Smarter Weather

Variables available in a dataset, with standard names, units, descriptions, and the time range of available data. Use before query_dataset to discover valid variable names. Example: {"dataset_id": "nbm_conus"}.

NWS watches, warnings, advisories. Point (city/ZIP/lat+lon): containing polygons. BBox or US state/DC/CONUS (codes, full names, US/national): intersecting polygons. A city miss is not a statewide all-clear — query the state or a bbox; never say regional inventory is impossible. NY/WA and "New York State"/"Washington State" are states; "New York"/"Washington" stay cities. Omit at for now; at (ISO-8601 UTC) is the snapshot then. Empty = all-clear or purged (~24h). alert_id = detail+geometry, ignores at. Ex: {"location":"WI","events":["Tornado Warning"]}.

Current weather right now at a location from two independent sources in one call: the RTMA gridded analysis (exact-point values, updated sub-hourly) and the nearest METAR station observation (ground truth with raw METAR, flight category). Use the analysis for point-accurate values and the station for verification. Analysis fields: temperature_2m, dew_point_2m, relative_humidity_2m (derived here from temperature and dew point; listed in analysis.derived), wind_speed_10m, wind_direction_10m, wind_gusts_10m, surface_pressure (station pressure at ground level, not sea-level pressure), visibility, cloud_cover, cloud_ceiling. Analysis values are SI by default; pass units "imperial" or "metric" to convert them (labels in analysis.unit_labels; get_forecast defaults to imperial). nearest_station is the station's own report, unconverted. For a forecast, use get_forecast. Example: {"location": "Pella, IA"}.

Complete weather overview for a location: current conditions, daily forecast (SPC threats, severity; view "detailed" adds CAPE, UV, full day/night periods), active alerts, and outlooks in one call. Pre-aggregated across NBM, HRRR, GFS, RTMA, and SPC and unit-converted server-side. Primary weather tool; reach for lower-level tools only when you need raw observations or a specific dataset. Times are UTC ISO 8601; quote daily[].astro.local to users, not UTC. If forecast.location.timezone_resolved is false, zone unknown: state times as UTC, never as local. Accepts a place name directly. Examples: {"location": "Denver"} or {"location": "Portland, OR", "days": 5} or {"lat": 41.4, "lon": -92.9}.

NBM probabilistic guidance for one weather aspect: percentiles (p10-p90), exceedance probabilities and ensemble spread. Use this for any question about odds, ranges, potential or confidence ("how much could we get", "range of possible highs", "how sure is this") -- a deterministic forecast value cannot answer one. Reading the percentiles: p50 is the most likely outcome, p90 is the reasonable worst case when the risk is the high end (snow totals, wind, rainfall), and p10 when the risk is the low end (cold, visibility, ceiling). A single percentile is not the forecast -- report the likely value with the tail that matters, and label which is which. Aspects: precip (PoP, QPF + percentiles), snow (accumulation percentiles, >1/2/4in probabilities, snow level, snow-type probability), ice (ice accretion, freezing-rain / ice-pellet probabilities), temperature (`daily` high/low p10/p50/p90: the NBM value -/+ 1.28 stddev; hourly temp + stddev), wind (speed/gust percentiles), severe (thunderstorm probability plus NBM CWASP, a severe-environment index, both in percent: severe_weather_prob_p50 is the median CWASP value and severe_weather_prob_above_75 the probability CWASP exceeds 75; NBM has no tornado, hail or damaging-wind odds; SPC's: get_outlooks (guidance) hazard=severe), aviation (LIFR/IFR/MVFR visibility + ceiling odds), confidence (ensemble stddev; low = settled, high = details still in play). Labels in unit_labels. Example: {"location": "Denver", "aspect": "snow", "hours": 72}.

Blended hourly forecast: temperature, feels-like, humidity, wind, precipitation probability/amount, conditions, and icon per hour. Snapped to the current hour so hourly[0] is "now". Timestamps are UTC ISO 8601; convert to the local timezone before presenting. Ask for the days you need up front -- one call with days: 4 beats four calls. ALWAYS check hourly_coverage before answering about a specific hour: it reports first_time and last_time (the window the rows actually span), sample_interval_hours (past the first day rows are every 2-3h, not every hour), and truncated: true when upstream returned less than you asked for. If the hour the user cares about is after last_time, say the forecast does not reach that far yet rather than answering from the nearest row you do have. For one stretch of time ask for that stretch with hours_from/hours_to: it comes back hour by hour even where the full range would be sampled. Accepts a place name or coordinates. Examples: {"location": "Portland, OR", "days": 2} or {"lat": 41.88, "lon": -87.63, "hours_from": 36, "hours_to": 48}.

