Query Open Data
at Lightning Speed

# Monthly temperature + boundary-layer height, Berlin
curl "https://api.jiskta.com/api/v1/climate/query" \
  -H "X-API-Key: sk_live_your_key" \
  -G -d "lat_min=52.3" -d "lat_max=52.7" \
     -d "lon_min=13.0" -d "lon_max=13.7" \
     -d "time_start=2023-01" -d "time_end=2023-12" \
     -d "variables=t2m,blh" -d "format=csv" -d "aggregate=monthly"

# ERA5 at 0.25° · global · 2013–present
# lat,lon,year_month,t2m_mean,blh_mean
# 52.3750,13.1250,2023-01,275.4,342.1
# 52.3750,13.1250,2023-02,277.1,401.3

# Monthly nighttime radiance for Paris, 2020–2024
curl "https://api.jiskta.com/api/v1/climate/query" \
  -H "X-API-Key: sk_live_your_key" \
  -G -d "lat=48.85" -d "lon=2.35" \
     -d "time_start=2020-01" -d "time_end=2024-12" \
     -d "variables=viirs" \
     -d "format=csv" -d "aggregate=monthly"

# NASA Black Marble · 0.005° (~500 m) · global · 2020–present
# lat,lon,year_month,viirs_mean
# 48.8525,2.3525,2020-01,64.3  ← nW/cm²/sr
# 48.8525,2.3525,2020-02,61.8
# ...combine with no2 for env-economic analysis

# WHO exceedance for a named city — no lat/lon math needed
curl "https://api.jiskta.com/api/v1/climate/query" \
  -H "X-API-Key: sk_live_your_key" \
  -G -d "area=paris" \
     -d "time_start=2023-01-01" -d "time_end=2023-12-31" \
     -d "variables=no2" -d "threshold=25"

# 400K+ named areas — cities, regions, countries
# area=london · area=berlin · area=osm:54094
# lat,lon,hours_above,total_hours,pct_above
# 48.8500,2.3500,312,8760,3.56
# 48.9500,2.3500,248,8760,2.83

# Average hour-of-day NO₂ profile — city-level daily cycle
curl "https://api.jiskta.com/api/v1/climate/query" \
  -H "X-API-Key: sk_live_your_key" \
  -G -d "area=madrid" \
     -d "time_start=2023-01-01" -d "time_end=2023-12-31" \
     -d "variables=no2" -d "aggregate=diurnal"

# hour,no2_mean
# 0,11.3   # low at midnight
# 8,18.7   # morning rush peak
# 17,17.2  # evening peak

# Air quality + meteorology + nightlights — 1 request
curl "https://api.jiskta.com/api/v1/climate/query" \
  -H "X-API-Key: sk_live_your_key" \
  -G -d "area=london" \
     -d "time_start=2023-01" -d "time_end=2023-12" \
     -d "variables=no2,t2m,viirs" \
     -d "format=csv" -d "aggregate=monthly"

# lat,lon,year_month,no2_mean,t2m_mean,viirs_mean
# 51.5000,−0.1000,2023-01,14.2,275.1,42.6
# 3 datasets · 1 endpoint · 3 credits/month

# 10-year NO₂ trend — anywhere in Europe, 3 lines
from jiskta import JisktaClient

client = JisktaClient("sk_live_your_key")

df = client.query(
    area="london",
    start="2013-01", end="2024-12",
    variables=["no2"], aggregate="trend"
)
# Returns pandas DataFrame:
# slope  intercept  r2    n
# -0.49  18.2       0.87  144   ← −0.49 µg/m³/year

# pip install jiskta  ·  works with any area= or lat/lon