RAINFALL ESTIMATES OVER SOUTH AMERICA
Galdino V. Mota and Edward J. Zipser Department of Meteorology - University of Utah
135 S 1460 E Rm.819, Salt Lake City, Utah, 84112 – USA email: [email protected]
RESUMO
Os algoritmos de precipitação provenientes do satélite “Tropical Rainfall Measuring Mission” (TRMM) foram investigados e comparados com estimativas de precipitação de outros satélites e pluviômetros. Apesar das estimativas consideradas terem apresentado boa concordância em longo prazo, discrepâncias foram observadas nas análises médias mensais, sazonais e regionais, que podem estar relacionadas com problemas de amostragem. As altas taxas de chuva do infra-vermelho podem ser devidas às super-estimativas provenientes de nuvens altas. Outra possível explicação para as discrepâncias é que não há suficiente pluviômetros no algorítimo usado para fornecer estimativas representativas de chuva. Outras possíveis explicações sugeridas podem estar relacionadas com as propriedades físicas das massas de ar no continente.
1 – INTRODUCTION
There is no doubt that rainfall constitutes the
most important meteorological and climatological variable in the tropics. The distribution of rainfall and its variability in South America is very important in understanding the local, regional, and large-scale circulations that are driven by latent heat sources in the continent. The role of the South Atlantic Convergence Zone (SACZ), El Niño/Southern Oscillation (ENSO), tropical latent heat sources in the continent, the Andes topography, the Altiplano, the Brazilian Highlands, etc., modulate the distribution of rainfall variability over South America. Many studies have presented climatologies and short-term descriptions of rainfall using rain gauges from conventional networks and from field experiments to understand this variability, such as Figueroa and Nobre (1990), Marengo et al. (2001), Poveda and Mesa (2000), among others.
The inaccessibility of many regions in South America, such as high mountain ranges, dense forests, and deserts, impedes measurements of rainfall. Fortunately, with the advance in remote sensing technologies by satellites it is possible now to better investigate the characteristics and distribution of estimated precipitation in those regions, albeit indirectly.
Comparisons of satellite products with rain gauges estimates have been made in an attempt to evaluate the rainfall distribution over the tropics. A detailed description of rainfall estimates, using the Geostationary Environmental Satellite (GOES) Precipitation Index (GPI), was presented in Arkin and Meisner (1987). Comparisons of GPI’s 3-year means for the period of December 1981 - November 1984 with
published descriptions of long-term mean rainfall of station observations provided by Hoffman (1975) and Jaeger (1976) were made. The comparisons made by Arkin and Meisner showed good agreement between GPI and the long-term mean over the tropical oceans and good qualitative agreement of the large-scale features over South America. However, high estimates by GPI compared with those same published climatologies of Hoffman and Jaeger were noted over the Americas. GPI estimates presented more extensive areas of heavy rainfall than those of the published climatologies, with a slight tendency for greater peak values during the rainy season.
Janowiak and Arkin (1991) have investigated the seasonal and interannual variations of rainfall estimates in South America using cloud-top temperature measurements during 1986-1987 (a warm ENSO event), and during 1988-1989 (a cold ENSO phase). By comparing rainfall estimates with the expected rainfall anomalies related to ENSO they concluded that for most regions the GPI rainfall variations behaved as expected during both periods.
Large discrepancies were found in the comparisons made by McCollum et al. (2000) over Africa using 7 years of rain gauges and satellites analysis, both estimates obtained from the Global Precipitation Climatology Project (GPCP). In examining a region in the north of South America (10°S – 10°N and 75° – 65°W), having correspondingly large amounts of monthly rainfall as that observed in equatorial Africa, they found less discrepancies between gauges and satellite estimates than those observed in Africa during all 7 years. Their investigations identified different possible explanations for the large discrepancies in
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central equatorial Africa. The first is that there may not be enough rain gauges in the GPCP’s Global Precipitation Climatology Center (GPCC) analysis to provide accurate estimates of rainfall. However, a comparison between the 7 years average of GPCC dataset with a long-term climatology indicates that GPCC is similar to long-term averages, suggesting that GPCC is not underestimating rainfall in Africa.
