Significant efforts are made to eliminate biases from models and observations, especially at operational centres. However, these biases still significantly impact the quality of assimilated data products. In the case of numerical weather prediction, residual biases can result in suboptimal utilization of available data or even render them unusable. In climate research based on re-analyzed datasets, it can be difficult to distinguish between accurate signals and trends from inaccurate ones caused by biases in models and data.This study used a detection algorithm written in the R language to perform statistical computing and data analysis. The algorithm was applied to a synthetic study utilizing pseudo-stations based on ERA5 to simulate and detect instrumental effects. Rather than using observational data from real-world sources, the study generated artificial scenarios to guarantee the quality of the data assessment.ERA5 is a well-known atmospheric reanalysis product that was used to create simulated or pseudo-weather stations. These stations were designed to mimic actual stations but were generated computationally to enable controlled experimentation. The study constructed twenty-five pseudo-stations in Frankfurt, Germany, within the latitude 49–50° and longitude 8–9° in the Northern Hemisphere. The study utilized the ERA5 land surface dataset of hourly 2-m air temperature of September in 2013 and 2014. The study tool significantly improves data quality assessment by evaluating the synthetic dataset's precision, dependability, and general robustness. It introduces a range of factors to assess the degree to which the data quality can be enhanced and maintained, including station movements, errors, and noise.To determine the likelihood of the threshold correlation occurring at our confirmed noise threshold, the correlation values occurring at 1.53 for each locational trial were extracted. Our threshold correlation was evaluated to see if it occurred within a likely range of correlations occurring at 1.53 degrees of noise, where 0.9744052 is less than 0.9744667 but greater than 0.9781093. This process helps improve detection methods for data anomalies, contributing to advancements in data quality assessment.

Introduction HYDROCOASTAL is a two year project funded by ESA, with the objective to maximise exploitation of SAR and SARin altimeter measurements in the coastal zone and inland waters, by evaluating and implementing new approaches to process SAR and SARin data from CryoSat-2, and SAR altimeter data from Sentinel-3A and Sentinel-3B. Optical data from Sentinel-2 MSI and Sentinel-3 OLCI instruments will also be used in generating River Discharge products. New SAR and SARin processing algorithms for the coastal zone and inland waters will be developed and implemented and evaluated through an initial Test Data Set for selected regions. From the results of this evaluation a processing scheme will be implemented to generate global coastal zone and river discharge data sets. A series of case studies will assess these products in terms of their scientific impacts. All the produced data sets will be available on request to external researchers, and full descriptions of the processing algorithms will be provided Objectives The scientific objectives of HYDROCOASTAL are to enhance our understanding of interactions between the inland water and coastal zone, between the coastal zone and the open ocean, and the small scale processes that govern these interactions. Also the project aims to improve our capability to characterize the variation at different time scales of inland water storage, exchanges with the ocean and the impact on regional sea-level changes The technical objectives are to develop and evaluate new SAR and SARin altimetry processing techniques in support of the scientific objectives, including stack processing, and filtering, and retracking. Also an improved Wet Troposphere Correction will be developed and evaluated. Presentation The presentation will describe the different SAR altimeter processing algorithms that are being evaluated in the first phase of the project, and present results from the evaluation of the initial test data set. It will focus particularly on the performance of the new algorithms over inland water.