---
id: "jena-weather-1h"
slug: "jena-weather-1h"
name: "Jena Climate & Weather Telemetry"
domain: "Meteorology & Climate"
taskType: "time_series_forecasting"
numRows: 8784
numColumns: 23
targetColumn: "target_0"
primaryMetric: "Timestral-123M SQL"
primaryScore: "0.3316"
parquetUrl: "https://huggingface.co/datasets/carlahq/demo-timeseries-benchmark-containers/resolve/main/containers/jena-weather-1h/dataset.parquet"
spreadsheetUrl: "https://docs.google.com/spreadsheets/d/1h6bjnpm9IVjolRMZdRXdlNz_yhJZ9gl8hKhnwcg-a5g/edit?usp=sharing"
canonicalUrl: "https://carlahq.eu/datasets/jena-weather-1h"
---

# Dataset: Jena Climate & Weather Telemetry (jena-weather-1h)

> High-resolution meteorology benchmark spanning 8,784 hourly observations across 21 atmospheric telemetry variables from a single sensor station. Designed for day-ahead 24-hour multivariate weather forecasting to support renewable energy dispatch and environmental operations.

## Dataset Overview

| Attribute | Value |
| :--- | :--- |
| **Task Type** | `time_series_forecasting` |
| **Business Domain** | Meteorology & Climate |
| **Target Column** | `target_0` |
| **Rows (Instances)** | 8,784 |
| **Features (Columns)** | 23 |
| **Primary Evaluation Metric** | Timestral-123M SQL |
| **Best Benchmark Score** | **0.3316** |
| **Prediction Horizon** | 24 steps |
| **Seasonality** | 24 |
| **Time Frequency** | Hourly |
| **Timestamp Column** | `timestamp` |
| **Entity / ID Column** | `id` |

## Business Decision Context
**Context:** Grid operators, municipal utilities, and agritech platforms require precise 24-hour day-ahead atmospheric forecasts to schedule wind and solar generation dispatch, manage district heating thermal inertia, and plan agricultural spraying windows.
**Objective:** Forecast 21 interdependent atmospheric and thermodynamic state variables over an hourly 24-step horizon (H=24) using historical multi-sensor telemetry.
**Decision Impact:** Accurate day-ahead atmospheric forecasting minimizes costly ancillary grid reserve activations and imbalance penalties from wind/solar ramping errors. Under-forecasting temperature or wind triggers thermal overload or unhedged peak-load spot purchases, while over-forecasting yields curtailed renewable capacity and stranded reserve commitments.

## Feature Schema
| Column Name | Data Type | Role | Distinct Values | Missing Values (%) | Description |
| :--- | :--- | :--- | :--- | :--- | :--- |
| `id` | `categorical` | identifier | 1 | 0 | Unique identifier for the recording weather station entity |
| `timestamp` | `datetime` | timestamp | 8,784 | 0 | ISO 8601 recording time of atmospheric observation at hourly resolution |
| `target_0` | `numeric` | target | 6,988 | 1 | Atmospheric surface pressure recorded at station elevation |
| `target_1` | `numeric` | target | 6,797 | 1 | Ambient dry-bulb air temperature measured at 2 meters elevation |
| `target_10` | `numeric` | target | 8,305 | 1 | Atmospheric air density computed from pressure, temperature, and moisture content |
| `target_11` | `numeric` | target | 2,848 | 1 | Mean horizontal wind speed measured by anemometer at mast height |
| `target_12` | `numeric` | target | 3,763 | 1 | Peak wind gust velocity observed during the hourly recording interval |
| `target_13` | `numeric` | target | 6,857 | 1 | Compass azimuth direction from which the wind is blowing |
| `target_14` | `numeric` | target | 58 | 1 | Total liquid precipitation accumulated over the hourly interval |
| `target_15` | `numeric` | target | 308 | 1 | Cumulative duration of rainfall events observed within the hour |
| `target_16` | `numeric` | target | 4,742 | 1 | Downwelling shortwave solar irradiance incident on a horizontal surface |
| `target_17` | `numeric` | target | 4,839 | 1 | Incident photon flux density available for photosynthesis (400-700 nm spectral band) |
| `target_18` | `numeric` | target | 4,874 | 1 | Peak instantaneous photosynthetically active radiation recorded during the hour |
| `target_19` | `numeric` | target | 6,837 | 1 | Internal operating temperature of the telemetry data acquisition system |
| `target_2` | `numeric` | target | 6,847 | 1 | Potential temperature adjusted adiabatically to a reference pressure of 1000 mbar |
| `target_20` | `numeric` | target | 3,569 | 1 | Atmospheric mole fraction of carbon dioxide measured at the mast intake |
| `target_3` | `numeric` | target | 6,396 | 1 | Temperature to which air must be cooled to become saturated with water vapor |
| `target_4` | `numeric` | target | 5,776 | 1 | Ratio of current absolute vapor pressure to saturation vapor pressure |
| `target_5` | `numeric` | target | 6,296 | 1 | Maximum vapor pressure possible at prevailing ambient air temperature |
| `target_6` | `numeric` | target | 5,360 | 1 | Partial pressure exerted by water vapor in the atmosphere |
| `target_7` | `numeric` | target | 4,775 | 1 | Difference between saturation vapor pressure and actual vapor pressure |
| `target_8` | `numeric` | target | 4,313 | 1 | Mass ratio of water vapor to total moist air mass |
| `target_9` | `numeric` | target | 5,364 | 1 | Molar concentration of water vapor molecules per mole of moist air |

