellite products against rain gauge observations is shown in Table 1 and Table 2. The sparse nature of the rain gauge network in the region was a disadvantage for providing the categorical matrices for each of the 6 grid cells over the study region hence, mean values for each year were used (Table 1 and Table 2). Probability Of Detection (POD) which measures the fraction of gauge observations that are correctly detected in the satellite was found to be mostly greater than 0.9. Similarly, the fraction of the volume of rainfall that is detected correctly, HI, was greater than 0.9, indicating a high rainfall detection capability of both satellite products. Furthermore, the high detection capabilities were confirmed by the low VFAR (less than 0.2) and very low VIM (less than 0.05) for all years. Critical Success Index (SCI) and Volumetric Critical Success Index (VCSI) were respectively greater than 0.6 and 0.8 for both satellite products.
The study found that, TRMM and FEWS-REF sometimes underestimate the observed rainfall values by at least 30% and 20% respectively. TRMM and FEWS-REF had reasonably high rainfall detection capabilities relative to gauge as evident in the POD, HI and SCSI in Table 1 and Table 2. FAR and VFAR values were low for both TRMM and FEWS-REF, indicating that very few rainfall events were captured by the satellites and not necessarily the gauge. This could be attributed to random and systematic errors associated with the satellite products including (but not limited to), its inability to capture extreme events due to its polar orbiting, sampling frequency  , non-uniform field of view of sensors  , the diurnal cycle of rainfall  and the uncertainties in the rainfall retrieval algorithms   . Nicholson et al., 2003  observed that gauge biases are usually smaller compared to biases in the satellite estimates.
Microscale validation of TRMM and FEWS satellite estimates with automatic rain gauge measurements have been carried out over Ashanti region of Ghana. First inter- comparison between ARG and GMet SRG measurements was performed, showing a good agreement with correlation of 0.99, which was an indication that both gauges provided consistent rainfall measurements over the period under study. Gauge satellite validation results showed good agreement with correlation coefficients of 0.6 and 0.7 for TRMM and FEWS with SRG, and 0.87 and 0.86 for TRMM and FEWS with ARG respectively. Although the Pearson correlation values are relatively high, there is an evidence of a slight under/over estimation of rainfall over the study period. This is likely attributable to the sparse gauge network over the study area or atmospheric sampling errors associated with satellite estimates.
Probability of Detection (POD) and Volumetric Hit Index (VHI) were found to be greater than 0.9. The Volumetric Critical Success Index (VCSI) was 0.9 and 0.8 respectively for TRMM and FEWS with low False Alarm Ratio (FAR) and insignificant Volumetric Miss Index (VMI). This is an indication that rainfall events were well captured by both satellite products, with few misses. In general, relatively low biases and RMSE values were observed for all years. Consistently, biases were below 1.3 and 0.8 for TRMM and FEWS-RFE respectively, implying a possibility for overestimation of average rainfall by at least 30% when using TRMM and underestimation by 20% when using FEWS-RFE products. Furthermore, there were very good rainfall events and volume detection capabilities by both TRMM and FEWS-RFE, relative to gauge. In addition, rainfall onsets, peaks and cessations as well as the dry spells were well captured in TRMM and FEWS-RFE, relative to gauge. Although TRMM and FEWS- RFE sometimes under/overestimated rainfall in the study area, these satellite products have shown good potential for use in agricultural and other hydro-climatic impact studies.
Within the DACCIWA work project, 17 portable optical rain gauges with a minute resolution have been deployed over the Ashanti region, Ghana since June 2015. The DACCIWA gauge network is expected to provide high quality rainfall data over the study region. Hence, this rainfall dataset will improve our understanding on the rainfall types, enhance satellite validation and serve as useful database for future climatic studies.
We are thankful to Mr. Elvis Agypong for data downloading and regular maintenance of the project field sites. The research leading to these results has received funding from the European Union 7th Framework Programme (FP7/2007-2013) under Grant Agreement No. 603502 (EU project DACCIWA: Dynamics-aerosol-chemistry-cloud interactions in West Africa)
Pearson’s Correlation Co-efficient:
where is the satellite estimates and is the gauge measurement, is the arithmetic mean, is the standard deviation and is the Pearson Correlation Coefficient (r Î −1,1).
Probability Of Detection (POD) is given as
False Alarm Ratio (FAR) is given as
Critical Success Index (CSI) is given as
Volumetric Hit Index (VHI) is given as
Volumetric False Alarm Ratio (VFAR) is given as
Volumetric Miss Index (VMI) is given as
Volumetric Critical Success Index (VCSI) is given as
where Hit (H) is the rain correctly detected, Miss (M) is the rainfall observed but not detected, False (F) is the rain detected but not observed, is the satellite estimate, is the rain gauge measurement and t is the threshold value given to be zero.
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