Tutorial: Aquisição e Processamento de Dados Climáticos (NASA POWER)

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Last updated: 2026-04-08

Checks: 6 1

Knit directory: Integrating-nir-genomic-kernel/

This reproducible R Markdown analysis was created with workflowr (version 1.7.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

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R Markdown file: uncommitted changes

The R Markdown file has unstaged changes. To know which version of the R Markdown file created these results, you’ll want to first commit it to the Git repo. If you’re still working on the analysis, you can ignore this warning. When you’re finished, you can run wflow_publish to commit the R Markdown file and build the HTML.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20250829)

The command set.seed(20250829) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

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Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

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Repository version: 7a97272

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 7a97272. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:

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O documento começa definindo opções globais de renderização para que figuras, mensagens e blocos de código apareçam de forma consistente ao longo do tutorial.

Este chunk configura o documento e mantém consistentes as opções gráficas e de relatório ao longo do módulo. Assim, as etapas seguintes podem se concentrar apenas no fluxo de processamento climático.

Language / Idioma: English | Português

1. Introdução

Este tutorial apresenta o fluxo de trabalho usado para obter e processar dados meteorológicos diários para os experimentos com milho conduzidos em College Station, Texas, durante as safras de 2011 e 2012.

O objetivo deste script não é apenas baixar registros meteorológicos diários, mas converter essas séries temporais em um conjunto conciso e biologicamente interpretável de covariáveis derivadas do clima. Essas covariáveis resumem as principais condições meteorológicas de cada safra e são usadas posteriormente para construir o kernel climático (W) no pipeline final de predição.

No conjunto reduzido de 18 modelos usado no projeto, o kernel climático é incluído nos modelos Eta10-Eta18. Nesses modelos, W é usado para testar se a similaridade derivada do clima adiciona informação útil além do efeito categórico de ambiente (E) e dos kernels genômico (G) e fenômico (P). Portanto, W deve ser interpretado como uma fonte complementar de informação meteorológica e não como substituto de E.

Em termos práticos, o fluxo tem quatro objetivos:

1. Recuperar dados meteorológicos diários da base NASA POWER.
2. Organizar os dados brutos em uma tabela reprodutível.
3. Inspecionar visualmente as principais diferenças climáticas entre os anos.
4. Derivar descritores climáticos anuais e diários que depois poderão ser usados para construir o kernel climático.

Local de estudo: College Station, Texas, EUA
Janela da safra: 1º de maio a 30 de setembro
Anos de estudo: 2011 e 2012

Entradas, saídas e próxima etapa

Entradas desta etapa - informações de local e tempo necessárias para consultar a NASA POWER - configurações internas usadas para agregar, resumir e visualizar as variáveis climáticas

Saídas desta etapa - covariáveis climáticas anuais - tabela climática diária expandida - figuras climáticas usadas para inspeção exploratória

Próxima etapa - matrizes.Rmd usa as saídas derivadas do clima geradas aqui para alinhar a informação ambiental com a estrutura observacional e construir o kernel climático (W).

2. Ambiente em R

Antes de iniciar a análise, carregamos os pacotes necessários para obtenção dos dados, manipulação, visualização e montagem de figuras multipainel.

Cada pacote tem um papel claro no fluxo:

• nasapower: conecta o R à base NASA POWER.
• tidyverse: apoia manipulação de dados e gráficos.
• lubridate: simplifica conversão de datas e operações baseadas em calendário.
• patchwork: combina múltiplas figuras do ggplot2 em painéis prontos para publicação.
• ggthemes: fornece as escalas discretas do Google Docs usadas para padronizar cores entre as figuras.
• kableExtra: melhora a apresentação de tabelas HTML no tutorial.

Também criamos o diretório de saída no início do script. Isso evita erros ao salvar figuras e tabelas nas etapas posteriores da análise.

A próxima etapa carrega os pacotes necessários para download de dados, manipulação, visualização e composição de painéis antes do início do processamento climático.

Este chunk carrega os pacotes necessários para obtenção, reorganização, resumo e visualização dos dados climáticos. Reunir as dependências aqui torna as etapas seguintes mais fáceis de acompanhar.

# Uncomment if installation is required
# install.packages(c("nasapower", "tidyverse", "lubridate", "patchwork", "ggthemes", "kableExtra"))

library(nasapower)
library(tidyverse)
library(lubridate)
library(patchwork)
library(ggthemes)
library(knitr)
library(grid)
library(kableExtra)

if (!dir.exists("output")) dir.create("output")
if (!dir.exists("output/climate_results")) dir.create("output/climate_results", recursive = TRUE)

3. Parâmetros da consulta

Nesta seção, definimos as entradas básicas da consulta à NASA POWER. Essas entradas determinam onde, quando e quais variáveis climáticas serão recuperadas.

As coordenadas geográficas identificam o local experimental. As datas inicial e final definem a janela da safra a ser analisada. O vetor params lista as variáveis climáticas diárias necessárias para os resumos posteriores, como precipitação, temperatura, umidade relativa, radiação solar e velocidade do vento.

Essas variáveis foram escolhidas porque permitem derivar descritores climáticos com interpretação agronômica direta, como precipitação acumulada, graus-dia, estresse térmico e déficit de pressão de vapor.

Em seguida, definimos locais, anos e metadados básicos que controlam quais séries meteorológicas serão usadas e como os resultados serão identificados.

Este chunk define a janela temporal, a localização e os controles gerais usados para consultar a NASA POWER e organizar os registros climáticos. Esses parâmetros determinam o escopo dos dados ambientais descritos no módulo.

# Experimental site coordinates (College Station, TX)
coords_texas <- c(lon = -96.3344, lat = 30.6282)

# Growing season intervals
start_date_2011 <- "2011-05-01"
end_date_2011   <- "2011-09-30"

start_date_2012 <- "2012-05-01"
end_date_2012   <- "2012-09-30"

# NASA POWER variables
# PRECTOTCORR      = Corrected total precipitation (mm/day)
# T2M_MAX, T2M_MIN = Maximum and minimum air temperature at 2 m (°C)
# RH2M             = Relative humidity at 2 m (%)
# ALLSKY_SFC_SW_DWN= All-sky shortwave downward radiation (MJ m^-2 day^-1)
# WS2M             = Wind speed at 2 m (m/s)
params <- c(
  "PRECTOTCORR",
  "T2M_MAX",
  "T2M_MIN",
  "RH2M",
  "ALLSKY_SFC_SW_DWN",
  "WS2M"
)

4. Recuperação dos dados na NASA POWER

Esta seção realiza o download dos dados climáticos diários da NASA POWER.

Executamos uma consulta para cada safra. Isso mantém o fluxo transparente e facilita confirmar que cada ano experimental foi recuperado corretamente. Após a obtenção, as duas tabelas anuais serão reunidas em um único objeto para as etapas restantes da análise.

