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Observation and Forecast","monthly_downloads":564,"total_dependent_repos":2,"total_dependent_packages":1,"readme":"# climate \u003cimg src=\"man/figures/logo.png\" align=\"right\" width=\"150\" /\u003e\n\n\u003c!-- badges: start --\u003e\n\n[![R-CMD-check](https://github.com/bczernecki/climate/workflows/R-CMD-check/badge.svg)](https://github.com/bczernecki/climate/actions)\n[![HTML5 check](https://github.com/bczernecki/climate/actions/workflows/html5-check.yaml/badge.svg?branch=master)](https://github.com/bczernecki/climate/actions/workflows/html5-check.yaml)\n[![Codecov test\ncoverage](https://codecov.io/gh/bczernecki/climate/branch/master/graph/badge.svg)](https://app.codecov.io/gh/bczernecki/climate?branch=master)\n[![CRAN\nstatus](https://www.r-pkg.org/badges/version/climate)](https://cran.r-project.org/package=climate)\n[![CRAN RStudio mirror\ndownloads](https://cranlogs.r-pkg.org/badges/climate)](https://cran.r-project.org/package=climate)\n[![](https://cranlogs.r-pkg.org/badges/grand-total/climate?color=brightgreen)](https://cran.r-project.org/package=climate)\n\u003c!-- badges: end --\u003e\n\nThe goal of the  **climate** R package is to automatize downloading of *in-situ* meteorological\nand hydrological data from publicly available repositories:\n\n- OGIMET [(ogimet.com)](http://ogimet.com/index.phtml.en) - up-to-date collection of SYNOP dataset\n- University of Wyoming - atmospheric vertical profiling data (http://weather.uwyo.edu/upperair/)\n- National Oceanic \u0026 Atmospheric Administration - Earth System Research Laboratories - Global Monitoring Laboratory [(NOAA)](https://gml.noaa.gov/ccgg/trends/)\n- Polish Institute of Meteorology and Water Management - National Research Institute [(IMGW-PIB)](https://dane.imgw.pl/)\n- National Centers for Environmental Information, National Oceanic \u0026 Atmospheric Administration - National Climatic Data Center - Integrated Surface Hourly (ISH) [(NOAA)](https://www.ncei.noaa.gov/pub/data/noaa/)\n\n \n## Installation\n\nThe stable release of the **climate** package from the [CRAN](https://CRAN.R-project.org) repository can be installed with:\n\n``` r\ninstall.packages(\"climate\")\n```\n\nIt is also possible to install the most up-to-date development version of **climate** from [GitHub](https://github.com/bczernecki/climate) with:\n\n``` r\nlibrary(remotes)\ninstall_github(\"bczernecki/climate\")\n```\n\n## Overview\n\n### Meteorological data\n\n- 🌍 [**meteo_ogimet**](https://bczernecki.github.io/climate/reference/meteo_ogimet.html) -\nDownloading hourly and daily meteorological data from the SYNOP stations available in the ogimet.com collection.\nAny meteorological (aka SYNOP) station working under the World Meteorological Organization framework after year 2000 should be accessible.\nTwo backends are available and selected automatically: raw **SYNOP decoding** (`source = \"synop\"`, default for `interval = \"hourly\"`) and **HTML scraping** (`source = \"html\"`, default for `interval = \"daily\"`).\nCountry-level bulk downloads are supported via the `country_name` argument (SYNOP backend only).