Bias Correction Scheme Research Articles

Bias Correction of Zero-Inflated RCM Precipitation Fields: A Copula-Based Scheme for Both Mean and Extreme Conditions

Abstract Changes in extreme precipitation due to climate change often require the application of methods to bias correct simulated atmospheric fields, including extremes. Most existing bias correction techniques (i) only focus on the bias in the mean value or on the extreme values separately, and (ii) exclude zero values from analysis, even though their presence is significant in daily precipitation. We developed a copula-based bias correction scheme that is suitable for zero-inflated daily precipitation data to correct the bias in mean as well as in extreme precipitation at any specific statistical quantile. In considering the whole of Germany as a test bed, the proposed scheme is found to work well across the entire study area, including the German Alpine regions. The joint distribution between observed and regional climate model (RCM)-derived precipitation is developed through copulas. In particular, the joint distribution is modified to make it discrete at zero in order to account for zero values. The benefit of considering zero precipitation values is revealed through the improved performance of bias correction both in the mean and extreme values. Second, the quantile that best captures the bias (whether in the mean or any extreme value) is determined for a specific location and varies spatially and seasonally. This relaxation in selecting the location-specific optimal quantile renders the proposed methodology spatially transferable. By acknowledging possible changes in extreme precipitation due to climate change, the proposed scheme is expected to be suitable for climate change impact assessments for extreme events worldwide.

An Alternative Bias Correction Scheme for CrIS Data Assimilation in a Regional Model

Bias correction (BC) is a crucial step for satellite radiance data assimilation (DA). In this study, the traditional airmass BC scheme in the National Centers for Environmental Prediction (NCEP) Gridpoint Statistical Interpolation (GSI) is investigated for Cross-track Infrared Sounder (CrIS) DA. The ability of the airmass predictors to model CrIS biases is diagnosed. Correlations between CrIS observation-minus-background ( O − B) samples and the two lapse rate–related airmass predictors employed by GSI are found to be very weak, indicating that the bias correction contributed by the airmass BC scheme is small. A modified BC scheme, which directly calculates the moving average of O − B departures from data of the previous 2 weeks with respect to scan position and latitudinal band, is proposed and tested. The impact of the modified BC scheme on CrIS radiance DA is compared with the variational airmass BC scheme. Results from 1-month analysis/forecast experiments show that the modified BC scheme removes nearly all scan-dependent and latitude-dependent biases, while residual biases are still found in some channels when the airmass BC scheme is applied. Smaller predicted root-mean-square errors of temperature and specific humidity and higher equivalent threat scores are obtained by the DA experiment using the modified BC scheme. If O − B samples are replaced by observation-minus-analysis ( O − A) samples for bias estimates in the modified BC scheme, the forecast impacts are reduced but remain positive. A convective precipitation case that occurred on 21 August 2016 is investigated. Using the modified BC scheme, the atmospheric temperature structure and the geopotential height structures near trough/ridge areas are better resolved, resulting in better precipitation forecasts.

Comparison of assimilating all‐sky and clear‐sky infrared radiances from Himawari‐8 in a mesoscale system

This study examines the advantages of infrared all‐sky radiance (ASR) assimilation over traditional clear‐sky radiance (CSR) assimilation using a mesoscale LETKF data assimilation system. To effectively assimilate ASR data from the Himawari‐8 geostationary satellite, a cloud‐dependent quality‐control procedure and an observation error model were developed. A single humidity band to be assimilated and thinning distance were determined based on observation error statistics. The operational pre‐processing and parameter settings, such as observation errors and an adaptive bias correction for CSR, were incorporated into the LETKF data assimilation system.A comparison of the impacts of assimilating ASRs and CSRs was accomplished using single‐cycle and 10‐day cycle assimilation experiments. Study results revealed that ASR assimilation provided a higher degree of improvement in the first‐guess fit for conventional observations and satellite retrievals with respect to temperature, moisture and wind. Furthermore, ASR assimilation displayed a more stable improvement in the prediction of a severe rainfall event because it has more universal data coverage than CSR. Adaptive bias correction schemes with two different sets of predictors for ASRs were tested and revealed the difficulty in extracting additional information for the assimilation of no‐bias corrected ASR due to complicated bias factors. This was in contrast to the CSR assimilation, where bias correction had a positive impact.

