Tool Reference

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.

Returns: query, candidates, partial

{
  "query": "Springfield"
}

{
  "query": "bost",
  "fuzzy": true,
  "limit": 3
}

{
  "query": "50219"
}

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}.

One of: location, or lat + lon

Returns: location, units, forecast, data_status, widget

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}.

One of: location, or lat + lon

Returns: location, units, forecast, data_status, widget

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"}.

One of: location, or lat + lon

Returns: location, units, analysis, nearest_station

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"]}.

One of: location, or lat + lon, or bbox, or alert_id

Returns: location, alert_id, alerts, alert, valid_time, widget

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).

One of: location, or lat + lon

Returns: location, units, as_of, windows, quantization_step, nowcast, derived, notes, _sources, data_status

{
  "location": "Chicago, IL",
  "windows": [
    "24h"
  ]
}

{
  "lat": 41.89,
  "lon": -87.65,
  "windows": [
    "1h",
    "6h"
  ],
  "at": "2026-09-30T12:00:00Z"
}

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}.

One of: location, or lat + lon

Returns: location, aspect, hours, units, series, daily

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.

Returns: query, domain, note, hits

{
  "query": "how much rain fell in the last day",
  "lat": 39.74,
  "lon": -104.99,
  "limit": 3
}

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"}.

One of: location, or lat + lon

Returns: location, domain, datasets, freshness

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"}.

Returns: dataset_id, detail, attribution

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}.

One of: location, or lat + lon

Returns: location, series, attribution

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.

Returns: overall, generated_at, sources, advisories, note

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"}.

One of: location, or lat + lon

Returns: location, current_time, daylight, moon, calendar

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.

Returns: lat, lon, place

Compare forecast variables across multiple locations side-by-side in one batched call. Returns a distilled per-location series matrix for direct comparison -- prefer this over N sequential forecast calls. Locations accept place names directly. Example: {"locations": [{"location": "Denver"}, {"location": "Boulder, CO"}], "variables": ["temperature_2m", "precipitation_probability"], "hours": 48}.

Returns: variables, hours, comparisons, errors, attribution

Find the optimal time window for an activity based on weather criteria. Scans the forecast and returns daylight-aware periods matching all conditions. Criteria are expressed in the selected units system (default imperial: °F, mph, miles, feet). Example: {"location": "Boulder, CO", "criteria": {"min_temperature": 55, "max_wind_speed": 15, "max_precipitation_probability": 20}, "hours": 72, "activity_duration_hours": 3}.

One of: location, or lat + lon

Returns: location, units, windows, criteria_applied, daylight_only, daylight_filter, activity_duration_hours, sun_times, message

{
  "location": "Boulder, CO",
  "criteria": {
    "min_temperature": 55,
    "max_wind_speed": 15,
    "max_precipitation_probability": 20
  },
  "hours": 72,
  "activity_duration_hours": 3
}

{
  "location": "Girdwood, AK",
  "criteria": {
    "max_precipitation_probability": 30
  },
  "daylight_only": true
}

Aggregate a weather variable over one or more time periods. Returns server-computed totals, maxima, minima, or averages per period. Period start/end times should use the user's local timezone boundaries (not UTC midnight). Response includes the converted value and unit per period. Period-ending totals (1 h rain/snow, 6 h buckets, MRMS QPE) sum windows ending in (start, end]. period_total variables need periods on their bucket grid (UTC-aligned for 3 h/6 h buckets such as precipitation_6h; hourly totals fit any hour); off-grid periods error naming the grid. Past the data: partial: true, frames_used/frames_expected, covered_start/covered_end. Ideal for questions like "total rainfall today and tomorrow" or "peak wind speed this weekend". For rain that already fell, use get_observed_precipitation. Accepts a place name directly. Example: {"location": "Portland, OR", "variable": "precipitation", "aggregation": "sum", "periods": [{"start": "2026-07-08T07:00:00Z", "end": "2026-07-09T07:00:00Z", "label": "Today"}]}.