## FEV Benchmark Leaderboard (AutoGluon Time-Series Suite)
Empirical probabilistic evaluation across standard foundation models and baselines:

| Rank | Method / Model | Family | Organization | SQL Win Rate (%) | SQL Skill Score | MASE Win Rate (%) | Median Latency | Execution |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | **Timestral-209M** | Foundation Model | — | 87.7% | 50.39 | 86.0% | 0.51s | Server (CUDA / PyTorch) |
| 2 | **Timestral-123M (Carla Engine)** | Foundation Model | — | 86.8% | 50.18 | 85.0% | 0.32s | WebGPU (Browser / Sheets) |
| 3 | **TimeRSI** | Closed-Source API | — | 84.8% | 51.24 | 83.8% | 2512s | Server (CUDA / PyTorch) |
| 4 | **t0-gamma** | Foundation Model | — | 84.4% | 49.42 | 81.8% | 4.38s | Server (CUDA / PyTorch) |
| 5 | **Timestral-28M** | Foundation Model | — | 79.2% | 48.42 | 77.0% | 0.11s | Server (CUDA / PyTorch) |
| 6 | **timesfm-3** | Foundation Model | — | 78.5% | 48.99 | 77.0% | 3.41s | Server (CUDA / PyTorch) |
| 7 | **ChakraTS** | Baseline | — | 78.2% | 48.14 | 79.5% | 14.22s | Server (CUDA / PyTorch) |
| 8 | **Lingjiang2.0** | Baseline | — | 73.9% | 47.02 | 71.3% | 8.89s | Server (CUDA / PyTorch) |
| 9 | **Chronos-2** | Foundation Model | — | 70.4% | 47.28 | 68.4% | 0.84s | Server (CUDA / PyTorch) |
| 10 | **timeh2o-v1-tiny** | Foundation Model | — | 69.9% | 45.49 | 67.0% | 0.19s | Server (CUDA / PyTorch) |
| 11 | **Timestral-5M** | Foundation Model | — | 69.0% | 46.19 | 67.7% | 0.04s | Server (CUDA / PyTorch) |
| 12 | **Toto-2.0-2.5B** | Baseline | — | 67.5% | 44.42 | 65.7% | 5.58s | Server (CUDA / PyTorch) |
| 13 | **t0-beta** | Foundation Model | — | 67.4% | 46.65 | 64.5% | 2.29s | Server (CUDA / PyTorch) |
| 14 | **Toto-2.0-1B** | Baseline | — | 66.4% | 44.43 | 65.5% | 2.48s | Server (CUDA / PyTorch) |
| 15 | **TiRex-2** | Foundation Model | — | 65.8% | 45.50 | 63.4% | 0.27s | Server (CUDA / PyTorch) |

## Provenance & Downloadable Artifacts
- **Dataset Parquet File:** [Direct Parquet Download](https://huggingface.co/datasets/carlahq/demo-timeseries-benchmark-containers/resolve/main/containers/jena-weather-1h/dataset.parquet)
- **Google Sheets Interactive Demo:** [Open in Google Sheets](https://docs.google.com/spreadsheets/d/1h6bjnpm9IVjolRMZdRXdlNz_yhJZ9gl8hKhnwcg-a5g/edit?usp=sharing)
- **Academic Paper / Citation:** [Read Research Paper](https://www.bgc-jena.mpg.de/wetter/)
- **Canonical HTML Page:** [https://carlahq.eu/datasets/jena-weather-1h](https://carlahq.eu/datasets/jena-weather-1h)