Atmospheric and Space Scientists
Life, Physical, and Social Science Occupations- O*NET-SOC コード
- 19-2021.00
Investigate atmospheric phenomena and interpret meteorological data, gathered by surface and air stations, satellites, and radar to prepare reports and forecasts for public and other uses. Includes weather analysts and forecasters whose functions require the detailed knowledge of meteorology.
職業名とタスク記述は、公表された英語のまま表示します。ラベル（タスクの種類を含む）は翻訳しています。
AI露出度
- データ出典: BLS公表時点: 2026-08
非常に高い· 相対
低い4段階の相対区分非常に高い職業群単位の値
尺度・母数・出典
4段階の相対区分（低い / 中程度 / 高い / 非常に高い）
BLS雇用見通し表の詳細職業831件が母数。値は NEM(全国雇用マトリクス)コード単位で付与されるため、同じ NEM コードの職業は同じバンドになります
- データ出典: Anthropic公表時点: 2026-03
0.038
0.000ここに掲載された値の範囲0.745尺度・母数・出典
観測エクスポージャー指数、公開されたまま0–1
O*NETタスクへの対応づけが基準
SOC 2018の職業単位で公表された値で、同じSOC 2018コードを持つO*NET職業はすべてこの値になります
- データ出典: ILO公表時点: 2025
0.54
0.09ここに掲載された値の範囲0.70職業群単位の値
尺度・母数・出典
生成AI露出度指数、公開されたまま0–1
ISCO-08の細分類（4桁）単位で公表された値です。米国労働統計局（BLS）の公式対応表（ISCO-08→2010 SOC、2010 SOC→2018 SOC）を公表どおり適用してこの職業に結び付けており、対応は全部または一部です
この出典がどのような性格の値か
BLSの区分は絶対水準ではなく相対順位であり、一次測定でもありません。複数の既存研究が付けた職業別パーセンタイル順位を4段階にまとめた値です。雇用や賃金の予測でもなく、導入確率でもなく、自動化と増強を区別しません。
AI 曝露度（OpenAI ルーブリック）
評価済みタスク 27 件 · β ≥ 0.5 のタスク 25 件（92.6%）
β = 直接的な露出（E1）+ 0.5 × ツール利用時の露出（E2）。原典リポジトリの定義に従います。
- 原典の単位：O*NET 27.2 のタスク → O*NET 31.0 の職業コード
- 評価済みタスクはすべて O*NET 31.0 のタスク一覧にあります。
- 出典
- OpenAI "GPTs are GPTs" exposure rubric
- 版
- gh-main-0471612
- ライセンス
- MIT License, Copyright (c) 2024 OpenAI
タスク
O*NET® 31.0 Database のタスク記述です。コアタスクを先に表示します。
| タスク | 種類 | β（OpenAI） |
|---|---|---|
| Broadcast weather conditions, forecasts, or severe weather warnings to the public via television, radio, or the Internet or provide this information to the news media. | コア | 0.5 |
| Gather data from sources such as surface or upper air stations, satellites, weather bureaus, or radar for use in meteorological reports or forecasts. | コア | 0.5 |
| Prepare forecasts or briefings to meet the needs of industry, business, government, or other groups. | コア | 0.5 |
| Measure wind, temperature, and humidity in the upper atmosphere, using weather balloons. | コア | 0 |
| Analyze climate data sets, using techniques such as geophysical fluid dynamics, data assimilation, or numerical modeling. | コア | 0.5 |
| Formulate predictions by interpreting environmental data, such as meteorological, atmospheric, oceanic, paleoclimate, climate, or related information. | コア | 0.5 |
| Perform managerial duties, such as creating work schedules, creating or implementing staff training, matching staff expertise to situations, or analyzing performance of offices. | コア | 0.5 |
| Consult with other offices, agencies, professionals, or researchers regarding the use and interpretation of climatological information for weather predictions and warnings. | コア | 0.5 |
| Analyze historical climate information, such as precipitation or temperature records, to help predict future weather or climate trends. | コア | 0.5 |
| Prepare weather reports or maps for analysis, distribution, or use in weather broadcasts, using computer graphics. | コア | 0.5 |
| Apply meteorological knowledge to issues such as global warming, pollution control, or ozone depletion. | コア | 0.5 |
| Develop or use mathematical or computer models for weather forecasting. | コア | 1 |
| Interpret data, reports, maps, photographs, or charts to predict long- or short-range weather conditions, using computer models and knowledge of climate theory, physics, and mathematics. | コア | 0.5 |
| Conduct meteorological research into the processes or determinants of atmospheric phenomena, weather, or climate. | コア | 0.5 |
| Prepare scientific atmospheric or climate reports, articles, or texts. | コア | 1 |
| Speak to the public to discuss weather topics or answer questions. | コア | 1 |
| Develop computer programs to collect meteorological data or to present meteorological information. | コア | 1 |