\n\n- 🌍 [**meteo_noaa_hourly**](https://bczernecki.github.io/climate/reference/meteo_noaa_hourly.html) -\nDownloading hourly NCEI/NOAA Integrated Surface Hourly (ISH) meteorological data - Some stations have \u003e 100 years long history of observations\n\n- 🌍 [**meteo_noaa_co2**](https://bczernecki.github.io/climate/reference/meteo_noaa_co2.html) -\nDownloading monthly CO2 measurements from Mauna Loa Observatory\n\n- 🌍 [**sounding_wyoming**](https://bczernecki.github.io/climate/reference/sounding_wyoming.html) - \nDownloading measurements of the vertical profile of atmosphere (aka rawinsonde data)\n\n- 🇵🇱 [**meteo_imgw**](https://bczernecki.github.io/climate/reference/meteo_imgw.html) - \nDownloading hourly, daily, and monthly meteorological data from the Polish met service across \nall types of stations (i.e. SYNOP/CLIMATE/PRECIP ) available in the danepubliczne.imgw.pl collection. \nIt is a wrapper for `meteo_monthly()`, `meteo_daily()`, `meteo_hourly()` and `meteo_imgw_datastore()` which gives access from montly to even to 10-min dataset.\n\n  \n### (Polish) Hydrological data\n\n- 🇵🇱 [**hydro_imgw**](https://bczernecki.github.io/climate/reference/hydro_imgw.html) - \nDownloading hourly, daily, and monthly hydrological data from stations available in the\ndanepubliczne.imgw.pl collection.\nIt is a wrapper for previously developed set of functions such as: `hydro_monthly()`, and `hydro_daily()`\n\n- 🇵🇱 [**hydro_imgw_datastore**](https://bczernecki.github.io/climate/reference/hydro_imgw_datastore.html) - \nDownloading hourly and subhourly hydrological data from the IMGW-PIB hydro telemetry stations.\n\n### Auxiliary functions and datasets\n\n- 🌍 **stations_ogimet** - Downloading information about all stations available in the selected\ncountry in the Ogimet repository\n- 🌍 **nearest_stations_ogimet** - Downloading information about nearest stations to the selected point using Ogimet repository\n- 🌍 **nearest_stations_noaa** - Downloading information about nearest stations to the selected point available for the selected country in the NOAA ISH meteorological repository\n- 🇵🇱 **nearest_stations_imgw** - List of nearby meteorological or hydrological IMGW-PIB stations in Poland\n- 🇵🇱 **imgw_meteo_stations** - Built-in metadata from the IMGW-PIB repository for meteorological stations, their geographical coordinates, and ID numbers\n- 🇵🇱 **imgw_hydro_stations** - as above, for hydrological stations\n- 🇵🇱 **stations_meteo_imgw_telemetry** - Downloading complete and up-to-date information about coordinates for IMGW-PIB telemetry meteorological stations\n- 🇵🇱 **stations_hydro_imgw_telemetry** - Downloading complete and up-to-date information about coordinates for IMGW-PIB telemetry hydrological stations\n\n- 🌍 [**synop_parser**](https://bczernecki.github.io/climate/reference/synop_parser.html) - Decoding raw SYNOP meteorological messages into structured R lists or data frames. For a full walkthrough see the [SYNOP Messages vignette](https://bczernecki.github.io/climate/articles/synop_parser.html).\n\n## Example 1\n#### Download hourly dataset from NCEI/NOAA ISH meteorological repository:\n\n``` r\nlibrary(climate)\nnoaa = meteo_noaa_hourly(station = \"123300-99999\", year = 2018:2019) # station ID: Poznan, Poland\nhead(noaa)\n```\n\n| year | month | day | hour | lon   | lat    | alt | t2m | dpt2m | ws | wd  | slp    | visibility |\n|------|-------|-----|------|-------|--------|-----|-----|-------|----|-----|--------|------------|\n| 2019 | 1     | 1   | 0    | 16.85 | 52.417 | 84  | 3.3 | 2.3   | 5  | 220 | 1025.0 | 6000       |\n| 2019 | 1     | 1   | 1    | 16.85 | 52.417 | 84  | 3.7 | 3.0   | 4  | 220 | 1024.2 | 1500       |\n| 2019 | 1     | 1   | 2    | 16.85 | 52.417 | 84  | 4.2 | 3.6   | 4  | 220 | 1022.5 | 1300       |\n| 2019 | 1     | 1   | 3    | 16.85 | 52.417 | 84  | 5.2 | 4.6   | 5  | 240 | 1021.2 | 1900       |\n\n## Example 2\n#### Finding a nearest meteorological stations in a given country using NCEI/NOAA ISH data source (used in Ex. 1):\n\n``` r\n# find 100 nearest UK stations to longitude 1W and latitude 53N :\n\nnearest_stations_ogimet(country = \"United Kingdom\",\n  date = Sys.Date(),\n  add_map = TRUE,\n  point = c(-1, 53),\n  no_of_stations = 100\n)\n```\n\n| wmo_id | station_names             | lon       | lat      | alt | distance [km] |\n|--------|---------------------------|-----------|----------|-----|---------------|\n| 03354  | Nottingham Weather Centre | -1.250005 | 53.00000 | 117 | 28.04973      |\n| 03379  | Cranwell                  | -0.500010 | 53.03333 | 67  | 56.22175      |\n| 03377  | Waddington                | -0.516677 | 53.16667 | 68  | 57.36093      |\n| 03373  | Scampton                  | -0.550011 | 53.30001 | 57  | 60.67897      |\n| 03462  | Wittering                 | -0.466676 | 52.61668 | 84  | 73.68934      |\n| 03544  | Church Lawford            | -1.333340 | 52.36667 | 107 | 80.29844      |\n| ...    | ...                       | ...       | ...      | ... | ...           |\n\n![100 nearest stations to given coordinates in UK](http://iqdata.eu/kolokwium/uk.png)\n\n## Example 3\n#### Downloading daily (or hourly) data from a global (OGIMET) repository knowing its ID (see also `nearest_stations_ogimet()`):\n\n``` r\n# Daily summary — uses HTML backend by default\no = meteo_ogimet(date = c(Sys.Date() - 5, Sys.Date() - 1), \n                 interval = \"daily\",\n                 station = 12330)\nhead(o)\n```\n\n| station_ID | Date       | TempCAvg | TempCMax | TempCMin | TdAvgC | HrAvg | WindDir | WindInt | WindGust | PressHp | Precmm | TotClOct | lowClOct | SunD1h | VisKm |\n|------------|------------|----------|----------|----------|--------|-------|---------|---------|----------|---------|--------|----------|----------|--------|-------|\n| 12330      | 2019-12-21 | 8.8      | 13.2     | 4.9      | 5.3    | 79.3  | SSE     | 11.4    | 39.6     | 995.9   | 1.8    | 3.6      | 2.0      | 6.7    | 21.4  |\n| 12330      | 2019-12-20 | 5.4      | 8.5      | -1.2     | 4.5    | 92.4  | ESE     | 15.0    | NA       | 1015.0  | 0.0    | 6.4      | 0.6      | 1.0    | 8.0   |\n| 12330      | 2019-12-19 | 3.8      | 10.3     | -3.0     | 1.9    | 89.6  | SW      | 7.1     | NA       | 1020.4  | 0.0    | 5.2      | 5.9      | 2.5    | 14.1  |\n| 12330      | 2019-12-18 | 6.3      | 9.0      | 2.2      | 4.1    | 84.8  | S       | 9.2     | NA       | 1009.2  | 0.0    | 5.7      | 2.7      | 1.4    | 12.2  |\n| 12330      | 2019-12-17 | 4.9      | 7.6      | 0.3      | 2.9    | 87.2  | SSE     | 7.2     | NA       | 1010.8  | 0.1    | 6.2      | 4.6      | NA     | 13.0  |\n\n``` r\n# Hourly observations — decoded from raw SYNOP messages by default\nh = meteo_ogimet(date = c(\"2009-12-01\", \"2009-12-04\"),\n                 interval = \"hourly\",\n                 station  = 12330)\nhead(h)\n```\n\n| date                | station | t2m  | dpt2m | rel_hum | tmax  | tmin  | wd  | ws  | gust | press  | slp    | precip | Nt | snow |\n|---------------------|---------|------|-------|---------|-------|-------|-----|-----|------|--------|--------|--------|----|------|\n| 2009-12-01 00:00:00 | 12330   | 2.0  | 0.0   | 93      | NA    | NA    | 210 | 5   | NA   | 1007.4 | 1016.3 | NA     | 8  | NA   |\n| 