P r o c e. d i n g s. 1st Workshop. Madrid, Spain September 2002

Transcription

1 P r o c e e d i n g s 1st Workshop Madrid, Spain September 2002

2 AN INTERCOMPARISON OF GAUGE, RADAR AND SATELLITE RAINFALL IN THE TROPICS Augusto J. Pereira Filho 1, Andrew J. Negri 2, and P. T. Nakayama 3 Abstract The above measurement systems were used to estimate and to measure 24-hour precipitation accumulation over the São Paulo weather radar (SPWR) surveillance area. The Convective- Stratiform Technique (CST) calibrated by data from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) was compared to radar-derived and gauge-measured rainfall for the summer months of 2001 and A total of 150 days were used to compute the daily areal mean and its standard deviation in a 300 km 2 area centered at the SPWR. Pixel-by-pixel radar and satellite rainfall estimates were also compared by means of the cross-correlation coefficient, the mean square difference and associated phase and amplitude differences. Preliminary results indicate better agreement between radar-derived and gauge-measured areal means. A log-log scatter diagram of daily areal means and their standard deviation shows that the areal mean rainfall explains more than 87%, 88% and 96% of the gauge, radar and satellite areal rainfall variance, respectively. Radar and gauge tend to be more dispersive due to their inherent larger errors and biases. Furthermore, the gauge network did not detect precipitating systems with very low areal means. Radar-derived, satellite-derived and gauge-measured total areal mean rainfall accumulation were 823 mm, 746 mm and 1224 mm, respectively. The gauge sampling area is half of the two others, approximately. Most of the deficiencies associated with these three measurement systems were identified. The rain gauge network lacks spatial representativeness under sparse convection. Ground clutter suppression/contamination and range effects are the most significant errors associated with the SPWR. Under-sampling, low information content and miss-calibration under orographic effects are major sources of the satellite errors. Finally, it is shown how to integrate all three systems to improve the spatial and temporal distribution of precipitation for water resources purposes in Brazil. 1. Introduction Brazil has one of the largest drainage systems of the Earth. The Brazilian portion of the Amazon basin alone is almost 65% of its 8.5 million km 2 area. The other 35% of its basins have been heavily used for hydroelectric power generation, especially in South and Southeast Brazil. Nearly 85% of all electric power comes from such plants. In the past few years, electrical energy consumption has increased faster than new investments in the sector. 1 Dept. of Atmospheric Sciences, USP, São Paulo , Brazil 2 Laboratory for Atmospheres, NASA/GSFC, Greenbelt, MD, USA 3 Dept. of Water and Electrical Energy, São Paulo , Brazil

3 Thus, the hydroelectric plants are consuming more and more of the available water resources. To optimise the use of these water resources, better spatial and temporal quantification of precipitation is needed. Brazil has a good network of rain gauges. Its network measures 24-hour rainfall accumulations. This system is reliable, but takes more than 3 months to the data to become available. Recently, several automatic networks have been deployed to reduce that lag time. On the other hand, the number of weather radars is also increasing. For instance, the Amazon Surveillance System (SIVAM) will have a total of ten weather radars, but they will cover only a fraction of the total area of the Amazon basin. To cover the whole country, 140 weather radars would be needed, or an investment that a developing country can make only in the long-term. Therefore, a feasible short-term alternative is the use of satellites to estimate rainfall. In the present work, the CST developed by Negri and Adler (2002) is compared to gaugemeasured and radar-derived 24-hour rainfall accumulation. An observational campaign was carried out between October 2000 and January 2002 to archive all three data sets available over the Eastern portion of São Paulo State. Fig. 1 shows the location of the SPWR used in this work. Half of the SPWR surveillance area is over the Atlantic Ocean. The long-term annual mean rainfall accumulation in the Eastern São Paulo State ranges from 1200 mm inland to more than 4500 mm along the Mar mountain range close to the seashore (Barros et al., 1987). Figure 1. Map of Eastern São Paulo State, Brazil. The SPWR is at 23 o 36' 00'' South and 45 o 58' 20" West. Inner and outer radii are 120 km and 240 km, respectively. The DAEE rain gauge network is also shown. Data sets are compared within the 300 km 2 area. Table 1. Sequence of the days used for satellite, radar and gauge 24-hour precipitation comparisons. Day indicated by DD/MM/YYYY, where DD = day, MM = month and YYYY = year. Most rainfall events are caused by induced heat-island effects and sea breeze circulation (Pereira Filho, 1999), orographic lifting (Blanco and Massambani, 1997), mesoscale convective systems (Silva Dias, 1999), fronts (Satyamurty et al., 1990) and the South Atlantic Converge Zone - SACZ (Rocha and Gandú, 1996). Large-scale features such as El Ninõ (e.g., ) and La Ninã (e.g., ) episodes (Gan and Rao, 1991) also modulate these rainfall systems. Thus, a variety of rainfall patterns are available to compare these three important and relevant sources of precipitation data.

