Precipitation

Data Source and Integrity

The foundation of this dashboard is the high-quality, open meteorological data provided by the Swedish Meteorological and Hydrological Institute (SMHI). We extend our sincere gratitude to SMHI for maintaining and providing access to their robust Application Programming Interfaces (APIs).

• Network Scope: The system is connected to SMHI’s network of over 2,000 weather stations, both active and historical, across Sweden.
• Historical Depth: The dataset is exceptionally rich, with continuous records for numerous stations extending back to the early 20th century, some as far as 1910. This long-term perspective is critical for robust climate trend analysis and for contextualizing modern extreme events against historical variability.
• Live Data Processing: All data is extracted and processed on-the-fly, ensuring users access the most current and complete dataset available from SMHI.

Scientific Methodology and Analytical Capabilities

The dashboard performs several key analytical procedures to convert raw precipitation data into scientifically valid metrics.

1. Core Precipitation Metrics:

• Precipitation Trends: Visualizes the temporal changes in rainfall, allowing for the identification of increasing or decreasing patterns over seasonal, annual, and decadal scales.
• Extreme Periods Identification: Algorithmically identifies and reports the wettest and driest periods on record for the selected station and specified timeframe.

2. Anomaly Detection using the Z-Score Method:
A central feature of this tool is the detection of precipitation anomalies. For this, we employ the Monthly Z-Score method, a standardized statistical technique widely used in climatology for seasonal anomaly detection.

• Definition: A Z-score (or standard score) quantifies how many standard deviations a given data point (e.g., monthly precipitation in May 2020) is from the mean of the population (e.g., all May months in the historical record).
• Calculation: For each month of the year, the tool calculates the long-term mean and standard deviation from the entire historical dataset for that station. The anomaly for a specific instance of that month is then calculated as:
  Z = (X - μ) / σ
  where:
  • X is the monthly precipitation value.
  • μ is the historical mean precipitation for that specific month.
  • σ is the historical standard deviation for that specific month.
• Interpretation: A Z-score of +2.0, for example, indicates that the rainfall for that month was two standard deviations above the historical average—a significant positive anomaly indicative of an extremely wet month. Conversely, a score of -2.0 indicates a severe drought condition. This method effectively normalizes the data, allowing for direct comparison between different seasons and regions.

3. Application to Cloudburst Analysis:
In the Swedish context, intense, short-duration rainfall events—cloudbursts—pose a significant risk to urban infrastructure. The inability of sealed surfaces to absorb rapid runoff leads to urban flooding. This tool aids in this analysis by:

• Providing the historical context to assess the rarity of a recent high-precipitation event.
• Helping to identify stations and regions with historical susceptibility to extreme positive anomalies.

Technical Architecture

The dashboard is built on a modern Business Intelligence (BI) and data processing stack. It functions by:

1. Accepting a user’s selection of a weather station.
2. Querying the relevant SMHI API endpoint in real-time.
3. Receiving and parsing the historical data series.
4. Performing the statistical calculations (trends, means, Z-scores) dynamically within the browser or on a server.
5. Rendering the results through interactive charts and maps.

Future Development

This platform represents an initial framework for precipitation analysis. Planned advanced functionalities, contingent on user interest and support, include:

• Advanced Spatiotemporal Analysis: Integration of GIS-based interpolation (e.g., Kriging) to generate continuous precipitation surfaces across the country, moving beyond point-based station data.
• Multi-Variable Analysis: Incorporation of other critical meteorological variables from SMHI APIs, such as temperature, evaporation, and soil moisture, for a more holistic environmental assessment.
• Customizable Thresholds: Allowing users to define custom thresholds for extreme event detection based on specific project needs.