Assimilating satellite sea‐surface salinity data from SMOS, Aquarius and SMAP into a global ocean forecasting system

Measuring Sea‐Surface Salinity (SSS) from space is a relatively recent technique that relies on L‐band radiometry that has evolved to a point where useful information is provided every few days. The impact of assimilating satellite SSS data is investigated using the global FOAM ocean forecasting system. This system assimilates daily satellite SSS products from the ESA Soil Moisture and Ocean Salinity (SMOS), NASA Aquarius and Soil Moisture Active Passive (SMAP) missions equatorward of 40°N/S, in addition to other observing systems. The data are assimilated using a 3D‐Var scheme that includes an observation bias correction scheme to estimate spatially and temporally varying bias estimates for each SSS satellite.The SSS assimilation is tested over 2 years and 3 months covering the 2015/2016 El Niño with assessment focussed on the tropical regions, particularly in the Pacific. Consistent reductions in root‐mean‐square errors are found in all tropical regions from each of the satellite SSS datasets. The largest improvements of up to about 8% are found in the tropical Pacific from assimilating both the SMOS and SMAP datasets together. The largest impact is in the intertropical convergence zone (ITCZ) in the central Pacific during 2015 and early 2016 where the surface salinity is reduced by about 0.03 pss on average, correcting for too little precipitation. A smaller magnitude, large‐scale reduction in the SSS is also seen in the tropical Pacific that increases the modelled surface stratification and leads to reduced vertical mixing. Changes to the SSS in the ITCZ lead to changes in the meridional gradients of SSS that affects the sea surface height and surface currents, both of which are improved in the SMOS assimilation experiment compared to externally produced observation‐based datasets. The results suggest that assimilation of SMOS and SMAP satellite data is now of an appropriate quality for operational implementation.

Assimilation of MWHS and MWTS radiance data from the FY-3A satellite with the POD-3DEnVar method for forecasting heavy rainfall

An ensemble three-dimensional variational assimilation method based on Proper Orthogonal Decomposition (referred to as POD-3DEnVar) can not only provide flow-dependent covariances through the evolving ensemble of short-range forecasts, but also obtain directly the analysis field without an iterative process. Using the POD-3DEnVar method, a regional hybrid variational ensemble data assimilation system (referred to as POD-HVEDAS) is constructed with the Community Radiative Transfer Model (CRTM) as the observation operator. Observations from Microwave Humidity Sounder (MWHS) and Microwave Temperature Sounder (MWTS) onboard Fengyun-3A are simultaneously assimilated into the Weather Research and Forecasting (WRF) model by the POD-HVEDAS. Bias correction and quality control schemes for MWHS and MWTS microwave radiance are applied in the system. Three experiments (Con, Hybrid-DA, and POD-DA) are designed to investigate the assimilation ability of the POD-HVEDAS for heavy rainfall over the Yangtze River. The assimilation effects of POD-3DEnVar and WRFDA-Hybrid assimilation method are compared. The results show that the POD-HVEDAS can assimilate MWHS and MWTS microwave radiance effectively, and give a better precipitation forecast than that of the WRFDA-Hybrid method. And it is found that the assimilation increments of both POD-3DEnVar and WRFDA-Hybrid methods all show flow dependent characteristic. However, the improvements in the background wind, temperature and humidity fields by the POD-DA experiment are more obvious than those of the Hybrid-DA experiment, which play key roles in improving the accuracy of precipitation forecast. These promising results suggest initiulizing limited-area models with POD-3DEnVar method that assimilate microwave radiances is beneficial. In the future, POD-3DEnVar method will be used to study multiple cases to fully assess the robustness of POD-HVEDAS.

Improved Estimation for a New Class of Parametric Link Functions in Binary Regression

We develop nearly unbiased maximum likelihood estimators for a new class of asymmetric link functions proposed recently in the statistic literature by Lemonte and Bazan (TEST 27, 597–617 2018). These authors have introduced a broad class of parametric link functions in binary regression that contains as special cases both symmetric as well as asymmetric links. We discuss maximum likelihood estimation for the model parameters and derive a closed-form expression for the second order bias of these estimators. The second order bias can be easily computed as an ordinary weighted least-squares regression and is then used to define bias corrected maximum likelihood estimators. Monte Carlo simulation experiments are conducted in order to investigate the performance of the corrected estimators. The numerical results reveal that the bias correction scheme yields nearly unbiased estimates without increasing the mean squared errors. Empirical applications are considered for illustrative purposes.