4 2. Methodology An observational effort was carried out between October 2000 and January 2002 over the São Paulo Weather Radar (SPWR) area to archive all available gauge, radar data satellite rainfall files within the 300 km 2 shown in Fig. 1. The GOES-east data sets were processed at NASA and the SPWR at the Hydraulic Technological Centre (CTH), University of São Paulo. The Water and Electrical Energy Department (DAEE) network measured rain gauge daily totals. Rainfall accumulation were estimated between 1000 UTC of the previous day to 1000 UTC of the day to match the rain gauge accumulation period. Each of the three measurement systems are presented below as well as the analysis procedures to compare the respective data sets for the dates shown in Table Rain gauge data More than one thousand rain gauges from the DAEE network are available (Fig. 1) in São Paulo State. A subset of just fifty-five 24-hour rainfall accumulation rain gauges was used in this work. These rain gages had good quality control and very few missing data. They also yield a fairly uniform areal distribution through out the radar surveillance domain in São Paulo (not shown). Most rain gages are at altitudes in between 500 m and 900 m. Fewer gages are at near and far range as well as at higher and lower altitudes. The rainfall accumulation is measured daily at 0700 LT. Two major sources of errors in areal rain gage measurements are point-dependency and wind effects (Brock and Richardson, 2001), but both of them were not corrected in the present work. 2.2 Weather radar data The volume scan of the SPWR is composed of 20 elevation angles between 1.0 and 30.8 degrees and 360 azimuth angles for each elevation angle. The down range resolution is 0.5 km between 0 and 60 km, 1.0 km between 60 and 120 km and 2.0 km between 120 and 240 km. The MDS is 10 dbz. The return power is converted to effective reflectivity at one byte digital resolution. A ground clutter filter is applied before obtaining average rainfall rates at Constant Altitude Plan Position Indicator (CAPPI) at 2 x 2 km 2 horizontal resolution. Rainfall accumulations were integrated from 5- minute 3-km CAPPI with 4-bit rainfall rate resolution between 0.0 to mm h -1. Afterwards, the resolution was decrease to 4 x 4 km 2 to match that of the satellite. Most days selected had close to the maximum number of possible CAPPI (288). Until 4 January 2000, the SPRW processing system applied the MP relationship (Marshall and Palmer, 1948) to convert the effective reflectivity into rainfall rates. Afterwards, the a parameter in the MP was arbitrarily changed to 400 to reduce observed larger biases (Fig. 2). Figure 2. Difference between radar-derived (Pr) and gauge-measured (Pg) 24-hour precipitation as a function of distance from the SPWR for January and February A second order polynomial was fitted to the data (red curve). After Pereira Filho and Nakayama (2001). 2.3 Satellite data and the CST technique Available GOES-east IR (11 µm) measurements were used in this work in conjunction with the convective-stratiform technique (CST) developed by Adler and Negri (1988) and improved by Negri and Adler (2002) to estimate rainfall rates. This satellite samples the entire hemisphere every 30