Several other studies have investigated the characteristics and distribution of the rainfall estimated by IR measurements over South America. However, it is well known that the cloud-top temperatures measured remotely by IR probes on board satellites do not describe directly the physical processes occurring in clouds and their consequent precipitation. Thus, to better estimate the precipitation in the tropics, alternative rainfall algorithms more closely related to the hydrometeors and the physical processes in-cloud, such as those used by the Precipitation Radar (PR) and Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) on board the TRMM satellite, should be considered.
One of the major goals of the TRMM is to provide rainfall estimates over the tropics. In order to reach this goal “calibrations” against other sources of data and comparisons of TRMM algorithms with IR and gauge estimates have been made in a global context. Adler et al. (2000) used 1 year of TRMM data to compare the combined radar-radiometer data from TRMM to adjust IR data with rain gauges and with GPCP. Regional comparisons, e.g., Nicholson and Some (2000) used a dense rain gauge network in Africa to validate TRMM rainfall estimates, other satellites estimates and combined products, from May to September 1998. Negri and Adler (2002) applied a satellite infrared technique and brightness temperatures from the TMI for rain rate estimation, from January to April 1999 in order to investigate the diurnal variability of rainfall and the relative contributions from convective and stratiform precipitation in Amazonia.
However, no study to date has attempted a systematic, multiyear, comprehensive comparison of IR, passive microwave, and radar estimates in the South America region. In order to better understand the distribution of rainfall estimates over unique environments of South America the current study investigates the performance of TRMM algorithms, i.e., PR and TMI rainfall estimates, and compares them with other rainfall estimates. The distribution of precipitation systems observed by the PR is also analyzed. The two TRMM algorithms used here are from the PR – designated 3A25, and from the TMI – designated 2A12 (Kummerow et al. 2000). These rainfall algorithms are compared with GPI (Arkin and Meisner, 1987); with
GPCP combined algorithm composed of microwave, IR, and gauges; and with the gauge-only analysis (GPCC, Huffman et al. 1997). The description of all these algorithms is presented in the following section. 2. DATA AND METHODS
The TRMM satellite, a U.S.-Japan project, has been collecting meteorological data almost continuously since December 1997, with the intent of monitoring rainfall over the tropics and subtropics (between 35°N and 35°S). This study uses the first 3 years of data (Version 5) collected by PR and TMI instruments on board of the TRMM satellite.
The PR instrument operates at 13.8 GHz frequency. The PR swath is about 215 km wide, and its horizontal and vertical resolution at nadir are respectively 4.3 × 4.3 km and 250 m. The TMI instrument is a conically scanning 53° inclination radiometer that operates at 10.7, 19.3, 21.3, 37.0 and 85.5 GHz. Its swath is about 760 km wide, and its horizontal resolution is 10.7 × 7 km and 5 × 7 km at 37 and 85 GHz, respectively. Due to the inclined orbit of TRMM satellite there are different sampling rates between low and high latitudes. The range of latitudes of 30 to 35° (north or south) has twice the samples as a 5° range of latitudes near the equator. Sampling problems also exist due to the fact that PR views only a portion of the TMI swath. Furthermore, due to the non-sun-synchronous orbit of TRMM, the satellite would overfly a given location at a different time every day in order to complete the diurnal cycle in approximately 46 days. That type of orbit introduces differences between TRMM (PR and TMI) rainfall estimates and GPI, which uses data every 3 hours from geosynchronous satellites, every day. Kummerow et al. (1998) provide detailed descriptions of PR and TMI instruments.
The TMI algorithm used here is named 2A12. It provides rainfall rates and vertical structure of hydrometeors and latent heat based in the nine channels of TMI. The rainfall estimated by 2A12 over land is based mainly upon thresholds of brightness temperatures (Tb) associated with ice scattering signatures by hydrometeors. Over water the output of 2A12 depends upon a Bayesian algorithm using hydrometeor profiles from cloud resolving models.
The PR data used in this study is the 3A25 algorithm, which provides monthly near-surface rainfall estimates in 5° × 5° grid boxes (Kummerow et al., 2000). The 3A25 algorithm includes in the rainfall estimate the number of pixels from 1 to 3 and with reflectivity ≥ 17 dBZ.
