Correction Log

Switched primary model from JMA MSM v2.0 to v2.1 following Open-Meteo API update. v2.1 includes improved urban canopy parameters that reduce our required correction magnitude for nighttime UHI effects by approximately 0.3°C. Verification against AMeDAS shows reduced systematic warm bias in Shinjuku and Ikebukuro overnight forecasts. No action required from users — forecasts automatically improved.

Identified systematic 1.2°C warm bias in Ginza afternoon forecasts (12:00-16:00 JST) during sea breeze days. Root cause: our sea breeze arrival model was predicting arrival 45-60 minutes too late for Ginza, causing the temperature drop to be mistimed. Adjusted the Ginza sea breeze arrival parameter from -90 minutes (relative to Shimbashi) to -105 minutes based on analysis of 40 sea breeze events from the 2024 season. MAE for Ginza afternoon forecasts improved from 1.8°C to 1.1°C. Reported by user T.S. in Minato — thank you.

Added elevated platform wind correction for Akihabara based on field measurements. The JR and Metro viaducts create measurable wind channeling that our previous model didn't capture. Added +1.2 m/s adjustment to Akihabara wind forecasts when synoptic wind direction is between 60° and 120° (east-southeast to east-northeast flow). Correction validated against 3 months of anemometer data from our Akihabara logger. Wind MAE reduced from 3.1 m/s to 2.6 m/s for this district.

Updated spring pollen corridor model with 2024 verification data. Cedar pollen distribution patterns showed a shift in the primary transport corridor from 260°-270° (west) to 255°-265° (west-southwest), likely due to urban development changing surface roughness in western Tokyo suburbs. Adjusted our high-pollen flag directions accordingly. The correction is subtle — roughly 5-10% improvement in pollen day detection — but matters for users with allergies.

Major correction to Ikebukuro winter night forecasts. Our cold air drainage model was underestimating the magnitude of katabatic flow from the Saitama plateau by approximately 40%. This caused Ikebukuro minimum temperature forecasts to be systematically 1.5-2.5°C too warm on clear, calm winter nights. Revised the drainage coefficient from 0.6°C per 10m elevation difference to 1.0°C per 10m, based on comparison with 3 winters of AMeDAS data from stations along the drainage path. MAE for Ikebukuro winter night forecasts improved from 2.4°C to 1.3°C. Multiple users reported this discrepancy — thank you all.

Updated typhoon rain band model following verification against Typhoon Ampil (August 2024) and Typhoon Shanshan (August 2024). Our previous model over-predicted rain intensity in the left-rear quadrant (relative to storm track) by approximately 30%. Adjusted the asymmetric rain field parameters to better match observed rain gauge data. The correction primarily affects forecasts for western Tokyo wards during west-passing typhoons. Forecast skill (measured by Brier score for >10mm/3h events) improved from 0.42 to 0.51.

Added non-linear UHI amplification factor for extreme heat events (daily max > 35°C). Our linear correction model was under-predicting peak temperatures in Shinjuku, Shibuya, and Ikebukuro during the August 2024 heatwave by 1.5-2.5°C. Analysis showed that UHI amplification increases non-linearly above 32°C ambient temperature, likely due to increased HVAC waste heat and reduced convective efficiency at extreme temperatures. Added quadratic term to UHI correction: UHI_effective = base_UHI * (1 + 0.08 * (T_ambient - 32)) for T_ambient > 32°C. Peak temperature MAE during extreme heat improved from 2.7°C to 1.4°C.

Revised sea breeze penetration model for all bayfront districts. The 2023 season showed that our sea breeze arrival times were systematically 30-45 minutes too early for Shimbashi and 20-30 minutes too late for Ginza. Root cause was an oversimplified pressure gradient threshold — we were using a single threshold for all bayfront districts, but the local geometry (bay orientation, shoreline shape, building density) creates district-specific lags. Implemented separate arrival parameters for each district based on 2023 season analysis. Sea breeze timing accuracy (within ±30 minutes) improved from 58% to 74%.

Adjusted Arakawa River rain corridor width from 2km to 3km based on radar analysis of 50+ summer thunderstorm events. The wider corridor better captures the observed rain enhancement along the river valley. Also added a secondary corridor along the Kanda River (Akihabara to Nihonbashi) that was identified through spatial clustering of rain gauge data. These changes increased our rain detection rate for Akihabara and Kanda-Sudacho by 12% without increasing false alarms.

Added Roppongi Hills downdraft parameter to wind model. Field measurements during February 2024 showed persistent 2-3 m/s downdrafts on the north and east sides of the Roppongi Hills tower on sunny afternoons, with gusts to 5 m/s. Added a downdraft flag for Roppongi when: solar radiation > 600 W/m², wind direction 180°-270° (south to west), and wind speed at 850hPa > 6 m/s. This is a qualitative flag ("expect downdrafts") rather than a quantitative forecast, but it provides useful guidance for cyclists and pedestrians in the area.

Implemented bay-effect fog prediction model for Minato, Shinagawa, and Shimbashi. The model triggers when: bay surface temperature < 850hPa temperature by > 4°C, wind direction 120°-170°, and wind speed 8-15 m/s. Validated against JMA fog observations from Tokyo Haneda and Tokyo AMeDAS stations. Detection rate for bay fog events: 72%. False alarm rate: 18%. This is our first fog-specific product and represents a significant gap filled in our forecast portfolio.

Documented and modeled the tsuyu (rainy season) arrival gradient across Tokyo districts. Analysis of 2022 and 2023 data showed that bayfront districts see persistent tsuyu rain 5-7 days before inland wards. Added a district-specific tsuyu onset parameter to our seasonal model. The gradient is now reflected in our long-range outlooks during the May-June transition period. This is a low-confidence forecast (tsuyu onset has high interannual variability) but the spatial pattern is robust.

Formalized user report processing workflow. Previously, user-reported forecast errors were handled ad-hoc via email. Implemented a structured ticketing system that logs every report, assigns it to a team member for investigation, and tracks resolution. Since implementation, we've processed 47 user reports, of which 23 led to identifiable model improvements, 12 were consistent with expected forecast uncertainty, 8 were due to temporary local conditions we can't model, and 4 were user measurement errors. Thank you to everyone who has reported discrepancies — you make our forecasts better.

Added Tokyo Station Yaesu exit wind acceleration to our wind hazard flags. Measurements showed 1.3-1.5x wind acceleration in the Yaesu plaza on westerly flow days due to converging flows around the station building. Added a "strong wind, Yaesu" flag when ambient westerly winds exceed 10 m/s. This is primarily a safety feature for cyclists and pedestrians in the area.

Completed initial calibration of all six district correction factors against the full 2020-2022 AMeDAS observation record. This was our first comprehensive validation exercise and established the baseline accuracy figures we publish: 78% of 24-hour temperature forecasts within ±1.5°C, 65% of rain timing forecasts within ±2 hours. The calibration involved approximately 200,000 forecast-observation pairs and took 3 months to complete. All subsequent corrections build on this foundation.