Submitted as: development and technical paper
26 Jun 2023
Submitted as: development and technical paper |  | 26 Jun 2023
Status: this preprint is currently under review for the journal GMD.

RICHARD 1.0 – Routine for the Isolation of Chemical Hotspots in Atmospheric Research Data

Christian Scharun, Roland Ruhnke, and Peter Braesicke

Abstract. Here, we introduce version 1.0 of the RICHARD algorithm, a Routine for the Isolation of Chemical Hotspots in Atmospheric Research Data, e.g. in satellite measurement datasets (level-2 and above) or atmospheric chemistry model output. The overall goal of the algorithm is to identify "hotspot" areas in which local enhancements of an atmospheric constituent can only occur due to strong local emissions. To detect hotspot areas, we use a mathematical method that combines spatiotemporal proxy data for calibration purposes and a selection algorithm in a novel way. For each input file family (e.g. the near surface mixing ratio or the tropospheric partial column of atmospheric constituents as a function of longitude and latitude) we define a structure quotient by which the algorithm decides - based on a threshold value - whether at a particular iteration step the hotspot area criteria are met and if they are kept for further calculations or not. The python based command line tool RICHARD 1.0 comes with a set of implemented features like an automated generator for user-defined patterns and an analysis tool to determine the optimal threshold value for a given dataset.

For testing purposes of RICHARD 1.0, we use simulations of the atmosphere and chemistry modeling framework ICON-ART, a joint development of the German Weather Service and Max-Planck-Institute for Meteorology in Hamburg. We comprehensively explore different aspects of RICHARD using ICON-ART model output datasets. We provide an analysis of the decision making process coded in RICHARD, and provide a detailed look at the competing effects of emissions and advection. Here, we also consider the direction and speed of the wind that affect the advection of prescribed (and thus known) emissions in the model and look at the resulting tracer mixing ratios as to evaluate the sensitivity of the algorithm and its ability to identify objectively hotspots of strong emissions, based on the self-determined threshold values.

The results show that RICHARD can identify frequently (or continuously) emitting localised sources as hotspots. Furthermore, the algorithm is able to distinguish between an actual emission source and other circumstances that lead to enhanced tracer concentrations, e.g. as caused by wind conditions and associated transport processes. In addition, the in ICON-ART prescribed emission source strengths are detected and quantified regardless of overlying transport features with only a small error of about 5 %. This increases significantly the accuracy of determined source strengths compared to other methods that we have explored.

RICHARD 1.0 is a novel comprehensive tool for the identification and quantification of emission hotspots and uses a novel workflow that includes spatiotemporal proxy data as well as a selection algorithm. Here, we present a model-based proof of concept that is already fully transferable to applications using satellite measurement data.

Christian Scharun et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2023-91', Anonymous Referee #1, 17 Jul 2023
    • AC1: 'Reply on RC1', Christian Scharun, 30 Sep 2023
  • RC2: 'Comment on gmd-2023-91', Anonymous Referee #2, 01 Sep 2023

Christian Scharun et al.

Christian Scharun et al.


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Short summary
The identification and quantification of greenhouse gas (GHG) emissions is an important task for monitoring mitigation strategies under climate change. With RICHARD 1.0, we developed a novel approach using spatiotemporal proxy data and a selection algorithm to detect GHG emission hotspots. By using a one year dataset of global climate model output we showed that RICHARD is able to determine and quantify the source strengths of GHG emission hotspots much more precisely than conventional methods.