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The GPI rainfall algorithm was developed by Arkin and Meisner (1987) in an attempt to estimate tropical rainfall using thresholds of cloud-top temperatures derived from IR measurements. The estimation technique is simply the product of the mean fractional coverage of cloud colder than 235 K in a 2.5º × 2.5º grid box, the length of the averaging period in hours and a constant of 3 mm h-1. That is, , where GPI is in millimeters, Fc is the fractional cloudiness (a dimensionless number between 0 and 1), and t is the length of the period (hours) for which Fc was the mean fractional cloudiness.
tFGPI c3=
The major advantage of this rainfall estimate is that it is based in IR measurements, which are available in a high time resolution over most regions of the globe from geostationary and polar orbiting satellites.
The GPCP was established by the World Climate Research Program (WCRP) in 1986 to address the necessary coverage and accuracy of precipitation on global scale. The Version 2 Combined Precipitation Dataset, used in this study, includes a gridded analysis based on observation-only dataset that are gauge measurements and satellite estimates of rainfall (Huffman et al., 1997; and Susskind et al., 1997). Preliminary analyses have shown general agreement with previous studies of global precipitation. However, there are systematic differences with standard climatologies at regional scales.
The GPCC analyses are based on interpolation of station data to regular 0.5º × 0.5º grid boxes. These regular points are averaged to provide area-mean monthly total precipitation on 2.5º × 2.5º grids of latitude-longitude (Rudolf, 1993; Huffman et al., 1997).
The original gridded data of GPI, GPCP, GPCC, and GPCC NORMAL are available in 2.5º of latitude per 2.5º of longitude. However, for these comparisons the dataset was aggregated into 5º × 5º grids in order to match the TRMM rainfall data.
The first 3 years’ data of PR and TMI rainfall estimates, from December 1997 to November 2000, are compared with those of GPI, GPCP, and GPCC. The 3-year averages integrated over the South American continent and oceans are compared.
In order to compare the seasonal distribution of estimated rainfall, gridded individual maps, and differences between PR, TMI, and GPI with GPCC are displayed. 3. RESULTS AND DISCUSSION
The 3-year averages of rainfall estimates (mm
per year), from the period of December 1997 to November 2000, are displayed in Figure 1. A qualitative
analysis of the rainfall estimates illustrates interesting patterns over South America. All panels show regions of maximum estimated rainfall coincident with the climatological descriptions of rainfall in South America and adjoining areas. For example, the rainfall maximum in continent is located over the Gulf of Panama and western Colombia, which agrees to the regions that receive rainfall as high as 5,000 mm per year as described by Poveda and Mesa (2000) and Figueroa and Nobre (1990).
Secondary maxima are observed in Figure 1 over northwest Amazonia, over the coastal regions adjacent to the Amazon River delta and over southeast South America. The climatological position of the Intertropical Convergence Zone (ITCZ) around the 5°N parallel in the Atlantic Ocean is well-estimated by all satellite algorithms.
Although the regions of maximum rainfall estimates depicted in the panels of Figure 1 show qualitative similarities among themselves, some differences in magnitudes are observed among them. Both TMI and GPI tend to estimate higher than PR, and GPCC in the western Colombia, Panama, Gulf of Panama, and in the Amazonia. On the other hand, PR estimates tend to be higher than all others in southeast South America.
The percentage differences of total rainfall estimates over all grid boxes in South America are summarized in the Tables 1-3. The columns in Table 1 are related to the total rainfall estimates of GPCC over all grids in the continent, and the percentage differences (PD) were calculated as follow:
nn
nn
nn
GPCCGPCCXPD ∑∑−∑∗= /)(100 , where n
is the total number of grid boxes over land, and X corresponds to the total rainfall estimates over land by PR, TMI, and GPI, in the respective columns. Note that the differences of GPCC vary from year-to-year from all estimates other than GPCP that is about 6% higher than GPCC during all three periods. The differences are higher in the first 2 years (Dec.1997-Nov.1998 and Dec.1998-Nov.1999) than in the third one (Dec.1999-Nov.2000). The PR estimates appear to be close to GPCC estimates for the total 3-year mean, where the estimates of PR are 4% higher than those of GPCC. However, notice that large systematical differences between TMI as well as GPI against GPCC are observed; where TMI and GPI are 29% and 19% higher than GPCC, respectively. Note that GPCP, that is a product blended of passive microwave, IR, and GPCC gauge analysis, is 6% higher than GPCC.
