Zonal statistics of climate indicators for Brazilian municipalities

Introduction

Climate indicators are used on several statistical models for many research areas and are specially important for modelling Climate Sensitive Diseases (CSD) incidence. Those models usually adopts a lattice structure, where its data is aggregated at administrative boundaries (e.g. disease incidence), but climate indicators are usually presented in a continuous regular grid format.

To make climate indicators compatible with lattice structures, zonal statistics may be adopted. Zonal statistics are descriptive statistics calculated using a set of cells that spatially intersects a given spatial boundary. For each boundary in a map, statistics like average, maximum value, minimum value, standard deviation, and sum are obtained to represent the cell’s values that intersect the boundary.

I created some zonal statistics of climate indicators datasets for Brazilian municipalities from some climate data products.

Zonal ERA5-Land

1950-2022:

2023:

The ERA5-Land data (Muñoz-Sabater et al. 2021) presents hourly climate indicators at 0.1° × 0.1° horizontal resolution with global coverage, from 1950 to present.

Selected indicators hourly data were downloaded and aggregated at daily basis to compute zonal statistics for the Brazilian municipalities.

The following indicators and aggregations were computed.

ERA5-Land indicators	Daily time-aggregating functions	Spatial zonal statistics
Temperature (2m)	mean, max, min	max, min, stdev, count
Dewpoint temp. (2m)	mean	max, min, stdev, count
\(u\) component of wind	mean	max, min, stdev, count
\(v\) component of wind	mean	max, min, stdev, count
Surface pressure	mean	max, min, stdev, count
Total precipitation	sum	max, min, stdev, count, sum

A paper with the full methodology was published in the Environmental Data Science journal.

Usage statistics

Usage statistics of this and other datasets are available here.

Zonal BR-DWGD

1961-01-01 to 2024-03-20:

The BR-DWGD dataset (Xavier et al. 2022) presents daily meteorological data interpolated to a grid with 0.1° × 0.1° of spatial resolution for the Brazilian territory, with daily data starting on January 1, 1961. It uses data from several weather stationsrain gauges in its interpolation methods, cross-validated to the selection of the best method for each weather indicator.

The following weather indicators are available from the BR-DWGD study: precipitation (mm), minimum temperature (°C), maximum temperature (°C), solar radiation (MJ⋅m−2 ), wind speed at 2m height (m⋅s−1) and relative humidity (%).

The following zonal statistics were computed.

BR-DGWD indicators	Daily time-aggregating functions	Spatial zonal statistics
Temperature	max, min	max, min, stdev, count
Relative humidity	mean	max, min, stdev, count
\(u\) component of wind	mean	max, min, stdev, count
Evapotranspiration	mean	max, min, stdev, count
Solar radiation	mean	max, min, stdev, count
Precipitation	sum	max, min, stdev, count, sum

Zonal TerraClimate

The TerraClimate (Abatzoglou et al. 2018) dataset presents monthly meteorological data interpolated to a grid with 0.04° × 0.04° (1/24th degree) of spatial resolution with world cover, with monthly data from January, 1958, to December, 2021.

The following weather indicators are available from the TerraClimate study: Actual Evapotranspiration (mm), Climate Water Deficit (mm), Potential evapotranspiration(mm), Precipitation (mm), Runoff (mm), Soil Moisture (mm), Downward surface shortwave radiation (W/m2), Snow water equivalent (mm), Minimum temperature (°C), Maximum temperature (°C), Vapor pressure (kPa), Wind speed (m/s), Vapor Pressure Deficit (kpa) and Palmer Drought Severity Index.

The following zonal statistics were computed.

brclimr::product_info(product = "terraclimate")