Accuracy of daily estimation of grass reference evapotranspiration using ERA-Interim reanalysis products with assessment of alternative bias correction schemes

This study aims at assessing the accuracy of estimating daily grass reference evapotranspiration (PM-ETo) computed with ERA-Interim reanalysis products, as well as to assess the quality of reanalysis products as predictors of daily maximum and minimum temperature, net radiation, dew point temperature and wind speed, which are used to compute PM-ETo. With this propose, ETo computed from local observations of weather variables in 24 weather stations distributed across Continental Portugal were compared with reanalysis-based values of ETo (ETo REAN). Three different versions of these reanalysis-based ETo were computed: (i) an (uncorrected) ETo based on the individual weather variables for the nearest grid point to the weather station; (ii) the previously calculated ETo corrected for bias with a simple bias-correction rule based only on the nearest grid point; and (iii) the ETo corrected for bias with a more complex rule involving all grid points in a 100 km radius of the weather station. Both bias correction approaches were tested aggregating data on a monthly, quarterly and a single overall basis. Cross-validation was used to allow evaluating the uncertainties that are modelled independently of any forcing; with this purpose, data sets were divided into two groups. Results show that ETo REAN without bias correction is strongly correlated with PM-ETo (R2>0.80) but tends to over-estimate PM-ETo, with the slope of the regression forced to the origin b0 ≥ 1.05, a mean RMSE of 0.79 mm day−1, and with EF generally above 0.70. Cross-validation results showed that using both bias correction methods improved the accuracy of estimations, in particular when a monthly aggregation was used. In addition, results showed that using the multiple regression correction method outperforms the additive bias correction leading to lower RMSE, with mean RMSE of 0.57 and 0.64 mm day−1 respectively. The selection of the bias correction approach to be adopted should balance the ease of use, the quality of results and the ability to capture the intra-annual seasonality of ETo. Thus, for irrigation scheduling operational purposes, we propose the use of the additive bias correction with a quarterly aggregation.

Assimilation of atmospheric infrared sounder radiances with WRF-GSI for improving typhoon forecast

The Atmospheric Infrared Sounder (AIRS) can provide the profile information on atmospheric temperature and humidity in high vertical resolution. The assimilation of its radiances has been proven to improve the Numerical Weather Prediction (NWP) in global models. In this study, regional assimilation of AIRS radiances was carried out in a community assimilation system, using Gridpoint Statistical Interpolation (GSI) coupled with the Weather Research and Forecasting (WRF) model. The AIRS channel selection, quality control, and radiances bias correction were examined and illustrated for optimized assimilation. The bias correction scheme in the regional model showed that corrections on most of the channels produce satisfactory results except for several land surface channels. The assimilation and forecast experiments were carried out for three typhoon cases (Saola, Damrey, and Haikui in 2012) with and without including AIRS radiances. Results show that the assimilation of AIRS radiances into the WRF/GSI model improves both the typhoon track and intensity in a 72-hour forecast.

Development of bias correction scheme for high resolution precipitation forecast

An increase in heavy rainfall and floods have been observed over South Korea due to recent abnormal weather. In this perspective, the high-resolution weather forecasts have been widely used to facilitate flood management. However, these models are known to be biased due to initial conditions and topographical conditions in the process of model building. Theretofore, a bias correction scheme is largely applied for the practical use of the prediction to flood management. This study introduces a new mean field bias correction (MFBC) approach for the high-resolution numerical rainfall products, which is based on a Bayesian Kriging model to combine an interpolation technique and MFBC approach for spatial representation of the error. The results showed that the proposed method can reliably estimate the bias correction factor over ungauged area with an improvement in the reduction of errors. Moreover, it can be seen that the bias corrected rainfall forecasts could be used up to 72 hours ahead with a relatively high accuracy.

