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Article

Brazilian Annual Precipitation Analysis Simulated by the Brazilian Atmospheric Global Model

1
Center for Weather Forecasting and Climate Studies (CPTEC), National Institute for Space Research (INPE), Cachoeira Paulista 12630-000, Brazil
2
Department of Physics, Federal University of Santa Maria (UFSM), Santa Maria 97105-900, Brazil
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Water 2023, 15(2), 256; https://doi.org/10.3390/w15020256
Received: 20 November 2022 / Revised: 27 December 2022 / Accepted: 3 January 2023 / Published: 7 January 2023
(This article belongs to the Section Hydrology)

Abstract

:
The strategy for assessing simulations produced by climate models established as part of the Atmospheric Model Intercomparison Project (AMIP) delivers an outline for model analysis, verification/validation, and intercomparison. Numerical models are continuously being developed to find the best representation for the amount and distribution of precipitation in Brazil to improve the country’s precipitation forecast. This article describes the key features of the Brazilian Global Atmospheric Model (BAM) (developed by the Center for Weather Forecasting and Climate Studies of the National Institute for Space Research (CPTEC/INPE)) and analyses of its performance for annual rainfall climate simulations. This study considered the representation of the annual precipitation in Brazil mainly during the rainy season in the central part of Brazil by the BAM. The model was run over the 1990 to 2015 period using spectral Eulerian model dynamics with a 70-horizontal resolution of approximately 1.0 × 1.0 and 42 vertical sigma levels. The analysis was divided into two stages: the annual precipitation and the rainy season precipitation. Model precipitation analyses were performed using statistical methods, such as the mean and standard deviation, comparing modeled data with observed data from two datasets, data from the XAV (observed data from INMET, ANA, and DAEE), and the Climate Prediction Center (CPC). In general, the BAM model simulations reasonably replicated the configuration of the spatial distribution of precipitation in the Brazilian territory almost entirely, especially compared with the XAV. The accumulated precipitation in the southern region presented great variation, accumulating from 750 mm year 1 in the extreme south to 1750 mm year 1 in the north of this region. Average values of the BAM accumulated precipitation ranged from 1000 to 2000 mm year 1 , within the expected average, compared to observed values of 750–1500 mm year 1 (CPC and XAV, correspondingly). Although there was an underestimation of the accumulated precipitation by the model, the model reasonably reproduced the precipitation during the rainy season. The performed assessment identified model aspects that need to be improved.

1. Introduction

Precipitation is one of the most important meteorological variables in defining climate. Dissimilarities in the precipitation regime, which comprise the frequency, quantity, and distribution, play a crucial role in the water supply for consumption, agricultural crops, and the maintenance of the Brazilian energy sector through the generation of energy from hydroelectric plants, among other uses [1,2,3].
Brazil (BR), the largest country in South America (SA), has a heterogeneous spatial and temporal precipitation distribution [4,5,6], which is the result of a complex interaction of several atmospheric phenomena, which act at different spatial scales, and storms. The main systems that influence precipitation in South America are frontal systems, cyclones and cold fronts, mesoscale convective complexes, cyclonic systems, atmospheric blocking, the South Atlantic Convergence Zone (SACZ), lines of instability (LI), upper tropospheric cyclonic vortices (UTCVs), and others.
Southeastern Brazil, which has a high population density and the largest industrial hub in the country, has an important and well-defined annual precipitation distribution [7]. The rainfall regime can be separated into two seasons: the rainy season, from October to March, and the dry season, from April to September [4,6,8].
According to Gan et al. [8], Marengo et al. [9], Wang et al. [10], and Gan et al. [11], when the precipitation regime is divided into a dry season and a rainy season, it is associated with the South American Monsoon System (SAMS), which extends from the north to the midwest and southeast of Brazil, and it is responsible for supplying watersheds in most of the Brazilian territory. The SAMS is key for the rainfall regime mainly because of its importance for hydroelectricity generation and the agricultural base.
In addition, another atmospheric phenomenon associated with SAMS is the South SACZ, which is characterized by a wide range of deep convection and, consequently, high precipitation in parts of the southeast of BR, persisting for at least four uninterrupted days [12,13,14,15]. During the active phases of the SACZ, substantial precipitation in a short period can cause floods and landslides, generating a sequence of socioeconomic damage and even deaths. The absence of the SACZ, or even a lower frequency of occurrence of SACZ events, can adversely affect the water supply and result in a water and energy crisis in the country [16].
Since 1995, the Center for Weather Forecasting and Climate Studies of the National Institute for Space Research (CPTEC/INPE) has implemented climate research using an atmospheric global circulation model (AGCM) initially acquired from the Center for Ocean–Land–Atmosphere Studies (COLA) in the USA. Cavalcanti et al. [17] and Marengo et al. [18] assessed and acknowledged the performance of the first Atmospheric Model Intercomparison Project (AMIP)-type climate simulation made with CPTEC/COLA AGCM.
Though the latter CPTEC-AGCM version was proficient to reproduce the climatological atmospheric circulation main features, improper methodical errors at high latitudes (spurious precipitation) in short (10 days) and long (30 years) integrations originated [19].
Given this scenario and given the importance of the precipitation that occurs during the months from October to March, researchers from the CPTEC/INPE developed the Brazilian Atmospheric Model (BAM), which is the atmospheric component of the Brazilian Earth System Model (BESM) developed primarily for climate-change-related studies [20,21] to improve weather and climate forecast simulations in Brazil, aiming to incorporate better representation of the systems that act in Brazil.
Therefore, considering the strong socioeconomic influence of the precipitation in the country, this is a short study to evaluate the performance of the BAM model in terms of its representation of the rainy season in the SAMS region.