<list>
├─tmax: <list>
│ ├─link: "https://terraclimate.nyc3.cdn.di..."
│ ├─name: "Maximum temperature"
│ ├─detail: "Average for month"
│ ├─unit: "°C"
│ ├─date_range: "Monthly, 1958-01 to 2021-12"
│ └─stats: <list>
│   ├─min: "tmax_min"
│   ├─max: "tmax_max"
│   ├─mean: "tmax_mean"
│   └─sd: "tmax_sd"
├─tmin: <list>
│ ├─link: "https://terraclimate.nyc3.cdn.di..."
│ ├─name: "Minimum temperature"
│ ├─detail: "Average for month"
│ ├─unit: "°C"
│ ├─date_range: "Monthly, 1958-01 to 2021-12"
│ └─stats: <list>
│   ├─min: "tmin_min"
│   ├─max: "tmin_max"
│   ├─mean: "tmin_mean"
│   └─sd: "tmin_sd"
├─ppt: <list>
│ ├─link: "https://terraclimate.nyc3.cdn.di..."
│ ├─name: "Precipitation"
│ ├─detail: "Monthly total"
│ ├─unit: "mm"
│ ├─date_range: "Monthly, 1958-01 to 2021-12"
│ └─stats: <list>
│   ├─min: "ppt_min"
│   ├─max: "ppt_max"
│   ├─mean: "ppt_mean"
│   ├─sd: "ppt_sd"
│   └─sum: "ppt_sum"
├─aet: <list>
│ ├─link: "https://terraclimate.nyc3.cdn.di..."
│ ├─name: "Actual Evapotranspiration"
│ ├─detail: "Monthly total"
│ ├─unit: "mm"
│ ├─date_range: "Monthly, 1958-01 to 2021-12"
│ └─stats: <list>
│   ├─min: "aet_min"
│   ├─max: "aet_max"
│   ├─mean: "aet_mean"
│   ├─sd: "aet_sd"
│   └─sum: "aet_sum"
├─def: <list>
│ ├─link: "https://terraclimate.nyc3.cdn.di..."
│ ├─name: "Climate Water Deficit"
│ ├─detail: "Monthly total"
│ ├─unit: "mm"
│ ├─date_range: "Monthly, 1958-01 to 2021-12"
│ └─stats: <list>
│   ├─min: "def_min"
│   ├─max: "def_max"
│   ├─mean: "def_mean"
│   ├─sd: "def_sd"
│   └─sum: "def_sum"
├─pdsi: <list>
│ ├─link: "https://terraclimate.nyc3.cdn.di..."
│ ├─name: "Palmer Drought Severity Index"
│ ├─detail: "At end of month"
│ ├─unit: "unitless"
│ ├─date_range: "Monthly, 1958-01 to 2021-12"
│ └─stats: <list>
│   ├─min: "PDSI_min"
│   ├─max: "PDSI_max"
│   ├─mean: "PDSI_mean"
│   └─sd: "PDSI_sd"
├─pet: <list>
│ ├─link: "https://terraclimate.nyc3.cdn.di..."
│ ├─name: "Potential evapotranspiration"
│ ├─detail: "Monthly total"
│ ├─unit: "mm"
│ ├─date_range: "Monthly, 1958-01 to 2021-12"
│ └─stats: <list>
│   ├─min: "pet_min"
│   ├─max: "pet_max"
│   ├─mean: "pet_mean"
│   ├─sd: "pet_sd"
│   └─sum: "pet_sum"
├─q: <list>
│ ├─link: "https://terraclimate.nyc3.cdn.di..."
│ ├─name: "Runoff"
│ ├─detail: "Monthly total"
│ ├─unit: "mm"
│ ├─date_range: "Monthly, 1958-01 to 2021-12"
│ └─stats: <list>
│   ├─min: "q_min"
│   ├─max: "q_max"
│   ├─mean: "q_mean"
│   ├─sd: "q_sd"
│   └─sum: "q_sum"
├─soil: <list>
│ ├─link: "https://terraclimate.nyc3.cdn.di..."
│ ├─name: "Soil Moisture"
│ ├─detail: "Total column, at end of month"
│ ├─unit: "mm"
│ ├─date_range: "Monthly, 1958-01 to 2021-12"
│ └─stats: <list>
│   ├─min: "soil_min"
│   ├─max: "soil_max"
│   ├─mean: "soil_mean"
│   ├─sd: "soil_sd"
│   └─sum: "soil_sum"
├─srad: <list>
│ ├─link: "https://terraclimate.nyc3.cdn.di..."
│ ├─name: "Downward surface shortwave radia..."
│ ├─unit: "W/m2"
│ ├─date_range: "Monthly, 1958-01 to 2021-12"
│ └─stats: <list>
│   ├─min: "srad_min"
│   ├─max: "srad_max"
│   ├─mean: "srad_mean"
│   ├─sd: "srad_sd"
│   └─sum: "srad_sum"
├─swe: <list>
│ ├─link: "https://terraclimate.nyc3.cdn.di..."
│ ├─name: "Snow water equivalent"
│ ├─detail: "At end of month"
│ ├─unit: "mm"
│ ├─date_range: "Monthly, 1958-01 to 2021-12"
│ └─stats: <list>
│   ├─min: "swe_min"
│   ├─max: "swe_max"
│   ├─mean: "swe_mean"
│   ├─sd: "swe_sd"
│   └─sum: "swe_sum"
├─vap: <list>
│ ├─link: "https://terraclimate.nyc3.cdn.di..."
│ ├─name: "Vapor pressure"
│ ├─detail: "Average for month"
│ ├─unit: "kPa"
│ ├─date_range: "Monthly, 1958-01 to 2021-12"
│ └─stats: <list>
│   ├─min: "vap_min"
│   ├─max: "vap_max"
│   ├─mean: "vap_mean"
│   └─sd: "vap_sd"
├─vpd: <list>
│ ├─link: "https://terraclimate.nyc3.cdn.di..."
│ ├─name: "Vapor Pressure Deficit"
│ ├─detail: "Average for month"
│ ├─unit: "kPa"
│ ├─date_range: "Monthly, 1958-01 to 2021-12"
│ └─stats: <list>
│   ├─min: "vpd_min"
│   ├─max: "vpd_max"
│   ├─mean: "vpd_mean"
│   └─sd: "vpd_sd"
└─ws: <list>
  ├─link: "https://terraclimate.nyc3.cdn.di..."
  ├─name: "Wind speed"
  ├─detail: "Average for month"
  ├─unit: "m/s"
  ├─date_range: "Monthly, 1958-01 to 2021-12"
  └─stats: <list>
    ├─min: "ws_min"
    ├─max: "ws_max"
    ├─mean: "ws_mean"
    └─sd: "ws_sd"

The results are availabe as parquet files, available at Zenodo and can be also accessed with the brclimr R package.

References

Abatzoglou, John T., Solomon Z. Dobrowski, Sean A. Parks, and Katherine C. Hegewisch. 2018. “TerraClimate, a High-Resolution Global Dataset of Monthly Climate and Climatic Water Balance from 1958-2015.” Scientific Data 5 (January). https://doi.org/10.1038/sdata.2017.191.

Muñoz-Sabater, Joaquín, Emanuel Dutra, Anna Agustí-Panareda, Clément Albergel, Gabriele Arduini, Gianpaolo Balsamo, Souhail Boussetta, et al. 2021. “ERA5-Land: A State-of-the-Art Global Reanalysis Dataset for Land Applications.” Earth System Science Data 13 (9): 4349–83. https://doi.org/10.5194/essd-13-4349-2021.

Xavier, Alexandre C., Bridget R. Scanlon, Carey W. King, and Aline I. Alves. 2022. “New Improved Brazilian Daily Weather Gridded Data (1961).” International Journal of Climatology, December. https://doi.org/10.1002/joc.7731.