2009-12-01 06:00:00 | 12330   | 1.0  | -1.0  | 92      | NA    | NA    | 200 | 3   | NA   | 1009.8 | 1018.8 | NA     | 8  | NA   |\n| 2009-12-01 12:00:00 | 12330   | 3.0  | 1.0   | 93      | 5.8   | 2.9   | 230 | 4   | NA   | 1011.5 | 1020.4 | NA     | 8  | NA   |\n| 2009-12-01 18:00:00 | 12330   | 2.0  | 0.0   | 93      | NA    | NA    | 240 | 3   | NA   | 1013.3 | 1022.1 | NA     | 7  | NA   |\n\n``` r\n# Country-level bulk download — all stations in a country for a given day\npoland = meteo_ogimet(interval      = \"hourly\",\n                      country_name  = \"Poland\",\n                      date          = c(\"2009-12-15\", \"2009-12-15\"))\nnrow(poland)  #\u003e several hundred rows (one per observation per station)\n```\n\n## Example 4\n#### Downloading monthly/daily/hourly meteorological/hydrological data from the Polish (IMGW-PIB) repository:\n\n``` r\nm = meteo_imgw(interval = \"monthly\", rank = \"synop\", year = 2000, coords = TRUE)\nhead(m)\n```\n\n| rank  | id        | X        | Y        | station   | yy   | mm | tmax_abs | tmax_mean | tmin_abs | tmin_mean | t2m_mean_mon | t5cm_min | rr_monthly |\n|-------|-----------|----------|----------|-----------|------|----|----------|-----------|----------|-----------|--------------|----------|------------|\n| SYNOP | 353230295 | 23.16228 | 53.10726 | BIAŁYSTOK | 2000 | 1  | 5.3      | 0.4       | -16.5    | -4.5      | -2.1         | -23.5    | 34.2       |\n| SYNOP | 353230295 | 23.16228 | 53.10726 | BIAŁYSTOK | 2000 | 2  | 10.6     | 4.1       | -10.4    | -1.4      | 1.3          | -12.9    | 25.4       |\n| SYNOP | 353230295 | 23.16228 | 53.10726 | BIAŁYSTOK | 2000 | 3  | 14.8     | 6.2       | -6.4     | -1.0      | 2.4          | -9.4     | 45.5       |\n| SYNOP | 353230295 | 23.16228 | 53.10726 | BIAŁYSTOK | 2000 | 4  | 27.8     | 17.9      | -4.6     | 4.7       | 11.5         | -8.1     | 31.6       |\n| SYNOP | 353230295 | 23.16228 | 53.10726 | BIAŁYSTOK | 2000 | 5  | 29.3     | 21.3      | -4.3     | 5.7       | 13.8         | -8.3     | 9.4        |\n| SYNOP | 353230295 | 23.16228 | 53.10726 | BIAŁYSTOK | 2000 | 6  | 32.6     | 23.1      | 1.0      | 9.6       | 16.6         | -1.8     | 36.4       |\n\n``` r\nh = hydro_imgw(interval = \"daily\", year = 2010:2011)\nhead(h)\n```\n\n| id        | station | riv_or_lake | date       | hyy  | idhyy | dd | H   | Q   | T  | mm | thick |\n|-----------|---------|-------------|------------|------|-------|----|-----|-----|----|----|-------|\n| 150210180 | ANNOPOL | Wisła (2)   | 2009-11-01 | 2010 | 1     | 1  | 287 | 436 | NA | 11 | NA    |\n| 150210180 | ANNOPOL | Wisła (2)   | 2009-11-02 | 2010 | 1     | 2  | 282 | 412 | NA | 11 | NA    |\n| 150210180 | ANNOPOL | Wisła (2)   | 2009-11-03 | 2010 | 1     | 3  | 272 | 368 | NA | 11 | NA    |\n| 150210180 | ANNOPOL | Wisła (2)   | 2009-11-04 | 2010 | 1     | 4  | 268 | 352 | NA | 11 | NA    |\n| 150210180 | ANNOPOL | Wisła (2)   | 2009-11-05 | 2010 | 1     | 5  | 264 | 336 | NA | 11 | NA    |\n| 150210180 | ANNOPOL | Wisła (2)   | 2009-11-06 | 2010 | 1     | 6  | 260 | 320 | NA | 11 | NA    |\n\n## Example 5\n#### Create Walter \u0026 Lieth climatic diagram based on downloaded data\n\n``` r\nlibrary(climate)\nlibrary(dplyr)\n\ndf = meteo_imgw(interval = \"monthly\", rank = \"synop\", year = 1991:2019, station = \"POZNAŃ\") \ndf2 = select(df, station:t2m_mean_mon, rr_monthly)\n\nmonthly_summary = df2 %\u003e% \n  group_by(mm) %\u003e% \n  summarise(tmax = mean(tmax_abs, na.rm = TRUE), \n            tmin = mean(tmin_abs, na.rm = TRUE),\n            tavg = mean(t2m_mean_mon, na.rm = TRUE), \n            prec = sum(rr_monthly) / n_distinct(yy))            \n\nmonthly_summary = as.data.frame(t(monthly_summary[, c(5,2,3,4)])) \nmonthly_summary = round(monthly_summary, 1)\ncolnames(monthly_summary) = month.abb\nprint(monthly_summary)\n```\n\n|      | Jan   | Feb   | Mar  | Apr  | May  | Jun  | Jul  | Aug  | Sep  | Oct  | Nov  | Dec   |\n|------|-------|-------|------|------|------|------|------|------|------|------|------|-------|\n| prec | 37.1  | 31.3  | 38.5 | 31.3 | 53.9 | 60.8 | 94.8 | 59.6 | 40.5 | 39.7 | 35.7 | 38.6  |\n| tmax | 8.7   | 11.2  | 17.2 | 23.8 | 28.3 | 31.6 | 32.3 | 31.8 | 26.9 | 21.3 | 14.3 | 9.8   |\n| tmin | -15.0 | -11.9 | -7.6 | -3.3 | 1.0  | 5.8  | 8.9  | 7.5  | 2.7  | -2.4 | -5.2 | -10.4 |\n| tavg | -1.0  | 0.5   | 3.7  | 9.4  | 14.4 | 17.4 | 19.4 | 19.0 | 14.3 | 9.1  | 4.5  | 0.8   |\n\n``` r\n# create plot with use of the \"climatol\" package:\nclimatol::diagwl(monthly_summary, mlab = \"en\", \n                 est = \"POZNAŃ\", alt = NA, \n                 per = \"1991-2019\", p3line = FALSE)\n```\n\n![Walter and Lieth climatic diagram for Poznan, Poland](http://iqdata.eu/kolokwium/poznan.svg)\n\n## Example 6\n#### Download monthly CO2 dataset from Mauna Loa observatory\n\n``` r\nlibrary(climate)\nlibrary(ggplot2)\nlibrary(ggthemes)\n\nco2 = meteo_noaa_co2()\nhead(co2)\nco2$date = ISOdate(co2$yy, co2$mm, 1)\nggplot(co2, aes(date, co2_avg)) + \n  geom_line()+ geom_smooth()+\n  theme_bw()+\n  labs(\n    title = \"Carbon Dioxide (CO2)\",\n    subtitle = paste0(\"Mauna Loa Observatory \"),\n    caption = \"data source: NOAA\n    visualization: Bartosz Czernecki / R climate package\",\n    x = \"\",\n    y = \"ppm\"\n)\n\n```\n\n![CO2 monthly concentration, Mauna Loa observatory](http://iqdata.eu/kolokwium/co2_chart.svg)\n\n## Example 7\n#### Use \"climate\" inside python environment via rpy2\n\n```python\n# load required packages\nfrom rpy2.robjects.packages import importr\nimport rpy2.robjects as robjects\nimport pandas as pd\nimport datetime as dt\n\n# load climate package (make sure that it was installed in R before)\nimportr(\"climate\")\n# test functionality e.g. with meteo_ogimet function for New York - La Guardia:\ndf = robjects.r[\"meteo_ogimet\"](interval = \"daily\", station = 72503,\n                                date = robjects.StrVector([\"2022-05-01\", \"2022-06-15\"]))\n# optionally - transform object to pandas data frame and rename columns + fix datetime:\nres = pd.DataFrame(df).transpose()\nres.columns = df.colnames\nres[\"Date\"] = pd.TimedeltaIndex(res[\"Date\"], unit=\"d\") + dt.datetime(1970,1,1)\nres.head\n\n\u003e\u003e\u003e res[res.columns[0:7]].head()\n```\n\n| station_ID | Date       | TemperatureCAvg | TemperatureCMin | TdAvgC | HrAvg |\n|------------|------------|-----------------|-----------------|--------|-------|\n| 72503.0    | 2022-06-15 | 23.5            | 19.4            | 10.9   | 45.2  |\n| 72503.0    | 2022-06-14 | 25.0            | 20.6            | 16.1   | 59.0  |\n| 72503.0    | 2022-06-13 | 20.4            | 17.8            | 16.0   | 74.8  |\n| 72503.0    | 2022-06-12 | 21.3            | 18.3            | 12.0   | 57.1  |\n| 72503.0    | 2022-06-11 | 22.6            | 17.8            | 8.1    | 40.1  |\n\n## Example 8\n#### Decode raw SYNOP messages with `synop_parser()`\n\nThe `synop_parser()` function decodes FM-12 SYNOP meteorological messages into structured R objects.