A gridded European global dataset based on in-situ observations

Gerard Van Der Schrier,Else J.M Van Den Besselaar,Wouter Knap,Albert M.G Klein Tank,Marieke Dirksen

doi:10.5194/ems2021-365

Abstract

&lt;p&gt;Within the EOBS project, one of the objectives is to provide an (ensemble) gridded data set of global radiation. &lt;em&gt;In-situ&lt;/em&gt; observations of daily sums of global radiation are combined with daily sunshine duration records to construct a dataset for daily global radiation that goes back to 1950. A generalization of the commonly used Angstrom-Prescott formula is used to relate daily values of sunshine duration to global radiation, where optimal values of the parameters in this model are found by allowing for variations in the latitude and with the seasons. A quality control procedure based on the physical limits of&amp;#160; global radiation - latitude and yearday dependent - is applied to the data.&lt;/p&gt;&lt;p&gt;Based on this dataset, a gridded dataset for daily global radiation is produced with a resolution of 0.1 degree, covering Europe. The density of the combined networks of radiation and sunshine duration measurements hugely varies in space and time and this inhomogeneity is likely to give variations in space and time of the confidence of the gridded dataset. A method for enhancing the spatial analysis of daily global radiation from a sparse network is by incorporating information on the spatial covariance in the global radiation fields determined from high&amp;#8208;resolution measurements available in the past. Here we use satellite-based daily observations of downwards surface shortwave radiation from the CERES (Clouds and the Earth's Radiant Energy System) dataset for this purpose.&lt;/p&gt;&lt;p&gt;This approach is inspired by the reduced space optimal interpolation (RSOI) method, and the dominant patterns of variability are calculated using Self Organizing Maps (SOMs). Before reducing the dimension of the CERES dataset to 15 patterns, seasonal trends were removed. SOMs comprise a class of unsupervised neural networks that organize input geospatial data into a user-defined number of outputs (nodes) obtained by iteratively adjusting the nodes to resemble the input data. The training of this unsupervised artificial neural network is entirely data driven.&lt;/p&gt;&lt;p&gt;In the presentation, the similarity between the gridded dataset and the underlying station data is quantified, and a comparison against the CMSAF SARAH dataset is presented.&lt;/p&gt;

Highlights

OSA1.3 : Meteorological observations from GNSS and other space-based geodetic observing techniques OSA1.7: The Weather Research and Forecasting Model (WRF): development, research and applications
OSA3.5: MEDiterranean Services Chain based On climate PrEdictions (MEDSCOPE)
UP2.1 : Cities and urban areas in the earth- OSA3.1: Climate monitoring: data rescue, atmosphere system management, quality and homogenization 14:00-15:30

Summary

Introduction

OSA1.3 : Meteorological observations from GNSS and other space-based geodetic observing techniques OSA1.7: The Weather Research and Forecasting Model (WRF): development, research and applications. EMS Annual Meeting Virtual | 3 - 10 September 2021 Strategic Lecture on Europe and droughts: Hydrometeorological processes, forecasting and preparedness Serving society – furthering science – developing applications: Meet our awardees ES2.1 - continued until 11:45 from 11:45: ES2.3: Communication of science ES2.2: Dealing with Uncertainties

Results

Conclusion