5 minutes with a spatial resolution of 4 x 4 km 2. To each satellite pixel is assigned a brightness temperature within one byte. An average of 45 images per day were available for the entire period of analysis, so most days were monitored adequately. Noteworthy, GOES-east measurements are fewer over South America whenever significant weather needs to be tracked at higher sampling rates over the North Atlantic, such as hurricanes and tropical storms. The CST technique was calibrated with the Tropical Rain Measuring Mission (TRMM) microwave imager (TMI) over Western Amazon (Negri and Adler, 2002). It was also verified against monthly rainfall averages with 1 o x 1 o spatial resolution estimated with rain gauge measurements in Northeast Brazil. Briefly, the CST technique identifies local minima in the brightness temperature array and filters out thin, non-precipitating cirrus clouds. The brightness temperatures are transformed into rainfall rates of 18.9 mm h -1 and 2.6 mm h -1 for convective and stratiform precipitation, respectively. Details of the CST technique are found in Negri and Adler (2002). In the present study, daily rainfall accumulation at 4 x 4 km 2 resolution is estimated by averaging all rainfall rate estimates and multiplying the result by 24 hours. Advection effects are not considered. This simple method has two major limitations when applied to daily precipitation estimation. Namely, its sampling frequency and information content. The first always affects precipitation pulses less than 1 hour long, twice the sampling interval associated with the Nyquest frequency (Brock and Richardson, 2001). The second is a consequence of adopting only two of an infinite number of possible rain rates. Nevertheless, the CST technique was originally developed for long term rainfall variability studies (Anagnostou et al., 1999; Negri et al., 2000; Negri and Adler, 2002). 2.4 Data analysis Satellite and radar estimates and gauge measurements of 24-hour rainfall accumulation were compared by means of their spatial average and standard deviation. The mean-square difference (MSD) between radar and satellite estimates was obtained at 4 x 4 km 2 resolution. The MSD can be divided into two components associated with phase differences (φ) and another with amplitude differences (α) (Tacks, 1985): where, MSD φ = [σ(p r ) σ(p s )] 2 + [µ(p r )-µ(p s ] 2 MSD α = 2(1-ρ)σ(P r ) σ(p s ) P s = Satellite rainfall estimation (mm), P r = Radar rainfall estimation (mm), σ( ) = spatial standard deviation operator, µ( ) = spatial mean operator ρ = cross-correlation between P s and P r. MSD = MSD= ( P s P r ) 2 = MSD φ + MSD α (1) Thus, it is simple to identify and to quantify spatial differences between radar and satellite rainfall estimates caused by spatial shift (phase) and amount (amplitude) of precipitation. If radar estimates were deemed more accurate than the corresponding satellite estimates or vice-versa, the MSD would become the mean-square error (MSE). 3. Results Fig. 3 shows sequences of daily areal mean rainfall accumulation, the percentage of the MSD associated with phase differences and the cross-correlation coefficient for the dates indicated in Table 1. The total area did not weight the gauge average. Fig. 3 indicates good agreement on the

6 areal mean tendencies with frequent higher peaks for gauge-estimated rainfall averages since measurements are only available over the continent and most summer rainfall systems are continental, as is shown below. Large differences between gauge comparing to radar and satellite are generally caused by under-sampling. The daily cross-correlation coefficient between radarderived and satellite-derived 24-hour rainfall is always less than 0.7. Also, the MSD is dominated by phase errors. On average, It accounts for 70% of the differences. These two estimators tend to have strong fluctuations caused by different precipitation patterns as mentioned above. Scatter diagrams of gauge-measured against radar-derived and satellite-derived areal mean as well as of radar and satellite alone are shown in Fig. 4. The coefficient of variance between gaugeradar, gauge-satellite and radar-satellite are 0.60, 0.31 and 0.44, respectively. It indicates better overall agreement between gauge measurements and radar estimates of rainfall. Radar rainfall estimates tend to be higher than satellite for events less than 10 mm and lower above this limit. Radar range errors tend to be more significant for wide spread precipitation. On the other hand, the lower sampling rate of the satellite measurements is unable to detect rapidly evolving systems. Figure 3. Time series of rainfall spatial mean for satellite, radar and gauge (top), the percentage of the mean-square phase difference (middle) and the cross-correlation coefficient (bottom) between satellite and radar 24-hour precipitation accumulation. Numbers indicate dates given in Table 1. Also, these rapidly evolving convective-type precipitation systems have larger rainfall rate spatial gradients that the CST does not take into account. Around 80% of all precipitating systems yield radar-derived areal means less than 10 mm. Thus, in most summer days analyzed satellite underestimates the rainfall. It has a better performance for wide spread precipitation associated with fronts, jets and SACZ. This last system type is often observed during January and February (Rocha and Gandú, 1996). In fact, their frequency decreases in the summer of 2001 and caused a severe drought in the Center-East and in the Southeast of Brazil, where most hydroelectric power plants are located.

