diff --git a/README.md b/README.md
index 9ab125c..b9df1f9 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
 # limaair
 
-This package downloads pollutant data from the [SENAMHI air quality network](https://www.senamhi.gob.pe/?p=calidad-del-aire) located in Lima, capital of Peru.
+This package downloads pollutant data from the [SENAMHI air quality network](https://www.senamhi.gob.pe/?p=calidad-del-aire), meteorological data from [SENAMHI automatic weather staions](https://www.senamhi.gob.pe/servicios/?p=estaciones), and  sounding data from [Jorge Chávez airport](https://en.wikipedia.org/wiki/Jorge_Ch%C3%A1vez_International_Airport) located in Lima, capital of Peru.
 The returned `data.frame` is a complete dataset.
 This means that missing data by missing hours is padded out by `NA`.
 
@@ -19,22 +19,30 @@ devtools::install_github("quishqa/limaair")
 ```
 
 ## How to use
-`limaair` (until now) has one function: **`download_senamhi_pol()`**.
+`limaair` has three functions: 
+ - **`download_senamhi_pol()`**.
+ - **`download_senamhi_met()`**.
+ - **`donwload_airport_sounding()`**
 
-To use it, you need the air quality station (AQS) code (`aqs_code`),
+To use `download_senamhi_pol()`, you need the air quality station (AQS) code (`aqs_code`),
 the pollutant code (`pol_code`),
 and the start and end date of the download (`start_date` and `end_date`).
+And, to use `download_senmahi_met()`, you need to know the automatic weather station (AWS) code 
+(`aws_code`)
 
-`limaair` has two datasets to check `aqs_code` and `pol_code` values:
+`limaair` has three datasets to check `aqs_code`, `pol_code`, and `aws_code` values:
 
 ```R
 library(limaair)
 
-# To check SENAMHI AQS code, name, lat and lon
+# To check SENAMHI AQS code, name, latitude and longitude
 senamhi_aqs
 
 # To check SENAMHI AQS pollutant code, name and units
 senamhi_params
+
+# To check SENAMHI AWS code, name latitude and longitude
+senamhi_aws
 ```
 
 ## Examples
@@ -96,6 +104,31 @@ cm_pm25 <- download_senamhi_pol(aqs_codes = cm_sb_code,
                                 to_df = TRUE) # Look here!
 ```
 
+### Downloading meteorological data  from automatic weather stations
+
+Following the same philosophy as `download_senamhi_pol` function, you can use `download_senamhi_met`
+to download data for Temperature (°C), Precipitation (mm/hour), Relative Humidity (%), Wind speed (m/s) and direction (°). You only need to know the `aws_code`, which for some cases is the same as the `aqs_code` (**except for Campo de Marte**). In this example, We download meteorological data 
+for Campo de Marte AWS
+```R
+cm_code <- 112181 # senamhi_aws
+start_date <- "01/01/2019"
+end_date <- "28/02/2019"
+
+cm_met <- download_senamhi_met(112181, "01/01/2019", "28/02/2019")
+```
+Like `download_senamhi_pol`, you can download data for multiple AWS.
+
+### Downloading sounding data from Jorge Chavez airport
+
+The `download_airport_sounding` function download sounding data from [Wyoming University sounding 
+repository](https://weather.uwyo.edu/upperair/sounding.html). Default values are set for [Jorge 
+Chavez International Airport](https://en.wikipedia.org/wiki/Jorge_Ch%C3%A1vez_International_Airport), so you can also use this function to download
+sounding data  for other airports ;)
+```R
+# Downloading sounding for 15/01/2019 at 12z
+jc_sounding <- download_airport_sounding("15/01/2019")
+```
+
 ### Exporting to csv
 If you prefer to process the data in another software,
 you can export the downloaded data to a csv.