\nFor a detailed guide including all parameters and output formats, see the [SYNOP Messages vignette](https://bczernecki.github.io/climate/articles/synop_parser.html).\n\n```r\nlibrary(climate)\n\nsynop_code = \"AAXX 01004 88889 12782 61506 10094 20047 30111 40197 53007 60001 81541\"\n\n# Decode a single message — returns a named list\nresult = parser(synop_code)\nresult$station_id$value        #\u003e \"88889\"\nresult$air_temperature$value   #\u003e 9.4\nresult$wind_speed$value        #\u003e 6\nresult$visibility$value        #\u003e 40000\nresult$sea_level_pressure$value #\u003e 1019.7\n```\n\n```r\n# Return a tidy data frame with one row per message\ndf = parser(synop_code, as_data_frame = TRUE)\ndf\n```\n\n| station_type | station_id | region | obs_day | obs_hour | wind_unit | wind_estimated | visibility | cloud_cover | wind_direction | wind_speed | air_temperature | dewpoint_temperature |\n|--------------|------------|--------|---------|----------|-----------|----------------|------------|-------------|----------------|------------|-----------------|----------------------|\n| AAXX         | 88889      | III    | 1       | 0        | KT        | FALSE          | 40000      | 6           | 150            | 6          | 9.4             | 4.7                  |\n\n| station_pressure | sea_level_pressure | pressure_tendency | pressure_change | precipitation_amount | precipitation_time | cloud_base_min | cloud_base_max | low_cloud_type | middle_cloud_type | high_cloud_type | low_cloud_amount | source                  |\n|------------------|--------------------|-------------------|-----------------|----------------------|--------------------|----------------|----------------|----------------|-------------------|-----------------|------------------|-------------------------|\n| 1011.1           | 1019.7             | 0                 | 7               | 0                    | 6                  | 1500           | 2000           | 5              | 4                 | 1               | 1                | AAXX 01004 88889 12782… |\n\n```r\n# Decode multiple SYNOP messages at once\nmsgs = c(\n  \"AAXX 01004 88889 12782 61506 10094 20047 30111 40197 53007 60001 81541\",\n  \"AAXX 10124 26477 32560 83102 10156 20106 38528 40128 52003 60001 333 56017\"\n)\ndf2 = parser(msgs, as_data_frame = TRUE)\nnrow(df2)       #\u003e 2\ndf2$station_id  #\u003e c(\"88889\", \"26477\")\ndf2$source      # original SYNOP strings preserved in last column\n\n```\n\n## Acknowledgment\n\nOgimet.com, University of Wyoming, and Institute of Meteorology and Water Management - National Research Institute (IMGW-PIB), National Oceanic \u0026 Atmospheric Administration (NOAA) - Earth System Research Laboratory, Global Monitoring Division and Integrated Surface Hourly (NOAA ISH) are the sources of the data.\n\n## Contribution\n\nContributions to this package are welcome. \nThe preferred method of contribution is through a GitHub pull request. \nFeel also free to contact us by creating [an issue](https://github.com/bczernecki/climate/issues).\n\n\n## Citation\n\nTo cite the `climate` package in publications, please use [this paper](https://doi.org/10.3390/su12010394):\n\nCzernecki, B.; Głogowski, A.; Nowosad, J. Climate: An R Package to Access Free In-Situ Meteorological and Hydrological Datasets for Environmental Assessment. 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