Choose from a selection of data formats and spatiotemporal resolutions. For nationwide/ regional risk assessment, we supply monthly summaries either on a regular grid in addition to providing hazard index maps and daily weather ensembles for town and cities. The API provides a variety of acute seasonal climate risk data:
- Changes in the intensity or frequency of heatwaves/ cold waves
- Changes in the intensity or frequency of rainfall extremes
- Changes in drought extremes
- Changes in aridity/ humidity extremes
- Changes in hail risk/ convective precipitation
- Changes in windstorm damage
Gridded departure maps (often referred to as ‘anomalies’ in the meteorology community) show the 10th, 30th, 50th, 70th and 90th centiles of monthly means of daily average temperatures and percentage of climatology precipitation from a 50-member ensemble. These hazard datasets are supplied either in ASCII format, or as a CSV list of anomalies for each town and city. Daily time series are supplied for the same towns and cities as a 100-member ensemble, which combines both our proprietary statistical forecast system and modified numerical weather prediction model data.
Departures are referenced to the following climatology datasets:- UK. Precipitation: 1962 to 2016 [1], temperature projected to start date
- Spain. Precipitation: 1962 to 2012 [2], temperature projected to start date
- Turkey. Precipitation: 1962 to 2015 [3], temperature projected to start date
Hazard indices are supplied as seasonal (3 month) and next 2 full month graphical summaries. These charts are produced using daily weather simulations for town and cities and show the increase/ decrease in the likelihood of daily precipitation/ heat/ cold weather events above a 80th frequency of occurrence. They make use of 100 ensemble members; 50% from Weather Logistics’ model, and 50% from a Copernicus Climate Change Service multi-model mean [4].
For more experienced data scientists or climate risk analysts, we also supply spatially correlated daily weather time series as a 100 member ensemble. Ensemble members rather than being random are grouped into blocks of 10, representing different confidence intervals. Therefore all individual members should be combined to report a complete assessment of daily weather likelihoods and intensities. These GeoJSON and CSV outputs are supplied for a fixed set of town and city. Meteorological variables and there units are fully described in the JSON outputs.
Citations
[1] Contains modified public sector information licensed under the Open Government Licence v3.0. Met Office (2016): UKCP09: Met Office gridded land surface climate observations – long term averages at 5km resolution. Centre for Environmental Data Analysis, 2022
[2] Contains modified Deutscher Wetterdienst, spatiotemporally re-formatted, 2022
[3] SPREAD (Spanish PREcipitation At Daily scale), Serrano Notivoli, Roberto ; De Luis, Martín; Beguería, Santiago; Saz, Miguel Ángel, url: http://hdl.handle.net/10261/141218, Accessed May 2022
[4] Contains modified Copernicus Climate Change Service information 2022. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains
****Disclaimer.
Seasonal climate forecasts supplied by Weather Logistics Ltd are advisory in nature, and therefore we cannot accept liability or responsibility for their use within any commercial, academic or any other application environment. We cannot compensate for any misuse or connected activities that relies upon this climate information or any of our 3rd party meteorological output data. No parties shall therefore be responsible in the case of loss of life, business or any other liability incurred. Processes, methods and weather and climate prediction software is copyright to Weather Logistics Ltd. 2014-2022, all rights reserved. Forecasts are probabilistic in nature, indicating the likelihood of daily weather events of various intensities and do not provide deterministic or time specific information about individual weather events or their sequences.