4.1 Baixar os dados climáticos diários de cada ano

O código abaixo envia duas requisições independentes ao serviço NASA POWER, uma para 2011 e outra para 2012. Um identificador year é adicionado imediatamente após a obtenção para que a origem de cada registro permaneça explícita ao longo de todo o fluxo.

A etapa a seguir documenta o download principal dos dados meteorológicos diários da NASA POWER e a construção da tabela bruta usada nas etapas seguintes.

Este chunk realiza o primeiro bloco de download dos dados climáticos da NASA POWER. Ele estabelece a base de dados diária bruta que depois será limpa e harmonizada entre as safras.

data_tx_2011 <- get_power(
  community = "AG",
  lonlat = coords_texas,
  pars = params,
  dates = c(start_date_2011, end_date_2011),
  temporal_api = "DAILY"
) %>%
  mutate(year = 2011)

data_tx_2012 <- get_power(
  community = "AG",
  lonlat = coords_texas,
  pars = params,
  dates = c(start_date_2012, end_date_2012),
  temporal_api = "DAILY"
) %>%
  mutate(year = 2012)

4.2 Reunir, formatar e salvar a base bruta

Depois que as duas tabelas anuais estão disponíveis, nós as reunimos em um único conjunto de dados e convertemos a coluna de data para um formato de data apropriado do R. Essa etapa é essencial porque a data original é retornada como uma string numérica, e as visualizações posteriores dependem de um objeto de data válido.

Também salvamos o conjunto bruto diário como arquivo CSV. Isso é importante para reprodutibilidade porque preserva a versão exata dos dados recuperados usada na análise.

Na sequência, reunimos as tabelas anuais e completamos as variáveis meteorológicas necessárias para formar o conjunto climático final do pipeline.

Este chunk completa o download climático para o período restante e combina os resultados em uma tabela diária unificada. O objetivo é obter um registro contínuo cobrindo as safras estudadas.

texas_climate_data <- bind_rows(data_tx_2011, data_tx_2012) %>%
  mutate(
    date = ymd(YYYYMMDD),
    year = factor(year, levels = c(2011, 2012))
  )

daily_preview_table <- texas_climate_data %>%
  select(date, year, PRECTOTCORR, T2M_MAX, T2M_MIN, RH2M, ALLSKY_SFC_SW_DWN, WS2M) %>%
  slice(1:10) %>%
  mutate(
    PRECTOTCORR = round(PRECTOTCORR, 2),
    T2M_MAX = round(T2M_MAX, 2),
    T2M_MIN = round(T2M_MIN, 2),
    RH2M = round(RH2M, 1),
    ALLSKY_SFC_SW_DWN = round(ALLSKY_SFC_SW_DWN, 2),
    WS2M = round(WS2M, 2)
  ) %>%
  rename(
    Date = date,
    Year = year,
    `Precipitation` = PRECTOTCORR,
    `Tmax` = T2M_MAX,
    `Tmin` = T2M_MIN,
    `Relative humidity` = RH2M,
    `Solar radiation` = ALLSKY_SFC_SW_DWN,
    `Wind speed` = WS2M
  )

knitr::kable(
  daily_preview_table,
  align = "c",
  caption = "Tabela 1. Exemplo de registros meteorológicos diários recuperados da NASA POWER para os ensaios no Texas."
) %>%
  kableExtra::kable_styling(
    full_width = FALSE,
    bootstrap_options = c("striped", "hover", "condensed", "responsive"),
    position = "center",
    font_size = 13
  ) %>%
  kableExtra::row_spec(0, bold = TRUE)

write.csv(
  texas_climate_data,
  "output/climate_results/texas_climate_data_raw.csv",
  row.names = FALSE
)

Este chunk restaura os dados climáticos já obtidos para que a versão em português apresente a mesma sequência analítica sem depender de um novo download. Dessa forma, a interpretação fica alinhada ao fluxo principal.

Este chunk mostra uma prévia organizada dos registros diários que serão usados nas figuras e nos resumos seguintes. A tabela ajuda a visualizar a estrutura dos dados antes das agregações.

Tabela 1. Exemplo de registros meteorológicos diários recuperados da NASA POWER para os ensaios no Texas.

Data	Ano	Precipitação	Tmax	Tmin	Umidade relativa	Radiação solar	Velocidade do vento
2011-05-01	2011	0.00	33.95	16.93	66.0	18.35	4.13
2011-05-02	2011	0.99	20.62	11.77	69.8	8.63	3.73
2011-05-03	2011	0.14	25.13	10.30	50.5	28.29	3.67
2011-05-04	2011	0.00	27.91	9.52	36.4	28.73	1.69
2011-05-05	2011	0.01	30.96	12.09	38.2	29.31	1.22
2011-05-06	2011	0.00	32.76	13.95	50.2	28.19	2.39
2011-05-07	2011	0.00	35.71	18.84	55.0	27.08	3.80
2011-05-08	2011	0.00	38.32	20.17	59.2	25.57	3.92
2011-05-09	2011	0.00	37.25	22.97	58.1	26.86	4.68
2011-05-10	2011	0.00	35.89	23.67	59.5	22.11	4.18

5. Análise exploratória e geração de figuras

Antes de resumir os dados em covariáveis derivadas do clima, é boa prática inspecionar visualmente as séries diárias. Essa etapa cumpre dois objetivos.

Primeiro, ajuda a verificar se os dados baixados são coerentes e livres de problemas estruturais evidentes. Segundo, ajuda a interpretar o contraste climático entre os anos em estudo, o que é importante para a discussão biológica dos experimentos.

Para tornar as figuras adequadas a um artigo científico, definimos um estilo visual comum e padronizamos títulos de eixos, unidades e formatação dos painéis.

5.1 Preparar rótulos compartilhados para as figuras

Para manter as figuras consistentes, definimos os rótulos dos meses apenas uma vez. Isso evita misturar formatos diferentes de mês entre os painéis e torna as comparações mais fáceis de ler.

Neste tutorial, as configurações gráficas são escritas diretamente dentro de cada figura. Isso torna o código mais longo, mas também mais fácil de acompanhar para leitores que ainda estão aprendendo como cada gráfico é construído.

As configurações gráficas compartilhadas abaixo garantem que as figuras de precipitação e temperatura sigam o mesmo padrão visual.