One of: location, or lat + lon

Returns: location, variable, aggregation, periods, message, attribution

AirNow air quality at a location (CONUS): current overall AQI plus per-pollutant detail (PM2.5, ozone, PM10 concentrations) and the AirNow AQI forecast. AQI scale: 0-50 good, 51-100 moderate, 101-150 unhealthy for sensitive groups, 151-200 unhealthy, 201-300 very unhealthy, 301+ hazardous. pollutants=["aqi"] (default) is the cheap headline call; add pollutant keys or include_forecast=true when the user digs in. Example: {"location": "Boise", "pollutants": ["aqi", "pm25"], "include_forecast": true}.

One of: location, or lat + lon

Returns: location, current, forecast, widget

METAR surface observations from weather stations: temperature, wind, visibility, ceiling, flight category, raw METAR. Nearest mode (default) returns the closest N stations to a location; station mode returns history for a specific ICAO identifier. Examples: {"location": "Denver", "n": 3} or {"station": "KJFK", "hours": 6}.

One of: location, or lat + lon

Returns: location, station, observations

Nearest RAOB (radiosonde) vertical soundings to a point. Each sounding carries: profile (pressure-indexed thermodynamics: pressure_hpa, height_m, temperature_c, dewpoint_c, wind arrays), wind_profile (height-indexed winds for hodographs/shear), and derived indices (sbcape/mucape/mlcape + cin, lifted_index, k_index, total_totals, pwat_mm, freezing_level_m, lcl/lfc/el, bulk_shear_0_6km_kt). Soundings launch at 00Z/12Z so data can be hours old. Example: {"location": "Norman, OK"}.

One of: location, or lat + lon

Returns: location, soundings

Hurricane Hunter aircraft reconnaissance for active tropical systems: vortex data message (VDM) center fixes — pressure, max flight-level wind, eye — and high-density (HDOB) flight-level observations. These are aircraft measurements, not surface weather stations (use get_observations for METAR). Use get_tropical for NHC cones, tracks, and watches; this tool is what the aircraft measured. kind=hdob returns the newest 200 records (about 1–2 h of flight; truncated says when more exist). Empty when no aircraft has flown in the window.

Returns: active, storms, feature_count, truncated, features

Real-time lightning near a location: GLM satellite flash count (30km/10min) and MRMS ground-truth lightning density + 30-minute probability. The summary field is ready-to-use. A zero flash count means no lightning inside that window -- report it as a quiet observation scoped to the window in scope, never as a data gap. Only call when storms may be active or the user asks about lightning. Example: {"location": "Tampa"}.

One of: location, or lat + lon

Returns: location, scope, lightning

Radar-identified storm cells near a location, merging NEXRAD Level III algorithm output from the nearest radar site: storm tracks (cell position, movement, forecast positions), hail index (probability of hail/severe hail + max expected size), mesocyclone detections (rotation), and TVS (tornado vortex signatures). Use during active convection to see what the radar algorithms flag. An empty result means no detected cells -- common outside active storms. Example: {"location": "Norman, OK"}.

One of: location, or lat + lon

Returns: location, summary, tracks, detections

Recent NWS Local Storm Reports (LSRs) -- verified reports of tornadoes, hail, damaging winds, flooding near a location. Use to confirm severe weather occurrence or assess reported damage. valid_time is event occurrence (UTC); cite in local time. Example: {"location": "Wichita", "hours": 12, "type": "H"}.

One of: location, or lat + lon

Returns: location, hours, reports

Hazard outlooks affecting a location. severe: SPC convective (day 1-8 categorical + tornado/wind/hail probabilities); fire: SPC fire weather; rain: WPC Excessive Rainfall (days 1-3); heat: NWS HeatRisk index (0-4, days 1-3); temperature/precipitation: CPC 6-10 day, 8-14 day, week 3-4 and monthly outlooks (favored category + probability bin; period=seasonal: 3-month leads). include_narrative=true adds the forecaster discussion (SWO, FWD, QPFERD, or the period's CPC PMD). Empty means no outlook covers the point, not a failure. Examples: {"location": "Moore, OK", "hazard": "severe", "include_narrative": true} or {"location": "Denver", "hazard": "temperature"}.