Calibrating Climate Model Ensembles for Assessing Extremes in a Changing Climate

AbstractClimate models serve as indispensable tools to investigate the effect of anthropogenic emissions on current and future climate, including extremes. However, as low‐dimensional approximations of the climate system, they will always exhibit biases. Several attempts have been made to correct for biases as they affect extremes prediction, predominantly focused on correcting model‐simulated distribution shapes. In this study, the effectiveness of a recently published quantile‐based bias correction scheme, as well as a new subset selection method introduced here, are tested out‐of‐sample using model‐as‐truth experiments. Results show that biases in the shape of distributions tend to persist through time, and therefore, correcting for shape bias is useful for past and future statements characterizing the probability of extremes. However, for statements characterized by a ratio of the probabilities of extremes between two periods, we find that correcting for shape bias often provides no skill improvement due to the dominating effect of bias in the long‐term trend. Using a toy model experiment, we examine the relative importance of the shape of the distribution versus its position in response to long‐term changes in radiative forcing. It confirms that the relative position of the two distributions, based on the trend, is at least as important as the shape. We encourage the community to consider all model biases relevant to their metric of interest when using a bias correction procedure and to construct out‐of‐sample tests that mirror the intended application.

Evaluation and Bias Correction of Satellite-Based Rainfall Estimates for Modelling Flash Floods over the Mediterranean region: Application to Karpuz River Basin, Turkey

This study investigates the utility of satellite-based rainfall estimates in simulating flash floods in Karpuz River Basin, Turkey, characterized by limited rain gauge network. Global Satellite Mapping of Precipitation (GSMaP) product was evaluated with the rain gauge network at daily and monthly time-scales considering seasonality, elevation zones, extreme events and rainfall intensity thresholds. Statistical analysis indicated that GSMaP shows acceptable linear correlation coefficient with rain gauges, however, suffers from significant underestimation bias. Statistical measures exhibited a remarkable deterioration with increasing elevation-following a linear relationship; for example, percent bias was found to increase by a rate of 11.7% with every 400 m interval. A multiplicative bias correction scheme was devised, and Hydrological River Basin Environmental Assessment Model (Hydro-BEAM) was implemented to simulate flash floods driven by the uncorrected/corrected GSMaP data. Analysis of intensity thresholds revealed that appropriate threshold selection is critically important for the bias correction procedure. The hydrological model was calibrated for flash flood events during October–December 2007 and 2012 and validated during October–December, 2009 and 2010. Flash floods simulations were improved by the local bias correction procedure applied to the GSMaP data, but the degree of improvement varied from one period to another. The results of the study indicate that bias factors incorporating multiple variables such as extreme events and elevation variability have the potential to further improve flood simulations.

Bias correction of daily satellite precipitation data using genetic algorithm

Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) was producted by blending Satellite-only Climate Hazards Group InfraRed Precipitation (CHIRP) with Stasion observations data. The blending process was aimed to reduce bias of CHIRP. However, Biases of CHIRPS on statistical moment and quantil values were high during wet season over Java Island. This paper presented a bias correction scheme to adjust statistical moment of CHIRP using observation precipitation data. The scheme combined Genetic Algorithm and Nonlinear Power Transformation, the results was evaluated based on different season and different elevation level. The experiment results revealed that the scheme robustly reduced bias on variance around 100% reduction and leaded to reduction of first, and second quantile biases. However, bias on third quantile only reduced during dry months. Based on different level of elevation, the performance of bias correction process is only significantly different on skewness indicators.

Validation of satellite-based rainfall in Kalahari

Water resources management in arid and semi-arid areas is hampered by insufficient rainfall data, typically obtained from sparsely distributed rain gauges. Satellite-based rainfall estimates (SREs) are alternative sources of such data in these areas. In this study, daily rainfall estimates from FEWS-RFE∼11 km, TRMM-3B42∼27 km, CMOPRH∼27 km and CMORPH∼8 km were evaluated against nine, daily rain gauge records in Central Kalahari Basin (CKB), over a five-year period, 01/01/2001-31/12/2005. The aims were to evaluate the daily rainfall detection capabilities of the four SRE algorithms, analyze the spatio-temporal variability of rainfall in the CKB and perform bias-correction of the four SREs. Evaluation methods included scatter plot analysis, descriptive statistics, categorical statistics and bias decomposition. The spatio-temporal variability of rainfall, was assessed using the SREs' mean annual rainfall, standard deviation, coefficient of variation and spatial correlation functions. Bias correction of the four SREs was conducted using a Time-Varying Space-Fixed bias-correction scheme. The results underlined the importance of validating daily SREs, as they had different rainfall detection capabilities in the CKB. The FEWS-RFE∼11 km performed best, providing better results of descriptive and categorical statistics than the other three SREs, although bias decomposition showed that all SREs underestimated rainfall. The analysis showed that the most reliable SREs performance analysis indicator were the frequency of “miss” rainfall events and the “miss-bias”, as they directly indicated SREs' sensitivity and bias of rainfall detection, respectively. The Time Varying and Space Fixed (TVSF) bias-correction scheme, improved some error measures but resulted in the reduction of the spatial correlation distance, thus increased, already high, spatial rainfall variability of all the four SREs. This study highlighted SREs as valuable source of daily rainfall data providing good spatio-temporal data coverage especially suitable for areas with limited rain gauges, such as the CKB, but also emphasized SREs' drawbacks, creating avenue for follow up research.

