Precipitable water vapour forecasting: a tool for optimizing IR observations at Roque de los Muchachos Observatory

TL;DR

This study validates the Weather Research and Forecasting (WRF) model for accurate precipitable water vapour forecasting, demonstrating its effectiveness as an operational tool to optimize infrared astronomical observations at Roque de los Muchachos Observatory.

Contribution

The paper presents a validated, operational WRF model for PWV forecasting at ORM, with high accuracy over 24- and 48-hour horizons, improving IR observation planning.

Findings

High correlation between forecasts and GNSS data (R > 0.9).

Forecast errors are approximately 1.75-1.99 mm for 24- and 48-hour predictions.

Operational implementation of WRF enhances IR observation scheduling.

Abstract

We validate the Weather Research and Forecasting (WRF) model for precipitable water vapour (PWV) forecasting as a fully operational tool for optimizing astronomical infrared (IR) observations at Roque de los Muchachos Observatory (ORM). For the model validation we used GNSS-based (Global Navigation Satellite System) data from the PWV monitor located at the ORM. We have run WRF every 24 h for near two months, with a horizon of 48 hours (hourly forecasts), from 2016 January 11 to 2016 March 4. These runs represent 1296 hourly forecast points. The validation is carried out using different approaches: performance as a function of the forecast range, time horizon accuracy, performance as a function of the PWV value, and performance of the operational WRF time series with 24- and 48-hour horizons. Excellent agreement was found between the model forecasts and observations, with R = 0.951 and R = 0.904 for the 24- and 48-h forecast time series respectively. The 48-h forecast was further improved by correcting a time lag of 2 h found in the predictions. The final errors, taking into account all the uncertainties involved, are 1.75 mm for the 24-h forecasts and 1.99 mm for 48 h. We found linear trends in both the correlation and RMSE of the residuals (measurements - forecasts) as a function of the forecast range within the horizons analysed (up to 48 h). In summary, the WRF performance is excellent and accurate, thus allowing it to be implemented as an operational tool at the ORM.