Clairwave

By Clairwave

Overview

Clairwave gives ChatGPT grounded ocean-acoustics answers instead of estimates from memory. Ask about any point in the world ocean and the plugin runs propagation physics on Clairwave's platform: parabolic-equation transmission loss along a bearing, sonar-equation detection ranges, and full 3D Bellhop transmission-loss volumes, driven by Clairwave-served bathymetry, seasonal sound-speed profiles and seabed acoustic parameters. It also covers ships: search live AIS positions by name or MMSI, list vessels near a point, estimate a vessel's radiated-noise source level from its class, speed and dimensions, fetch a photo, open an MMSI-keyed 3D hull model from the open shipshape fleet, and compute the power-summed noise level at a fixed site such as a fish farm or hydrophone. Every location accepts a place name ("outside Halifax", "Strait of Hormuz") as well as coordinates. Every simulation result carries a replication bundle (sound-speed profile, bottom parameters, bathymetry transect, grid) and a run id whose files are downloadable, plus a link that opens the run visualized in the Clairwave web platform with no sign-in. Typical uses: sonar and hydrophone planning, marine-mammal noise exposure screening, aquaculture noise monitoring, teaching ocean acoustics, checking a ship's audibility at a receiver, and exploring how season and seabed change propagation. No account is required. Compute calls share a platform-wide budget of about 20 per minute. Results are climatology-based and are not a substitute for in-situ measurements.

Tools

about

ChatGPT

What Clairwave provides, which models/data back each tool, and limits.

estimate_detection_range

ChatGPT

Sonar detection range along a bearing from a location (lat/lon or place). Transmission loss (TL) is ALWAYS computed: RAM parabolic equation with the location's bathymetry, seasonal sound speed (month, default: current month) and seabed. Every other sonar-equation term is optional — anything not given gets a stated, typical default listed in assumptions with its source. Tell the user which values were assumed. Detection threshold DT is derived from processing gain (unless given): detection index d from pd/pfa (default 0.5 / 1e-4 → d ≈ 13.8), then passive (incoherent energy detector): DT = 5·log10(d / (B·T)), B = analysis band, T = integration_time_s (default 1 s); active (matched filter): DT = 10·log10(d / (2·B·τ)), B = bandwidth_hz (default 100 Hz LFM), τ = pulse_length_s (default 0.5 s). DT is in-band SNR, so it can be negative — that IS the processing gain. mode="passive": SE = SL − TL − (NL − DI) − DT. Default target = merchant ship (bulk carrier, AIS 70, 200 m @ 12 kn, JOMOPANS-ECHO decidecade band level) at 6 m; receiver ½ water depth (≤ 30 m), omni (DI 0 — give directivity_index_db for an array). frequency_hz is optional: the tool always also evaluates the target's MOST DETECTABLE 1/3-octave band in 50–2000 Hz (best_band) — ships are loudest there; a user frequency is reported as asked. If you give source_level_db without frequency_hz, the range is evaluated at 50 Hz. mode="active" (monostatic, frequency_hz required): SE = SL − 2·TL + TS − (NL − DI) − DT, TL from the sonar to the target depth. Defaults: hull sonar at 7 m, SL 220 dB, TS 10 dB (submarine-sized, beam aspect), target ½ water depth (≤ 50 m), DI 20 dB (hull array). Noise-limited only: reverberation is NOT included (often the limit in shallow water). NL (unless given) = Wenz ambient noise at the CURRENT forecast wind at the point (Clairwave weather), moderate shipping, in the analysis band. Units: dB re 1 µPa (SL @ 1 m).

get_bathymetry

ChatGPT

Seafloor depth from Clairwave-served global bathymetry (~450 m resolution). Location: lat/lon or place (name resolved to a water point, echoed in location). Without a bearing: depth at the point. With a bearing (compass degrees, 0 = north): a depth profile along that transect out to range_km.

get_sound_speed_profile

ChatGPT

Seasonal sound-speed profile c(z) at a location (lat/lon or place name, e.g. "Halifax approaches") for a calendar month (1-12), from Clairwave-served seasonal temperature/salinity climatology, plus the seabed parameters (compressional/shear speed, density ratio, attenuation, sediment type) at the same point. Cached per 0.1 degree.

get_vessel

ChatGPT

Everything about one vessel: live AIS position/track and static particulars, plus its 3D model (unique photo-derived model if generated, else the class archetype) with GLB URL (bow=+Z, up=+Y) and platform links.

get_vessel_photo

ChatGPT

Photograph of a vessel from Wikimedia Commons, with attribution.

habitat_received_level

ChatGPT

Broadband received level (dB re 1 uPa) at a fixed site — a fish farm, reef, hydrophone or marine protected area — from the ships around it, power-summed. Each vessel's source level comes from its AIS class, speed and size; transmission loss is a fast broadband model, so this is a screening estimate (use run_transmission_loss for exact site physics). hours_back = 0 uses the live snapshot; > 0 reconstructs a time series from the AIS archive in 10-minute bins (max/median/quiet levels). Location: lat/lon or place. Show open_url to the user as a clickable link.

resolve_place

ChatGPT

Turn a place name ('outside Halifax', 'Strait of Hormuz', 'Bergen, Norway') into water coordinates. Maritime gazetteer first (ports resolve to their approaches, straits/seas to a representative point), then OpenStreetMap. Points on land or shallower than min_depth_m are walked seaward along the gazetteer bearing (or the nearest direction) until deep enough; the original point and the snap are reported. offshore_km pushes the point further out along seaward_bearing_deg. All location tools also accept place directly, so a separate call is only needed to inspect.

run_bellhop_volume

ChatGPT

Full 3D transmission-loss volume around a source (lat/lon or place) with Bellhop (all bearings), stored on the platform under a run id. Show open_url to the user as a clickable link: it opens this run visualized. Returns the run id, the replication metadata (SSP, seabed, bounding box) and file links (uint8 TL cube .npy + JSON sidecar). Any frequency and radius (keep radius <= ~50 km for reasonable run times).

run_transmission_loss

ChatGPT

Run a physically grounded transmission-loss simulation (RAM parabolic equation) from a source at (lat, lon or place, depth) along a compass bearing. Bathymetry, the seasonal sound-speed profile (month) and seabed parameters are fetched for the location automatically. Returns TL vs range at the receiver depths (default: source depth, 10 m, 100 m, mid-water), grid statistics, and the full replication bundle. Show open_url to the user as a clickable link.

search_vessels

ChatGPT

Search live AIS vessels by name or MMSI prefix (global feed).

vessel_source_level

ChatGPT

Underwater radiated-noise source level of a ship (monopole, dB re 1 uPa m, 6 m reference source depth): decidecade spectrum (10 Hz - 100 kHz; model bands to 31.5 kHz, validated 20 Hz - 20 kHz) and broadband level, from the JOMOPANS-ECHO reference spectrum model (MacGillivray & de Jong 2021, J. Mar. Sci. Eng. 9:369), fitted to 1862 measured ship passes. Individual ships scatter about +/-6 dB rms per band. Give an MMSI (live AIS type/speed/length are used) or explicit AIS ship_type / speed / length. vessel_class overrides the AIS mapping (e.g. "vehicle_carrier", which has no AIS type). Missing speed = the class reference speed; missing length = the class mean length of the measured ships. The spectrum is usable directly as sl_spectrum.