For some time I used the brilliant original ClimaCell integration available via HACS from Renato Rossi. I found it to be very useful for local forecasts as well as air pollution and pollen. It’s got animated icon’s and very easy to configure/tweak. The out of the box Weather card didn’t work or do it justice to the card, so I use the ha-card-weather-conditions card by Renato and hukoeth https://github.com/r-renato/ha-card-weather-conditions.
Renato’s integration was put into competition with the native climacell integration introduced in 2021.3.
That integration now works well with the tomorrow.io v4 API and so I decided to transition to the native integration which is python based and seemingly having a longer future.
The problem is the lovelace card ha-card-weather-conditions didn’t talk to the new integration. So this guide is to share how I mapped things back to the card and restored the great card to it’s rightful position on my dashboard.
The new integration uses the standard weather entity so I had to map that back to variables that the card could use. Here’s how I did it:
In configuration.yml:
# For adding Pollen:
- platform: template
sensors:
cc_grass_pollen_index_mod:
friendly_name: Pollen Grass climacell
icon_template: 'mdi:flower'
value_template: >-
{% if is_state('sensor.climacell_grass_pollen_index', 'none') %}
0
{% elif is_state('sensor.climacell_grass_pollen_index','very_low') %}
1
{% elif is_state('sensor.climacell_grass_pollen_index','low') %}
2
{% elif is_state('sensor.climacell_grass_pollen_index','medium') %}
3
{% elif is_state('sensor.climacell_grass_pollen_index','high') %}
4
{% elif is_state('sensor.climacell_grass_pollen_index','very_high') %}
5
{% else %}
Unknown
{% endif %}
cc_tree_pollen_index_mod:
friendly_name: Pollen Tree climacell
icon_template: 'mdi:tree'
value_template: >-
{% if is_state('sensor.climacell_tree_pollen_index', 'none') %}
0
{% elif is_state('sensor.climacell_tree_pollen_index','very_low') %}
1
{% elif is_state('sensor.climacell_tree_pollen_index','low') %}
2
{% elif is_state('sensor.climacell_tree_pollen_index','medium') %}
3
{% elif is_state('sensor.climacell_tree_pollen_index','high') %}
4
{% elif is_state('sensor.climacell_tree_pollen_index','very_high') %}
5
{% else %}
Unknown
{% endif %}
cc_weed_pollen_index_mod:
friendly_name: Pollen Weed climacell
icon_template: 'mdi:sprout'
value_template: >-
{% if is_state('sensor.climacell_weed_pollen_index', 'none') %}
0
{% elif is_state('sensor.climacell_weed_pollen_index','very_low') %}
1
{% elif is_state('sensor.climacell_weed_pollen_index','low') %}
2
{% elif is_state('sensor.climacell_weed_pollen_index','medium') %}
3
{% elif is_state('sensor.climacell_weed_pollen_index','high') %}
4
{% elif is_state('sensor.climacell_weed_pollen_index','very_high') %}
5
{% else %}
Unknown
{% endif %}
For the others sensors this worked fine:
template:
- sensor:
- name: cc_temperature
state: "{{ state_attr('weather.climacell_daily','temperature') }}"
unit_of_measurement: °F
- name: cc_humidity
state: "{{ state_attr('weather.climacell_daily','humidity') }}"
- name: cc_pressure
state: "{{ state_attr('weather.climacell_daily','pressure') }}"
unit_of_measurement: Hg
- name: cc_current_conditions
state: "{{ states.weather.climacell_daily.state }}"
# feels_like
- name: cc_visibility
state: "{{ state_attr('weather.climacell_daily','visibility') }}"
- name: cc_wind_bearing
state: "{{ state_attr('weather.climacell_daily','wind_bearing') }}"
- name: cc_wind_speed
state: "{{ state_attr('weather.climacell_daily','wind_speed') }}"
- name: cc_O3
state: "{{ state_attr('weather.climacell_daily','ozone') }}"
- name: cc_weather_condition_0d
state: "{{ state_attr('weather.climacell_daily','forecast')[0].condition }}"
- name: cc_weather_condition_1d
state: "{{ state_attr('weather.climacell_daily','forecast')[1].condition }}"
- name: cc_weather_condition_2d
state: "{{ state_attr('weather.climacell_daily','forecast')[2].condition }}"
- name: cc_weather_condition_3d
state: "{{ state_attr('weather.climacell_daily','forecast')[3].condition }}"
- name: cc_weather_condition_4d
state: "{{ state_attr('weather.climacell_daily','forecast')[4].condition }}"
- name: cc_temperature_maximum_0d
state: "{{ state_attr('weather.climacell_daily','forecast')[0].temperature }}"
unit_of_measurement: °F
- name: cc_temperature_maximum_1d
state: "{{ state_attr('weather.climacell_daily','forecast')[1].temperature }}"