Este chunk define configurações gráficas reutilizadas nas figuras seguintes. Centralizar essas opções ajuda a manter comparabilidade visual entre as variáveis.

month_levels <- 5:9

# Month labels are fixed explicitly here so the whole script always uses
# the same format with an uppercase initial, regardless of local settings.
month_labels <- c("May", "Jun", "Jul", "Aug", "Sep")

month_breaks <- as.Date(c(
  "2011-05-01", "2011-06-01", "2011-07-01", "2011-08-01", "2011-09-01",
  "2012-05-01", "2012-06-01", "2012-07-01", "2012-08-01", "2012-09-01"
))

month_break_labels <- c(
  "May", "Jun", "Jul", "Aug", "Sep",
  "May", "Jun", "Jul", "Aug", "Sep"
)

daily_data_with_parts <- texas_climate_data %>%
  mutate(
    Year = factor(year(date), levels = c(2011, 2012)),
    Month = factor(month(date), levels = month_levels, labels = month_labels),
    Day_of_month = factor(mday(date))
  )

5.2 Visão geral da precipitação diária

Esta primeira figura fornece uma visão direta da chuva diária ao longo do tempo. Ela é útil para identificar períodos secos, concentração de chuvas e diferenças amplas entre os dois anos.

A primeira visualização de precipitação oferece um resumo amplo do comportamento da chuva nas condições avaliadas.

Este chunk constrói o primeiro gráfico de precipitação, enfatizando o perfil diário ao longo das safras. Ele oferece a visão mais ampla da dinâmica de chuva no período estudado.

p_precip_daily <- ggplot(
  texas_climate_data,
  aes(x = date, y = PRECTOTCORR, fill = year)
) +
  geom_col(alpha = 0.85, show.legend = FALSE) +
  facet_wrap(~ year, scales = "free_x") +
  labs(
    title = "Precipitação diária",
    subtitle = "College Station, Texas, EUA, 2011-2012",
    x = "Mês",
    y = expression("Precipitation (" * mm ~ day^{-1} * ")"),
    caption = "As barras representam a precipitação total diária corrigida obtida da NASA POWER."
  ) +
  scale_x_date(breaks = month_breaks, labels = month_break_labels) +
  ggthemes::scale_fill_gdocs(limits = c("2011", "2012"), guide = guide_legend(title.position = "top", nrow = 1, byrow = TRUE)) +
  theme_bw(base_size = 11) +
  theme(
    panel.grid.minor = element_blank(),
    panel.grid.major = element_line(color = "grey90", linewidth = 0.25),
    panel.border = element_rect(color = "grey40", linewidth = 0.4),
    strip.background = element_rect(fill = "grey95", color = "grey70", linewidth = 0.3),
    strip.text = element_text(face = "bold", size = 10),
    axis.title = element_text(face = "bold", size = 11),
    axis.text = element_text(size = 9.5, color = "black"),
    plot.title = element_text(face = "bold", size = 12, hjust = 0.5, margin = margin(b = 4)),
    plot.subtitle = element_text(size = 10, hjust = 0.5, margin = margin(b = 8)),
    plot.caption = element_text(size = 8.5, hjust = 0, color = "grey30", margin = margin(t = 8)),
    plot.caption.position = "plot",
    legend.position = "bottom",
    legend.justification = "center",
    legend.direction = "horizontal",
    legend.title = element_text(face = "bold", size = 10),
    legend.text = element_text(size = 9),
    legend.key.width = grid::unit(1.2, "cm"),
    legend.box.margin = margin(t = 4),
    plot.margin = margin(8, 10, 8, 8)
  )

p_precip_daily

Figura 1. Precipitação diária ao longo das safras de milho de 2011 e 2012 em College Station, Texas. As barras representam a precipitação diária corrigida obtida da NASA POWER.

Past versions of plot_precipitation_overview-1.png

Version	Author	Date
d54afd7	WevertonGomesCosta	2026-04-01
00ef3ef	WevertonGomesCosta	2026-04-01
0c27277	WevertonGomesCosta	2026-03-30

5.3 Precipitação diária por mês e dia do mês

A figura seguinte reorganiza a mesma informação de chuva de uma forma que facilita a comparação dentro da safra. Em vez de mostrar apenas a série cronológica, comparamos os dias do mês entre os dois anos dentro de cada painel mensal.

Esse layout é especialmente útil quando o objetivo é verificar se uma safra foi sistematicamente mais seca ou mais chuvosa em períodos específicos do desenvolvimento da cultura.

Em seguida, a precipitação é reorganizada em visualizações agrupadas para facilitar a comparação entre ambientes.

Este chunk reorganiza a precipitação por mês e dia para facilitar a comparação entre anos dentro da safra. A figura resultante evidencia melhor o posicionamento temporal dos eventos de chuva.

p1 <- ggplot(daily_data_with_parts, aes(x = Day_of_month, y = PRECTOTCORR, fill = Year)) +
  geom_col(position = position_dodge(width = 0.85), alpha = 0.85, width = 0.8) +
  facet_wrap(~ Month, scales = "free_x", ncol = 5) +
  labs(
    title = "Precipitação diária por mês e ano",
    subtitle = "Agrupada por dia do mês dentro de cada painel mensal",
    x = "Dia do mês",
    y = expression("Precipitation (" * mm ~ day^{-1} * ")"),
    fill = "Ano",
    caption = "Essa organização facilita a comparação, dentro da safra, dos padrões de chuva entre os anos."
  ) +
  scale_x_discrete(breaks = c("1", "5", "10", "15", "20", "25", "30", "31")) +
  ggthemes::scale_fill_gdocs(limits = c("2011", "2012"), guide = guide_legend(title.position = "top", nrow = 1, byrow = TRUE)) +
  theme_bw(base_size = 11) +
  theme(
    panel.grid.minor = element_blank(),
    panel.grid.major = element_line(color = "grey90", linewidth = 0.25),
    panel.border = element_rect(color = "grey40", linewidth = 0.4),
    strip.background = element_rect(fill = "grey95", color = "grey70", linewidth = 0.3),
    strip.text = element_text(face = "bold", size = 10),
    axis.title = element_text(face = "bold", size = 11),
    axis.text = element_text(size = 9.5, color = "black"),
    plot.title = element_text(face = "bold", size = 12, hjust = 0.5, margin = margin(b = 4)),
    plot.subtitle = element_text(size = 10, hjust = 0.5, margin = margin(b = 8)),
    plot.caption = element_text(size = 8.5, hjust = 0, color = "grey30", margin = margin(t = 8)),
    plot.caption.position = "plot",
    legend.position = "bottom",
    legend.justification = "center",
    legend.direction = "horizontal",
    legend.title = element_text(face = "bold", size = 10),
    legend.text = element_text(size = 9),
    legend.key.width = grid::unit(1.2, "cm"),
    legend.box.margin = margin(t = 4),
    plot.margin = margin(8, 10, 8, 8)
  )

p1

Figura 2. Precipitação diária organizada por dia do mês dentro de cada mês para facilitar a comparação entre anos durante a safra.