2. Materials and Methods

2.1. Study Area

For this analysis, the area of study was the Brazilian territory, which is located between latitudes 6 N and 35 S and longitudes 34 W and 75 W focused on the central region of Brazil, where the monsoon system is acting during the summer. Figure 1 shows the Brazilian territory with its five regions, according to the Brazilian Institute of Geography and Statistics (IBGE), which is responsible for dividing the country based on natural, social, cultural, and economic aspects. The delimitation of the regions is shown only to locate the reader during the discussion of the results. R1 corresponds to the northern region, R2 corresponds to the center-western region, R3 corresponds to the northeastern region, R4 corresponds to the southeastern region, and R5 corresponds to the southern region.

2.2. General Circulation Model and Climate Data

The Brazilian Global Atmospheric Model (BAM) was developed entirely by the global modeling group at the Center for Weather Forecast and Climate Studies of the National Institute for Space Research (CPTEC/INPE), and it has been described and widely evaluated in recent years by Figueroa et al. [21], Cavalcanti and Raia [22], Souza et al. [23], Cavalcanti et al. [24], Guimarães et al. [25], Coelho et al. [19], Guimarães et al. [26], Baker et al. [27], Lima [28], and Coelho et al. [29]. The BAM-3D is currently used operationally to perform numeric forecasts for weather at subseasonal and seasonal scale.
The BAM version 1.2 used in this study is a spectral Eulerian model with a 70-horizontal resolution of approximately 1.0 × 1.0 and 42 vertical sigma levels (32 levels in the troposphere and 10 in the stratosphere), with the top of the model at 2 hPa.
The BAM configurations are shown in Table 1, adapted from Figueroa et al. [21]. Here, we used the revised version of the simplified Arakawa–Shubert deep convection scheme [30]; the shallow convection developed by Tieldke [31]; microphysics from Morrison et al. [32]; the IBIS-CPTEC surface model [33]; the longwave radiation (CLIRAD-LW) and the shortwave radiation scheme developed by Tarasova et al. [34] and Holtslag et al. [35] for the planetary boundary layer (PBL), which is referred to as dry-PBL.
The BAM results were generated from an initial condition of temperature, humidity, wind speed, surface pressure, and geopotential surface from the ERA-40 reanalysis [36], observed sea surface temperature (SST) data from NOAA (monthly) [37], and the lower boundary condition over the oceans provided by CPTEC/INPE. Detailed descriptions about the BAM model can be found in Coelho et al. [19].
Comparisons of the simulation model with two observed daily datasets were made. The first was described by Xavier et al. [38], who obtained observed data from meteorological stations around Brazil (3625 rain gauge and 735 weather stations) for 1980–2013. These data were provided from the Brazilian National Institute of Meteorology (INMET), the National Water Agency (ANA), and the São Paulo Department of Water and Electricity (DAEE) and were interpolated at high spatial resolution (0.25 × 0.25 ), updated to 2015 (XAV) (the data are freely available at https://utexas.app.box.com/v/xavier-etal-ijoc-data, accessed on 20 September 2020).
The second dataset was from the Climate Prediction Center of the National Oceanic and Atmospheric Administration (CPC—NOAA). The data are part of the CPC Global Unified Gauge-Based Analysis of Daily Precipitation project, which consists of combining available NOAA station data into a single product. The data are from 30,000 stations collected from the Global Telecommunications System (GTS)’s daily reports and from hydrography and agricultural agency reports around the world [39]. The spatial resolution of the data was firstly 0.125 latitude–longitude grid and was interpolated using the optimal interpolation (OI) of Gandin [40] to 0.5 × 0.5 , and the temporal resolution was daily from 1979 to the present, from Xie et al. [41] (available at https://psl.noaa.gov/data/gridded/data.cpc.globalprecip.html, accessed on 20 September 2020).
The analyzed period was 1990 to 2015, expressed according to the concomitant data interval between datasets, totaling 26 years. It was necessary to standardize the spatial resolution of the datasets, and for that, interpolation was performed on the CPC and XAV datasets to a grid of approximately 1.0 × 1.0 with the linear interpolation method using the Interactive Data Language software (IDL) so that the contrasts between the results were coherent.

2.3. Data Analysis

The analysis was divided into two phases: in the first phase, the accumulated annual precipitation climatology was analyzed to produce the first view of the annual precipitation distribution; in the second phase, the accumulated monthly precipitation in the rainy season in central Brazil (October–March) was analyzed. For the two analyses, the means and differences between the model simulations and the observed data (BAM-XAV and BAM-CPC) were performed according to each situation.
Finally, the BAM data and CPC-XAV data were subjected to a correlation analysis using Pearson’s correlation coefficient (r). The Student’s t-test was used to analyze the statistical significance of the results, according to Wilks [42].