Table 2 shows the percentage differences related to the total rainfall estimates of GPI over all grids in the
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(a) (b)
(c) (d)
Figure 1. Three years mean (of each 5° × 5° box) of rainfall (mm yr-1) estimate by a) PR, b) TMI, c) GPI, and d) GPCC, in the period of December 1997-November 2000.
Table 1. Percentage difference of total rainfall over all boxes in South America between TRMM, GPI, and GPCP with respect to GPCC.
PERIOD PR vs. GPCC
TMI vs. GPCC
GPI vs. GPCC
GPCP vs. GPCC
DEC.1997 - NOV. 1998 7.9 31.2 23.6 5.6 DEC.1998 - NOV. 1999 5.3 33.1 22.6 6.4 DEC.1999 - NOV. 2000 0.2 22.9 11.3 5.8
DEC.1997 - NOV. 2000 4.2 28.8 18.8 5.9
Table 2. Percentage differences of total rainfall over all boxes in South America between PR, TMI, and GPCP with respect to GPI. PERIOD PR vs. GPI TMI vs. GPI GPCP vs. GPI
DEC.1997 - NOV. 1998 -12.8 6.1 -14.6 DEC.1998 - NOV. 1999 -14.2 8.5 -13.2 DEC.1999 - NOV. 2000 -10.0 10.4 -5.0
DEC.1997 - NOV. 2000 -12.3 8.4 -10.8
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Table 3. Percentage difference of total rainfall over ocean between PR, TMI, and GPCP with respect to GPI.
PERIOD PR vs. GPI TMI vs. GPI GPCP vs. GPI DEC.1997 - NOV. 1998 -3.8 19.2 11.1 DEC.1998 - NOV. 1999 3.5 21.8 14.5 DEC.1999 - NOV. 2000 4.0 22.4 17.8
DEC.1997 - NOV. 2000 1.2 21.1 14.4
continent. The percentage differences (PD) were calculated as follow:
, where n is the
total number of grid boxes over land, and X corresponds to the total rainfall estimates over land by PR, TMI, and GPCP, in the respective columns. The same procedure was adopted to calculate the percentage differences over the ocean, as shown in Table 3.
nn
nn
nn
GPIGPIXPD ∑∑−∑∗= /)(100
Note that the PR estimates are 12% lower than those of GPI over land, and the GPCP estimates are 11% lower than GPI. On the other hand, TMI estimates 8% higher than GPI does (Table 2). However, a different pattern in the comparisons over the ocean is presented in Table 3. The PR estimates are 4% lower than GPI in the first-year period, and 4% higher than GPI in the other 2-year periods. In contrast to the comparisons with GPI over land, TMI estimates are 21% higher than those of GPI over water. Note that GPCP is 11% lower than GPI over land, where GPCP is combined with satellites and gauges. On the other hand, GPCP is 14% higher than GPI over water, where GPCP is a product of passive microwave and IR only.
The comparisons of the 3-year mean and the annual differences showed several important characteristics of the rainfall estimated by different algorithms. The larger differences between TMI and GPI over GPCC than between PR over GPCC and the different performances of the TRMM algorithms versus the GPI algorithms over land and ocean are obvious in the previous analysis of long-term averages. Further comparisons using seasonal analysis of rainfall estimates are presented next.
Figure 2 shows time series of the total seasonal rainfall (mm month-1) estimated by PR, TMI, GPI, GPCP, and GPCC for all 5° × 5° continental grids. Additionally, the time series of the climatologies of GPCC (NORMAL) is included in Figure 2. In general, the GPCC seasonal averages show good agreement with GPCC NORMAL, except during the El Niño event of 1997-98, where the GPCC estimates are lower than those
of GPCC NORMAL in D97-JF98 and MAM98. The main findings observed for the seasonal means are: • TMI has higher estimates than GPCC during all
periods, with percentage differences with respect to GPCC varying from 16 to 66%. The greatest percentage differences are observed during SON.
• GPI estimates are greater than those of GPCC during SON; however small absolute differences are observed during JJA.
• PR estimates are higher than GPCC during of SON and JJA, with percentages differences with respect to GPCC varying from 5 to 41%. PR estimates during DJF and MAM are lower than those of GPCC. The general analysis over the ocean (not shown)
shows that TMI and GPCP are higher than PR and GPI; and that PR estimates agree better with those of GPI over the ocean than over land.