One of: location, or lat + lon

Returns: location, hazard, day, outlooks, heat_risk, narrative

Expert forecaster text products. type=afd: Area Forecast Discussion. type=hwo: Hazardous Weather Outlook. type=now: WFO short-term NOW. type=fwf/hls/esf: local fire weather / hurricane local statement / hydrologic discussion. type=mcd: SPC Mesoscale Discussion. type=mpd: WPC Mesoscale Precipitation Discussion (flash flood). type=swo/fwd/ero: national outlook discussions. type=tcd/tcp/tcm/twd/two: NHC tropical text (type=two is the text TWO, not GIS nhc_two). type=pmd: WPC/CPC desk discussion (pass awips_id for a specific desk, e.g. PMDSPD). type=pwo: SPC public weather outlook. National types (swo/fwd/ero/tcd/tcp/tcm/twd/two/pmd/pwo) need no location; day selects the outlook day for swo and fwd. summary_only=true returns the pipeline LLM summary without the full body. Examples: {"location": "Des Moines", "type": "afd"} or {"type": "swo", "day": 2, "summary_only": true}.

Returns: location, type, products

Active NHC (National Hurricane Center) tropical systems: forecast cones, track lines, forecast points, coastal watches/warnings, and 7-day Tropical Weather Outlook formation areas -- Atlantic + East Pacific. Each feature carries a kind (cone | track | points | watch_warning | outlook_area) plus storm name, intensity, and timing properties. include_geometry=true adds full GeoJSON geometries (large). An empty result means no active tropical activity. Aircraft reconnaissance fixes and flight-level observations are in get_tropical_observations. Example: {} or {"include_geometry": true}.

Returns: active, feature_count, features

NWS daily climate data: type=reports returns CLI daily climate reports (observed high/low/precip vs normals per station); type=records returns RER record event reports (record highs/lows/rainfall actually set). Filter by wfo (3-letter office, e.g. DMX), station, date (YYYY-MM-DD), start/end range, or hours lookback. Examples: {"type": "records", "hours": 48} or {"type": "reports", "wfo": "DMX", "date": "2026-07-04"}.

Returns: type, results

Day-of-year climate normals (NCEI 1991-2020 30-year averages) for a US location, from the nearest station with a record. Returns normal high, normal low, and normal mean for each date in the window, plus the station and how far away it is. Use this whenever a question needs a baseline rather than a forecast: "is this warm for October?", "what is a typical high here in January?", "how does this week compare to normal?". Pair it with get_forecast to say how far above or below normal the coming days run. Covers dates by day of year, so it answers for any date, past or future -- these are long-period averages, not a forecast and not observed history for a specific year.