3. Results

The annual averages of the accumulated precipitation from 1990 to 2015 are shown in Figure 2. In general, the model reproduced the expected precipitation configurations when compared to the two observed datasets. The highest values of the average accumulated precipitation presented in the simulations (ranging from 1500 to 3250 mm year 1 ) occurred in the northern region, with some underestimation mostly in the northwest areas of this region and with respect to the CPC dataset.
During autumn and winter, the precipitation in the northern region occurs due to the condensation of humid air advected by east winds from the ITCZ, and during spring and summer the region is under the influence of the SAMS; consequently, the northern region is characterized by year-round precipitation [43].
The accumulated precipitation in the southern region presented great variation, accumulating from 750 mm year 1 in the extreme south to 1750 mm year 1 in the north of this region. The southern portion of the region exhibited underestimation compared to the observed data (XAV (1500–2250 mm year 1 ) and CPC (1500–2000 mm year 1 ), as shown in Figure 2B,C, respectively.
The southern region is characterized by a uniform distribution of precipitation during the year, with accumulated values of 1050 to 1750 mm year 1 [44]. This precipitation regime is due to the frequent passage of frontal systems and the performance of mesoscale convective systems (SCMs) in the region [45,46]. In addition to these, there are other phenomena that influence the precipitation, such as frontogenetic, cyclogenetic, and blocking conditions, as well as climatic phenomena, such as the El Niño–Southern Oscillation [47].
In the southeastern and midwest regions, the average values of the accumulated precipitation simulated by BAM ranged from 1000 to 2000 mm year 1 , within the expected average, compared to the values presented by the XAV and CPC (1500 and 750 mm year 1 , in both datasets). The model simulated the maximum precipitation in the coastal part of southeastern Brazil, which was a little higher than the observed datasets, evident in the figures of the differences (Figure 2D,E).
These accumulated precipitation values were mainly due to rain during October to March when the region is also under the effect of the SAMS (see Supplementary Material, for better understanding, where the accumulated precipitation in each month in the period from October to March and the total accumulated for the same period for the three datasets are shown). The main system that typifies this stage is the occurrence of the SACZ, which increases precipitation levels in a short period, and during the winter, the frequency and amount of precipitation are lower [48].
Finally, the region in which the lowest precipitation accumulations were observed was the northeastern region. The accumulated values simulated by BAM ranged from 250 mm year 1 in the central part of the northeast, characterizing the northeastern semiarid area, to 1250 mm year 1 in regions close to the Brazilian southeast, and 1750 mm year 1 on the coast. The largest accumulations in the south of this region occur as a result of dissimilarities in the SACZ position, and in the coastal part, it occurs due to the influence of the trade winds [49].
The main factors that favor precipitation in the northeastern region are, in autumn, the dislocation of the ITCZ, in which the ascending convection is over the region and, during the summer, the performance of systems such as the upper tropospheric cyclonic vortices (UTCVs), characterized by subsidence, where convection activity can similarly be observed proximate to the center when the UTCV is over the continent [50].
The simulations were above the observed average (250 mm year 1 to 1250 mm year 1 in both sets) in the southern part of the northeast region. The winter is the season with the lowest precipitation in the region.