unit_of_measurement: °F
- name: cc_temperature_maximum_2d
state: "{{ state_attr('weather.climacell_daily','forecast')[2].temperature }}"
unit_of_measurement: °F
- name: cc_temperature_maximum_3d
state: "{{ state_attr('weather.climacell_daily','forecast')[3].temperature }}"
unit_of_measurement: °F
- name: cc_temperature_maximum_4d
state: "{{ state_attr('weather.climacell_daily','forecast')[4].temperature }}"
unit_of_measurement: °F
- name: cc_temperature_minimum_0d
state: "{{ state_attr('weather.climacell_daily','forecast')[0].templow }}"
unit_of_measurement: °F
- name: cc_temperature_minimum_1d
state: "{{ state_attr('weather.climacell_daily','forecast')[1].templow }}"
unit_of_measurement: °F
- name: cc_temperature_minimum_2d
state: "{{ state_attr('weather.climacell_daily','forecast')[2].templow }}"
unit_of_measurement: °F
- name: cc_temperature_minimum_3d
state: "{{ state_attr('weather.climacell_daily','forecast')[3].templow }}"
unit_of_measurement: °F
- name: cc_temperature_minimum_4d
state: "{{ state_attr('weather.climacell_daily','forecast')[4].templow }}"
unit_of_measurement: °F
- name: cc_precipitation_intensity_0d
state: "{{ state_attr('weather.climacell_daily','forecast')[0].precipitation }}"
- name: cc_precipitation_probability_0d
state: "{{ state_attr('weather.climacell_daily','forecast')[0].precipitation_probability }}"
- name: cc_precipitation_probability_1d
state: "{{ state_attr('weather.climacell_daily','forecast')[1].precipitation_probability }}"
- name: cc_precipitation_probability_2d
state: "{{ state_attr('weather.climacell_daily','forecast')[2].precipitation_probability }}"
- name: cc_precipitation_probability_3d
state: "{{ state_attr('weather.climacell_daily','forecast')[3].precipitation_probability }}"
- name: cc_precipitation_probability_4d
state: "{{ state_attr('weather.climacell_daily','forecast')[4].precipitation_probability }}"
This established the sensors that I needed to then populate the card as follows:
I created a manual card and you can paste this in and make sure the variables match above (or your naming scheme:
type: custom:ha-card-weather-conditions
name: Allen, TX
language: en
animation: true
pollen:
weed:
entity: sensor.cc_weed_pollen_index_mod
min: 0
max: 5
low: 1
high: 3
grass:
entity: sensor.cc_grass_pollen_index_mod
min: 0
max: 5
low: 1
high: 3
tree:
entity: sensor.cc_tree_pollen_index_mod
min: 0
max: 5
low: 1
high: 3
weather:
icons_model: climacell
current:
sun: sun.sun
moon_phase: sensor.moon
current_conditions: sensor.cc_current_conditions
temperature: sensor.cc_temperature
humidity: sensor.cc_humidity
pressure: sensor.cc_pressure
visibility: sensor.cc_visibility
wind_bearing: sensor.cc_wind_bearing
wind_speed: sensor.cc_wind_speed
forecast: true
forecast:
temperature_high:
day_1: sensor.cc_temperature_maximum_0d
day_2: sensor.cc_temperature_maximum_1d
day_3: sensor.cc_temperature_maximum_2d
day_4: sensor.cc_temperature_maximum_3d
day_5: sensor.cc_temperature_maximum_4d
temperature_low:
day_1: sensor.cc_temperature_minimum_0d
day_2: sensor.cc_temperature_minimum_1d
day_3: sensor.cc_temperature_minimum_2d
day_4: sensor.cc_temperature_minimum_3d
day_5: sensor.cc_temperature_minimum_4d
precipitation_probability:
day_1: sensor.cc_precipitation_probability_0d
day_2: sensor.cc_precipitation_probability_1d
day_3: sensor.cc_precipitation_probability_2d
day_4: sensor.cc_precipitation_probability_3d
day_5: sensor.cc_precipitation_probability_4d
icons:
day_1: sensor.cc_weather_condition_0d
day_2: sensor.cc_weather_condition_1d
day_3: sensor.cc_weather_condition_2d
day_4: sensor.cc_weather_condition_3d
day_5: sensor.cc_weather_condition_4d
precipitation_intensity:
day_1: sensor.cc_precipitation_intensity_0d
air_quality:
o3: sensor.cc_o3
pm25: sensor.climacell_particulate_matter_2_5_mm
pm10: sensor.climacell_particulate_matter_10_mm
no2: sensor.climacell_nitrogen_dioxide
co: sensor.climacell_carbon_monoxide
so2: sensor.climacell_sulfur_dioxide
epa_aqi: sensor.climacell_us_epa_air_quality_index
epa_health_concern: sensor.climacell_us_epa_health_concern
I’m not using the China weather or fire sensor but you should be able to model that from the above. We don’t have access to the Feels Like values as they aren’t provided now by the integration - maybe in the future?!
Good luck and thanks Renato/all. As always contribute a more elegant solution if you have one!