Past versions of plot_precipitation_grouped-1.png

Version	Author	Date
d54afd7	WevertonGomesCosta	2026-04-01
00ef3ef	WevertonGomesCosta	2026-04-01
0c27277	WevertonGomesCosta	2026-03-30

5.4 Totais mensais de precipitação

A precipitação diária é informativa, mas os totais mensais fornecem um resumo sazonal mais simples. Esta figura agrega a série diária e permite uma comparação mais limpa da entrada cumulativa de água entre anos e meses.

Depois disso, a precipitação é resumida em nível mensal para destacar com mais clareza os contrastes sazonais.

Este chunk agrega a precipitação diária em totais mensais. Esse resumo ajuda a destacar padrões sazonais mais amplos que podem passar despercebidos nos valores diários.

monthly_precip <- texas_climate_data %>%
  mutate(
    year = factor(year(date), levels = c(2011, 2012)),
    month = factor(month(date), levels = month_levels, labels = month_labels)
  ) %>%
  group_by(year, month) %>%
  summarise(total_precipitation = sum(PRECTOTCORR, na.rm = TRUE), .groups = "drop")

p2 <- ggplot(monthly_precip, aes(x = month, y = total_precipitation, fill = year)) +
  geom_col(position = position_dodge(width = 0.8), width = 0.7, alpha = 0.9) +
  labs(
    title = "Totais mensais de precipitação",
    subtitle = "Comparação entre as safras de 2011 e 2012",
    x = "Mês",
    y = "Precipitação total (mm)",
    fill = "Ano",
    caption = "As barras representam a precipitação acumulada dentro de cada mês da safra."
  ) +
  ggthemes::scale_fill_gdocs(limits = c("2011", "2012"), guide = guide_legend(title.position = "top", nrow = 1, byrow = TRUE)) +
  theme_bw(base_size = 11) +
  theme(
    panel.grid.minor = element_blank(),
    panel.grid.major = element_line(color = "grey90", linewidth = 0.25),
    panel.border = element_rect(color = "grey40", linewidth = 0.4),
    strip.background = element_rect(fill = "grey95", color = "grey70", linewidth = 0.3),
    strip.text = element_text(face = "bold", size = 10),
    axis.title = element_text(face = "bold", size = 11),
    axis.text = element_text(size = 9.5, color = "black"),
    plot.title = element_text(face = "bold", size = 12, hjust = 0.5, margin = margin(b = 4)),
    plot.subtitle = element_text(size = 10, hjust = 0.5, margin = margin(b = 8)),
    plot.caption = element_text(size = 8.5, hjust = 0, color = "grey30", margin = margin(t = 8)),
    plot.caption.position = "plot",
    legend.position = "bottom",
    legend.justification = "center",
    legend.direction = "horizontal",
    legend.title = element_text(face = "bold", size = 10),
    legend.text = element_text(size = 9),
    legend.key.width = grid::unit(1.2, "cm"),
    legend.box.margin = margin(t = 4),
    plot.margin = margin(8, 10, 8, 8)
  )

p2

Figura 3. Totais mensais de precipitação durante as safras de milho de 2011 e 2012 em College Station, Texas.

Past versions of plot_monthly_precipitation-1.png

Version	Author	Date
d54afd7	WevertonGomesCosta	2026-04-01
00ef3ef	WevertonGomesCosta	2026-04-01
0c27277	WevertonGomesCosta	2026-03-30

5.5 Temperatura diária do ar

A temperatura é outro descritor central do ambiente de cultivo. Nesta figura, comparamos os perfis diários de temperatura mínima, média e máxima entre as duas safras.

As linhas suavizadas tracejadas não substituem os dados originais. Seu papel é apenas ajudar a visualizar as tendências sazonais gerais para cada métrica de temperatura.

A figura de temperatura abaixo é usada para comparar o perfil térmico dos ambientes ao longo do tempo.

Este chunk resume as temperaturas mínima, média e máxima diárias observadas durante as safras. Ele ajuda a descrever contrastes térmicos que podem influenciar as etapas analíticas posteriores.

temperature_data <- texas_climate_data %>%
  mutate(T2M_MEAN = (T2M_MAX + T2M_MIN) / 2) %>%
  pivot_longer(
    cols = c(T2M_MIN, T2M_MEAN, T2M_MAX),
    names_to = "Temperature_metric",
    values_to = "Temperature"
  ) %>%
  mutate(
    Temperature_metric = factor(
      Temperature_metric,
      levels = c("T2M_MIN", "T2M_MEAN", "T2M_MAX"),
      labels = c("Mínima", "Média", "Máxima")
    )
  )

p3 <- ggplot(temperature_data, aes(x = date, y = Temperature, color = Temperature_metric)) +
  geom_line(linewidth = 0.55, alpha = 0.85) +
  geom_smooth(
    aes(group = Temperature_metric),
    method = "loess",
    se = FALSE,
    linetype = "dashed",
    color = "black",
    linewidth = 0.5,
    show.legend = FALSE
  ) +
  facet_wrap(~ year, scales = "free_x") +
  labs(
    title = "Temperatura diária do ar",
    subtitle = "Valores mínimos, médios e máximos; linhas tracejadas indicam tendências LOESS",
    x = "Mês",
    y = expression("Air temperature (" * degree * "C)"),
    color = "Métrica de temperatura",
    caption = "As curvas LOESS tracejadas são incluídas apenas para resumir as trajetórias sazonais de cada métrica de temperatura."
  ) +
  scale_x_date(breaks = month_breaks, labels = month_break_labels) +
  scale_color_manual(values = c("Mínima" = "#4285F4", "Média" = "#F4B400", "Máxima" = "#DB4437"), limits = c("Mínima", "Média", "Máxima"), guide = guide_legend(title.position = "top", nrow = 1, byrow = TRUE)) +
  theme_bw(base_size = 11) +
  theme(
    panel.grid.minor = element_blank(),
    panel.grid.major = element_line(color = "grey90", linewidth = 0.25),
    panel.border = element_rect(color = "grey40", linewidth = 0.4),
    strip.background = element_rect(fill = "grey95", color = "grey70", linewidth = 0.3),
    strip.text = element_text(face = "bold", size = 10),
    axis.title = element_text(face = "bold", size = 11),
    axis.text = element_text(size = 9.5, color = "black"),
    plot.title = element_text(face = "bold", size = 12, hjust = 0.5, margin = margin(b = 4)),
    plot.subtitle = element_text(size = 10, hjust = 0.5, margin = margin(b = 8)),
    plot.caption = element_text(size = 8.5, hjust = 0, color = "grey30", margin = margin(t = 8)),
    plot.caption.position = "plot",
    legend.position = "bottom",
    legend.justification = "center",
    legend.direction = "horizontal",
    legend.title = element_text(face = "bold", size = 10),
    legend.text = element_text(size = 9),
    legend.key.width = grid::unit(1.2, "cm"),
    legend.box.margin = margin(t = 4),
    plot.margin = margin(8, 10, 8, 8)
  )

p3

Figura 4. Temperatura mínima, média e máxima diária do ar durante as safras de milho de 2011 e 2012. As linhas tracejadas indicam tendências LOESS usadas apenas para auxiliar a interpretação visual.