Analyzing the differences between all datasets (Figure 2D–F), the difference in the values between the BAM and XAV was relatively low, which suggests that the results of the model simulations were very close to the most realistic dataset for Brazilian conditions. The resolution of the XAV is higher than the CPC, and the XAV data source is focused on analysis of Brazil, while the CPC contains global data with sources from different countries. The large difference between the observed datasets is shown in Figure 2F. With this, it is possible to affirm that the BAM simulations were close to reality, with a reasonable representation of the observed precipitation.
The BAM model’s annual standard deviation for the accumulated precipitation varied from 100 to 150 mm year 1 for all the study areas (figure not shown). Related to the XAV data, the highest standard deviation values were in the northern and southern regions (300–400 mm year 1 ), which means that the values were further from the mean. The standard deviation related to the CPC showed the highest values in the northern (above 500 mm year 1 ) and southern (approximately 350 mm year 1 ) regions.
Figure 3 shows the monthly average of the accumulated precipitation during the extended summer months, from October to March, using the BAM simulations (Figure 3A), XAV data (Figure 3B), and CPC data (Figure 3C), as well as the differences between the simulations and the observed data (Figure 3D,E) and between the two observed datasets (Figure 3F). The BAM simulations showed the highest values in the western portion of the northern region and extended to the southeast of the country, with monthly accumulations ranging from 160 to more than 300 mm month 1 , while the XAV and CPC data presented the highest values throughout the extent of northern, central, and southeastern Brazil (220–370 mm month 1 ), indicating an underestimation of the precipitation values in this region by the BAM, mainly in the southern part of the northern and central regions, where the differences reached 168 mm and 224 mm month 1 in relation to the XAV and CPC data, respectively.
As well as the annual average, it is possible to affirm that the results of the model simulations are close to the results obtained by the observed data of XAV, evidenced by Figure 3D. The differences between BAM–CPC and CPC–XAV are pronounced in the average of the rainy season, mainly in the north and northeast of the country.
However, in the coastal region of southeastern Brazil, the BAM simulated values were close to those of the XAV and CPC data, with even a slight overestimation by the model. Furthermore, supplementary studies of the model were performed during the SACZ episodes that occurred from 1992 to 2015 to assess whether the model simulated the SACZ conditions. The model reproduced the spatial distribution of the SACZ precipitation in the analyzed period (data not shown).
In the southern region, the model simulated a wide variation in precipitation, from 40 mm month 1 in the southern half to 160 mm month 1 in the extreme north, different from the XAV and CPC, which retained an almost continuous precipitation performance, ranging from 130 to 190 mm month 1 , excluding the southern extreme (Figure 3A) and the southern coast (Figure 3B) (less than 100 mm month 1 ). These values resulted in an underestimation by the model across the region.
In the northeastern region, the BAM presented the lowest values, ranging from 10 mm month 1 in the central part and 70 mm month 1 further within the continent, in agreement with both observed datasets, except for a small portion in the south, in which the model overestimated the precipitation values compared to the CPC.
The correlation between the BAM simulations and the both datasets are shown in Figure 4. The correlation between the XAV and BAM was positive in almost all the Brazilian territory, ranging from 0 to 0.5 (Figure 4A), except for a small region in the extreme south of Brazil. However, the highest correlation values were found in the central region of Brazil (between 0.25 and 0.50), which were more significant, according to the Student’s t-test (95% confidence). The correlation between the CPC and BAM (Figure 4B) ranged, with the highest positive correlation in southeastern Brazil.