7 Figure 4. Scatter diagram of gauge spatial mean rainfall against radar and satellite ones (left) as well as satellite against radar (right). It is indicated the respective variance coefficients. Scatter diagrams of areal rainfall means against their respective standard deviations for gauge, satellite and radar are shown in Fig. 5. The log-log scatter diagrams show surprisingly good agreement among the three estimators of 24-hour precipitation accumulation. The coefficients of variance are 0.96, 0.88 and 0.87 for satellite, radar and gauge. Thus, the spatial mean rainfall explains most of its spatial variance. Figure 5: Scatter diagram of satellite, radar and gauge spatial mean rainfall against their corresponding spatial standard deviation for all dates indicate in Table 1. Also, big (small) spatial means have relative small (big) spatial variance compared to the its mean. Radar measurements are able to detect precipitating systems with areal means as small as 0.01 mm (Fig. 5). These systems tend to be associated with isolated convection. But, radar as well as gauge measurements present larger dispersions around the adjusted power curve (not shown). It is an indication of their inherent errors discussed in Section 2.

8 Figure 6. Maps of satellite (left) and radar (right) 24-hour rainfall accumulation. Color scale indicates relative accumulation (mm) for each date shown at the top of each map. Latitudes, longitudes and geographic contours are also shown. Dates shown in the format YYMMDD. Some examples of precipitation fields are shown in Fig. 6. Top left (right) maps correspond a maximum positive (negative) cross-correlation coefficient. Middle left maps correspond to the maximum areal mean difference between satellite-derived and radar-derived areal mean accumulation. This difference was caused by radar calibration problems during early Middle right maps for a typical case when satellite severely underestimates precipitation, especially over the ocean. Bottom left maps are for similar maximum areal means. Finally, bottom right maps typifies most summer events that under-sampling and the CST missed the short-lived, convective portion of the precipitating system. The total accumulation fields for radar-derived and satellite-derived 24-hour rainfall accumulation is shown in Fig. 7. Both precipitation fields clearly indicate the most common problems with these two measurement systems. It is evident in the radar-derived precipitation field the most common errors that is underestimation by ground clutter suppression, overestimation by ground clutter contamination, range effect that clearly shows up beyond 60 km range, and signal processing. On the other hand, satellite-derived rainfall severely underestimated the convective type of precipitation (but do not shown any range biases) and also the shallow convection due to orographic precipitation at the coast as well as over the ocean.

