Datasets:

CMU-OSPO
/

protostar-climate-submission-template

city_id stringclasses 210 values	year int64 2.02k 2.03k	metric stringclasses 4 values	predicted_value float64
USC00042863	2,019	annual_avg_temp	null
USC00042863	2,019	total_precip_in	null
USC00042863	2,019	hottest_day_temp	null
USC00042863	2,019	cdd	null
USC00042863	2,020	annual_avg_temp	null
USC00042863	2,020	total_precip_in	null
USC00042863	2,020	hottest_day_temp	null
USC00042863	2,020	cdd	null
USC00042863	2,021	annual_avg_temp	null
USC00042863	2,021	total_precip_in	null
USC00042863	2,021	hottest_day_temp	null
USC00042863	2,021	cdd	null
USC00042863	2,022	annual_avg_temp	null
USC00042863	2,022	total_precip_in	null
USC00042863	2,022	hottest_day_temp	null
USC00042863	2,022	cdd	null
USC00042863	2,023	annual_avg_temp	null
USC00042863	2,023	total_precip_in	null
USC00042863	2,023	hottest_day_temp	null
USC00042863	2,023	cdd	null
USC00042863	2,024	annual_avg_temp	null
USC00042863	2,024	total_precip_in	null
USC00042863	2,024	hottest_day_temp	null
USC00042863	2,024	cdd	null
USC00042863	2,025	annual_avg_temp	null
USC00042863	2,025	total_precip_in	null
USC00042863	2,025	hottest_day_temp	null
USC00042863	2,025	cdd	null
USC00166584	2,019	annual_avg_temp	null
USC00166584	2,019	total_precip_in	null
USC00166584	2,019	hottest_day_temp	null
USC00166584	2,019	cdd	null
USC00166584	2,020	annual_avg_temp	null
USC00166584	2,020	total_precip_in	null
USC00166584	2,020	hottest_day_temp	null
USC00166584	2,020	cdd	null
USC00166584	2,021	annual_avg_temp	null
USC00166584	2,021	total_precip_in	null
USC00166584	2,021	hottest_day_temp	null
USC00166584	2,021	cdd	null
USC00166584	2,022	annual_avg_temp	null
USC00166584	2,022	total_precip_in	null
USC00166584	2,022	hottest_day_temp	null
USC00166584	2,022	cdd	null
USC00166584	2,023	annual_avg_temp	null
USC00166584	2,023	total_precip_in	null
USC00166584	2,023	hottest_day_temp	null
USC00166584	2,023	cdd	null
USC00166584	2,024	annual_avg_temp	null
USC00166584	2,024	total_precip_in	null
USC00166584	2,024	hottest_day_temp	null
USC00166584	2,024	cdd	null
USC00166584	2,025	annual_avg_temp	null
USC00166584	2,025	total_precip_in	null
USC00166584	2,025	hottest_day_temp	null
USC00166584	2,025	cdd	null
USC00280734	2,019	annual_avg_temp	null
USC00280734	2,019	total_precip_in	null
USC00280734	2,019	hottest_day_temp	null
USC00280734	2,019	cdd	null
USC00280734	2,020	annual_avg_temp	null
USC00280734	2,020	total_precip_in	null
USC00280734	2,020	hottest_day_temp	null
USC00280734	2,020	cdd	null
USC00280734	2,021	annual_avg_temp	null
USC00280734	2,021	total_precip_in	null
USC00280734	2,021	hottest_day_temp	null
USC00280734	2,021	cdd	null
USC00280734	2,022	annual_avg_temp	null
USC00280734	2,022	total_precip_in	null
USC00280734	2,022	hottest_day_temp	null
USC00280734	2,022	cdd	null
USC00280734	2,023	annual_avg_temp	null
USC00280734	2,023	total_precip_in	null
USC00280734	2,023	hottest_day_temp	null
USC00280734	2,023	cdd	null
USC00280734	2,024	annual_avg_temp	null
USC00280734	2,024	total_precip_in	null
USC00280734	2,024	hottest_day_temp	null
USC00280734	2,024	cdd	null
USC00280734	2,025	annual_avg_temp	null
USC00280734	2,025	total_precip_in	null
USC00280734	2,025	hottest_day_temp	null
USC00280734	2,025	cdd	null
USC00286055	2,019	annual_avg_temp	null
USC00286055	2,019	total_precip_in	null
USC00286055	2,019	hottest_day_temp	null
USC00286055	2,019	cdd	null
USC00286055	2,020	annual_avg_temp	null
USC00286055	2,020	total_precip_in	null
USC00286055	2,020	hottest_day_temp	null
USC00286055	2,020	cdd	null
USC00286055	2,021	annual_avg_temp	null
USC00286055	2,021	total_precip_in	null
USC00286055	2,021	hottest_day_temp	null
USC00286055	2,021	cdd	null
USC00286055	2,022	annual_avg_temp	null
USC00286055	2,022	total_precip_in	null
USC00286055	2,022	hottest_day_temp	null
USC00286055	2,022	cdd	null

End of preview. Expand in Data Studio

📋 This is the submission template for the Protostar Challenge: Climate Risk Intelligence. Copy it into your Hugging Face account, fill it in, and delete this box when you are done.

Copy: hf download CMU-OSPO/protostar-climate-submission-template --repo-type dataset --local-dir my-submission, then hf upload <your-name>/<dataset-name> my-submission . --repo-type dataset (or create a new dataset on the Hub and upload these files).

Fill in the predicted_value column of submission.csv: the city, year and metric of all 5,880 rows (210 cities × 7 years × 4 metrics) are already there. Check it with python code/check_submission.py.

Replace every <...> placeholder in this README, including <Team Name> in the header.

Add your notebooks or scripts to code/.

Make the dataset public and paste its link in the Submission tab of the challenge Space.

Keep the tag protostar-climate-2026 in the header: it is how submissions are found.

: Protostar Climate Challenge Submission

Negative result: no

Forecasts of yearly weather for 210 U.S. cities, 2019–2025, made as of the end of 2018 (no data that reveals 2019–2025 weather), for the Protostar Challenge: Climate Risk Intelligence.

Team

Name	Hugging Face username	Program
	<@username>	<e.g., Heinz MSPPM, Tepper BS Business>

Method

Model: <e.g., 10-year average plus a linear trend, per city>
Features: <e.g., the last 30 years of each metric>
How we checked it: <e.g., practice run on 2011–2018: CDD MAE = ... °F-days>

Policy Idea

What risk did we notice? <...>
What would we do about it, and who would do it? <...>
Why is it sustainable? <...>

Feedback to the Ecosystem

What got in the way: <...>
What would fix it: <...>

Files

File	Content
`submission.csv`	Our forecasts: `city_id,year,metric,predicted_value`
`code/`

Metric codes: annual_avg_temp (°F), total_precip_in (inches), hottest_day_temp (°F), cdd (°F-days). City IDs: the station ID from the city_info table of the training dataset (e.g., Pittsburgh = USW00094823).

How to Reproduce

<e.g., install the packages in code/requirements.txt>
<e.g., run code/forecast.ipynb>

Data

External data: <none, or: name, link, years, forecast or analysis>

Training data: CMU-OSPO/protostar-climate-train-to-2018, from:

Lai, Y., & Dzombak, D. A. (2019). Compiled historical daily temperature and precipitation data for selected 210 U.S. cities. Carnegie Mellon University. https://doi.org/10.1184/R1/7890488.v6
Lai, Y., & Dzombak, D. A. (2019). Use of historical data to assess regional climate change. Journal of Climate, 32(14), 4299–4320. https://doi.org/10.1175/JCLI-D-18-0630.1

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