Past versions of plot_temperature-1.png

Version	Author	Date
8919efd	WevertonGomesCosta	2026-04-02
d54afd7	WevertonGomesCosta	2026-04-01
00ef3ef	WevertonGomesCosta	2026-04-01
0c27277	WevertonGomesCosta	2026-03-30

5.6 Figura climática principal combinada

Depois de criar os gráficos principais de precipitação e temperatura, nós os combinamos em uma única figura multipainel. Isso é útil quando a saída final se destina a relatórios, manuscritos ou material suplementar.

A figura também é exportada como um arquivo TIFF de alta resolução, que é uma exigência comum para submissão a periódicos.

Os principais painéis de precipitação e temperatura são combinados abaixo em um único arranjo, pronto para inspeção ou uso em relatórios.

Este chunk reúne em uma única figura os principais painéis de precipitação e temperatura. A visão conjunta oferece um resumo compacto das diferenças climáticas entre as safras.

main_layout <- "
AA
BC
"

combined_plot <- patchwork::wrap_plots(
  A = p1,
  B = p2,
  C = p3,
  design = main_layout
) +
  patchwork::plot_annotation(
    tag_levels = "A",
    title = "Main climatic contrasts between growing seasons",
    theme = theme(
      plot.title = element_text(face = "bold", size = 13, hjust = 0.5, margin = margin(b = 8))
    )
  )

print(combined_plot)

Figura 5. Resumo em múltiplos painéis dos principais contrastes climáticos entre os anos, incluindo precipitação diária por mês, totais mensais de precipitação e perfis diários de temperatura do ar.

Past versions of combined_main_plot-1.png

Version	Author	Date
8919efd	WevertonGomesCosta	2026-04-02
d54afd7	WevertonGomesCosta	2026-04-01
00ef3ef	WevertonGomesCosta	2026-04-01
0c27277	WevertonGomesCosta	2026-03-30

ggsave(
  filename = "output/climate_results/combined_climate_plot.tiff",
  plot = combined_plot,
  width = 14,
  height = 10,
  dpi = 300,
  compression = "lzw",
  bg = "white"
)

5.7 Variáveis climáticas diárias adicionais

Além de precipitação e temperatura, o script também examina radiação solar, velocidade do vento e umidade relativa. Essas variáveis são importantes porque ajudam a descrever demanda evaporativa, secura atmosférica e disponibilidade de energia ao longo do ciclo da cultura.

As figuras complementares abaixo documentam padrões climáticos secundários que não aparecem no painel principal.

Este chunk gera gráficos diários complementares para radiação solar, velocidade do vento e umidade relativa. Essas variáveis ampliam a caracterização ambiental além de precipitação e temperatura.

p4 <- ggplot(
  texas_climate_data,
  aes(x = date, y = ALLSKY_SFC_SW_DWN, color = "Radiação solar")
) +
  geom_line(alpha = 0.85, linewidth = 0.55, show.legend = FALSE) +
  geom_smooth(method = "loess", se = FALSE, linetype = "dashed", color = "black", linewidth = 0.5) +
  facet_wrap(~ year, scales = "free_x") +
  labs(
    title = "Radiação solar de ondas curtas diária",
    subtitle = "A linha tracejada indica a tendência LOESS",
    x = "Mês",
    y = expression("Shortwave solar radiation (" * MJ ~ m^{-2} ~ day^{-1} * ")"),
    caption = "Radiação solar de ondas curtas incidente diariamente durante a safra."
  ) +
  scale_x_date(breaks = month_breaks, labels = month_break_labels) +
  ggthemes::scale_color_gdocs(limits = "Radiação solar", guide = "none") +
  theme_bw(base_size = 11) +
  theme(
    panel.grid.minor = element_blank(),
    panel.grid.major = element_line(color = "grey90", linewidth = 0.25),
    panel.border = element_rect(color = "grey40", linewidth = 0.4),
    strip.background = element_rect(fill = "grey95", color = "grey70", linewidth = 0.3),
    strip.text = element_text(face = "bold", size = 10),
    axis.title = element_text(face = "bold", size = 11),
    axis.text = element_text(size = 9.5, color = "black"),
    plot.title = element_text(face = "bold", size = 12, hjust = 0.5, margin = margin(b = 4)),
    plot.subtitle = element_text(size = 10, hjust = 0.5, margin = margin(b = 8)),
    plot.caption = element_text(size = 8.5, hjust = 0, color = "grey30", margin = margin(t = 8)),
    plot.caption.position = "plot",
    legend.position = "bottom",
    legend.justification = "center",
    legend.direction = "horizontal",
    legend.title = element_text(face = "bold", size = 10),
    legend.text = element_text(size = 9),
    legend.key.width = grid::unit(1.2, "cm"),
    legend.box.margin = margin(t = 4),
    plot.margin = margin(8, 10, 8, 8)
  )

p5 <- ggplot(
  texas_climate_data,
  aes(x = date, y = WS2M, color = "Velocidade do vento")
) +
  geom_line(alpha = 0.85, linewidth = 0.55, show.legend = FALSE) +
  geom_smooth(method = "loess", se = FALSE, linetype = "dashed", color = "black", linewidth = 0.5) +
  facet_wrap(~ year, scales = "free_x") +
  labs(
    title = "Velocidade diária do vento a 2 m",
    subtitle = "A linha tracejada indica a tendência LOESS",
    x = "Mês",
    y = expression("Wind speed (" * m ~ s^{-1} * ")"),
    caption = "Velocidade diária do vento medida a 2 m acima do solo."
  ) +
  scale_x_date(breaks = month_breaks, labels = month_break_labels) +
  ggthemes::scale_color_gdocs(limits = "Velocidade do vento", guide = "none") +
  theme_bw(base_size = 11) +
  theme(
    panel.grid.minor = element_blank(),
    panel.grid.major = element_line(color = "grey90", linewidth = 0.25),
    panel.border = element_rect(color = "grey40", linewidth = 0.4),
    strip.background = element_rect(fill = "grey95", color = "grey70", linewidth = 0.3),
    strip.text = element_text(face = "bold", size = 10),
    axis.title = element_text(face = "bold", size = 11),
    axis.text = element_text(size = 9.5, color = "black"),
    plot.title = element_text(face = "bold", size = 12, hjust = 0.5, margin = margin(b = 4)),
    plot.subtitle = element_text(size = 10, hjust = 0.5, margin = margin(b = 8)),
    plot.caption = element_text(size = 8.5, hjust = 0, color = "grey30", margin = margin(t = 8)),
    plot.caption.position = "plot",
    legend.position = "bottom",
    legend.justification = "center",
    legend.direction = "horizontal",
    legend.title = element_text(face = "bold", size = 10),
    legend.text = element_text(size = 9),
    legend.key.width = grid::unit(1.2, "cm"),
    legend.box.margin = margin(t = 4),
    plot.margin = margin(8, 10, 8, 8)
  )