9 Figure 7. Maps of satellite (left) and radar (right) total rainfall accumulation for the entire data set (Table 1). Color scale indicates total rainfall (mm). Numbers in the radar map (right) identifies areas where the total rainfall was underestimated by ground clutter suppression (1, 2 and 3) and contamination (4), range effect (e.g., 5) and signal processing (e.g., 7). 4. Conclusions Spatial phase differences between radar and satellite rainfall estimates account for 70% of the MSD. It tends to increase with the size of the rainfall event. Large (small) rainfall events have relative small (large) spatial variance. A log-log relationship between the spatial mean and the spatial standard deviation of radar and satellite estimates and rain gauge measurements of 24- hour rainfall accumulation indicates that small spatial means have greater probability of detection with the radar measurements. On the other hand, the satellite spatial mean explains 96% of the spatial standard deviation. It shows that the satellite estimates are less susceptible to systematic errors when compared to both the SPWR and the rain gauge network. It is apparent that all three measurement systems are limited. The SPWR rainfall estimates were affected by electronic calibration, distance effects and ground clutter filtering. The CST failed to assign stratiform precipitation in many instances perhaps because of the lower troposphere, but more limiting is the satellite sampling rate that is greater than the life span of most convective cells. Rain gauges are not able to capture the spatial variability of local convection events. In most instances in which the rain gage network yielded a relatively greater areal mean rainfall than either satellite or radar were due to fewer irregular sampling. Overall, satellite was the most reliable measurement system with an average of 45-sample d -1. None of the three measurement systems are good enough to make reliable estimates of rainfall alone, but they can provide useful information and be integrated to produce daily accumulations with the least error for instance, with a statistical analysis scheme (Pereira Filho et al., 1998). Further research is under way to that end. As mentioned early, satellite rainfall estimates are of importance to water resources management in Brazil. The CST have been used in climatology and life studies. There are clear indication that it might yield reasonable estimates for daily rainfall accumulations. It might be necessary in this case to increase the information contend from 2 bit to 4 bit to improve the estimates over areas with strong local convection. Nevertheless, this is one of the first attempts to verify its performance against radar and gauge measurements. Acknowledgments - This research was sponsored by the National Council for Scientific and Technological Development (CNPq), under grant /98-3. Appreciation is extended to CTH and DAEE for providing radar and rain gauge data, as well as to NASA/GSFC for the GOES-east CST rainfall estimates. The first author also would like to thank the WMO for the support to participate in the IPWG workshop on precipitation measurements in Madrid, Spain.

10 5. References Anagnostou, E. N., A. J. Negri, and R. F. Adler, A satellite infrared technique for diurnal rainfall variability studies. J. Geophys. Res., 104, Barros, M. T. L., B. P. F. Braga Jr., and A. J. Pereira Filho, Rainfall climatology within the Ponte Nova weather radar surveillance area, São Paulo. Proc. 7 th Brazilian Hydrology and Water Resources Symp., Bahia, 2,1-16. (Available in Portuguese). Blanco, C. M. R., and O. Massambani, Orographic enhancement in São Paulo - Brazil: a preliminary case study. Prepr. 28 th Conf. Radar Meteor., Texas, Amer. Meteor. Soc., Brock, F. V., and S. J. Richardson, Meteorological measurement systems. Oxford Univ. Press, 290 pp. Gan, M. A., and V. B. Rao, Surface cyclogenesis over South America. Mon. Wea. Rev., 119, Marshall, J. S., and Palmer, The distribution of raindrops with size. J. Meteor., 5, Negri, A. J., and R. F. Adler, A TRRM-calibrated rainfall algorithm applied over Brazil. J. Geophys. Res., 107 (D), , E. N. Anagnostou, and R. F. Adler, A 10-yr climatology of Amazonian rainfall derived from passive microwave satellite observations. J. Appl. Meteor., 39, Pereira Filho, A. J., Radar measurements of tropical summer convection: urban feedback on flash floods. Prepr. 29 th Conf. Radar Meteor., Montreal, AMS, , and P. T. Nakayama, Intercomparison of radar rainfall estimates and rain gage measurements in São Paulo, Brazil. Proc. 5 th Int. Symp. Hydrological Applications of Weather Radar, Kyoto, Japan, BR2, 6 pp., K. C. Crawford, and C. Hartzell, Improving WSR-88D hourly rainfall estimates. Wea. Forecasting, 13, Rocha, A. M. G. C., and A. W. Gandù, The South Atlantic Convergence Zone. Climanalise, Special edition, (Available in Portuguese). Satyamurti, P., C. C. Ferreira, and M. A. Gan, Cyclonic vortices over South America. Tellus, 42A, Silva Dias, M. A. F., Storms in Brazil. In: Hazards and Disasters Series, Storms, Vol. II, R. Pielke Sr., R. Pielke Jr., Eds., Routledge, Tacks, L. L., A two-step scheme for the advection equation with minimized dissipation and dispersion errors. Mon. Wea. Rev., 113,