Climatologies
Weather Logistics’ climatologies are based on a current year estimation of average weather conditions. To produce this data, we calculate the regional changes in precipitation and temperature over the past 30 years. We then project forward using change factors that adjusts past observational records to current equivalent daily weather hazards. Climatology simulations provide users with daily information for the next season in the absence of predictive knowledge about the current state of the El Nino Southern Oscillation or other modes of internal climate variability e.g., Arctic ice coverage or sea temperatures. This contrasts with our seasonal climate forecasts, which make use of a combination of climate hazard signals to predict the seasonal departure from the baseline climatology.
Forecasts
Seasonal climate forecasts provide outlooks for the next full three calendar months. Specifically, Weather Logistics’ statistical model provides downscaled daily weather information on a 5km scale, which has been interpedently and impartially assessed by the National Physical Laboratory. Our forecast approach has proven both reliable and well calibrated. We generate a set of 50 simulations of temporally and spatially correlated weather information using a stochastic weather generator. These time series simulations are combined in a 50:50 ratio with numerical weather prediction data to deliver better calibrated forecasts that are neither over nor under confident. As our ‘benchmark’ forecast we use a multi-model average of Copernicus Climate Change Service (C3S). This is modified to capture local weather using our spatiotemporal downscaling and stochastic weather generator. The distribution of daily weather occurrence/ exceedance curves (event likelihoods and severities) is adjusted for both approaches using a non-linear change function that reflects wetter/ drier or warmer/ cooler conditions as well as the local response to interval climate variability. We have trained our seasonal forecasts on observations to correct for individual model biases, and uniquely make use of jet stream characterisations to predict local weather extremes.
Output Type: GeoJSON Daily time series + CSV Daily time series + PNG
Precipitation Index/ Heat Index/ Cold Index
Hazards charts show the likelihood of extreme daily precipitation, heatwaves or coldwaves for a selection of town and city locations. The index scale run from 1 = least extreme, to 9 = most extreme, with 5 indicating weather conditions comparable with the 2017 to 2021 climatology. Hazard indices are produced using a shift-of-the-tails approach for which the 80th centiles of the daily forecast and coincident observations are compared. For the precipitation index wet days are assessed (80th centile where precipitation > 1mm) whereas minimum and maximum temperatures are used respectively for the heat and cold indexes. The analysis includes all 100 ensemble members in a 50:50 ratio from both Weather Logistics’ statistical approach and the C3S multi-model average. ERA5-land Reanalysis (assimilated observations) are used as an appropriate reference point, covering the five years from 2017 to 2021 inclusive.
Analysis charts show hazard index maps as bubble charts that individually cover the next two months following the forecast start date, and a seasonal average that combines information for the next three months.
GeoJSON Data Format
Output Type: PNG
{root}/Output_Visuals/JSON/
mid-summer forecast issued on 10th May 2022
{"metadata": {
"Site Name": "Salisbury", {Point location where weather hazard data is interpolated to}
"WGS84 Coordinate": [ {Standard global coordinate system}
51.074, {Latitude in degrees north of the Equator}
-1.7936 {Longitude in degrees east of Greenwich}
],
"Start Date": "10th May, 2022", {Initiation/ valid date of the forecast release}
"Type": "Baseline climatology", {Description of the data product}
"Model": "Weather Logistics Ltd (10 members)", {Providers and number of simulations}
"Output Format": "Daily weather ensemble time series", {output structure/ frequency}
"Variable": "Hail", {meteorological output variable}
"Units": "mm/s * 1000" {meteorological output units}
},
"data": {
"columns": [
"06/01/2022", … {forecast time in month/day/year format}
"08/31/2022"
],
"index": [
"Ensemble #1", … {number allocated to the daily time series simulation}
"Ensemble #10"
],
"data": [
[
0.16 … {daily output value in the specified units}
]]}
}
Example CSV output
{root}/Output_Visuals/CSV/
mid-summer forecast issued on 10th May 2022
,06/01/2022, … 08/31/2022 {forecast time in month/day/year format}
Ensemble#1,0.16, … 0.26 {daily output value in the specified units in GeoJSON file for the labelled ensemble number}
PNG outputs
{root}/Output_Visuals/PRECIPITATION_INDEX/
{root}/Output_Visuals/HEAT_INDEX/
{root}/Output_Visuals/COLD_INDEX/
Other configurations …
Future Climate
Monthly mean temperatures (in degrees Celsius) and daily precipitation accumulations in millimetres. Spatial uncertainties are incorporated into this dataset through the inclusion of weather simulations from neighbouring 5-kilometre grid cells (Southwest, Northwest, Southeast and Northeast). Daily time series provide a set of realistic weather events for any projected climate up to the year 2040, showing the adjustments in daily temperature and precipitation used in Weather Logistics’ seasonal climate prediction software.
Output Type: ASCII grid + GeoJSON Daily time series + CSV Daily time series
Monthly Anomalies
{root}/WEATHERDOCKER/FORECAST_OUTPUTS/Monthly/Anom_Temperature/
{root}/WEATHERDOCKER/FORECAST_OUTPUTS/Monthly/Anom_Temperature/
{root}/WEATHERDOCKER/FORECAST_OUTPUTS/Monthly/Rainfall/Anom_Rainfall/
Monthly mean departures from the representative current year climatological average in degrees Celsius for temperature analyses or percentage of climate (%) for precipitation.
Output Type: ASCII grid + CSV list for town/ city locations
ASCII formats for United Kingdom, Spain, and Turkey Geographies:
{root}/FORECAST_OUTPUTS/Monthly/Anom_Temperature/
{root}/FORECAST_OUTPUTS/Monthly/Rainfall/Anom_Rainfall/
UNITED KINGDOM (EPSG:27700) Bottom-left centre cell: 49.84N, -10.70E
NCOLS 192 {number of columns of weather data grid points}
NROWS 249 {number of rows of weather data grid points}
XLLCENTER -225160.85671291745 {British National Grid Easting coordinate for centre of bottom-left grid cell}
YLLCENTER 29788.11610251431 {British National Grid Northing coordinate for centre of bottom-left grid cell}
CELLSIZE 5000.0 {Regular width spacing for weather data grid points in metres}
NODATA_VALUE -9999 {Fill value for missing or oceanic/ lake grid cells}
*****
SPAIN (EPSG:23030) Bottom-left centre cell: 35.71°N, -9.67°E
NCOLS 241 {number of columns of weather data grid points}
NROWS 188 {number of rows of weather data grid points}
XLLCENTER -104015.42271117808 {ED50/ UTM zone 30N Northing coordinate for centre of bottom-left grid cell}
YLLCENTER 3972363.4626177787 {ED50/ UTM zone 30N Easting coordinate for centre of bottom-left grid cell}
CELLSIZE 5000.0 {Regular width spacing for weather data grid points in metres}
NODATA_VALUE -9999 {Fill value for missing or oceanic/ lake grid cells}
*****
TURKEY (EPSG:23035) Bottom-left centre cell: 35.82°N, 26.04°E
NCOLS 340 {number of columns of weather data grid points}
NROWS 171 {number of rows of weather data grid points}
XLLCENTER 413280.00073572306 {ED50 / UTM zone 35N Easting coordinate for centre of bottom-left grid cell}
YLLCENTER 3964409.325085106 {ED50 / UTM zone 35N Easting coordinate for centre of bottom-left grid cell}
CELLSIZE 5000.0 {Regular width spacing for weather data grid points in metres}
NODATA_VALUE -9999 {Fill value for missing or oceanic/ lake grid cells}
*****
**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.