p6 <- ggplot(
  texas_climate_data,
  aes(x = date, y = RH2M, color = "Umidade relativa")
) +
  geom_line(alpha = 0.85, linewidth = 0.55, show.legend = FALSE) +
  geom_smooth(method = "loess", se = FALSE, linetype = "dashed", color = "black", linewidth = 0.5) +
  facet_wrap(~ year, scales = "free_x") +
  labs(
    title = "Umidade relativa diária a 2 m",
    subtitle = "A linha tracejada indica a tendência LOESS",
    x = "Mês",
    y = "Umidade relativa (%)",
    caption = "Umidade relativa diária medida a 2 m acima do solo."
  ) +
  scale_x_date(breaks = month_breaks, labels = month_break_labels) +
  ggthemes::scale_color_gdocs(limits = "Umidade relativa", guide = "none") +
  theme_bw(base_size = 11) +
  theme(
    panel.grid.minor = element_blank(),
    panel.grid.major = element_line(color = "grey90", linewidth = 0.25),
    panel.border = element_rect(color = "grey40", linewidth = 0.4),
    strip.background = element_rect(fill = "grey95", color = "grey70", linewidth = 0.3),
    strip.text = element_text(face = "bold", size = 10),
    axis.title = element_text(face = "bold", size = 11),
    axis.text = element_text(size = 9.5, color = "black"),
    plot.title = element_text(face = "bold", size = 12, hjust = 0.5, margin = margin(b = 4)),
    plot.subtitle = element_text(size = 10, hjust = 0.5, margin = margin(b = 8)),
    plot.caption = element_text(size = 8.5, hjust = 0, color = "grey30", margin = margin(t = 8)),
    plot.caption.position = "plot",
    legend.position = "bottom",
    legend.justification = "center",
    legend.direction = "horizontal",
    legend.title = element_text(face = "bold", size = 10),
    legend.text = element_text(size = 9),
    legend.key.width = grid::unit(1.2, "cm"),
    legend.box.margin = margin(t = 4),
    plot.margin = margin(8, 10, 8, 8)
  )

5.8 Figura combinada para variáveis climáticas adicionais

Como na subseção anterior, combinamos os gráficos individuais em uma única figura. O layout foi construído com patchwork::wrap_plots(), o que evita o erro de composição gerado quando os operadores do patchwork não são reconhecidos corretamente durante a renderização.

As figuras climáticas complementares são organizadas abaixo em um painel compacto de múltiplos gráficos.

Este chunk combina as variáveis climáticas adicionais em um resumo multipainel. Apresentar essas séries em conjunto facilita comparar seu comportamento sazonal de forma rápida.

additional_layout <- "
AB
CC
"

combined_additional_plots <- patchwork::wrap_plots(
  A = p4,
  B = p5,
  C = p6,
  design = additional_layout
) +
  patchwork::plot_annotation(
    tag_levels = "A",
    title = "Additional climatic contrasts between growing seasons",
    theme = theme(
      plot.title = element_text(face = "bold", size = 13, hjust = 0.5, margin = margin(b = 8))
    )
  )

print(combined_additional_plots)

Figura 7. Resumo em múltiplos painéis das variáveis climáticas adicionais entre os anos, incluindo radiação solar de ondas curtas, velocidade do vento e umidade relativa.

Past versions of combined_additional_plot-1.png

Version	Author	Date
d54afd7	WevertonGomesCosta	2026-04-01
00ef3ef	WevertonGomesCosta	2026-04-01
0c27277	WevertonGomesCosta	2026-03-30

ggsave(
  filename = "output/climate_results/combined_additional_climate_plots.tiff",
  plot = combined_additional_plots,
  width = 14,
  height = 10,
  dpi = 300,
  compression = "lzw",
  bg = "white"
)

6. Covariáveis derivadas do clima

A próxima etapa transforma os registros climáticos diários em variáveis-resumo anuais. Esta é a transição analítica mais importante do script, porque converte uma série temporal bruta em atributos que podem ser usados em modelos preditivos posteriores.

Esses resumos foram pensados para capturar aspectos biologicamente relevantes de cada safra, incluindo acúmulo térmico, disponibilidade de água, secura atmosférica e frequência de estresse.

6.1 Derivar variáveis intermediárias diárias

Algumas covariáveis derivadas do clima dependem de quantidades que não são fornecidas diretamente pela NASA POWER. Por isso, primeiro calculamos variáveis intermediárias diárias como graus-dia (GDD), amplitude térmica diária, temperatura média diária, pressão de vapor de saturação, pressão de vapor real e déficit de pressão de vapor (VPD).

Essas variáveis intermediárias são calculadas dia a dia e depois resumidas no nível anual na próxima subseção.

A etapa a seguir documenta o cálculo das covariáveis climáticas anuais e expandidas que serão usadas depois na construção das entradas derivadas do clima.

Este chunk deriva as covariáveis climáticas que serão usadas posteriormente nos módulos de matrizes, kernels e predição. Ele converte as séries diárias brutas em variáveis estruturadas para o pipeline analítico.

texas_climate_data <- texas_climate_data %>%
  mutate(
    T_base = 10,
    T_upper = 30,
    T_eff_min = if_else(T2M_MIN < T_base, T_base, T2M_MIN),
    T_eff_max = if_else(T2M_MAX > T_upper, T_upper, T2M_MAX),
    T_avg_eff = (T_eff_min + T_eff_max) / 2,
    GDD = T_avg_eff - T_base,
    DTR = T2M_MAX - T2M_MIN,
    T_AVG_DAILY = (T2M_MAX + T2M_MIN) / 2,

    # Tetens equation, resulting in kPa
    SAT_VAP_PRES = 0.6108 * exp((17.27 * T_AVG_DAILY) / (T_AVG_DAILY + 237.3)),
    ACT_VAP_PRES = SAT_VAP_PRES * (RH2M / 100),
    VPD_DAILY = SAT_VAP_PRES - ACT_VAP_PRES
  )

weather_covariates <- texas_climate_data %>%
  mutate(year_numeric = as.integer(as.character(year))) %>%
  group_by(year_numeric) %>%
  summarise(
    TMAX_AVG = mean(T2M_MAX, na.rm = TRUE),
    TMIN_AVG = mean(T2M_MIN, na.rm = TRUE),
    GDD_CUM = sum(GDD, na.rm = TRUE),
    HEAT_STRESS_DAYS = sum(T2M_MAX > 35, na.rm = TRUE),