| Develop and deliver training on weather topics. | コア | 0.5 |
| Direct forecasting services at weather stations or at radio or television broadcasting facilities. | 補足 | 0.5 |
| Collect air samples from planes or ships over land or sea to study atmospheric composition. | 補足 | 0 |
| Conduct numerical simulations of climate conditions to understand and predict global or regional weather patterns. | 補足 | 0.5 |
| Design or develop new equipment or methods for meteorological data collection, remote sensing, or related applications. | 補足 | 0.5 |
| Conduct wind assessment, integration, or validation studies. | 補足 | 0.5 |
| Create visualizations to illustrate historical or future changes in the Earth's climate, using paleoclimate or climate geographic information systems (GIS) databases. | 補足 | 0.5 |
| Estimate or predict the effects of global warming over time for specific geographic regions. | 補足 | 0.5 |
| Teach college-level courses on topics such as atmospheric and space science, meteorology, or global climate change. | 補足 | 0.5 |
| Research the impact of industrial projects or pollution on climate, air quality, or weather phenomena. | 補足 | 0.5 |
職業情報
出典と帰属表示
This page includes information from the O*NET® 31.0 Database (https://www.onetcenter.org/database.html) by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA). Used under the CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/). O*NET® is a trademark of USDOL/ETA. AI Changing Work has modified all or some of this information: the O*NET-SOC code, title and task statements are reproduced in English without change; task-type labels are shown in the page's language and tasks are listed core first; any Korean occupation title shown on the Korean-language page is AI Changing Work's translation; any KSCO-8 unit groups linked to this occupation were paired with it by AI Changing Work's judgment, and the relation labels and statuses are AI Changing Work's additions. USDOL/ETA has not approved, endorsed, or tested these modifications.
Any AI exposure figures on this page are published by third parties, not by AI Changing Work, and none is part of the O*NET information. OpenAI publishes task-level scores (MIT License) for O*NET 27.2 task statements; each is shown next to the O*NET 31.0 task statement with the same task ID, whose wording can differ from the 27.2 statement that was scored. OpenAI also publishes occupation-level scores for O*NET-SOC codes in the same release, and any such score is shown on the O*NET occupation with the same code. Anthropic publishes an observed exposure index in the Anthropic Economic Index (CC-BY), and the U.S. Bureau of Labor Statistics publishes relative AI exposure categories (public domain); both are published per SOC code, and each value is shown on every O*NET occupation with that code. The International Labour Organization publishes a generative AI exposure index in ILO Working Paper 140 (CC BY 4.0) for ISCO-08 unit groups; AI Changing Work links those groups to O*NET occupations by applying the U.S. Bureau of Labor Statistics ISCO-08 to 2010 SOC and 2010 SOC to 2018 SOC crosswalks as published, without case-by-case selection, and these crosswalks match many groups only in part. Where several unit groups are linked, each group's published value is listed, and any summary shows only the lowest and highest of those values with the number of groups; no exposure figure is averaged or recalculated. Any employment figures are published by the U.S. Bureau of Labor Statistics for the SOC group containing this occupation. Each source is credited where its figures are shown.
O*NET OnLine: 19-2021.00 Atmospheric and Space Scientists
KSCO 코드·명칭: 한국표준직업분류(제8차 개정) — 통계청 고시 제2024-328호 (2024-07-01 고시, 2025-01-01 시행). 저작권법 제7조 제2호의 고시 항목이다. 명칭 표기(가운뎃점·띄어쓰기)는 해설서 2차 정오 반영판의 표기를 따랐으며, 고시 항목표와는 18개 명칭에서 가운뎃점 글리프나 띄어쓰기만 다르다. 통계청은 2025년 10월 국가데이터처로 개편되었다. 이 페이지의 KSCO 연결은 통계청·국가데이터처의 공식 연계표가 아니다.