There appears to be a systematically higher estimate by TMI, GPI, and PR than GPCC during the SON seasons in the continent, and higher estimates by TMI than the others over the ocean.
In order to evaluate the performance of regional rainfall estimates during the convective and nonconvective seasons, the total analysis over all grid boxes in the continent was separated in two regional analyses. The time series of the total seasonal rainfall estimates over land in the regions north and south of 10°S of latitude are shown in the Figures 2b and 2c, respectively.
The main feature shown in the time series of seasonal rainfall in the regions north of 10°S over land (Figure 2b) is that all estimates are lower during December-May of the El Niño event (1997-1998) than during December-May of the non-El Niño years of 1998-1999 and 1999-2000. This result agrees with previous studies showing negative anomalies of precipitation during the El Niño event over the northern regions of South America (Markham and McLain, 1977; Moura and Shukla, 1981; Ropelewski and Halpert, 1987).
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a) TOTAL RAINFALL (LAND ONLY)
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Figure 2. Time series of the (a) total seasonal rainfall estimates (mm month-1) per 5° × 5° latitude-longitude over land; (b) over the regions north; and (c) south of 10°S.
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Although TMI estimates tend to be higher than those of GPCC during all seasons in the boreal South America, the three SON and JJA-1999 seasons have the highest differences. GPI estimates are also much higher than GPCC during SON.
In the regions south of 10°S (Figure 2c) the three main features are: • GPCC estimates have good agreement with the
NORMAL. • TMI and GPI are generally higher than PR and
GPCC, especially during the rainy season (DJF).
• PR estimates are close to those of TMI and GPI, and higher than GPCC during SON. Note that during the winter of 1999 and 2000, PR estimates are higher than those of TMI, GPI, and GPCC. Additional comparisons of the seasonal
distribution of rainfall estimated by satellites and rain gauges are presented below.
The 3-year seasonal maps of PR, TMI, GPI, and GPCC estimates of rainfall in South America (not shown) present a generally well-defined annual cycle of rainfall in all four estimates. The main features such as the rainy season during DJF in most regions of the continent, the South Atlantic Convergence Zone (SACZ), and the ITCZ are represented in those analyses with rainfall maxima located in Amazonia (Satyamurty et al., 1998). During MAM the rainy season starts to progress towards the north, and the rainfall related with the ITCZ reaches the northern and northeastern coastal regions of Brazil, defining the rainy season in those regions. Most of the regions in central Brazil have a well-defined dry season during JJA, when rainfall is concentrated in the northwest of the continent. However, the regions in the southeast keep receiving rain related to frontal systems that eventually can reach northeastern Brazil during winter (Kousky, 1979). Rainfall estimated by PR, TMI, GPI, and GPCC migrates southeastward starting the rainy season in the center of the continent during SON. In summary, the general pattern of rainfall estimated by PR, TMI, GPI, and GPCC coincides with direct or indirect climatological descriptions of rainfall proposed by several authors, e.g., Figueroa and Nobre (1990), Horel et al. (1989), Nogués-Paegle and Mo (1997), among others.
Despite the general and qualitatively good agreement with climatologies, discrepancies in magnitudes of rainfall and misallocations of rainfall maxima are observed when careful comparisons are made between these rainfall estimates and those of other satellites and gauges.
The seasonal cycle of the differences between PR, TMI, and GPI analysis with rain gauges (GPCC) are shown in Figures 3, 4, and 5. Note that the differences between PR with GPCC displayed in
Figure 3 are generally negative, i.e., as already shown, PR estimates are lower than those of GPCC during all seasons other than SON. GPCC estimates appear to be higher by 100 mm per month than those of PR during the rainy season (DJF and MAM) in the northern coast of Brazil. On the other hand the highest positive differences (greater than 100 mm per month) between PR minus GPCC are observed mainly over southeast South America during SON, and the northwestern region in the continent during the rainy season of MAM and JJA.
The differences between TMI and GPCC (Figure 4) are mostly positive during the rainy seasons. However, during the dry season of JJA, most of grid boxes have TMI estimates lower than those of GPCC.
