Weathersight

Returns observations with 1991–2020 baseline comparisons at daily, weekly, or monthly granularity for a location and period. <br><b>When to use:</b> Graph or tabulate recent anomalies at a location for a specific period — e.g. 'how did last month compare to normal?' <br><b>Date format:</b> YYYYMMDD for ymd; use groupby (day/week/month) to control granularity. For 'last week' use YYYYMMDD of 7 days

Returns precomputed anomalous observations for locations in a given time period and scope. <br><b>When to use:</b> Survey anomalous weather at regional or global scale quickly without specifying individual cities. <br><b>Date format:</b> YYYY[MM[DD]]: YYYY for annual anomalies (scope=year), YYYYMM for monthly (scope=month), YYYYMMDD for weekly or daily (scope=week/day). <br><b>Performance:</b> Fas

Given a single week, month, or day, ranks locations whose weather best matches a set of criteria (most optional criteria met first; all MUST criteria satisfied by every result). Filter by lat/lon+radius or countries. <br><b>When to use:</b> Answer 'where has the best weather for week/month/day X?' <br><b>Date format:</b> scope=day + time=YYYYMMDD (forecast); scope=week + time=MMDD; scope=month + t

Given a location, ranks the weeks or months whose historical climatology best matches a set of weather criteria (most optional criteria met first; all MUST criteria satisfied by every result). <br><b>When to use:</b> Answer 'when is the best time to visit X for weather like Y?' Choose when_type=week or month over the 1996-2025 baseline. <br><b>Date format:</b> when_type: week|month|day. Historical

Returns anomalous weather conditions (breaking news) across the world or specified countries in the very recent past (observations) or immediate future (forecast). <br><b>When to use:</b> First call when looking for current or upcoming notable weather events globally or by country. <br><b>Date format:</b> No date params — always returns the most recent ~3 days of observations (breaking_type=obs) o

Returns monthly distributions of selected metrics for locations compared against a baseline period. <br><b>When to use:</b> Compute normalized and absolute deviations from a baseline, inter-annual and intra-annual variations within months and seasons. Check base_N and obs_N to assess data completeness. <br><b>Date format:</b> obs_years as YYYY or YYYY-YYYY range; months as MM or MM-MM (e.g. '06',

Returns monthly timeseries of selected climate trend metrics for one or more locations, suitable for long-term trend analysis. <br><b>When to use:</b> Retrieve long-term monthly timeseries for statistical trend analysis across any number of locations including globally. Use num_years and avg_N to filter sparse records. <br><b>Date format:</b> years as YYYY- range (e.g. '1975-' for 1975 to present)

Compares a location's observed weather period against the corresponding periods in a historical baseline distribution. <br><b>When to use:</b> Quantify how extreme a specific period was across all metrics at once — e.g. how anomalous was March 2025 at New York vs the 1991–2020 baseline. <br><b>Date format:</b> YYYY[MM[DD]]: YYYYMMDD for a specific day or short window (period_type DAY/WEEK), YYYYMM

Returns countries with their canonical country IDs used in all other API calls. <br><b>When to use:</b> Resolve country names to canonical IDs, or obtain the full list of supported countries. This list rarely changes. <br><b>Performance:</b> Fast — static data; safe to cache across calls. <br><b>Prerequisites:</b> None. <br><b>Investigate:</b> Call once if country-level filtering is needed; cache

Returns per-day observed values with percentiles and z-scores vs a sliding N-day window over a historical baseline. <br><b>When to use:</b> Graph heatwaves, cold spells, and other streaks by showing daily departures from normal. Also compare two separate periods or locations using z-scores or percentiles for normalized severity. <br><b>Date format:</b> YYYYMMDD for ymd_start and ymd_end. Keep rang

Returns cumulative degree-day indicators (heating/cooling/growing) for a location and period using ERA5 reanalysis data. <br><b>When to use:</b> Estimate energy demand anomalies or agricultural stress in a recent or upcoming period. <br><b>Date format:</b> YYYYMMDD for ymd_start and ymd_end. Maximum interval is 14 days per call. <br><b>Performance:</b> Computed on demand using ERA5; moderate speed

Finds days matching one or more compound weather conditions (e.g. hot + humid, rainy + windy) for a location. Returns up to 5000 most-recent matching events plus the total count. <br><b>When to use:</b> Count occurrences of compound weather events across years, study extreme or unusual day combinations, or identify streaks of such events. <br><b>Date format:</b> year_start/year_end as YYYY; month_

Returns extreme value analysis (return periods) for a specified location and metric. <br><b>When to use:</b> Quantify the statistical rarity of a record or near-record value. Typically called after other APIs establish that an observation is anomalous. <br><b>Performance:</b> Computed on demand; moderate speed. <br><b>Prerequisites:</b> Use /location to obtain a locid first. Prefer level='area' ov

Scores how well each forecast model actually performed at each of many locations, by comparing what the models predicted against what was then observed. Returns one score matrix per location, models by evaluation metrics, for a single weather metric at a single lead time, plus the winning model per location. <br><b>When to use:</b> Answer 'which forecast model is most accurate here?' at scale — to

Scores how well each forecast model performed at ONE location, across several forecast lead times and across a sliding time window. Shows both how fast accuracy decays as the forecast reaches further ahead, and how accuracy at a given lead time has varied over recent weeks. <br><b>When to use:</b> Answer 'how far ahead can I trust the forecast here?' or 'has the forecast been unusually poor here l

Returns meta information for a location given a name, locid, or lat/lon. <br><b>When to use:</b> Obtain the canonical locid needed by all location-specific API calls. Typically the first call in any workflow. <br><b>Performance:</b> Fast — precomputed lookup. <br><b>Prerequisites:</b> None — this is the entry point. <br><b>Investigate:</b> Call first whenever the user names a city or region; cache

Returns the catalogue of all supported weather metric identifiers with units and categories. <br><b>When to use:</b> Resolve metric IDs to human-friendly names, units, and categories. Call once and cache per session. <br><b>Performance:</b> Fast — static data; safe to cache for the lifetime of a session. <br><b>Prerequisites:</b> None. <br><b>Investigate:</b> Call once at session start; use metric

Returns recent weather records (all-time or period-of-record extremes) for the world or specified countries. <br><b>When to use:</b> Find which locations have broken or nearly broken records in a recent date window. Set history_years ≥ 15 to ensure a meaningful period of record. <br><b>Date format:</b> YYYYMMDD for ymd_start and ymd_end. Keep windows ≤ 3 months per call for reasonable performance.

Seasonal (monthly) forecast anomalies for the next six months, with the local climatology they are measured against. Each result gives, per metric: the ERA5 baseline mean (means), its interannual standard deviation (std), the predicted anomaly (anom, in degrees C / mm / km/h) and how unusual that is, both as standard deviations (norm) and as a percentile rank among the last 30 years (perc). <br><b

Returns aggregated metric values as a yearly or monthly timeseries for a location. <br><b>When to use:</b> Detect long-term trends and compute statistical significance (r-squared, p-value). Best suited for multi-decade spans. <br><b>Date format:</b> mm_dd_start/mm_dd_end as MMDD (day-of-year window, not a calendar year); year_start/year_end as YYYY. Note: Use mm_dd_start/mm_dd_end as MM when month

Returns statistical distributions of weather attributes (temperature, precipitation, wind, etc.) for a location and time period. <br><b>When to use:</b> Establish the climatological baseline — what is 'normal' for this location and period. Use before discussing anomalies. <br><b>Date format:</b> YYYY[MM[DD]]: YYYYMMDD for a specific week, YYYYMM for a month, YYYY for a seasonal or annual overview.