Improved estimation in a general multivariate elliptical model

The problem of reducing the bias of maximum likelihood estimator in a general multivariate elliptical regression model is considered. The model is very flexible and allows the mean vector and the dispersion matrix to have parameters in common. Many frequently used models are special cases of this general formulation, namely: errors-in-variables models, nonlinear mixed-effects models, heteroscedastic nonlinear models, among others. In any of these models, the vector of the errors may have any multivariate elliptical distribution. We obtain the second-order bias of the maximum likelihood estimator, a bias-corrected estimator, and a bias-reduced estimator. Simulation results indicate the effectiveness of the bias correction and bias reduction schemes.

Bias-corrected maximum likelihood estimation of the parameters of the generalized half-normal distribution

ABSTRACTCooray and Ananda introduced a two-parameter generalized Half-Normal distribution which is useful for modelling lifetime data, while its maximum likelihood estimators (MLEs) are biased in finite samples. This motivates us to construct nearly unbiased estimators for the unknown parameters of the model. In this paper, we adopt two approaches for bias reduction of the MLEs of the parameters of generalized Half-Normal distribution. The first approach is the analytical methodology suggested by Cox and Snell and the second is based on parametric Bootstrap resampling method. Additionally, the method of moments (MMEs) is used for comparison purposes. The numerical evidence shows that the analytic bias-corrected estimators significantly outperform their bootstrapped-based counterpart for small and moderate samples as well as for MLEs and MMEs. Also, it is apparent from the results that bias- corrected estimates of shape parameter perform better than that of scale parameter. Further, the results show that bias-correction scheme yields nearly unbiased estimates. Finally, six fracture toughness real data sets illustrate the application of our methods.

Impact of archeomagnetic field model data on modern era geomagnetic forecasts

A series of geomagnetic data assimilation experiments have been carried out to demonstrate the impact of assimilating archeomagnetic data via the CALS3k.4 geomagnetic field model from the period between 10 and 1590 CE. The assimilation continues with the gufm1 model from 1590 to 1990 and CM4 model from 1990 to 2000 as observations, and comparisons between these models and the geomagnetic forecasts are used to determine an optimal maximum degree for the archeomagnetic observations, and to independently estimate errors for these observations. These are compared with an assimilation experiment that uses the uncertainties provided with CALS3k.4. Optimal 20 year forecasts in 1990 are found when the Gauss coefficients up to degree 3 are assimilated. In addition we demonstrate how a forecast and observation bias correction scheme could be used to reduce bias in modern era forecasts. Initial experiments show that this approach can reduce modern era forecast biases by as much as 50%.

A bias-correction method for closed-loop identification of Linear Parameter-Varying systems

Due to safety constraints and unstable open-loop dynamics, system identification of many real-world processes often requiresgathering data from closed-loop experiments. In this paper, we present a bias-correction scheme for closed-loop identification of Linear Parameter-Varying Input–Output (LPV-IO) models, which aims at correcting the bias caused by the correlation between the input signal exciting the process and output noise. The proposed identification algorithm provides a consistent estimate of the open-loop model parameters when both the output signal and the scheduling variable are corrupted by measurement noise. The effectiveness of the proposed methodology is tested in two simulation case studies.