The GPI minus GPCC estimates (Figure 5) have similar behavior as TMI minus GPCC where the areas with maximum rainfall are higher than those of GPCC. High estimates of GPI exceeding 150 mm per month are observed in two grid boxes over southern Peru and Bolivia. However, GPI estimates are lower then GPCC by as much as 150 mm per month in the northern coast of South America.
It seems to be a consistent result that high estimates of TMI and GPI occur systematically in most grid boxes of South America during the seasons of greatest local convective activity. Furthermore, all three satellite algorithms tend to estimate higher than those of GPCC during SON seasons. These differences can be related with air mass properties in the continent during those seasons.
Nevertheless, PR estimates tend to be lower than those of GPCC in most grid boxes in the continent except in the regions of strong convective activity.
The comparisons between PR, GPCP, TMI, GPI, GPCC, and the climatology from GPCC in some selected regions are displayed in Figure 6.
The main features of the time series of monthly rainfall estimates in Argentina (Figure 6a) are described below: • PR and TMI estimates are generally higher
than all others during the rainy seasons. • The high rainfall from Precipitation Features
(PFs) with Mesoscale Convective Systems (MCSs) and intense MCSs (see Nesbitt et al., 2000) tends to follow the peaks of PR and TMI rainfall estimates. Similar characteristics of rainfall described
above are also observed in adjoining regions such as in southeast South America (not shown). There appears to be a tendency of PR overestimating rainfall from PFs with MCSs and intense MCSs in those regions. We speculate that random errors could occur due to the oversampling of PFs with MCSs and intense MCSs, especially over the regions in northeast Argentina, Paraguay, Uruguay, and southern Brazil. However, we cannot rule out the possibility that the PR algorithm
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Figure 3. PR minus GPCC seasonal means of rainfall estimates (mm month-1) during DJF, MAM, JJA, and SON.
may be overestimating rainfall from these systems without further research.
Several interesting behavior in rainfall estimates and precipitation features are observed in other regions, such as: • Over the high elevations in Peru/Bolivia
(Figure 6b), GPI estimates are much higher than TMI, PR, and GPCC during the rainy season.
• In the grid boxes over South Amazonia (Figure 6c) the TMI and GPI have approximately the same high estimates, compared with those of PR and GPCC.
• Over Colombia and Gulf of Panama (Figure 6d), a region with large concentrations of MCSs, we note that TMI has high estimates during the rainy seasons, while PR and GPI have intermediate and approximately the same estimates during all periods. The fact that
GPCC estimates are low during the rainy seasons in that region can be attributed to the absence of rain gauges in high rain areas.
• In the dry region of NE Brazil (Figure 6e) we observe that all rainfall estimates have good agreement during all periods other than in the dry season. During June-September PR, TMI, and GPI tend to estimate lower than GPCC does. These discrepancies appear to be related with the nonhomogeneous distribution of rainfall and rain gauges between the coast and the semiarid areas of NE Brazil, where a narrow belt along the east coast has twice as much rainfall than in the interior areas (Kousky and Chu, 1978). Furthermore, the regional analyses of the 3-
year rainfall estimates show that during the El Niño event of 1997-98 the rainfall estimated by PR, TMI, GPI, and GPCC is lower than during the non-El Niño
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Figure 4. TMI minus GPCC seasonal means of rainfall estimates (mm month-1) during DJF, MAM, JJA, and SON.
years in Amazonia, Colombia, and NE Brazil (See Figure 6). These results agree with the previous studies made by Markham and McLain (1977), and Poveda and Mesa (2000).
Major differences and similarities among rainfall estimates over South America and adjoining areas are pointed out in this section. Main findings of this work and plans for future research are presented next.
4. SUMMARY AND CONCLUDING REMARKS
Comparisons between different rainfall algorithms should be viewed taking into account the different physical characteristics of precipitation features observed over the tropics and subtropics, and the different physical assumptions inherent in the algorithms. Despite the fairly good agreement among PR, TMI, GPI, and GPCC rainfall algorithms of long-
term averages, discrepancies are observed for some specific months, seasons, and regional averages.
The overall comparisons of 3 years of rainfall estimates among satellites versus rain gauges show that PR, TMI, and GPI estimates are higher than those of GPCC. These analyses have shown a reasonable agreement between PR and GPCC for accumulated rainfall estimates over land for long-term averages. PR estimates tend to be only 4% higher than those of GPCC, while GPI and TMI estimates are systematically 19% and 29% higher than those of GPCC, respectively.