How accurate our forecasts have actually been near a location, measured against observed analysis truth. Returns bias (positive = the model runs high), mean absolute error, RMSE, and a skill score against local climatology, per model, weather variable, and forecast lead time; continuous and vector entries also carry persistenceSkillScore, skill against the analysis at forecast issue time (null means not enough persist pairs, not zero skill -- do not compare it to skillScore as if they shared a denominator), and analysisDisagreementMae, the analyses' own disagreement at that lead -- a floor on how good the forecast can look, not a skill score and not an excuse (null means the sibling row is missing or below minimumSamples); for probability forecasts, the Brier score and a reliability breakdown. Use this to qualify a forecast rather than assert it -- "NBM has been running 1.8F warm at 3-day leads near you, so treat that 72 as around 70" -- and to answer "how much should I trust this forecast", "is the model biased here", or "how accurate were you last month". Evidence comes at three scopes side by side: the exact point (strongest, slowest to accumulate), the neighborhood and the region (coarse pooled cells, not street-level). Prefer the most specific scope that has samples. Metrics below minimumSamples observations are withheld and listed under insufficientHistory with their count -- say history is still accumulating; do not treat thin numbers as evidence. Coverage is a rolling recent window over verified US variables, not all of history. Samples aren't matched across models, so never conclude one model beats another from these numbers. Each entry states the truth field it was measured against (one designated analysis per variable); never compare numbers with different truth values either. Each entry also states its regime: ALL (every observation, any weather) or a conditioned tier such as SEA:DJF (winter), SCN1:WINDY / SCN1:WET / SCN1:QUIET (what the forecast was showing), or JC1:NW (a circulation pattern). Pass the regime parameter to ask for a conditioned track record. It falls back: asking for SCN1:WINDY and getting regime ALL is a successful answer, not a missing one -- always read the regime field and qualify the claim with it, because "NBM runs warm here when it shows windy" and "NBM runs warm here" are different statements. Regimes overlap by construction across families, so entries under different regimes are alternative answers to one question; never compare or add them. Within SCN1: the labels are mutually exclusive. Entries with a categorical block answer a yes/no question instead of an error magnitude -- did it rain, at the thresholdMm stated on the entry -- with pod (of the times it happened, how often we called it), far (of the times we called it, how often it did not happen), and frequencyBias (above 1 = we call it too often). Use these for "will it actually rain" questions, where a small average error means nothing if the rain lands in the wrong hour. A null rate means the sample cannot answer it -- the event has not happened, or been forecast, enough times to divide by -- and must be reported as unknown, never as zero. The counts beside it are still evidence, and for a rare event they are often the whole answer: "it has only rained twice here in the record" is a useful thing to say.

One of: location, or lat + lon

Returns: location, units, minimumSamples, tracked, cells, skill, insufficientHistory

{
  "location": "Denver",
  "variable": "temperature_2m",
  "lead_hours": 72
}

{
  "lat": 39.7392,
  "lon": -104.9903
}

{
  "location": "Dallas, TX",
  "variable": "precipitation_probability",
  "lead_hours": 24
}

{
  "location": "Chicago",
  "variable": "temperature_2m",
  "regime": "SEA:DJF"
}

{
  "location": "Houston",
  "variable": "precipitation_gt_0p254mm",
  "lead_hours": 24
}

Measured forecast skill as a map of res-4 hexes inside a bounding box, sliced from the public skill map. Use this when the question is spatial ("where is NBM temperature skill weak across Colorado"), not "how good is the forecast at this point" — that is get_forecast_skill. Requires model, variable, lead_hours, and bbox {west,south,east,north}. lead_hours selects the containing published bucket (20 → NBM 24); a lead we have not published returns no slices rather than a nearby one. A hex is included when its center is inside the box; edge-overlapping hexes are dropped. The box cannot wrap the dateline. Each cell is [h3, samples, skill_score]; skill_score is unitless (fraction of climatological variance explained) and already gated at n ≥ 30. withheld lists hexes still accumulating. Do not compare slices across models or truths. This is CELL# only — never treat a hex as a person.

Returns: generated_at, atom_resolution, minimum_samples, bbox, slices

{
  "bbox": {
    "west": -105.2,
    "south": 39.5,
    "east": -104.7,
    "north": 40
  },
  "model": "nbm",
  "variable": "temperature_2m",
  "lead_hours": 24
}