Preparing for the Future Nankai Trough Tsunami: A Data Assimilation and Inversion Analysis From Various Observational Systems

AbstractThe future Nankai Trough tsunami is one of the imminent threats to the Japanese coastal communities that could potentially cause a catastrophic event. As a part of the countermeasure efforts for such an occurrence, this study analyzes the efficacy of combining tsunami data assimilation (DA) and waveform inversion (WI). The DA is used to continuously refine a wavefield model whereas the WI is used to estimate the tsunami source. We consider a future scenario of the Nankai Trough tsunami recorded at various observational systems, including ocean bottom pressure (OBP) gauges, global positioning system (GPS) buoys, and ship height positioning data. Since most of the OBP gauges are located inside the source region, the recorded tsunami signals exhibit significant offsets from surface measurements due to coseismic seafloor deformation effects. Such biased data are not applicable to the standard DA, but can be taken into account in the WI. On the other hand, the use of WI for the ship data may not be practical because a considerably large precomputed tsunami database is needed to cope with the spontaneous ship locations. The DA is more suitable for such an observational system as it can be executed sequentially in time and does not require precomputed scenarios. Therefore, the combined approach of DA and WI allows us to concurrently make use of all observational resources. Additionally, we introduce a bias correction scheme for the OBP data to improve the accuracy, and an adaptive thinning of observations to determine the efficient number of observations.

Recursive estimators of mean-areal and local bias in precipitation products that account for conditional bias

This paper presents novel formulations of Mean field bias (MFB) and local bias (LB) correction schemes that incorporate conditional bias (CB) penalty. These schemes are based on the operational MFB and LB algorithms in the National Weather Service (NWS) Multisensor Precipitation Estimator (MPE). By incorporating CB penalty in the cost function of exponential smoothers, we are able to derive augmented versions of recursive estimators of MFB and LB. Two extended versions of MFB algorithms are presented, one incorporating spatial variation of gauge locations only (MFB-L), and the second integrating both gauge locations and CB penalty (MFB-X). These two MFB schemes and the extended LB scheme (LB-X) are assessed relative to the original MFB and LB algorithms (referred to as MFB-O and LB-O, respectively) through a retrospective experiment over a radar domain in north-central Texas, and through a synthetic experiment over the Mid-Atlantic region. The outcome of the former experiment indicates that introducing the CB penalty to the MFB formulation leads to small, but consistent improvements in bias and CB, while its impacts on hourly correlation and Root Mean Square Error (RMSE) are mixed. Incorporating CB penalty in LB formulation tends to improve the RMSE at high rainfall thresholds, but its impacts on bias are also mixed. The synthetic experiment suggests that beneficial impacts are more conspicuous at low gauge density (9 per 58,000 km2), and tend to diminish at higher gauge density. The improvement at high rainfall intensity is partly an outcome of the conservativeness of the extended LB scheme. This conservativeness arises in part from the more frequent presence of negative eigenvalues in the extended covariance matrix which leads to no, or smaller incremental changes to the smoothed rainfall amounts.

C-GLORSv5: an improved multipurpose global ocean eddy-permitting physical reanalysis

Abstract. Global ocean reanalyses combine in situ and satellite ocean observations with a general circulation ocean model to estimate the time-evolving state of the ocean, and they represent a valuable tool for a variety of applications, ranging from climate monitoring and process studies to downstream applications, initialization of long-range forecasts and regional studies. The purpose of this paper is to document the recent upgrade of C-GLORS (version 5), the latest ocean reanalysis produced at the Centro Euro-Mediterraneo per i Cambiamenti Climatici (CMCC) that covers the meteorological satellite era (1980–present) and it is being updated in delayed time mode. The reanalysis is run at eddy-permitting resolution (1∕4° horizontal resolution and 50 vertical levels) and consists of a three-dimensional variational data assimilation system, a surface nudging and a bias correction scheme. With respect to the previous version (v4), C-GLORSv5 contains a number of improvements. In particular, background- and observation-error covariances have been retuned, allowing a flow-dependent inflation in the globally averaged background-error variance. An additional constraint on the Arctic sea-ice thickness was introduced, leading to a realistic ice volume evolution. Finally, the bias correction scheme and the initialization strategy were retuned. Results document that the new reanalysis outperforms the previous version in many aspects, especially in representing the variability of global heat content and associated steric sea level in the last decade, the top 80 m ocean temperature biases and root mean square errors, and the Atlantic Ocean meridional overturning circulation; slight worsening in the high-latitude salinity and deep ocean temperature emerge though, providing the motivation for further tuning of the reanalysis system. The dataset is available in NetCDF format at doi:10.1594/PANGAEA.857995.