Nevertheless, dense distribution of rain gauges within latitude-longitude grid boxes is required in order to have valid comparisons with spatial averages of rainfall from satellites. However, the number of rain gauges in most grid boxes of 2.5° × 2.5° latitude-longitude used in GPCC algorithm does not reach this requirement. Because of that we can safely infer that
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Figure 5. GPI minus GPCC seasonal means of rainfall estimates (mm month-1) during DJF, MAM, JJA, and SON.
Figure 6. Time series of monthly mean rainfall estimate (mm month-1) per 5 by 5° of latitude-longitude by PR, TMI, GPI, GPCC, and GPCC NORMAL over (a) Argentina, (b) Peru/Bolivia, (c) South Amazonia, (d) Colombia, and (e) Northeast Brazil.
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)
Figure 6. (Continued)
b
c))
d
e)
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4) Investigate the regional distribution of lightning associated with the precipitation features defined by PR, to determine whether some of the systematic differences between the rainfall algorithms would be a function of lightning occurrence and flash rate.
GPCC rainfall estimates are subject to significant errors. That is especially true for convective rainfall, which is a major component over virtually all of South America.
The seasonal analyses carried out in this investigation showed a tendency of TMI and GPI estimates being higher than those of PR and GPCC during the rainy seasons. The best agreement of all algorithms is observed during the austral winter when convection is less active than during the other seasons.
Acknowledgments. The author gratefully acknowledges many helpful comments and advice provided by Drs. Julia Nogués-Paegle and Steve Krueger. Thanks to Baike Xi and Steve S Nesbitt for their support processing most of the datasets used in this work. The author is supported by the NASA TRMM Grant # NAG5-9717; “Universidade Federal do Pará”; and CAPES BEX1387/99-5.
Furthermore, PR, TMI, GPI estimates tend to be higher than those of GPCC during September-November, especially in the regions of large occurrence of intense MCSs. We suspect those discrepancies could be related to random oversampling of PFs with MCSs and intense MCSs by PR. Another two possible explanations could be related to physical properties of air masses. First, the convective clouds formed under dry conditions could have high bases producing precipitation that partially evaporates before reaching the ground. The other explanation could be related to high concentration of aerosols associated, for example, with the vegetation burning during that season in the continent, leading to an abundance of cloud condensation nuclei, small droplets, and inefficient rain processes. However, it is not clear how these conditions might lead to overestimates by the PR.
REFERENCES
Adler, R. F., G. J. Huffman, D. T. Bolvin, S. Curtis,
and E. J. Nelkin, 2000: tropical rainfall distributions determined using TRMM combined with other satellite and rain gauge information. J. Appl. Meteor., 39, 2007-2023.
Arkin, P. A., and B. N. Meisner, 1987: The relationship
between large-scale convective rainfall and cold cloud over the Western Hemisphere during 1982-1984. Mon. Wea. Rev., 115, 51–74. The regional analyses in some selected regions
in South America have demonstrated some differences from one another. For example, Peru and Bolivia during the rainy season have unusually high estimates from GPI compared with all other algorithms. These high estimates of GPI could be related to problems in the algorithm showing rain where it does not necessarily exist, for example, if there were high occurrences of cirrus or nonprecipitating anvils there compared with the other regions.
Figueroa, S. N., and C. Nobre, 1990: Precipitation
distribution over Central and Western tropical South-America. Climanálise, 5, 36-44.
Horel, J. D., A. N. Hahmann, and J. E. Geisler, 1989:
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Although this work has provided a general description of the differences and similarities among the rainfall estimates, further investigations are needed in order to better understand the distribution of rainfall estimated in South America and the reasons for some of the biases in the estimates.
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Additional research might be made in order to explain the systematic differences among the estimates from satellite and gauges:
1) For better comparison of satellite estimates with rain gauges, additional datasets with much larger numbers of rain gauges should be used.
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2) Comparisons of TMI with PR estimates within PR swath should be made in order to distinguish differences in sampling from differences in the physics of the algorithms.
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3) Define regions using much finer grids than 5° × 5°, such as in the Altiplano, southeast and northwest South America, so that regional statistics are from similar physical and/or climatic regions.
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