How much rain fell: observed precipitation totals at a location. Use for "did it rain", "how much rain fell", "rain in the last 24 hours / since yesterday". Source: MRMS MultiSensor QPE Pass2 (radar bias-corrected to rain gauges) over 1/3/6/24/48/72 h windows (CONUS; 1 h only in Alaska and Hawaii). Values have about 2 mm (0.08 in) resolution, so 0 means under about 1 mm; data runs about an hour behind real time. Pass `at` (within 33 h) for totals ending at a past time (newest frame up to 3 h before it). With include_nowcast (default) it also returns a next-hour radar nowcast (CONUS): peak intensity (light/moderate/heavy), type and start time, not an amount. Forecast totals past the next hour: get_period_totals (planning); station reports: get_observations (observed).

Current data-freshness status of the weather platform: overall state, per-source states (ok / degraded / outage / no_signal), open incidents with cause attribution (provider outage vs internal processing delay), and active provider advisories. Use this when a user asks whether data is current, when other tools return surprisingly stale data, or before presenting time-critical weather. If a source is degraded or in outage, tell the user their data may be stale rather than presenting it as live. No inputs. Refreshed about every 5 minutes.

Complete temporal context for a location: local time, timezone, 14-day calendar with day names and Today/Tomorrow offsets, sunrise/sunset/solar times (from the weather pipeline's astro product), and moon phase. Use whenever you need to reason about dates, times, or daylight for a location -- including "what time is sunset?", "is it dark there now?", or "what day of the week is the 4th-day forecast?". If current_time.timezone_resolved is false, zone unknown: state times as UTC, never as local. Accepts a place name directly. Example: {"location": "Seattle"}.

Discover the datasets (model grids, analyses, observations) available at a location, keyed by dataset_id, with per-dataset freshness where tracked (latest model run as latest_run, latest_time, data_age_seconds, freshness_status). Only datasets covering the point are listed: its domain (CONUS/Alaska/Hawaii) plus global grids and the GOES full disks that see it. Use this to find dataset_id values for query_dataset, or to check whether data is current. Variables, units and standard names come from describe_dataset. Example: {"location": "Anchorage, AK"}.

Raw time series from a specific dataset for specific variables at a point. Power-user access to any gridded product (NBM, HRRR, RRFS, GFS, RTMA, MRMS, air quality, ...). Time modes: hours (next N hours, default 24), time_start+time_end (explicit ISO-8601 window), or latest=true (single most-recent value). reference_time pins a specific model run, and each returned series reports the run that served it (reference_time, or reference_times when a series mixes runs) — check it before comparing two runs, since a run older than about 48 hours may no longer be available. For blended forecasts use get_forecast instead. Examples: {"location": "Denver", "dataset_id": "rrfs_surface", "variables": ["temperature_2m"], "hours": 18} or {"lat": 41.4, "lon": -92.9, "dataset_id": "rtma_conus", "variables": ["temperature_2m"], "latest": true}.

Resolve coordinates to a human-readable place (city, state, county, timezone). Use when you have lat/lon but need a display name or the local timezone. Example: {"lat": 39.74, "lon": -104.99} -> Denver, Colorado, America/Denver.

Find which dataset answers a weather question when no dedicated tool fits: ranks the data catalog (model grids, radar, satellite, outlooks, observations) by the question's words and returns the best datasets with variable, units, cadence, a one-line why and the exact `call` (tool and arguments) to run next. Pass lat/lon, or `domain`, to keep only datasets covering the place; without one, Alaska/Hawaii twins fold into the CONUS dataset. Calls are ready for "now"; for a past time set `time_start`/`time_end` (or `at` where the tool supports it). Free; no network call.

Resolve a place to ranked candidates with lat/lon: city ("Denver"), "city, ST", ZIP ("50219"), or partial input with fuzzy=true ("bost" -> Boston); venues and addresses where enabled. Rows carry `type` place|zip|address|poi; pass a row's lat/lon to weather tools. `matched_via: "autosuggest"` marks rows found after the exact match missed; `partial` means a section timed out. Weather tools take `location` directly, venues included -- use this to disambiguate ("which Springfield?") or present choices.