4. Discussion

The southern region of Brazil is in a climate transition region. In the northern part, which is close to the SACZ region, the SMAS is influential during the summer since convection is favored by the surface heating and available moisture due to the circulation of both the subtropical Atlantic high and the low-level jet [51]. Furthermore, both in summer and the warmer months of the transition seasons, mesoscale convective complexes (MCC) are very present, related to severe weather with heavy rains and strong wind gusts [52].
In winter and the coldest months of the transition seasons, the more intense temperature gradient makes the baroclinic conditions more evident, and, thus, precipitation occurs mainly due to the strengthening of low-pressure centers (cyclogenesis) and the progression of frontal systems [44].
The BAM was able to capture the precipitation distribution characteristics in all stages analyzed for the northern, western, and southeastern regions. The model represented the characteristics of the monsoon system that is established in the region and extends from the center-western region to southeastern Brazil from October to March, although the precipitation model outputs needs improvement.
This result corroborates the results found by Moura et al. [53], in which the authors evaluated the performance of the BAM in representing the components of the water balance in the Amazon basin in the period 1979–2015. The authors showed that the model reproduced the spatiotemporal disparity of the water balance components but presented errors in the correct positioning of the maximum precipitation and an underestimation by 8.8% of the precipitation related to the ERA-Interim reanalysis. Additionally, Cavalcanti et al. [24] showed that while the model had deficiencies in precipitation differences in the southeast region, the characteristics of SAMS stood out in the simulations, particularly when associated with the precipitation periods of the winter and summer seasons and the moisture flow of the system.
The southern region of Brazil, represented by the BAM simulations, can be divided into two parts. In the southern half of the region, the model was unable to characterize the annual average precipitation scattering and quantity, with volumes much lower than those observed. In the northern part of the region, the precipitation was better represented by the BAM simulations. In this case, the best representation of precipitation may occur due to the effect of the region where the SACZ occurs to the north of the southern region, which was well simulated by the model [24], in which the precipitation to the north of the southern region was not underestimated. This also explains the low correlation values in the southern region (very close to 0), corresponding to the BAM simulations that were not representative of the precipitation, compared to the CPC and XAV.
The precipitation in the northeast region of Brazil was well characterized by the BAM in the two stages of the study. This result was also found in the analysis of Cavalcanti et al. [24], in which the authors showed that the region was well characterized by the model during the year, particularly in the months when rain is more present in the region (March, April, and May), and justified this result principally by the fact that this region is influenced by the Pacific and Atlantic oceans’ SST, whose influence is captured by the model.
The precipitation spatial distribution was well represented in the BAM simulations; however, the main BAM deficiency identified in this study was in the precipitation intensity in the northern, midwestern, southeastern, and southern regions. In both stages, the model was not able to properly position the maximum precipitation. The study carried out by Coelho et al. [19] analyzed the BAM-1.2 simulations using the same model parametrization used in this study, but the authors used two spatial resolutions, 100 and 180 km, and contrasted them. The authors identified that the model reproduced the spatial distribution of the daily precipitation, but the intensity was still underestimated compared to the observed data and the results presented in this study.
Coelho et al. [19] showed that by increasing the model resolution, the precipitation feature size was minimized and the results were closer to the observed data, but it still underestimated the observed precipitation. Coelho et al. [19] emphasized that the BAM reproduced the observed vertical profile of the average annual zonal temperature, the seasonal average atmospheric circulation, and the main climatological characteristics of precipitation, although with some biases. The largest biases were found in the top of the atmosphere simulations and in cloudy sky conditions, which were attributed to the simulation of a very transparent atmosphere for longwave and shortwave radiation under cloud conditions, compromising cloud and radiation interactions.