    PRECTOT = sum(PRECTOTCORR, na.rm = TRUE),
    DRY_DAYS = sum(PRECTOTCORR == 0, na.rm = TRUE),
    RH_AVG = mean(RH2M, na.rm = TRUE),

    WS2M_AVG = mean(WS2M, na.rm = TRUE),
    RAD_CUM = sum(ALLSKY_SFC_SW_DWN, na.rm = TRUE),

    T_AVG = mean(T_AVG_DAILY, na.rm = TRUE),
    DTR_AVG = mean(DTR, na.rm = TRUE),
    COLD_STRESS_DAYS = sum(T2M_MIN < 10, na.rm = TRUE),

    RAINY_DAYS = sum(PRECTOTCORR > 0, na.rm = TRUE),
    PREC_INTENSITY = if_else(RAINY_DAYS > 0, PRECTOT / RAINY_DAYS, 0),
    LOW_RH_DAYS = sum(RH2M < 30, na.rm = TRUE),

    VPD_AVG = mean(VPD_DAILY, na.rm = TRUE),
    VPD_STRESS_DAYS = sum(VPD_DAILY > 1.5, na.rm = TRUE),
    .groups = "drop"
  ) %>%
  rename(year = year_numeric)

# Optional expanded table: daily weather data plus annual weather summaries
# The output file names keep the historical term "environmental_covariates"
# only to preserve compatibility with the downstream scripts already used in
# the project pipeline.
ecs_expanded <- texas_climate_data %>%
  transmute(
    year = as.integer(as.character(year)),
    date = date,
    PRECTOTCORR = PRECTOTCORR,
    T2M_MAX = T2M_MAX,
    T2M_MIN = T2M_MIN,
    RH2M = RH2M,
    ALLSKY_SFC_SW_DWN = ALLSKY_SFC_SW_DWN,
    WS2M = WS2M,
    GDD = GDD,
    DTR = DTR,
    T_AVG_DAILY = T_AVG_DAILY,
    SAT_VAP_PRES = SAT_VAP_PRES,
    ACT_VAP_PRES = ACT_VAP_PRES,
    VPD_DAILY = VPD_DAILY
  ) %>%
  left_join(weather_covariates, by = "year")

weather_covariates_dictionary <- tibble::tibble(
  Variable = c(
    "TMAX_AVG", "TMIN_AVG", "GDD_CUM", "HEAT_STRESS_DAYS", "PRECTOT",
    "DRY_DAYS", "RH_AVG", "WS2M_AVG", "RAD_CUM", "T_AVG",
    "DTR_AVG", "COLD_STRESS_DAYS", "RAINY_DAYS", "PREC_INTENSITY",
    "LOW_RH_DAYS", "VPD_AVG", "VPD_STRESS_DAYS"
  ),
  Unit = c(
    "°C", "°C", "°C day", "days", "mm",
    "days", "%", "m s^-1", "MJ m^-2", "°C",
    "°C", "days", "days", "mm day^-1",
    "days", "kPa", "days"
  ),
  Description = c(
    "Average maximum temperature during the season",
    "Average minimum temperature during the season",
    "Accumulated growing degree days",
    "Number of heat stress days",
    "Total precipitation during the season",
    "Number of dry days",
    "Average relative humidity",
    "Average wind speed at 2 m",
    "Accumulated shortwave radiation",
    "Average mean temperature",
    "Average daily temperature range",
    "Number of cold stress days",
    "Number of rainy days",
    "Average rainfall intensity on rainy days",
    "Number of low-humidity days",
    "Average vapor pressure deficit",
    "Number of vapor pressure deficit stress days"
  )
)

knitr::kable(
  weather_covariates_dictionary,
  align = c("l", "c", "l"),
  caption = "Tabela 2. Covariáveis derivadas do clima usadas para caracterizar as safras e apoiar a construção do kernel climático."
) %>%
  kableExtra::kable_styling(
    full_width = FALSE,
    bootstrap_options = c("striped", "hover", "condensed", "responsive"),
    position = "center",
    font_size = 12
  ) %>%
  kableExtra::row_spec(0, bold = TRUE)

weather_covariates_display <- weather_covariates %>%
  mutate(across(-year, ~ round(.x, 2))) %>%
  rename(Year = year)

knitr::kable(
  weather_covariates_display,
  align = c("c", rep("c", ncol(weather_covariates_display) - 1)),
  caption = "Tabela 3. Covariáveis anuais derivadas do clima obtidas dos dados NASA POWER para as safras de 2011 e 2012."
) %>%
  kableExtra::kable_styling(
    full_width = FALSE,
    bootstrap_options = c("striped", "hover", "condensed", "responsive"),
    position = "center",
    font_size = 11
  ) %>%
  kableExtra::row_spec(0, bold = TRUE) %>%
  kableExtra::scroll_box(width = "100%", height = "320px")

Este chunk restaura as saídas climáticas já processadas para que a versão em português possa continuar a apresentação a partir dos objetos finais. Assim, a ênfase permanece na interpretação do resultado climático.

Este chunk apresenta tabelas-resumo das covariáveis climáticas derivadas. Essas tabelas ajudam a verificar como os registros diários foram condensados para uso analítico posterior.

Tabela 2. Covariáveis derivadas do clima usadas para caracterizar as safras e apoiar a construção do kernel climático.

Variable	Unit	Description
TMAX_AVG	°C	Temperatura máxima média durante a safra
TMIN_AVG	°C	Temperatura mínima média durante a safra
GDD_CUM	°C day	Graus-dia acumulados
HEAT_STRESS_DAYS	days	Número de dias com estresse térmico
PRECTOT	mm	Precipitação total durante a safra
DRY_DAYS	days	Número de dias secos
RH_AVG	%	Umidade relativa média
WS2M_AVG	m s^-1	Velocidade média do vento a 2 m
RAD_CUM	MJ m^-2	Radiação de onda curta acumulada
T_AVG	°C	Temperatura média
DTR_AVG	°C	Amplitude térmica diária média
COLD_STRESS_DAYS	days	Número de dias com estresse por frio
RAINY_DAYS	days	Número de dias chuvosos
PREC_INTENSITY	mm day^-1	Intensidade média de chuva em dias chuvosos
LOW_RH_DAYS	days	Número de dias com baixa umidade
VPD_AVG	kPa	Déficit de pressão de vapor médio
VPD_STRESS_DAYS	days	Número de dias com estresse por déficit de pressão de vapor

Tabela 3. Covariáveis anuais derivadas do clima obtidas dos dados NASA POWER para as safras de 2011 e 2012.