Render a weather map image for visual analysis. Simple form: pass product (a viz-catalog product_id like "mrms_qpe_01h_pass2_conus", "goes_truecolor_conus", "spc_day1_categorical", "hrrr_precip_hybrid_derived_conus" (future radar), "hrrr_subhourly_conus" (15-min Future Radar), "mrms_radar_nowcast_conus", "rtma_conus", "nbm_daily_temps", or "nexrad_l3:{SITE}:{PRODUCT}" for single-site radar, e.g. "nexrad_l3:TLX:N0B") plus a location and zoom (5=regional, 8=metro, 10=city). Composed form: pass scene -- a declarative scene document layering basemap + multiple weather products + active alerts + storm features + inline GeoJSON in one image (layers draw bottom-to-top, under basemap labels). Example scene: {"scene":"1.0","view":{"center":{"lat":43.8,"lon":-91.2},"zoom":8},"layers":[{"type":"weather","product":"goes_truecolor_conus"},{"type":"weather","product":"nexrad_l3:ARX:N0B"},{"type":"alerts","filter":{"events":["Tornado Warning"]},"onError":"skip"}]}. Alert filters (all optional, AND-combined): ids (specific alerts), events, severities, minSeverity (Extreme>Severe>Moderate>Minor>Unknown). Single-site radar keys: the address is nexrad_l3:{SITE}:{KEY} where KEY is N{tilt}{measurement} and tilt 0 is the 0.5 degree sweep -- N0B reflectivity (dBZ, where and how heavy), N0G base velocity (knots toward/away from the radar), N0S storm-relative velocity (storm motion removed, so a couplet is rotation rather than translation -- prefer it for rotation questions), N0C correlation coefficient (0-1, debris and hail), N0X differential reflectivity (dB). Legacy codes (N0V, N0R, N0Q) are accepted as aliases. Not every site produces every key; when a render reports which keys a site has, retry with one of those. Optional time (unix seconds): closest frame. Forecast (HRRR/nowcast/NBM) honors future times; analysis (MRMS/NEXRAD/RTMA/GOES) clamps to latest past. Pass time for future-radar asks — do not claim that capability is missing. Product ids must be real viz-catalog entries -- shorthand like "radar" or "reflectivity" is not one. Omit product for the default hybrid precip still. For Alaska and Hawaii prefer a local site or mrms_precip_hybrid_derived_alaska over CONUS mosaics, which do not cover them. Returns the rendered image plus per-layer resolved valid times.

{
  "location": "Tulsa, OK",
  "product": "nexrad_l3:TLX:N0B",
  "zoom": 8
}

{
  "lat": 39.74,
  "lon": -104.99,
  "product": "goes_truecolor_conus",
  "zoom": 5
}

{
  "location": "Chicago, IL",
  "product": "hrrr_precip_hybrid_derived_conus",
  "zoom": 7,
  "time": 1783554900
}

Render the nearest RAOB (radiosonde) sounding as a Skew-T log-P + hodograph chart image for visual analysis: temperature/dewpoint traces, wind barbs, height-banded hodograph, and a derived-indices table (CAPE/CIN, lifted index, PWAT, shear, LCL). Soundings launch at 00Z/12Z so data can be hours old. Use get_sounding for the raw profile numbers. Example: {"location": "Norman, OK"}.

One of: location, or lat + lon

National population-exposure headline for a risk-zone outlook product: how many people are inside risk bands at or above min_level. Powers headlines like "~57M people under major heat risk tomorrow". hazard=heat covers NWS HeatRisk days 1-3 (levels: 1 minor, 2 moderate, 3 major, 4 extreme). Pass product_id directly for other risk-zone products. Example: {"hazard": "heat", "min_level": 3}.

Returns: min_level, summaries

Civic POIs (schools, hospitals, airports, …) inside a caller-supplied GeoJSON Polygon or MultiPolygon. Not enabled for every caller: when off it returns 403 flag-disabled. Clip first; not a CONUS HeatRisk dissolve. Optional classes filters the civic allowlist. Example: {"geometry":{"type":"Polygon","coordinates":[[[-88.15,41.77],[-88.14,41.77],[-88.14,41.78],[-88.15,41.78],[-88.15,41.77]]]},"classes":["school"]}.

Returns: counts, features

Growing Degree Units (GDU / GDD) for a US location (CONUS, Alaska, Hawaii), computed from daily max/min temperatures. Pass a crop id (e.g. "corn", "soybean", "wheat") to use calibrated base/upper thresholds, or crop="custom" with base_temp_c (and optional upper_temp_c / method). Without season_start you get per-day GDU across the forecast horizon; WITH season_start (YYYY-MM-DD) you get the cumulative season-to-date total (observed history + today + forecast) plus a per-day cumulative series -- the number a grower tracks against crop milestones. Answers "how many growing degree days has my corn accumulated since May 1?" and "what's the GDU forecast this week?".