5. Conclusions

This study was a short analysis of the precipitation representation by the simulation using the BAM model version 1.2 in two stages, annual precipitation and precipitation, in the rainy season (October to March). The BAM simulations showed that the model reasonably represented the spatial distribution of precipitation in Brazil, except for the extreme south, for which the model did not. However, compared to the observed data, the BAM simulations were able to reproduce the maximum precipitation in the north region and the minimum precipitation in the northeast region in the annual mean and the maximum precipitation in central of Brazil during the rainy season.
In general, the BAM-1.2 model reasonably reproduced the climatological conditions of precipitation analyzed in this study, although with some biases. The study identified that the model needs development to sufficiently reproduce the intensity of the precipitation.
Enhancements in radiation fluxes and the interaction between cloud and radiation are perhaps the key points to address in the next version of the model to thereby improve future BAM simulations and forecasts for Brazil. A more in-depth study of the model compared to other meteorological variables is needed and is being developed for future papers, as well as some analyses of how the model represents systems such as the SACZ.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/w15020256/s1, Figure S1: Mean monthly precipitation (mm month 1 ) (1990–2015) during the extended summer (October–March), from BAM during October [A], November [B], December [C], January [D], February [E], and March [F]; Figure S2: Mean monthly precipitation (mm month 1 ) (1990–2015) during the extended summer (October–March), from XAV during October [A], November [B], December [C], January [D], February [E], and March [F]; Figure S3: Mean monthly precipitation (mm month 1 ) (1990–2015) during the extended summer (October–March), from CPC during October [A], November [B], December [C], January [D], February [E], and March [F]; Figure S4: Mean monthly precipitation (mm month 1 ) (1990–2015) during the extended winter (April–September), from BAM during April [A], May [B], June [C], July [D], August [E], and September [F]; Figure S5: Mean monthly precipitation (mm month 1 ) (1990–2015) during the extended winter (April–September), from XAV during April [A], May [B], June [C], July [D], August [E], and September [F]; Figure S6: Mean monthly precipitation (mm month 1 ) (1990–2015) during the extended winter (April–September), from CPC during April [A], May [B], June [C], July [D], August [E], and September [F]; Figure S7: Total accumulated precipitation (mm) (1990–2015) during the extended summer (October–March) from BAM; Figure S8: Total accumulated precipitation (mm) (1990–2015) during the extended summer (October–March) from XAV; Figure S9: Total accumulated precipitation (mm) (1990–2015) during the extended summer (October–March) from CPC.

Author Contributions

Conceptualization, C.B., N.T.B. and S.E.T.F.; methodology, C.B., N.T.B. and S.E.T.F.; validation, C.B. and F.V.R.; formal analysis, C.B., N.T.B. and S.E.T.F.; model data, D.C.d.S. and P.Y.K.; writing—original draft preparation, C.B.; writing—review and editing, C.B., N.T.B., S.E.T.F., F.V.R., D.P. and D.L.H.; supervision, N.T.B. and S.E.T.F.; project administration, S.E.T.F. and D.L.H.; funding acquisition, S.E.T.F. and D.L.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES) (process number 88881.148662/2017-01 and Finance Code 001).

Data Availability Statement

The data used in this study are available by contacting the corresponding author.

Acknowledgments

We would like to acknowledge the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES) for their support (processes 88881.148662/2017-01 and 001) and the National Institute for Space Research (INPE) and Federal University of Santa Maria for the availability of their infrastructure. The authors are also grateful to Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq).

