Philippines climate and weather data

This data is created using GPM data. It contains the accumulated precipitation for the periods October 11-18th, October 18-20th and the total over both periods.

This dataset contains: * windspeeds of Typhoon Nina * rainfall of Typhoon Nina * Priority Index of Typhoon Nina The predicted priority index of Typhoon Nina is produced by a machine learning algorithm that was trained on five past typhoons: Haiyan, Melor, Hagupit and Rammasun and Haima, It uses base line data for the whole country, combined with impact data of windspeeds and rains, and trained on counts by the Philippine government on houses damaged and completely destroyed. The output is a weighted index between partially damaged and completely damaged, where partially damaged is counted as 25% of the completely damaged. This has proven to give he highest accuracy. The absolute number of houses damaged / people affected is insufficiently validated at the moment, and should just be used for further trainng and ground-truthing. Scoring The model has an best r2 score of 0.794933727 and an accuracy of 0.699470899 Data sources: Administrative boundaries (P_Codes) - Philippines Government; Published by GADM and UN OCHA (HDX) Census 2015 (population) - Philippine Statistics Authority; received from UN OCHA (HDX) Avg. wind speed (mph) - University College London Typhoon path - University College London Houses damaged - NDRRMC Rainfall - GPM Poverty - Pantawid pamilyang pilipino program (aggregated) Roof and wall materials New geographical features All the columns with feat_ indicates the importance of that feature, if not present that feature was not used. learn_matrix name of the learning matrix with the 5 typhoons run_name unique run name (pickle files and csv files have this name for this model) typhoon_to_predict name of a new typhoon to predict val_accuracy accuracy based on 10 categories of damage 0% 10% 20% … val_perc_down perc of underpredicted categories val_perc_up perc of overpredicted categories Val_best_score best r2 score Val_stdev_best_score error on best score based on the CV Val_score_test r2 score on the test set (this should be around +- 5% of the previus number to not overfit Val_mean_error_num_houses average error on the number of houses val_median_error_num_houses median val_std_error_num_houses std deviation of the errors (lower is better) Algorithm developed by 510.global the data innovation initiative of the Netherlands Red Cross.

A collection of the strongest typhoon (tracks and windspeed) data for the Philippines with category levels 4 and 5. Data were scrapped from http://weather.unisys.com/.

**ADAM ID: **1001183\_31 Tropical storm during the period Jul 23 2025-Jul 30 2025 in Philippines, Japan, China. The center of the storm was located near latitude 31.2 longitude 121.4. WFP’s Automated Disaster Analysis and Mapping (ADAM) system is an operational system for collecting, analysing and mapping geospatial and socio-economic information following sudden onset humanitarian emergencies.

**ADAM ID: **1001255\_11 Tropical depression during the period Feb 04 2026-Feb 06 2026 in Philippines. The center of the storm was located near latitude 10.1 longitude 122.8. WFP’s Automated Disaster Analysis and Mapping (ADAM) system is an operational system for collecting, analysing and mapping geospatial and socio-economic information following sudden onset humanitarian emergencies.

**ADAM ID: **1001249\_25 Tropical depression during the period Jan 14 2026-Jan 20 2026 in Philippines. The center of the storm was located near latitude 17.1 longitude 133.0. WFP’s Automated Disaster Analysis and Mapping (ADAM) system is an operational system for collecting, analysing and mapping geospatial and socio-economic information following sudden onset humanitarian emergencies.

**ADAM ID: **1001236\_30 Tropical depression during the period Nov 24 2025-Dec 02 2025 in Viet Nam, Philippines. The center of the storm was located near latitude 14.0 longitude 111.5. WFP’s Automated Disaster Analysis and Mapping (ADAM) system is an operational system for collecting, analysing and mapping geospatial and socio-economic information following sudden onset humanitarian emergencies.

**ADAM ID: **1001234\_38 Tropical depression during the period Nov 04 2025-Nov 13 2025 in Philippines, China, Japan. The center of the storm was located near latitude 26.3 longitude 128.3. WFP’s Automated Disaster Analysis and Mapping (ADAM) system is an operational system for collecting, analysing and mapping geospatial and socio-economic information following sudden onset humanitarian emergencies.

**ADAM ID: **1001298\_28 Tropical storm during the period Aug 04 2026-Aug 13 2026 in Philippines. The center of the storm was located near latitude 33.8 longitude 170.2. WFP’s Automated Disaster Analysis and Mapping (ADAM) system is an operational system for collecting, analysing and mapping geospatial and socio-economic information following sudden onset humanitarian emergencies.

The International Best Track Archive for Climate Stewardship (IBTrACS) project is the most complete global collection of tropical cyclones available. It merges recent and historical tropical cyclone data from multiple agencies to create a unified, publicly available, best-track dataset that improves inter-agency comparisons. Fields available: SID: A unique storm identifier (SID) assigned by IBTrACS algorithm. ISO_TIME: Time of the observation in ISO format (YYYY-MM-DD hh:mm:ss) BASIN: Basin of the current storm position SUBBASIN: Sub-basin of the current storm position NATURE: Type of storm (a combination of the various types from the available sources) NUMBER: Number of the storm for the year (restarts at 1 for each year LAT: Mean position - latitude (a combination of the available positions) LON: Mean position - longitude (a combination of the available positions) WMO_WIND: Maximum sustained wind speed assigned by the responsible WMO agency WMO_PRES: Minimum central pressure assigned by the responsible WMO agency.