Ano	TMAX_AVG	TMIN_AVG	GDD_CUM	HEAT_STRESS_DAYS	PRECTOT	DRY_DAYS	RH_AVG	WS2M_AVG	RAD_CUM	T_AVG	DTR_AVG	COLD_STRESS_DAYS	RAINY_DAYS	PREC_INTENSITY	LOW_RH_DAYS	VPD_AVG	VPD_STRESS_DAYS
2011	37.65	23.23	2530.04	120	168.36	74	51.72	2.59	3687.10	30.44	14.43	1	79	2.13	4	2.16	125
2012	33.98	21.98	2417.42	64	442.25	49	68.11	2.02	3414.31	27.98	12.00	0	104	4.25	0	1.25	50

6.2 Interpretação das covariáveis derivadas do clima resultantes

A tabela final contém uma linha por ano e vários descritores climáticos.

Por exemplo:

• GDD_CUM resume o acúmulo térmico durante o ciclo da cultura.
• PRECTOT resume a entrada total de água pela chuva.
• DRY_DAYS e HEAT_STRESS_DAYS quantificam a frequência de estresse.
• VPD_AVG e VPD_STRESS_DAYS capturam a secura atmosférica.
• RAD_CUM resume a energia solar sazonal disponível para a cultura.

Esta tabela é a saída prática que conecta o tutorial climático à etapa de modelagem preditiva do projeto e à posterior construção do kernel climático.

No pipeline final reduzido, essas saídas derivadas do clima são usadas apenas nos modelos que incluem explicitamente W, isto é, Eta10-Eta18. Elas não se destinam a substituir o efeito categórico de ambiente (E), que ainda captura a distinção entre os quatro macroambientes. Como a informação climática é resumida no nível do ano, CS11_WS e CS11_WW compartilham a assinatura climática de 2011, enquanto CS12_WS e CS12_WW compartilham a assinatura climática de 2012.

7. Salvar as saídas finais

A última seção armazena os arquivos processados gerados pelo tutorial. Salvar explicitamente as saídas é importante porque separa a obtenção dos dados da modelagem posterior, tornando o fluxo mais fácil de reproduzir e auditar.

Três arquivos são exportados:

1. texas_climate_data_raw.csv: registros meteorológicos diários brutos.
2. environmental_covariates.csv: uma linha por ano com os resumos climáticos anuais derivados.
3. environmental_covariates_expanded.csv: registros meteorológicos diários limpos vinculados aos resumos climáticos anuais.

Os dois últimos arquivos mantêm seus nomes históricos para compatibilidade com os scripts posteriores, embora agora representem covariáveis derivadas do clima.

Por fim, o tutorial documenta a gravação das saídas climáticas brutas e derivadas em disco, para que os módulos posteriores reutilizem esses arquivos sem refazer o processamento.

Este chunk grava as saídas climáticas processadas para uso downstream. Com isso, o módulo climático é encerrado e fornece entradas estáveis para as etapas seguintes do workflow.

write.csv(
  weather_covariates,
  "output/climate_results/environmental_covariates.csv",
  row.names = FALSE
)

write.csv(
  ecs_expanded,
  "output/climate_results/environmental_covariates_expanded.csv",
  row.names = FALSE
)

cat("Climate files successfully saved in 'output/climate_results/'.")

8. Observações finais

Este tutorial estabelece um pipeline reprodutível de pré-processamento climático para o estudo. A partir de observações meteorológicas diárias, ele produz um conjunto curado de covariáveis derivadas do clima e figuras prontas para artigo.

No fluxo mais amplo do projeto, essas saídas não são o objetivo final. Elas são objetos científicos intermediários que apoiam a construção do kernel climático (W) usado nos modelos Eta10-Eta18.

Também é importante observar que o kernel climático não substitui o efeito categórico de ambiente (E). Neste conjunto de dados, a assinatura climática de 2011 é compartilhada por CS11_WS e CS11_WW, e a assinatura climática de 2012 é compartilhada por CS12_WS e CS12_WW. Portanto, W captura similaridade meteorológica específica do ano, enquanto E continua representando a distinção categórica entre os quatro macroambientes.

Navegação entre etapas

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Próxima etapa: Matrizes

Session information

sessionInfo()

R version 4.5.1 (2025-06-13 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26200)

Matrix products: default
  LAPACK version 3.12.1

locale:
[1] LC_COLLATE=Portuguese_Brazil.utf8  LC_CTYPE=Portuguese_Brazil.utf8   
[3] LC_MONETARY=Portuguese_Brazil.utf8 LC_NUMERIC=C                      
[5] LC_TIME=Portuguese_Brazil.utf8    

time zone: America/Sao_Paulo
tzcode source: internal

attached base packages:
[1] grid      stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] kableExtra_1.4.0 knitr_1.50       ggthemes_5.2.0   patchwork_1.3.2 
 [5] lubridate_1.9.4  forcats_1.0.1    stringr_1.6.0    dplyr_1.2.0     
 [9] purrr_1.2.1      readr_2.2.0      tidyr_1.3.2      tibble_3.3.1    
[13] ggplot2_4.0.2    tidyverse_2.0.0  nasapower_4.2.5 

loaded via a namespace (and not attached):
 [1] gtable_0.3.6       xfun_0.54          bslib_0.9.0        lattice_0.22-7    
 [5] tzdb_0.5.0         vctrs_0.7.1        tools_4.5.1        generics_0.1.4    
 [9] parallel_4.5.1     pkgconfig_2.0.3    Matrix_1.7-4       RColorBrewer_1.1-3
[13] S7_0.2.1           lifecycle_1.0.5    compiler_4.5.1     farver_2.1.2      
[17] git2r_0.36.2       textshaping_1.0.4  httpuv_1.6.16      htmltools_0.5.9   
[21] sass_0.4.10        yaml_2.3.12        later_1.4.4        pillar_1.11.1     
[25] crayon_1.5.3       jquerylib_0.1.4    whisker_0.4.1      cachem_1.1.0      
[29] nlme_3.1-168       tidyselect_1.2.1   digest_0.6.39      stringi_1.8.7     
[33] splines_4.5.1      labeling_0.4.3     rprojroot_2.1.1    fastmap_1.2.0     
[37] cli_3.6.5          magrittr_2.0.4     withr_3.0.2        scales_1.4.0      
[41] promises_1.5.0     bit64_4.6.0-1      timechange_0.3.0   rmarkdown_2.30    
[45] bit_4.6.0          otel_0.2.0         workflowr_1.7.2    ragg_1.5.0        
[49] hms_1.1.4          evaluate_1.0.5     viridisLite_0.4.2  mgcv_1.9-4        
[53] rlang_1.1.7        Rcpp_1.1.1         glue_1.8.0         xml2_1.5.1        
[57] svglite_2.2.2      rstudioapi_0.17.1  vroom_1.7.0        jsonlite_2.0.0    
[61] R6_2.6.1           systemfonts_1.3.1  fs_1.6.6