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

The following abbreviations are used in this manuscript:
AMIPAtmospheric Model Intercomparison Project
BAMBrazilian Atmospheric Model
CPTECCenter for Weather Forecasting and Climate Studies
INPENational Institute for Space Research
CPCClimate Prediction Center
SACZSouth Atlantic Convergence Zone
BRBrazil
SASouth America
SAMASouth American Monsoon System
ITCZIntertropical Convergence Zone
SSTSea surface temperature
BESMBrazilian Earth System Model
IBGMBrazilian Institute of Geography and Statistics
AGCMAtmospheric General Circulation Models
COLACenter for Ocean–Earth–Atmosphere Studies
NCEPNational Centers for Environmental Prediction
NOAANational Oceanic and Atmospheric Administration

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Figure 1. Delimitation of the Brazilian territory and its regions.
Figure 1. Delimitation of the Brazilian territory and its regions.
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Figure 2. Mean annual precipitation (mm year 1 ) (1990–2015) from the BAM (A), XAV (B), and CPC (C) (upper panel). Mean annual precipitation difference between the BAM and XAV (D), between the BAM and CPC (E), and between the CPC and XAV (F) (lower panel).
Figure 2. Mean annual precipitation (mm year 1 ) (1990–2015) from the BAM (A), XAV (B), and CPC (C) (upper panel). Mean annual precipitation difference between the BAM and XAV (D), between the BAM and CPC (E), and between the CPC and XAV (F) (lower panel).
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Figure 3. Mean monthly precipitation (mm month 1 ) (1990–2015) during the extended summer (October–March) from the BAM (A), XAV (B), and CPC (C) (upper panel). Mean monthly precipitation difference between the BAM and XAV (D), between the BAM and CPC (E), and between the CPC and XAV (F) (lower panel).
Figure 3. Mean monthly precipitation (mm month 1 ) (1990–2015) during the extended summer (October–March) from the BAM (A), XAV (B), and CPC (C) (upper panel). Mean monthly precipitation difference between the BAM and XAV (D), between the BAM and CPC (E), and between the CPC and XAV (F) (lower panel).
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Figure 4. Correlation between the precipitation data in relation to the XAV and BAM (A) and the CPC and BAM (B) (1990–2015). The observed and modeled simulated precipitation data (shaded) and the Student’s t-test significance (95% confidence) (red outlines).
Figure 4. Correlation between the precipitation data in relation to the XAV and BAM (A) and the CPC and BAM (B) (1990–2015). The observed and modeled simulated precipitation data (shaded) and the Student’s t-test significance (95% confidence) (red outlines).
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Table 1. BAM model configuration.
Table 1. BAM model configuration.
DynamicsEulerian (Spectral)
Deep ConvectionSimplified Arakawa–Schubert (SAS) Convection Scheme calibrated at CPTEC [30]
Shallow ConvectionTieldke [31]
MicrophysicsCliRad [32]
Planetary Boundary LayerDry-PBL [35]
SurfaceIBIS-2.6-CPTEC [33]
Shortwave and Longwave RadiationCliRad [34]
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Bresciani, C.; Boiaski, N.T.; Ferraz, S.E.T.; Rosso, F.V.; Portalanza, D.; de Souza, D.C.; Kubota, P.Y.; Herdies, D.L. Brazilian Annual Precipitation Analysis Simulated by the Brazilian Atmospheric Global Model. Water 2023, 15, 256. https://doi.org/10.3390/w15020256

AMA Style

Bresciani C, Boiaski NT, Ferraz SET, Rosso FV, Portalanza D, de Souza DC, Kubota PY, Herdies DL. Brazilian Annual Precipitation Analysis Simulated by the Brazilian Atmospheric Global Model. Water. 2023; 15(2):256. https://doi.org/10.3390/w15020256

Chicago/Turabian Style

Bresciani, Caroline, Nathalie Tissot Boiaski, Simone Erotildes Teleginski Ferraz, Flávia Venturini Rosso, Diego Portalanza, Dayana Castilho de Souza, Paulo Yoshio Kubota, and Dirceu Luis Herdies. 2023. "Brazilian Annual Precipitation Analysis Simulated by the Brazilian Atmospheric Global Model" Water 15, no. 2: 256. https://doi.org/10.3390/w15020256

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