arXiv subjects

Danny Parsons

Publications and source records attributed to Danny Parsons.

3 recordsLinked to original sources

arXiv2026

Improving Bias Correction Methods for Daily Rainfall Using a Markov Chain Approach

Accurate, localised rainfall information is essential for agricultural planning, climate risk assessment, and water resources management. Gridded climate products provide rainfall information over large areas but can lack the accuracy needed at local scales, often requiring bias correction before use in local impact studies. Local intensity scaling (LOCI) and quantile mapping (QM) are two widely used bias correction methods which adjust both rainfall frequency and intensity, but do not account for the temporal structure of daily rainfall. This can lead to biases in the representation of wet and dry spells. This study proposes integrating a two-state first-order Markov chain into existing bias correction methods through state-dependent rain day thresholds and rainfall adjustments, aimed at improving temporal structure. Two implementations of this framework are presented: Markov chain local intensity scaling (MC LOCI) and Markov chain quantile mapping (MC QM). The proposed methods were applied to AgERA5 reanalysis data with rainfall data from five stations in Zimbabwe. Results showed that the Markov chain methods improved the representation of rainfall persistence, onset, and wet and dry spell characteristics compared to LOCI and QM, while maintaining improvements in rain day frequency, mean and total rainfall. Improvements in event timing and daily rainfall amounts were limited. Results from five locations in Zimbabwe demonstrate that the proposed methods could be beneficial for crop simulation, hydrological modelling and other applications requiring accurate rainfall sequencing. Evaluation across additional regions and gridded products would establish the broader applicability of the proposed methods under a range of conditions.

stat.AP↗

arXiv2025

Bias correction of satellite and reanalysis products for daily rainfall occurrence and intensity

In data-sparse regions, satellite and reanalysis rainfall estimates (SREs) are vital but limited by inherent biases. This study evaluates bias correction (BC) methods, including traditional statistical (LOCI, QM) and machine learning (SVR, GPR), applied to seven SREs across 38 stations in Ghana and Zambia. We introduce a constrained LOCI method to prevent the unrealistically high rainfall values produced by the original approach. Results indicate that statistical methods generally outperformed machine learning, though QM tended to inflate rainfall. Corrected SREs showed high capability in detecting dry days (POD $\ge$ 0.80). The ENACTS product, which integrates numerous station records, was the most amenable to correction in Zambia; most BC methods reduced mean error at >70% of stations. However, ENACTS performed less reliably at an independent station (Moorings), highlighting the need for broader validation at locations not incorporated into the product. Crucially, even after correction, most SREs (except ENACTS) failed to improve the detection of heavy and violent rainfall (POD $\le$ 0.2). This limits their utility for flood risk assessment and highlights a vital research gap regarding extreme event estimation.

stat.AP↗

arXiv2025

Evaluating satellite and reanalysis rainfall estimates for climate services in agriculture: a comprehensive methodology

High-resolution rainfall estimates from satellite and reanalysis sources (SRE) could play a major role in improving climate services for agriculture. This is particularly relevant in regions that rely on rain-fed farming but lack a dense network of ground-based measurements to provide localised historical climate information, as in most of the Global South. However, there is a need for a framework which practitioners can use to determine the suitability of these estimated data for specific agricultural applications. This paper presents a comprehensive methodology for evaluating the ability of SRE to provide historical rainfall information for agricultural applications, primarily through comparison with ground-based measurements. The methodology comprises five main steps: data selection and pre-processing, spatial and temporal consistency checks, quantitative SRE-gauge comparisons, bias correction, and application specific summaries. The methodology makes use of graphical summaries, standard comparison metrics, and Markov chain models. We describe how users can apply this methodology to evaluate rainfall estimates for specific applications, complementing existing validation studies. Evaluation cases are presented to demonstrate the methodology using five widely used satellite and reanalysis rainfall products and ground-based measurements from 12 stations in Africa and the Caribbean. The case studies demonstrate how the methodology can be applied to examine multiple aspects of the rainfall estimates. While previous validation studies ask "Does the SRE estimate the true rainfall well?", this methodology provides means of establishing "To what extent can an SRE be used for this specific purpose?" and a comprehensive framework for this. This meets a major need for location specific rainfall information to improve climate information services for millions of small-holder farming households.

physics.ao-ph↗